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Hereditary Breast Cancer in Selected Populations: Screening RECQL Gene in
Ontario, Canada, and Exploring BRCA1/2 and PALB2 in the Caribbean
by
Humayun Ahmed
A thesis submitted in conformity with the requirements
for the degree of Master of Science
Institute of Medical Science
University of Toronto
© Copyright Humayun Ahmed 2018
ii
Hereditary Breast Cancer in Selected Populations: Screening RECQL Gene in
Ontario, Canada, and Exploring BRCA1/2 and PALB2 in the Caribbean
Humayun Ahmed
Master of Science
Institute of Medical Science
University of Toronto
2018
Abstract Hereditary breast cancer (i.e., cancer due to an inherited mutation) is imperfectly
understood: while 5 to 10 % of breast cancer is hereditary, only 50 % of breast cancer
susceptibility has been explained. Increased hereditary breast cancer understanding
fosters high-risk patient identification, prevention, early diagnosis, and targeted
treatment. To achieve increased knowledge of the hereditary breast cancer landscape,
both generation of new information and implementation of prior knowledge in new
settings are required. Accordingly, this project had two goals: Firstly, we screened
RECQL, a new susceptibility gene candidate, among Ontario breast cancer patients.
Secondly, we examined three established genes – namely, BRCA1, BRCA2, and PALB2
– among breast cancer patients in Trinidad and Tobago and Jamaica. Overall, we found
that further work was needed to validate RECQL in a heterogeneous population, and that
Caribbean screening and prevention could be ameliorated by the inclusion of certain
susceptibility genes on gene panels.
iii
Acknowledgements There are numerous individuals and organizations without whom the completion of
this thesis would not be possible.
Firstly, I would like to thank Dr. Mohammad Akbari, my direct supervisor, who
has supported me daily throughout the last two years. Mohammad, thank you for your
patience, your optimism, your belief in my ability to learn new things, your dedicated and
meticulous advising on all of our projects, your support in grant searching, and your
mentorship. I look forward to working with you in the future, and am deeply grateful for
your presence in my professional life.
Secondly, I would like to thank Dr. Steven Narod, who heads the Familial Breast
Cancer Research Unit at Women’s College Hospital and Research Institute alongside Dr.
Akbari. Dr. Narod consistently challenged me with novel projects and discussions, and has
been instrumental to my professional development within research.
I would also like to thank my advisory committee, which included Drs. Steven
Gallinger and Jordan Lerner-Ellis, both of whom were very supportive during my advisory
committee presentations, provided very constructive, clear feedback following these
presentations, and made themselves consistently available to me for consultations despite
their many commitments.
This work would also, of course, not have been possible without the generous help
of the entirety of the Familial Breast Cancer Research Unit at the Women’s College
Hospital and Research Institute. Special thanks is due to Robert Royer, who provided me
with a solid, meticulous introduction to the foundations of the protocols employed in breast
cancer genetics when I first joined the Familial Breast Cancer Research Unit; Shiyu Zhang,
who contributed an abundance of wet lab help to this project; Victoria Sopik, who was
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always available for comments and advice on joint papers; Erin Sellars, who carried out
the Sanger sequencing necessary to validating this project’s findings, coordinates our wet
lab operations, and was an invaluable resource where learning new protocols were
concerned; Ellen MacDougall, who consistently aligns all of our colleagues’ schedules;
and Marcia Llacuachaqui, who was always willing to lend an encouraging word and a
helping hand.
The Institute of Medical Science’s Master of Science (Medical Science) program
is another institution that deserves my sincerest gratitude: all of the IMS faculty and staff
with whom I have had the pleasure of interacting through my courses have solely been
helpful to the definition of my career and the execution of this publication. Special thanks
is due to Drs. Lucy Osborne and Steven Scherer, who taught MSC2010, a course which
immersed me in a range of key genetics and epidemiology concepts, and to Hazel Pollard,
who was always available to assist me with logistics.
Without external financial support, this work also would not have been possible. I
would thus like to extend my deepest thanks to the Province of Ontario and the University
of Toronto in awarding me the Ontario Graduate Scholarship (OGS), the Ministry of
Training, Colleges, and Universities and the University of Toronto for awarding me the
Queen Elizabeth II Graduate Scholarship in Science & Technology (QEII-GSST) and the
Institute of Medical Science for awarding me the Institute of Medical Science Entrance
Award.
Personally speaking, I would, of course, also like to thank my family and friends,
without whom it would not have been possible for me to complete this project.
v
Finally, I would like to thank all those women who participated in this project for
sharing their lives and their battles with us, in the hopes of furthering both their own
treatments and the future medical treatment possible for other women.
vi
Table of Contents
ACKNOWLEDGEMENTS ........................................................................................... III
LIST OF ABBREVIATIONS ..................................................................................... VIII
INTRODUCTION AND OBJECTIVES......................................................................... 1 1.1. INTRODUCTION ...................................................................................................... 1 1.2. OBJECTIVES ........................................................................................................... 5
1.2.1 Project One: Screening RECQL in Ontario, Canada ................................... 5 1.2.2 Project Two: BRCA1, BRCA2, and PALB2 in Trinidad and Tobago ........... 6 1.2.3 Project Three: BRCA1, BRCA2, and PALB2 in Jamaica ............................. 6
INTRODUCTION TO BREAST CANCER ................................................................... 7 2.1 Types of Breast Cancer: Sporadic, Hereditary, and Familial ......................... 7
2.2 RISK FACTORS ....................................................................................................... 8 2.2.1 Pregnancy and Childbirth............................................................................. 8 2.2.2 Age ................................................................................................................ 8 2.2.3 Behavioral and Environmental Influences.................................................... 9
2.3 GENETIC CONSIDERATIONS ................................................................................. 11 2.3.1 Molecular and Genetic Subtypes ................................................................ 11
2.4 SCREENING AND DIAGNOSTICS ........................................................................... 13 2.4.1 Screening..................................................................................................... 13 2.4.2 Diagnosis .................................................................................................... 15
2.5 MANAGEMENT .................................................................................................... 17 2.6 Treatment Limitations and Future Directions ................................................ 18
HEREDITARY BREAST CANCER ............................................................................ 21 3.1 HEREDITY AND BREAST CANCER ........................................................................ 21 3.2 UNDERSTANDING HEREDITARY BREAST CANCER: KEY TERMINOLOGY ............. 25
3.2.1 Mutation- and Gene-Level Terms ............................................................... 26 3.2.2 Tumor-Level Terms ..................................................................................... 32
3.3 BREAST CANCER SUSCEPTIBILITY GENES: GENERAL FUNCTIONS AND INVOLVED
PATHWAYS ..................................................................................................................... 33 3.3.1 Cell Division ............................................................................................... 33 3.3.2 DNA Damage Recognition and Response................................................... 34 3.3.3 DNA Repair ................................................................................................. 34 3.3.4 Other Functions: Cell Survival Post-Damage, Transcription, Apoptosis,
Senescence, Autophagy, and Angiogenesis ............................................................... 38 3.3.5 Joint Functions: Gene Interactions ............................................................ 38 3.3.6 Two Cancerous Categories: Oncogenes and Tumor Suppressors ............. 39
3.4 BREAST CANCER SUSCEPTIBILITY GENES: KNOWN MUTATION TYPES ............... 40 3.4.1 Structural Types .......................................................................................... 40 3.4.2 Functional Types ......................................................................................... 44
3.5 KNOWN BREAST CANCER SUSCEPTIBILITY GENES .............................................. 45 3.3.1 Breast cancer 1 (BRCA1) and breast cancer 2 (BRCA2) ........................... 45
vii
3.3.2 Tumor protein 53 (TP53, p53) .................................................................... 54 3.3.3 Phosphatase and tensin homolog (PTEN) .................................................. 57 3.3.4 Other high-penetrance genes ...................................................................... 58 3.3.5 Moderate-penetrance genes ........................................................................ 59 3.3.6 Plausible and former candidate genes ........................................................ 61 3.3.7 Low-penetrant loci ...................................................................................... 63
SCREENING RECQL GENE AMONG BREAST CANCER PATIENTS IN
ONTARIO, CANADA .................................................................................................... 64 4.1 RECQL: INTRODUCTION ..................................................................................... 64
4.2.1 Introduction................................................................................................. 64 4.2.2 Structure ...................................................................................................... 64 4.2.3 Function ...................................................................................................... 65
4.3 RECQL: BREAST CANCER SUSCEPTIBILITY GENE CANDIDACY IN SELECTED
POPULATIONS ................................................................................................................. 67 4.3.1 Our Lab’s Prior Paper................................................................................ 67 4.3.2 Other Papers ............................................................................................... 70
4.4 OUR PROJECT: SCREENING RECQL IN ONTARIO POPULATION ........................... 72 4.4.1 Rationale ..................................................................................................... 72 4.4.2 Objective ..................................................................................................... 74 4.4.3 Methodology ............................................................................................... 74 4.4.4 Results ......................................................................................................... 80 4.4.5 Discussion ................................................................................................... 86
OLD GENES, NEW CONTEXT: BRCA1, BRCA2, AND PALB2 IN THE
CARIBBEAN................................................................................................................... 90 5.1 CANCER IN SELECTED POPULATIONS .................................................................. 90 5.2 BREAST CANCER IN THE CARIBBEAN .................................................................. 91 5.3 PROJECT ONE: A SURVEY OF BRCA1, BRCA2, AND PALB2 MUTATIONS IN
WOMEN WITH BREAST CANCER IN TRINIDAD AND TOBAGO ............................................ 92 5.3.1 Rationale ..................................................................................................... 92 5.3.2 Objectives .................................................................................................... 93 5.3.3 Methods ....................................................................................................... 93 5.3.4 Results ......................................................................................................... 95 5.3.5 Discussion ................................................................................................. 102
5.4 PROJECT TWO: A HIGH FREQUENCY OF PALB2 MUTATIONS IN JAMAICAN
PATIENTS WITH BREAST CANCER .................................................................................. 103 5.4.1 Rationale ................................................................................................... 103 5.4.2 Objectives .................................................................................................. 104 5.4.3 Methods ..................................................................................................... 104 5.4.4 Results ....................................................................................................... 105 5.4.5 Discussion ................................................................................................. 109
FUTURE DIRECTIONS .............................................................................................. 111 6.1 RECQL .................................................................................................................. 111 6.2 BRCA1, BRCA2, & PALB2 IN THE CARIBBEAN ................................................... 113
REFERENCES .............................................................................................................. 114
viii
List of Abbreviations
ABRAXAS BRCA1-A Complex Subunit
ACACA Acetyl-CoA Carboxylase Alpha
ATM Ataxia Telangiectasia Mutated
ATR Ataxia Telangiectasia and Rad3-Related Protein
AURKA Aurora Kinase A
BAM Binary Alignment Map
BAP1 BRCA1-Associated Protein 1
BARD 1 BRCA1-Associated RING Domain 1
BASC BRAC1-associated genome surveillance complex
BER Base excision repair
BIR Break-induced repair
BLM Bloom syndrome RecQ-Like Helicase
BMI Body mass index
BRCA 1 Breast Cancer 1
BRCA 2 Breast Cancer 2
BRCC BRCA1/BRCA2-containing complex
BRCC3 BRCA1/BRCA2-Containing Complex Subunit 3
BRCC45 BRCA1/BRCA2-Containing Complex Subunit 45
BRCT Breast cancer carboxy-terminal domain
BRIP1 BRCA1-Interacting Protein C-Terminal Helicase 1
ix
BSE Breast Self-Examination
BWA Burrows Wheeler Aligner
CARPHA The Caribbean Public Health Agency
CBE Clinical Breast Examination
CCDC98 Coiled-Coil Domain Containing 98
CCND1 Cyclin D1
CDC Center of Disease Control
CDH1 Cadherin 1
CHEK2 Checkpoint Kinase 2
CNA Copy number alterations
CNV Copy number variations
COBRA1 Negative Elongation Factor Complex Member B (NELFB)
CT Computed Tomography
CTC Circulating Tumor Cells
dbSNP Single Nucleotide Polymorphism Database
DCIS Ductal carcinoma in-situ
DSB Double strand break
DSBR Double strand break repair
ER Estrogen receptor
ERBB2 Human Epidermal Growth Factor Receptor 2
ESR1 Estrogen Receptor 1
ETV6 ETS Variant 6
x
ExAC The Exome Aggregation Consortium
EXD2 Exonuclease 3'-5' Domain Containing 2
FANCD1 Breast Cancer 2
FANCN Fanconi Anemia, Complementation Group N
FGFR2 Fibroblast Growth Factor Receptor 2
GATK Genome Analysis Toolkit
GWAS Genome-wide association study
H19 Non-Protein Coding RNA 8
HBOC Hereditary breast and ovarian cancer
HDAC2 Histone Deacetylase 2
HDM2 Human MDM2 Proto-Oncogene
HER 2 Human Epidermal Growth Factor Receptor 2
HOXA5 Homeobox A5
HRR Homologous recombination repair
IDC Invasive ductal carcinoma
IHC Immunohistochemistry
ILC Invasive lobular carcinoma
IR Ionizing radiation/infrared radiation
IRB Institutional review board
ISH In-situ hybridization
ITPR2 Inositol 1,4,5-Trisphosphate Receptor Type 2
KPNA4 Karyopherin Subunit Alpha 4
xi
LCIS Lobular carcinoma in situ
LISP1 Liver-Specific Protein 1
LOH Loss of heterozygosity
LSP1 Lymphocyte-Specific Protein 1
MAF Minor allele frequency
MAP3K Mitogen-Activated Protein Kinase Kinase Kinase
MAP3K1 Mitogen-Activated Protein Kinase Kinase Kinase 1`
MLH1 MutL Homolog 1
MMEJ Microhomology-mediated end-joining
MMR Mismatch repair
MRI Magnetic resonance imaging
MRN MRE11-RAD50-NBN complex
MSH MutS Homolog
MSH2 MutS Homolog 2
MSH6 MutS Homolog 6
mTOR Mechanistic target of rapamycin
MYC Avian Myelocytomatosis Viral Oncogene Homolog
NBA1 BRISC and BRCA1 A Complex Member 1
NBN Nibrin
NER Nucleotide excision repair
NGS Next-generation sequencing
NHEJ Non-homologous end-joining repair
xii
NHLBI National Heart, Lung, and Blood Institute
NRG1 Neuregulin 1
OR Odds ratio
PALB2 Partner and Localizer of BRCA2
PARP Poly(ADP-Ribose) Polymerase
PCLAF PCNA clamp-associated factor
PET Positron-emission tomography
PI3K Phosphatidylinositol-4,5-bisphosphate 3-kinase
PIK3CA Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit
PMS2 PMS Homolog 2
PR Progesterone receptor
PTEN Phosphatase and Tensin Homolog
RAD50 RAD50 Double Strand Break Repair Protein
RAD51 RAD51 Recombinase
RAD51C RAD51 Paralog C
RAD51D RAD51 Paralog D
RAD51LI RAD51 Paralog B
RAP80 Ubiquitin Interaction Motif Containing 1
RASSF1A Ras Association Domain Family Member 1
RECQL RecQ Helicase-Like
RFC Replication Factor C
SAM Sequence Alignment Map
xiii
SCD Serine cluster domain
SDSA Synthesis-dependent strand annealing
SIPA1 Signal Induced Proliferation Associated 1
SMC1A Structural Maintenance of chromosome 1
SNP Single nucleotide polymorphism
SSB Single strand break
STK 11 Serine/Threonine Kinase 11
TBM Tandem base mutation
TCGA The Cancer Genomic Atlas
TGFB1 Transforming Growth Factor Beta 1
TMS1 Target of Methylation-Induced Silencing 1
TNBC Triple-negative breast cancer
TNM Tumor, Node, Metastasis
TNRC9 Trinucleotide Repeat-Containing Gene 9 Protein
TOX3 TOX High Mobility Group Box Family Member 3
TP53 Tumor Protein 53
UBXN1 UBX Domain Protein 1
UST Uronyl 2-Sulfotransferase
VEGF Vascular Endothelial Growth Factor
WCRI Women’s College Research Institute
WHO World Health Organization
WRN Werner Syndrome RecQ-Like Helicase
xiv
XRCC1 X-ray Repair Cross-Complementing Protein 1
ZM1Z1 Zinc Finger, MIZ-Type Containing 1
1
Chapter 1
Introduction and Objectives
1.1. Introduction
Breast cancer is the most common cancer in women around the world1, and the
second leading cause of cancer-related death in Canadian women2. In 2016 alone, an
estimated 25,700 Canadian women were diagnosed with breast cancer3, and an estimated
4,900 Canadian women died from breast cancer4. Worldwide, the number of affected
women was likely far higher: according to the most recent data available, which is from
2012, more than 1.7 million new breast cancer cases and 500,000 breast cancer deaths
occur among women worldwide every year, with developed countries generally having
higher rates than developing countries5.
Approximately 5 to 10 % of all breast cancer cases are hereditary cancer cases6,
meaning that the individual in question inherited the causative breast cancer mutation
from his or her parents. Another 10 % of all breast cancer cases are familial cancer
cases7, meaning that though their causal genetic mutations are not known, the patients
have a history of breast cancer in their immediate relatives. Both inheriting a breast
cancer-relevant mutation and having affected family members are significant breast
cancer risk factors: mutations in certain high-penetrance breast cancer susceptibility
genes may increase a woman’s lifetime breast cancer risk by as much as 80 %8, while the
presence of affected individuals in a woman’s family can make her lifetime breast cancer
risk up to twice as high as that of an individual without affected relatives, even
independently of any inherited mutations9. Identifying breast cancer susceptibility genes
2
and individuals who are at risk of developing breast cancer due to a mutation in those
genes is important from several perspectives. Firstly, knowing an individual’s genetic
make-up and isolating mutations in known susceptibility genes helps us take preventive
measures to reduce cancer risk: for example, mutations in BRCA1/2 are associated with
an up to 80 % life-time risk of developing cancer; patients with such mutations can be
advised to seek prophylactic measures such as double mastectomy, which has been
shown breast cancer risk by up to 95 %10. Secondly, knowing about breast cancer
susceptibility genes allows us to offer intensive screening to high-risk individuals (e.g.,
those with strong family history), allowing us to detect breast cancer at its early stages,
which ameliorates the chances of treatment succeeding. Finally, many biomarkers are
predictive not solely of breast cancer development, but also of the response to treatment –
and consequently also the prognosis – that a mutation carrier may have. As an example,
individuals with BRCA1 mutations are more likely than BRCA2 mutation carriers to be
both basal-like or triple negative (TN), which is also typically associated with a worse
prognosis11. If we are aware of the associations between certain biomarkers and certain
prognoses, then we can better predict not solely which patients will get breast cancer, but
also which patients have more aggressive cancers requiring more intensive treatments.
Also, genes – if they are found to be causal – may potentially be targeted via novel drugs
developed specifically to cater to them. For example, BRCA1 mutation carriers appear to
be more sensitive to cisplatinum therapy12.
It is clear, then, that susceptibility gene discovery is integral to oncology for a
range of reasons. Given the importance of discovery, and the relatively recent advent of
next-generation sequencing (NGS), a form of high-throughput modern sequencing in
3
which massive amounts of genetic information can be processed in parallel, one would
expect that gene discovery would be proceeding more quickly than ever before,
considering that more genetic material can now be analyzed more cost- and time-
efficiently. However, the vast majority of key breast cancer susceptibility genes (e.g.,
TP53, BRCA1, ATM, BRCA2, CDH1, CHEK2, PTEN, BARD1, NBN, and STK11) were
discovered by the 1990s13,14,15,; by contrast, since 2000, only one significant moderate-
penetrance breast cancer susceptibility gene – namely, PALB2 – has been discovered16.
This suggests that the vast majority of high-penetrance genes with relatively high
mutation frequencies in the general population have likely already been discovered. Next-
generation sequencing, which is gaining popularity, permits rapid, in-depth surveillance
of the entirety of the genome, but few high-penetrance genes have been found. Any
undiscovered genes would likely either be low-penetrance genes whose effects are only
expressed in a small percentage of mutation carriers, or exceedingly rare variants of high-
penetrance genes.
In search of just such rare high-penetrance breast cancer susceptibility genes, our
lab conducted whole-exome next-generation sequencing of two populations (from Poland
and Quebec) and reported a novel breast cancer susceptibility gene called RECQL17.
While many genes were found to have rare, truncating variants in either the Polish or the
Quebecois population, only had multiple rare truncating mutations in both populations. It
should also be noted that, in addition to being the only gene that harbored multiple rare
truncating mutations across both populations (and, consequently, the only gene that
yielded a promising odds ratio), RECQL is functionally likely to be a susceptibility gene.
Like the other four RecQ helicases, RECQL likely plays a role in one or more important
4
cellular functions related to DNA repair, replication, recombination, and transcription. It
is also a potential regulator of genomic integrity: when it is knocked out in mice
embryonic fibroblasts, a range of genomic complications occur, including spontaneous
chromosomal breakage, aneuploidy, and translocations; when both mouse and human
cells are depleted of it, they grow less, become more sensitive to ionizing radiation (IR),
experience a high rate of spontaneous sister chromatid exchange, and develop more
double-strand DNA breaks (dsDNA breaks)18,19,20. RECQL has also been shown to be
involved in non-homologous end joining DNA repair (NHEJ), a pathway in which
double-strand DNA breaks are fixed via the broken ends’ being directly ligated to one
another without the need for a homologous template, and with lengthening telomeres
independently of telomerase, the enzyme usually responsible for telomere elongation21.
For all these reasons, RECQL is thought to be a tumor suppressor: a gene that typically
discourages tumor formation, notably by preventing replication forks from stalling and
breaking, and whose loss of function via mutations thus leads to increased cancer
susceptibility22.
On these bases, our team identified RECQL as a breast cancer susceptibility gene
candidate. However, this initial discovery-oriented study was conducted in two
populations that are not indicative of the general population of Canada. Both Poland and
Quebec are regions with founder populations: populations that share genetic
characteristics because of shared ancestry. As such, even a very high coincidence of a
rare truncating mutation with breast cancer risk in such a population may not be probative
of that mutation’s predictive value in the general population, as this causative mutation
may well be a founder mutation: a mutation primarily restricted to the smaller, more
5
homogeneous populations in question. Follow-up work concerning RECQL in the context
of the general Canadian population was needed; this is where the first project that was
carried out here becomes relevant.
At the same time, as much as discovering and validating new breast cancer
susceptibility genes is of relevance, it is also important to examine existing breast cancer
susceptibility genes in populations with high breast cancer incidence, and about which we
have little established hereditary breast cancer information. The Caribbean, for example,
is a region with high breast cancer incidence, but relatively little in the way of formally
instituted national screening policies for unselected patients, many of whom could
potentially benefit from genetic testing and related prevention and treatment strategies.
To this end, we also carried out two projects geared at examining the frequency of
BRCA1, BRCA2, and PALB2 mutations in Trinidad and Tobago and Jamaica, two
Caribbean countries with a sparse genetic testing regulation landscape, but very high
incidences of breast cancer.
1.2. Objectives
1.2.1 Project One: Screening RECQL in Ontario, Canada
Objective 1: Screen the entire coding exons of the RECQL gene, plus 20 base
pairs of the intronic regions at each end of the exons, among 2,859 breast cancer cases
and 929 healthy women from Ontario to locate RECQL mutation carriers. Emphasis will
be placed on locating variants that are potentially pathogenic (i.e. truncating variants).
Objective 2: Estimate the odds ratio of developing breast cancer and possibly the
life-time risk of developing breast cancer associated with carrying a RECQL mutation.
6
1.2.2 Project Two: BRCA1, BRCA2, and PALB2 in Trinidad and Tobago
Objective: Examine the frequency of BRCA1/2 and PALB2 mutations in
unselected Trinidad and Tobago breast cancer cases to determine the contribution of
hereditary cases to that population.
1.2.3 Project Three: BRCA1, BRCA2, and PALB2 in Jamaica
Objective: Examine the frequency of BRCA1/2 and PALB2 mutations in
unselected Jamaican breast cancer cases to determine the contribution of hereditary cases
to that population.
7
Chapter 2
Introduction to Breast Cancer
2.1 Types of Breast Cancer: Sporadic, Hereditary, and Familial
Breast cancer can be caused by both germline and somatic mutations. Germline
mutations are mutations that are mostly inherited from one’s parents, and are thus present
in the genome since birth23. By contrast, somatic mutations are mutations that have not
been inherited from one’s parents, but have instead been acquired by the cell sporadically
through DNA replication and via other influences over the course of a lifetime24. If breast
cancer is caused by germline mutations, it is referred to as hereditary or inherited breast
cancer25. If it is caused by somatic mutations, it is referred to as sporadic breast cancer.
Further, if a given family has a history of breast cancer, then familial breast cancer
is said to be present. A family history is considered strong if a person’s family includes
two affected first-degree relatives (i.e., parents, children, or siblings); a strong family
history is associated with a higher risk of breast cancer, though not necessarily with
hereditary breast cancer (i.e., it is possible to have familial but not inherited breast
cancer)26. If there is any first-degree family history, then there is usually a two-fold
increased risk of breast cancer27; the higher the number of affected relatives, the higher
the relative risk28; finally, the less close the relationship between the relatives, the lower
the risk (e.g., risk is only 1.5-fold for second-degree relatives compared to two-fold for
first-degree relatives)29. When an individual has a family history of breast cancer but no
known genetic anomalies associated with the cancer, the cancer is said to be familial.
When, on the other hand, an individual has a family history of breast cancer and known
mutations in breast cancer susceptibility genes, the cancer is said to be hereditary.
8
2.2 Risk Factors
2.2.1 Pregnancy and Childbirth
Pregnancy and childbirth affect a woman’s risk of developing breast cancer.
Parity, or the number of pregnancies a woman has had, modifies breast cancer risk.
Nulliparity (i.e., having no pregnancies) is a breast cancer risk factor30. Generally, the
more pregnancies a woman has had, the less likely she is to develop breast cancer,
particularly if the first child is had before 20 years of age (in which case a 50 % reduction
in life-time breast cancer risk compared to nulliparous women has been observed)31.
However, it may be the case that increased parity, though it decreases the risk of luminal
A breast cancer, increases the risk of triple negative breast cancer32. This association,
however, as well as associations between parity and luminal B and HER2-overexpressing
breast cancers, are not presently clear33. Additionally, the longer a woman breastfeeds,
the lower her risk of developing breast cancer of the basal-like subtype, and the lower her
risk of recurrence and breast cancer death34. This has important epidemiological
implications, given that black women in the U.S. breastfeed significantly less than white
women, and experience nearly double the rate of triple-negative breast cancer35.
Additionally, as has already been mentioned, the age at which a woman first gives birth
also affects the association between parity and luminal A breast cancer: specifically, the
younger a woman is the first time she gives birth, the more decreased her risk of breast
cancer will be, assuming equivalent parity36.
2.2.2 Age
Age in general is decidedly a risk factor for breast cancer. The older a woman
gets, the more likely she is to get breast cancer37, with risk doubling approximately every
ten years until menopause, at which point risk continues to increase, but does so
9
comparatively slowly38. However, some breast cancers tend to occur somewhat earlier
than certain other cancers would: there is higher incidence of breast cancer than lung
cancer overall at younger ages, for example39. Breast cancers are frequently categorized
on the basis of whether they occur early (“early-onset”, “pre-menopausal”) or late in a
person’s life (“late-onset”, “post-menopausal”), and certain types of breast cancers are
more likely to occur early than other types. Invasive ductal carcinoma (IDC) and invasive
lobular carcinoma (ILC), for example, tend to affect mostly older women (ages 55 or
older), though certain subtypes of IDC (e.g., medullary) as well as LCIS involve earlier
onset, while some subtypes (e.g., mucinous) tend to involve unusually late onset.
More specifically, age at menarche and age at menopause are relevant age-related
risk factors for breast cancer. Typically, older age at menarche tends to be correlated with
a slightly decreased risk of certain more problematic cancers (i.e., triple negative and
luminal A, as well as possibly luminal B)40. However, relatively little has been confirmed
about the relationship between HER2-overexpressing cancer and age at menarche41. By
contrast, older age at menopause appears to be correlated with an increased risk of breast
cancer42.
2.2.3 Behavioral and Environmental Influences
Certain attributes of a woman’s life history are also risk factors for breast cancer.
Body-mass index (BMI), the ratio of a person’s weight to their height, tends to be
correlated with decreased risk of luminal A breast cancer and, in some studies, increased
risk of triple negative breast cancer when elevated in pre-menopausal
women43,44,45,46,47,48. In post-menopausal women, increased BMI has been associated with
an increased risk of breast cancer49. In pre-menopausal women, use of oral contraceptives
can also influence a woman’s breast cancer risk: specifically, use of oral contraceptives
10
tends to be correlated with decreased risk of luminal A breast cancer, and increased risk
of triple negative breast cancer50. Correspondingly, in post-menopausal women, use of
menopausal hormone therapy is associated with an increased risk of breast cancer51.
Finally, where substances are concerned, increased alcohol use (i.e., more than seven
drinks per week compared to none) appears to be correlated with a somewhat increased
risk of breast cancer52.
Personal factors aside, geography is also a risk factor for breast cancer.
Standardized mortality for breast cancer tends to be higher in developed rather than
developing countries53, with mortality being highest in Belgium at an age-standardized
rate of 111.9 per 100,00054. However, lack of awareness is a serious issue in
underdeveloped countries, where it was recently found that both prognostication and
survival rates are low due to lack of early diagnosis55. Studies have also suggested that
this increased mortality is more due to the environmental and lifestyle factors of a given
locality than to the genetics of its natives, considering that breast cancer risk tends to
assimilate to the risk of the population that a person has immigrated into within one or
two generations56.
Finally, family history of breast cancer is a risk factor, as could be expected given
the highly hereditary nature of certain types of breast cancer. If one or more first-degree
relatives (i.e., parents, siblings, or children) has had breast cancer, the risk of developing
any kind of breast cancer substantially increases: in fact, the risk of breast cancer in
individuals with a family history can be up to two times higher than the risk of breast
cancer in individuals without a family history.
11
It is worth mentioning at this point that several associations have been noted
between the aforementioned risk factors and luminal A cancer, but that significantly less
is known about the associations between these risk factors and other cancer types (i.e.,
luminal B, HER2-overexpressing, and triple negative).
2.3 Genetic Considerations
2.3.1 Molecular and Genetic Subtypes
While breast cancer can be described as per the above phenotypic attributes,
breast cancer can – and should – also be classified in terms of its genetics. Segmenting
breast cancer into molecular subtypes permits the assignment of treatment options that
align well with a specific cancer’s genetic characteristics. For several decades, we have
recognized that breast cancer is a highly heterogeneous condition, with variation
occurring both between tumors (inter-tumor variation) and within individual tumors
(intra-tumor variation)57. Despite this variation, advances in genetic technologies have
made it possible to segment breast cancer into five core molecular subtypes: luminal A,
luminal B, luminal B-like, HER2-positive, and basal-like. Cancers are classified into
subtypes on the basis of gene expression profiling of the tumor cells58.
Luminal A breast cancers tend to involve several characteristics. Firstly, luminal
A cancers tend to be estrogen receptor-positive (ER+)59, which means that they typically
respond to estrogen, and can thus be managed via drugs that interfere with estrogen
production or binding, as is further explained in Chapter Two. In a similar vein, they tend
to be progesterone-receptor positive (PR+)60, which again means they can be affected by
therapies that interfere with progesterone production and function. Luminal A cancers
also tend to be HER2-negative (HER2-)61, which means that they do not have amplified
12
or overexpressed HER2. Finally, luminal A tumors tend to have low quantities of the
protein Ki-67 (also known as MKI67), which is a protein that is associated with cell
proliferation. All of this may somewhat help us understand why luminal A breast cancers
are usually not as prognostically problematic: ER- and PR-positivity implies that these
cancers can be treated with the comparatively innocuous hormonal therapy rather than the
more damaging chemotherapy, HER2-negativity confers better prognosis, given that
HER2-positivity is associated with p53 abnormalities, lymphoid infiltration, and a high
mitotic index62, and lower quantities of Ki-67 potentially indicate that less cancerous cell
proliferation has occurred, explaining the luminal A association with smaller tumor sizes.
Luminal B breast cancers differ from luminal A cancers in a number of ways.
Like luminal A cancers, they tend to be both ER-positive and HER2-negative63.
However, unlike luminal A cancers, they tend to be PR-negative, making them less
susceptible to hormonal therapy. They may also have a higher Ki-67 index, which has
been found to be correlated with young age at diagnosis, larger tumors, invasion of the
lymph nodes, and p53 overexpression (in addition to HER2-positivity and ER-negativity,
which luminal B cancers nevertheless lack)64, and is thus both prognostically
disadvantageous and prone to conferring earlier recurrence.
Luminal B-like cancers resemble luminal B breast cancers in that they are ER-
positive65. However, unlike luminal B cancers, luminal B-like cancers involve
overexpression or amplification of HER2, which connotes a poorer prognosis. They also
involve any levels of Ki-67 and PR.
HER2-positive cancers, as their name suggests, involve solely HER2-positivity,
which involves a poorer prognosis66. In some breast cancer types, HER2-positivity also
13
tends to inversely correlate with ER and PR positivity67, which means that HER2-positive
cancers will often need to be treated with chemotherapy rather than with hormonal
therapy.
Finally, basal-like cancers are negative where all hormone receptors are
concerned: they lack ER, PR, and HER2 receptors (ER-negative, PR-negative, HER2-
negative)68. While HER2-negativity would usually connote a positive prognosis, the fact
that these cancers are hormone receptor-negative makes them unusually difficult to treat.
These subtypes are important because they have prognostic relevance. Some
subtypes respond better to chemotherapy and hormonal therapy than others: intuitively,
for example, subtypes lacking certain hormone receptors will not be responsive to
hormonal therapy.
2.4 Screening and Diagnostics
2.4.1 Screening
Breast cancer screening involves monitoring healthy women for signs of breast
cancer, with the aim of detecting any existing cancers as early as possible. Screening can
consist of a range of practices, including awareness-raising, physical examinations,
patient history collection, MRI, mammography, and risk assessment69. Breast self-
examination (BSE), in which the patient self-examines their breasts and reports changes
to their physician, is also a part of the screening process.
Typically, formal screening should be conducted once every one to three years,
depending on the age of the individual in question, and involves patient history collection
and a clinical breast examination (CBE), which is based on palpation of the breast for
symptoms of potential cancer, including lumps, nipple discharge, skin changes, and
14
asymmetric thickening70. Following a CBE in which potential symptoms are found,
individuals’ cancer risks are stratified: a woman may be deemed to be at normal or
increased risk of breast cancer. Risk is a function of a range of variables, including family
history and mutations, and can be calculated using a range of models (e.g., the empirical
Gail model, genetic models)71. A family history of breast cancer in first-degree relatives,
for example, confers a two-fold increased risk in breast cancer, and is considered strong if
two or more first-degree relatives are affected.
Further, certain mutated genes, as will be discussed in Chapter Four, confer an
increased risk of breast cancer; given that a considerable percentage of variation in breast
cancer risk is genetic, these need to be factored into risk as well. For example, TP53,
when mutated, confers a 50 to 60 % breast cancer risk by the age of 4572; PTEN, slightly
less severely, confers a 30 to 50 % risk by the age of 7073. Generally, women with first-
degree relatives below the age of 40 or with two affected first-degree relatives at any age
are considered to be a moderate risk74; women with two or more affected first-degree
relatives with at least one diagnosed under the age of 50 are considered high-risk75.
Finally, note that there are cases in which mutation frequency is dependent on family
history: if, for example, a strong family history involving six or more cases is present,
then it is probable that at least some of that familial cancer is associated with BRCA
mutations76. It is also noteworthy that risk of ovarian cancer is 30 % higher in mothers
and sisters of individuals with breast cancer77,78, given that the BRCA genes are
responsible for both ovarian and breast cancer contributions. Risk prediction also
involves analyzing some of the lifestyle factors described earlier.
15
At normal risk, women are encouraged to get a CBE everyone to three years if
they are between 20 to 39, and to get an annual CBE as well as a screening
mammography if they are 40 years or older79. At increased risk, women are told to get
annual mammograms and a CBE every six to twelve months if they are over 25 and have
previously undergone radiation; if they are under 25, then an annual CBE is
recommended80. For women aged 35 years and older, a risk assessment tool is available;
if these women end up having a five-year cancer risk of 1.7 % or greater, CBEs are
recommended every six to 12 months, alongside annual mammography81. In certain
special cases where risk is unusually high (greater than 20 % lifetime risk, based largely
on family history), women will be encouraged to get CBEs every six to 12 months
alongside annual mammography, and to consider an assortment of risk-reduction
strategies as well as annual MRI82. They will also be asked to begin getting these
assessments at an age that is five to ten years lower than that of the youngest breast
cancer case in the family83.
2.4.2 Diagnosis
2.4.2.1 Morphological and Molecular Assessments
If a woman is found to possess physical symptoms of breast cancer, screening
transitions into diagnosis. While breast cancer screening involves examining healthy
women for signs of potential cancer, diagnostic evaluations involve further examining
selected patients who are exhibiting symptoms. Post-the kind of imaging discussed
above, further diagnostic imaging in the form of mammography, ultrasonography, or
breast MRI may be performed to evaluate the breast84,85. This may be coupled with chest
X-rays, computerized tomography (CT) scans, bone scans, and positron emission
tomography (PET) scans to assess the stage of systemic disease86,87,88,89.
16
Typically, the next step following diagnostic imaging is an assessment of
pathology at the molecular and morphological levels, made possible by specimen
procurement and evaluation. Diseased tissue is obtained via core biopsy, fine-needle
aspiration, or surgical excision for morphological evaluation90. At this level, it is
important to consider that tissue preparation factors such as tissue handling, ischemic
time, the use of frozen rather than fresh sections, fixation, decalcification, and processing
all affect the effectiveness of evaluation91,92,93. Determination of gross tumor
characteristics can be problematized not solely by these logistical considerations, but also
by the situation of the tumor within the breast and the tumor cells’ dispersal amongst
normal cells94.
Following morphological examination, immunohistochemical (IHC) and
molecular tests are frequently used, particularly if morphological examination has been
ambiguous95. Specific tests can be used to gain certain molecular features. For example,
immunohistochemical analysis of paraffin-embedded tissue sections can determine
estrogen receptor (ER), progesterone receptor (PR), and Her-2/neu (HER2) status96, and
can be used to analyze RNA and DNA within these samples97. In-situ hybridization (ISH)
is sometimes used to confirm initial IHC assessments of HER2 amplification98.
2.4.2.2 Gene Tests
As much as the histological tumor markers isolated via these tests are of
prognostic value, and are routinely used to predict patient response to therapy, they are
limited in their utility by intra-tumor heterogeneity, a single tumor may possess a variety
of cells, some of them positive and some of them negative for the aforementioned
markers. As such, there is great interest in identifying new genes that can be included in
gene tests involving DNA microarrays and high-throughput reverse transcription –
17
polymerase chain reaction assays. Several such genomic assays already exist, including
MammaPrint [Agendia], which monitors 70 genes to predict the ten-year recurrence risk
after diagnosis of stage I or stage II breast cancer regardless of hormone receptor
status99,100; Prosigna Breast Cancer Prognostic Gene Signature Assay (formerly PAM50)
[NanoString], which monitors 58 genes to predict the risk of distant recurrence for post-
menopausal women within ten years of diagnosis of early-stage, hormone-receptor-
positive disease with up to three positive lymph nodes after five years of hormonal
therapy101,102; OncotypeDX [Genomic Health, Inc.], which monitors 21 genes to predict
the risk of recurrence in early-stage, hormone-receptor-positive breast cancer as well as
anticipated benefit from chemotherapy after surgery for invasive breast cancer, and the
risk of recurrence, risk of ipsilateral invasive breast cancer, and anticipated benefit from
radiation after surgery for ductal carcinoma in situ (DCIS)103; EndoPredict [Sividon
Diagnostics, Myriad], which monitors 12 genes to predict the risk of distant recurrence of
early-stage, hormone-receptor-positive, HER2-negative breast cancer with up to three
positive lymph nodes104; Breast Cancer Index [bioTheranostics], which monitors seven
genes to predict the five- and ten-year recurrence risk of node-negative, hormone-
receptor-positive breast cancer, as well as the anticipated benefit of extending hormone
therapy beyond five years (for a total of ten years of hormone therapy)105; and
Mammostrat [Clarient Diagnostic Services], which monitors five genes to predict
recurrence risk for early-stage, hormone-receptor-positive breast cancer106.
2.5 Management
After a diagnosis of breast cancer is confirmed, a range of breast cancer
treatments can be made available to the patient. Overall, breast cancer treatments can be
18
segmented into two categories: local treatments (i.e., surgery and radiation therapy),
which treat the tumor without affecting the remainder of the body, and systemic
treatments (i.e., chemotherapy, hormone therapy, and targeted therapy), which target
cancer cells all over the body, and tend to involve the use of drugs. Many women with
breast cancer will receive more than one type of treatment for their cancer.
2.6 Treatment Limitations and Future Directions
While several key treatments are available for breast cancer, breast cancer still
accounts for a significant portion of female cancer deaths. Furthermore, certain key types
of breast cancer, such as triple negative breast cancer (TNBC) – which accounts for 10 to
15 % of all female breast cancers107 – remain associated with higher likelihood of distant
recurrence and lower overall survival rates despite chemotherapy and surgery108; one
study notes that no substantial progress has been made therapeutically for TNBC in over
a decade109. Finally, it is important to note that even if management at first succeeds –
and it often does, with systemic agents being active in 90 % of primary breast cancers
and 50 % of metastases at the beginning of therapy – it may subsequently fail due to
resistance110 (i.e., the progression of a cancer that necessitates cancer cells’ becoming
immune to the therapy being administered). Resistance is not solely common – it is
expected where most treatments are concerned.
As such, it is clear that there still exists a need to find more effective breast cancer
treatments, particularly for certain categories of patients. For the most part, existing
clinical trials involve existing drugs in novel combinations with other existing drugs, or
with radiation and surgery111. Novel combinations of existing drugs aside, the eventual
goal where breast cancer susceptibility gene discovery is concerned is personalized
medicine112: treatment that is tailored to each individual patient, regardless of inter-
19
patient variability. This form of treatment is seen as having the potential to be more
successful than conventional therapy because of its highly targeted approach. At present,
not all patients respond well to existing cancer therapies: in fact, between 38 and 75 % of
all breast cancer patients fail to respond to a treatment. The principle behind personalized
medicine is that if treatments were to be tailored to individual patients’ genetics, then
they might better combat cancer by targeting precisely those pathways affected in a
certain tumor. A key first step in this kind of personalization of treatment is the
uncovering of biomarkers responsible for driving individual tumors, which include
susceptibility genes, and which may be both prognostic and predictive. While next-
generation technology, as has previously been mentioned, is getting increasingly
competent where this is concerned, and has found significantly more biomarkers than
were previously known, only 50 % of hereditary breast cancers currently have known
genetic bases113. As such, the search for biomarkers is by no means complete.
However, even in cases where the gene or mutation responsible for a given tumor
is known, personalized medicine’s success is not guaranteed114. For one, treatments
targeting that specific gene or mutation may not always be available. Many genes and
mutations do not map directly onto a relevant existing treatment. For example, while we
are aware that mutations to the PI3K-Akt pathway are causative in breast cancer, and that
tumors with PIK3CA mutations may be more sensitive to PI3K inhibitors, no PI3K
inhibitors are presently approved by the United States Food and Drug Administration115,
though several such drugs are in trials, and though inhibitors of the related mTOR
pathway are available (i.e., everolimus)116. Similarly, while it is known that BRCA1 and
BRCA2 carriers would likely benefit from poly (ADP-ribose) polymerase (PARP)
20
inhibitors, these drugs are not as yet definitively developed, though they are seen as likely
to be eventually successful117.
Even when such drugs do become approved for mainstream consumption,
however, personalized medicine will take some time to be adopted. Enhancing
physicians’ understanding of the implications of a range of genetic tests is time-
consuming, especially in light of the fact that many presently practicing physicians were
trained to manage breast cancer without too many concrete genetics-based guidelines.
Further, though new targeted drugs may be developed, they will be difficult to
immediately disseminate, as they will need to gain not solely functional clinical trial
results, but also regulatory and physician approval.
21
Chapter 3
Hereditary Breast Cancer
3.1 Heredity and Breast Cancer
While the majority of breast cancers are sporadic118 (i.e., originating from
mutations acquired by the somatic cells during life), some breast cancers are hereditary
(i.e., inherited by offspring from their parents via mutations in the germline DNA).
A significant proportion of cancers are known to be hereditary. A recent study
showed that, of twin pairs with cancer, approximately 38 % of monozygotic pairs and 26
% of dizygotic pairs were diagnosed with the same type of cancer119; further,
monozygotic and dizygotic twins whose co-twin was diagnosed with cancer had had a 14
% and 5 % increased cancer risk respectively. Overall, this study found that the
heritability for cancer in general was 33 %, with some types of cancer – namely, prostate
cancer, breast cancer, testicular cancer, kidney cancer, and melanoma – showcasing more
variability due to heredity than others120.
The fact that at least some proportion of breast cancers are hereditary has been
understood for almost two centuries: Paul Broca identified his wife’s family as the first-
ever hereditary breast cancer-prone family in 1866121, 122, and since that time, hereditary
breast cancer has been well-documented. We have also known that hereditary breast and
22
ovarian cancer syndrome (HBOC) – i.e., BRCA-associated increased risk of female and
male breast cancer, ovarian cancer, prostate cancer, pancreatic cancer, and melanoma123 –
exists since Lynch first investigated the syndrome. Finally, the link between hereditary
breast cancer and familial breast cancer has been studied for the past several decades:
Ashley’s 1969 “two-hit” model of cancer susceptibility suggests that those who test
positive for a certain gene or mutation – such as individuals who have relatives with
hereditary cancer – are more likely to develop cancer, as they have already taken one
“hit” by being related to affected individuals, and thus need only fewer excess “hits” than
the average individual to develop the disease124. More recently, a study in 2000 found
that breast cancer had a heritability of 27 % (i.e., the proportion of variability in
population disease risk due to genetic factors) among twins from Sweden, Denmark, and
Finland125. In 2016, following up on that 2000 study, the Nordic Twin Study of Cancer
(NorTwinCan) followed up with twins from Denmark, Finland, Norway, and Sweden for
an average of 32 years, and found that 31 % of breast cancer risk variability may be
associated with genetic factors (as well as a relatively significant estimate of shared
environment influence)126, particularly in pre-menopausal women127.
It is thought that heredity contributes in some way to 27 % of breast cancers.
However, only 5 to 10 % of all breast cancer cases have thus far been identified with a
strong inherited component128, and only 4 to 5 % have thus far been explained via
mutations in high-penetrance, autosomal dominantly-inherited mutations. Further, despite
the fact that familial breast cancer (i.e., cancer in individuals who have affected family
members) may seem especially likely to have a hereditary basis, given that families
possess shared genetic material, only 27 % of all familial breast cancer cases – which
23
within themselves comprise more than 13 % of all breast cancers – have been explained
by way of high- or moderate-penetrance mutations; the remaining 70 % of familial breast
cancer cases are yet to be assigned a genetic basis, problematizing early diagnosis and
treatment of high-risk individuals. It is estimated that known genetic risk loci can only
explain approximately 30 % of heritability129.
This is especially troubling considering that those women who have hereditary
breast cancer tend to be at a significantly higher lifetime risk of developing breast cancer
(e.g., up to 80 % in the case of certain mutations in BRCA1 or BRCA2), usually develop
cancer earlier in life than sporadic cases, and are likely to develop primary tumors in both
breasts rather than solely one130. Further, even women with solely a strong family history
– i.e., women with one or more first-degree female relatives with breast cancer – and no
known mutation have almost twice the risk of getting breast cancer as women without a
family history131. With more than one first-degree female relative with a history of breast
cancer, the risk becomes three to four times higher; the younger the relative was upon
diagnosis, too, the greater the woman’s chances of getting breast cancer. In addition to
being more likely to get cancer as a whole, then, women with hereditary or familial breast
cancer tend to display both earlier onset and increased severity when affected, and would
thus ideally merit planning around prevention and management as early as possible.
Were all breast cancer susceptibility genes to be documented, conducting the risk
estimations necessary to plan prevention and treatment – including screening,
prophylactic measures, and risk management – could become considerably easier. While
tracing the heredity of an individual’s cancer does not solve the problems of resistance
and intra- and inter-tumor heterogeneity, or guarantee the condition’s druggability,
24
preventive, prognostic, and treatment-related benefits arise from understanding the
genetic basis of a person’s condition. Preventively, for example, if a woman is known to
have a BRCA1/2 mutation, she may have an up to 80 % life-time risk of breast cancer;
annual monitoring, risk-reduction chemotherapy, and risk-reduction mastectomy may be
recommended to reduce risks. Some such preventive procedures are particularly
effective: namely, bilateral risk-reducing mastectomy has been shown to reduce the risk
of breast cancer by up to 95 % in women with BRCA1/2 mutations, and by up to 90 % in
women with a strong family history132, 133, 134, 135. Prognostically, at least one-third of
BRCA1 mutation carriers have triple-negative breast cancer (TNBC)136, which is
associated with a poorer prognosis, while BRCA2 mutations are associated with hormone
receptor-positive (HR+) cancers, which tend to have better prognoses. Even in cases
where no treatment exists, prognostic information may be valuable to life planning and
risk management. Finally, predictively, germline mutations are not solely relevant to
cancer susceptibility: they may also – like molecular subtype markers – help physicians
forecast the suitability of a given treatment for a given patient based on their cancer
genetics. For example, BRCA1-mutated tumors tend to respond less well to taxanes, but
may respond well to poly-ADP ribose polymerase (PARP) inhibitors and to cis-
platinum137.
Finally, if the genetic bases of breast cancer are not solely identified but also
successfully targeted through drug development, pinpointing the genetic bases of breast
cancer could lead to the advent of personalized medicine138: the long-anticipated cancer
care paradigm that aims to segment patients according to their individual genetics, and to
utilize molecular markers to predict and address individual variability in both clinical
25
outcomes and drug responses. While personalized medicine has not yet been successfully
implemented, the “one-dose-fits-all” approach to administering the therapies discussed in
Chapter Two has been largely ineffective. Between 38 and 75 % of all patients fail to
respond to a treatment139. Further, of those who do respond, many experience adverse
drug reactions: of all federally-approved breast cancer drugs in the United States, 16 %
have been shown to cause adverse reactions, with the number of deaths due to adverse
reactions exceeding 100,000 annually. With these numbers in place, as much as drug
development may not always achieve as quickly or as fittingly as desired, there is a moral
imperative to at least attempt to find and target the genetic bases of hereditary breast
cancer.
For all these reasons, identification of relevant breast cancer susceptibility genes
and especially predisposing mutations is of high significance to cancer care. Advances in
genomic technologies (e.g., next-generation sequencing) are key to the complementary
processes of gene discovery, cancer screening, and treatment-targeting, while functional
analysis is helpful to both proposing and validating candidate susceptibility genes; these
methodologies thus all also merit discussion in the context of hereditary breast cancer
genetics and epidemiology, particularly in light of the fact that their use is expected to
increase significantly as their costs decrease. This chapter will provide an introduction to
hereditary breast cancer, known breast cancer susceptibility gene functions, and specific
known genes and their clinical implications.
3.2 Understanding Hereditary Breast Cancer: Key Terminology
In order to understand hereditary breast cancer, it is important to understand
certain key genetics terms that are often used to characterize hereditary cancer.
26
3.2.1 Mutation- and Gene-Level Terms
3.2.1.1 Mutations and Polymorphisms
As was discussed in Chapter Two, hereditary breast cancer is the result of
germline mutations. By most definitions, mutations are variants that have a <1 %
prevalence in the population140. They may be pathogenic, beneficial, or neutral.
Contrastingly, polymorphisms are defined as variants that have a >1 % prevalence in the
population. Polymorphisms are not typically harmful or functionally impactful in any
way (although they can be), while mutations constitute changes that are often
phenotypically evident. Most genetic variants are benign polymorphisms that are
comparatively frequent in the population, while most pathogenic mutations are less
frequent141.
Polymorphisms include single nucleotide polymorphisms (SNPs), and copy
number variations (CNVs) (sometimes referred to as copy number alterations (CNAs) in
the context of somatic tumors).
3.2.1.1.1 Single Nucleotide Polymorphisms (SNPs)
Single nucleotide polymorphisms are among the most common types of genetic
variations among people, occurring once about every 300 nucleotides on average142,
meaning that there are over 171 million SNPs143 in the human genome.
Typically, SNPs have no effect on health or development, though they can be
utilized as markers of disease inheritance in families. However, some genes, when they
possess SNPs, have been associated with breast cancer. One study has found that a
polymorphism in Sipa1 (signal-induced proliferation-associated gene 1) may have a role
in germline predisposition to breast cancer metastasis, with specific SNPs within it
(G313A, G2760A, and C545T) being associated with positive lymph nodes and hormone
27
receptor-negative disease respectively. Another member of the RAD51 family,
RAD51L1, has also found to contain an SNP that is strongly associated with breast
cancer, as have TOX3 and TNRC9. Some specific SNPs have also been associated with
breast cancer: rs3803662, for example, is an SNP that was found to be associated with
increased breast cancer risk in Caucasian women in 2016. A total of 11 SNPs from genes
like FGFR2, MAP3K, CCND1, ZM1Z1, RAD51L11, ESR1, and UST were found to be
significantly associated with breast cancer in an Indian population; some of these were
SNPs previously identified in a Caucasian population. Databases like the NHGRI-EBI
GWAS Catalog, GWAS Central, dbSNP, and GWASdb have compiled detailed
information about both very significant and less-signficant genetic variants, including
SNPs.
3.2.1.1.2 Copy Number Variations (CNVs)
Copy number variations (CNVs) are the non-mutational equivalents of large
deletions and amplifications: they are changes in copy number that occur commonly
enough to be thought of as polymorphic in humans144. CNVs constitute approximately 12
% of the human genome145, and, like SNPs, can be used to trace difference between
individual humans as well as disease mechanisms146. Copy number variations are
sometimes referred to as copy number aberrations when they occur in sporadic tumors147.
Copy number variations have been known to play a role in breast cancer. In one
study148, cis-acting CNAs (i.e., those present on the same molecule of DNA as the gene
they regulate) were associated with distinct clinical outcomes, including an estrogen
receptor-positive (ER+) subgroup that nevertheless had a poor prognosis, two low-CNA
subgroups that had good prognoses across luminal A, ER-positive, and ER-negative
tumors, and an ERBB2-amplified subgroup with both ER-negative and ER-positive
28
subtypes. Another study found that germline DNA copy number variations can
potentially serve as prognostic factors for breast cancer recurrence149: specifically, the
presence of seven CNVs (two copy number gains and five copy neutral-loss of
heterozygosities) was found to be associated with changes in recurrence-free survival
(RFS).
3.2.1.2 Driver and Passenger Mutations
Most tumors contain more than one mutation: specifically, most tumors contain
many passenger mutations and very few driver mutations150. Driver mutations are those
mutations which actually cause the cancer in question, while passenger mutations are
background events that have no functional significance, but nevertheless accompany
drivers. Understandably, distinguishing between driver and passenger mutations within a
tumor can be challenging, given that cancer drivers are uncommon and that de novo
drivers frequently occur.
3.2.1.3 Genetic and Epigenetic Changes
While genetic changes are associated with breast cancer, not all inherited breast
cancer is due solely to them. Epigenetic changes (i.e., changes to protein expression
rather than to the DNA sequence) have also been implicated in increasing breast cancer
susceptibility151. Two key epigenetic changes relevant to breast cancer are DNA
methylation and histone modification152. DNA methylation tends to silence genes that
otherwise function normally. For example, hypermethylation of CpG islands (i.e., CpG
dinucleotide-rich regions) in the BRCA1 promoter has been associated with breast
cancer153. Less commonly, breast cancer-associated promoter hypermethylation also
occurs in ESR1, HOXA5, TMS1, and RASSF1A154. Histone modification can involve
either acetylation or methylation155: increased acetylation of histones tends to result in
29
gene activation156, while their methylation can sometimes result in inactivation157.
Specific instances of histone modification have been associated with breast cancer: for
example, the loss of the trimethylation of lysine 27 of H3 is associated with a negative
prognosis in breast cancer158. Aberrant hypermethylation of microRNAs is also present in
primary breast tumors159.
Specific genes are also known to be associated with epigenetic changes in breast
cancer. For example, hormone receptor (ER and PR) and HER2 expression are all
epigenetically regulated160: deacetylated histones interact with the methylated ER gene
promoter161, monomethylation of K302 stabilizes ER162, PR-negative tumors have
increased PR promoter hypermethylation163, HER2 neu-overexpressing tumors have
more methylated CpG islands164, and methylation of certain genes is correlated with
HER2 amplification165.
If epigenetic mechanisms of breast cancer susceptibility are better understood,
then epigenetic biomarkers may eventually be used to develop improved breast cancer
detection in blood166, and epigenetic changes may be targeted by relevant drugs (e.g.,
methyltransferase and histone desacetylase inhibitors) in the hopes of countering
epigenetic breast cancer susceptibility167.
3.2.1.4 Modes of Inheritance
Breast cancer can be inherited in several different manners, depending upon
which gene is causative to the inherited cancer. For the most part, however, breast cancer
is inherited in an autosomal dominant manner168 (i.e., one mutated BRCA allele is
sufficient to make breast cancer occur): this is the case with germline pathogenic variants
in BRCA1 and BRCA2169. Female carriers of mutations in ATM – i.e., women who are
30
heterozygous for mutated ATM – have been shown to have a 5 to 7 times greater relative
risk of breast cancer 170 .
3.2.1.5 Penetrance
Penetrance refers to the proportion of individuals carrying a certain gene or
mutation who express that gene’s or mutation’s phenotype171. High-penetrance genes are
those whose state is almost always reflected in the phenotype of carriers; intermediate-
penetrance genes are those whose genotype is often but not always apparent within the
phenotype; and low- or reduced-penetrance genes are those whose genotypic state is not
always apparent in the phenotype172.
Several of the most commonly alluded-to breast cancer susceptibility genes – e.g.,
BRCA1, BRCA2, TP53, and PTEN – are high-penetrance breast cancer genes173. The
identification of more such high-penetrance genes is critical, as the inheritance of a
mutation in such a gene may increase a woman’s lifetime breast cancer risk by as much
as 80 %.174 The bulk of such high-penetrance genes were discovered in the 1990s, mostly
using linkage analysis and positional cloning175. However, further attempts to find high-
risk genes have largely not been fruitful: the last high-risk gene – PTEN – was located in
1997176, and the last moderate-penetrance gene – PALB2 – was located in 2006177. As
much as these high-risk variants are of predictive and prognostic value where they do
occur, high-risk deleterious variants in genes like BRCA1, BRCA2, and TP53 occur in
less than 0.5 % of the population. As such, it has been hypothesized that familial risk is
not attributable solely to rare, high-risk mutations: it can also be caused by inheritance of
multiple, more common variants, each with a moderate associated increase in cancer
risk178.
31
This suggestion – that several variants rather than one variant can explain breast
cancer risk – is known as the polygenic model of cancer susceptibility179. The rare-variant
model of cancer susceptibility suggests that overall population cancer risk is largely
attributable to the joint action of several rare variants (i.e., SNPs with minor allele
frequencies (MAFs) of ≥ 1 % but ≤ 5 %, SNPs with minor allele frequencies of ≤ 1 %,
and deleterious mutations in high- and moderate-penetrance genes), with one mutation
causing a disorder in a given person180. This model argues that these variants – though
they are rare – are so many in number that the disorders they cause end up being common
in the population. Rare variants are typically located using candidate gene case-control
studies; such rare variants have been found to lead to an up to 20-fold increased risk
relative to the general population. By contrast, the common-variant model of cancer
suggests that common (MAF ≥ 5%) SNPs jointly increase cancer risk up to three-fold
compared to the general population. Common-variant searches are typically conducted
using the genome-wide association study (GWAS) method181. Specifically, regions with
high levels of linkage disequilibrium (LD) (i.e., with several alleles that are non-
randomly linked to one another at more than two loci) are located, and are then further
examined for cancer susceptibility. SNPs located in breast cancer are found at a rate of 5
to 10 % in the population182, and have been discovered to increase breast cancer risk by
up to around 1.25 times relative to the general population183.
As much as those who believe that more high-penetrance genes will be located
may see finding low-penetrance variants as less useful, considering that they only cause
cancer in certain cases, if we are to fully define breast cancer risk in every individual in
the spirit of personalized medicine, then even low-penetrance genes will need to be
32
identified and targeted, as some individuals possess no mutations in high-risk genes but
nevertheless present with breast cancer that appears to be hereditary. This is especially
the case given that low-penetrance genes tend to be common within the population, while
high-penetrance and moderate-penetrance genes tend to be rare. BRCA1 and BRCA2, for
example occur at a frequency of < 0.5% in the population184. As such, while searching for
high-penetrance genes in smaller populations remains of importance, pursuing large-scale
genome-wide association studies (GWAS) in search of low-penetrance variants that
cumulatively increase cancer risk is important185.
3.2.2 Tumor-Level Terms
3.2.2.1 Intra- and Inter-Tumor Heterogeneity
Intra-tumor heterogeneity refers to the fact that cancer cells within a single tumor
can differ in terms of their genetics, epigenetics, morphology, and behavior186. According
to Beca and Polyak, this is the “main obstacle to effective cancer treatment and
personalized medicine”, and is prevalent across the breast cancer continuum187. Examples
of intra-tumor heterogeneity within breast cancer include differing HER2 statuses
between a primary tumor and its metastasis or circulating tumor cells (CTCs), as well as
invasive breast cancer tumors with some cells with HER2 amplification and some cells
without it188. Furthermore, a recent study demonstrated that driver genetic aberrations
such as TP53 and PIK3CA somatic mutations may also exhibit intra-tumor heterogeneity.
Finally, tumors exhibit both spatial heterogeneity (i.e., differences between cells located
in different parts of a given tumor) and temporal heterogeneity (i.e., molecular
characteristics that change over time, either at the site of the original cancer or within a
metastatic recurrence). All this variation even within the scope of a single tumor
problematizes diagnosis and treatment: for example, a single-cell biopsy is highly
33
unlikely to yield genetic characteristics generalizable to the entirety of a tumor, and
multiple treatments may have to be applied to the same tumor in order to successfully kill
all involved cancer cells, given that different cells have different genetic characteristics.
Inter-tumor heterogeneity refers to the fact that distinct tumors within the same
individual – or tumors in the same tissue across different individuals – can vary in terms
of their genetics, epigenetic, morphology, and behavior. Inter-tumor heterogeneity has
been somewhat well-mapped in breast cancer, and is accounted for by classifying tumors
into the molecular subtypes discussed in Chapter Two.
3.3 Breast Cancer Susceptibility Genes: General Functions and Involved
Pathways
Breast cancer susceptibility genes have a range of functions in their wild type
state, including control of cell division, damaged or abnormal DNA recognition and
response, maintenance of cell survival post-damage, DNA repair, transcription, apoptosis,
senescence, autophagy, and angiogenesis. All of these functions jointly work to promote
genomic stability.
3.3.1 Cell Division
In terms of controlling cell division, breast cancer susceptibility genes typically
act to mediate cell division checkpoints. Prior to synthesis (S) phase, a sufficient quantity
and quality must have been done during the first growth (G1) phase to merit replication;
whether or not sufficient growth has been had is monitored via the G1/S checkpoint.
Similarly, following replication and the second phase of growth (G2), replication must
have been carried out correctly and further growth must have been done in order for cell
division (M phase) to occur; this is monitored by the G2/M checkpoint. Breast cancer
susceptibility genes involved in controlling these checkpoints include TP53189 (which
34
acts via p21 and cdk2 to stop cell division), CDH1901, STK11191, ATM192, CHEK2193, and
the MRE11-RAD50-NBN protein (MRN) complex194.
3.3.2 DNA Damage Recognition and Response
Sometimes, DNA can also become damaged. This can occur for a range of
reasons. Firstly, mutations in DNA polymerase195 – which typically maintains sequence
fidelity by proofreading the DNA and removing mismatched bases – can inactive its
proofreading function, resulting in up to a thousand-fold increase in the rate of
spontaneous mutations. Secondly, both direct-acting carcinogens (i.e., reactive
electrophiles that modify nucleotides) and indirect-acting carcinogens (i.e., unreactive
compounds that can be converted to cancer inducers if they are not correctly metabolized
and excreted by the body) can cause DNA damage196. Many different types of DNA
lesions can result197, including a missing base, an altered base, an incorrect base, a bulge
due to the deletion or insertion of a nucleotide, incorrectly linked pyrimidines, single-
strand breaks (SSBs), double-strand breaks (DSBs), cross-linked strands, and 3’-
deoxyribose fragments. If these lesions are left unrepaired, they can transform normal
cells into rapidly proliferating cells, causing cancer. In order for these lesions to be
repaired, they must first be detected; this is where some breast cancer susceptibility genes
are relevant. The BRCA1-associated genome surveillance complex (BASC), for example,
which includes the MRE11-RAD50-NBN protein (MRN) complex, is involved in DNA
damage recognition and response198, as are PTEN199, ATM200, and CHEK2201.
3.3.3 DNA Repair
Repairing damaged DNA post-detection is one of the major functions of breast
cancer susceptibility genes202 203 204 205 206 207 . Just as there are many forms of DNA
damage, there are many mechanisms for repairing DNA damaged by chemicals,
35
radiation, and other carcinogens. These can be broadly categorized as mismatch repair
(MMR), excision repair (spanning nucleotide excision repair and base excision repair),
homologous recombination repair (HRR), and non-homologous end-joining repair
(NHEJ).
3.3.3.1 Mismatch Repair
Mismatch repair (MMR), as its name suggests, involves identifying, removing,
and replacing an incorrect nucleotide that was recently incorporated into the newly-
synthesized daughter strand208. In other words, mismatch repair focuses on point
mutations that arise from errors in replication, during genetic recombination, and via base
deamination. Mismatch repair detects which strand is the parent strand via epigenetic
changes: older strands of DNA are typically more methylated, and the parent strand is
thus likely to contain methyl groups. Mammalian mismatch repair involves MSH2,
MSH6, MLH1, and PMS2209. Breast cancer susceptibility genes are relevant to mismatch
repair in that BRCA1 interacts with MSH2.
3.3.3.2 Base Excision Repair (BER)
While the mismatch repair pathway only targets mismatched base pairs, base
excision repair (BER) is responsible for recognizing and removing small, non-helix-
distorting base lesions from the genome, and specifically on damaged bases removable
via specific glycosylases210. These damaged bases, if not removed, could lead to
mispairing or DNA breaks during replication, thus also causing mutations. The damaged
bases that base excision repair targets can arise as a result of several mechanisms,
including deamination, oxidation, and alkylation. Base excision repair gene defects have
been shown to result in a higher mutation rate in organisms, implying that BER loss
could lead to the development of cancer. In terms of breast cancer, X-ray repair cross
36
complementing group 1 (XRCC1), a key BER gene, has been associated with breast
cancer: specifically, the XRCC1 Arg399Gln variant has been associated with increased
breast cancer risk in Asians and Africans211.
3.3.3.3 Nucleotide Excision Repair (NER)
Nucleotide excision repair (NER) specializes in recognizing and removing DNA
damage induced by ultraviolet light (UV)212. This kind of damage is characterized by
bulk DNA adducts, which are mostly thymine dimers. NER involves recognizing a short,
single-stranded segment of DNA that contains the lesion, removing it, and using the
undamaged single-stranded DNA remains and DNA polymerase to synthesize a short
complementary sequence, which is then integrated by DNA ligase. Nucleotide excision
repair has been implicated in cancer, and has specifically been linked to genomic
instability in breast cancer213. Further, both BRCA1 and TP53 have been associated with
nucleotide excision repair, and are prominent breast cancer susceptibility genes214.
3.3.3.4 Homologous Recombination Repair
While MR, BER, and NER are focused mostly on repairing single-nucleotide
damage, homologous recombination repair (HRR) repairs double-strand breaks
(DSBs)215. Double-strand breaks can be caused by a range of phenomena, including
ionizing radiation and stalled replication forks due to lesions in the DNA or repair
intermediates. If double-strand breaks are not repaired, they can, once processed, lead to
mutations, loss of heterozygosity (LOH), and lethal chromosomal rearrangements, all of
which can lead to cancer216.
In general, homologous recombination repair works as follows: Initially, after a
double-strand break occurs, sections of DNA around the 5’ ends of the break are removed
via resection. Next, an overhanging 3’ end of the broken DNA invades a similar DNA
37
molecule that is not broken. Afterwards, either the double-strand break repair (DSBR) or
the synthesis-dependent strand annealing (SDSA) pathway are pursued.
There are four different pathways that comprise HRR217: these are the double-
strand break repair (DSBR), synthesis-dependent strand annealing (SDSA), single-strand
annealing (SSA), and break-induced replication (BIR) pathways. Different pathways are
used during different periods of the cell cycle, as well as in different genomic situations;
each pathway has its unique attributes. The DSBR pathway, for example, is unique in that
it forms two Holliday junctions, and typically results in chromosomal crossover. The
SDSA pathway results in non-crossover products, and is the major homologous
recombination pathway used to repair double-strand breaks during mitosis. The SSA
pathway is used to repair double-strand breaks between two repeat sequences; it is unique
in that it uses the repeat sequences as its templates, and thus does not require a separate
homologous molecule of DNA. Finally, the BIR pathway is used when double-strand
breaks are encountered at replication forks during replication; not much is clear about its
molecular mechanisms.
Homologous recombination repair genes include some of the most common breast
cancer susceptibility genes (e.g., BRCA1, BRCA2, and PALB2)218, 219.
3.3.3.5 Non-Homologous End-Joining Repair (NHEJ)
Finally, the non-homologous end joining (NHEJ) also targets double-strand
breaks220. However, unlike homologous recombination repair, it does not require a
homologue to act as a template: instead, NHEJ just involves directly ligating the break
ends221. Typically, NHEJ uses the single-strand overhangs of the double-strand breaks to
guide where it will ligate, and is usually quite precise222. However, imprecise repair due
38
to incompatible overhangs can lead to loss of nucleotides, translocations, and telomere
fusions, all of which can contribute to cancer223. Further, if mutations cause the NHEJ
pathway to be inactivated, then a lower-fidelity pathway known as microhomology-
mediated end joining (MMEJ) takes over, and can lead to increased deletion of the DNA
sequence224. From a breast cancer perspective, BRCA1 has been implicated in non-
homologous end-joining repair (NHEJ)225.
3.3.4 Other Functions: Cell Survival Post-Damage, Transcription, Apoptosis,
Senescence, Autophagy, and Angiogenesis
Breast cancer susceptibility genes play a range of other genomic roles. Many are
involved in ensuring that the cell survives post-damage226 227 228 229 . This is primarily
accomplished through ubiquitin E3 ligase activity, and is dominantly carried out by the
BRCA1- and BRCA2-containing complex (BRCC), which includes BRCA1, BRCA2,
BARD1, and RAD51230. Some other breast cancer susceptibility genes are involved in
transcription231 232: BRCA1, for example, interacts with RNA polymerase II233. Others –
namely, STK11 – appear to regulate TP53-related apoptosis234, which is critical to the
adequate disposal of damaged or incorrectly proliferating cells. Some, like TP53, regulate
angiogenesis in tumors235, which, if left unregulated, can result in tumors that grow and
proliferate through increased vascularization.
3.3.5 Joint Functions: Gene Interactions
While certain breast cancer susceptibility genes are more directly involved in
some aspects of genomic stability maintenance than others, many of the genes we will
discuss in more detail later in this chapter work together. For example, BRCA1 and TP53
frequently interact, with BRCA1 regulating p53-dependent gene expression236, and p53
modulating BRCA1 expression237. PTEN also regulates p53 protein levels and activity
39
through several mechanisms238, and p53, in turn, regulates PTEN239. CHEK2 may be able
to regulate p53-dependent apoptosis independently of ATM240, but also works with ATM
to regulate apoptosis; ATM, in turn, regulates TP53 through HDM2 in humans, alongside
CHEK2, and directly via phosphorylation of a serine residue241; ATM can also
phosphorylate BRCA1242, and is required for phosphorylation and activation of CHEK2 in
response to infrared radiation (IR)243. Finally, PALB2, as its name suggests, interacts
directly with BRCA2 – and with BRCA1 – in homologous recombination repair244.
3.3.6 Two Cancerous Categories: Oncogenes and Tumor Suppressors
Breast cancer susceptibility genes can be categorized into two main functional
categories on the basis of their activity in their wild-type state, as well as their activity
when they become mutated.
Oncogenes are genes that result in increased breast cancer susceptibility when
they have unusually high function (i.e., if their protein expression is increased through
epigenetic mechanisms, or if amplification, duplication, or some other form of gain-of-
function mutation occurs at the DNA level)245. Prior to becoming overexpressed (i.e.,
prior to becoming problematic), these genes are known as proto-oncogenes246. Examples
of oncogenes relevant to breast cancer include ERBB2247, MYC248, CCND1249, and
PIK3CA250.
By contrast, tumor suppressors are genes whose normal function is to prevent
tumors. They do this via a range of mechanisms, including DNA repair, cell division
regulation, and programmed cell death251. In brief, by ensuring that cells cease dividing
when they need to, tumor suppressors prevent deregulation of the cell cycle (and, as a
consequence, cancer). However, when tumor suppressor genes experience loss-of-
function mutations, loss of heterozygosities (LOHs), or other changes resulting in
40
decreased function, they lose their ability to suppress tumors, and breast cancer
susceptibility thus increases252. Examples of tumor suppressor genes implicated in breast
cancer include TP53253, BRCA1254, BRCA2255, and PTEN256.
3.4 Breast Cancer Susceptibility Genes: Known Mutation Types
3.4.1 Structural Types
Breast cancer is caused by a range of mutation types, which can structurally be
divided into small-scale and large-scale mutations. Small-scale mutations include
substitutions, insertions, and deletions.
3.4.1.1 Substitutions
Substitutions are point mutations (i.e., mutations that affect a single nucleotide)
that involve the substitution of one base for another. Substitutions can be classified as
either transitions or transversions: transitions involve a change from a purine to a purine
or from a pyrimidine to a pyrimidine, and are thus typically less problematic than
transversions, which involve a change from a purine to a pyrimidine or a pyrimidine to a
purine, and thus alter the width of the DNA double helix257. If a substitution results in the
production of the same or a different but closely related amino acid (typically, the same
amino acid), it has no significant functional consequences, and is called a silent
mutation258. Not surprisingly, silent mutations do not typically result in cancerous
manifestations, as they do not alter the wild type. If a substitution results in a different
amino acid than the original amino acid, it is called a missense mutation259. Missense
mutations can have significant functional consequences, given the alteration of the
protein product that they imply. As such, they are quite common in breast cancer260.
Finally, if a substitution results in a stop codon and in the subsequent early truncation of a
protein, then it is known as a nonsense mutation261. This usually has significant
41
consequences, because the protein tends to lose function, and cellular functions are thus
compromised. Substitutions differ from insertions and deletions in that they substitute
one nucleotide base for another, but do not alter the overall number of bases in the DNA.
If substitutions occur at two or more adjacent nucleotides, they are referred to as tandem
base mutations (TBM)262. A large-scale survey of 560 breast cancers found twelve base
substitution signatures suggesting possible driver mutations across 93 protein-coding
cancer genes263.
3.4.1.2 Insertions
Insertions involve the addition of one or more nucleotide base pairs into a DNA
sequence. They can occur on both a small scale – e.g., with the insertion of only one
incorrect base pair – and a large scale (e.g., duplication of a single exon or the entire
gene)264. If the number of inserted nucleotides is not divisible by three, then an insertion
mutation can result in a frameshift mutation (i.e., in which all the amino acids following
the mutation are affected because of a changed reading frame)265; if the number of
inserted nucleotides is divisible by three, then the mutation is said to be in-frame.
3.4.1.3 Deletions
Deletions, conversely, involve the removal of one or more nucleotide base pairs
from a DNA sequence. Again, they can occur on both a small scale, involving a single
base pair, and on a large scale, involving an entire piece of a chromosome. As with
insertions, if deletions do not involve a multiple of three bases, then they can cause a
frameshift mutation, which typically has serious consequences. Deletions, like insertions,
can be either small-scale or large-scale (e.g., deletion of several exons or the entire gene).
42
The 560-breast-cancer survey mentioned above included six rearrangement
signatures that coincided with possible driver mutations, including deletions coinciding
with defective homologous-recombination-based DNA repair in BRCA1 and BRCA2266.
3.4.1.4 Insertion-Deletions (Indels)
While in evolutionary biology insertions and deletions are often cumulatively
called “indels”, insertion-deletions (indels) are, within the context of germline and
somatic mutation biology, another class of mutations entirely. In cancer studies, an indel
is a special mutation class that involves both an insertion and a deletion, typically near to
one another on the chromosome, which result in a net change in the number of
nucleotides. If the indel results in a net change of one to 50 nucleotides, then it is referred
to as a microindel267. As with insertions and deletions in general, if an indel produces a
net change of a number of nucleotides that is not divisible by three, then it is known as a
frameshift indel.
3.4.1.5 Large-Scale Mutations
Large-scale mutations may, for the most part, be interchangeably termed
“chromosomal rearrangements”. Chromosomal rearrangements are structural changes
that, unlike small-scale mutations, tend to involve the chromosome as a whole, or a large
component of the chromosome. Chromosomal rearrangements include amplifications and
duplications, large-scale deletions and loss of heterozygosity, inversions, and
translocations268.
3.4.1.5.1 Amplifications and Duplications
Amplifications are mutations that increase the number of copies of a gene in the
DNA (i.e. changes in copy numbers), or that increase the quantity of RNA or DNA made
from a given gene. Duplications, similarly, are mutations that increase the number of
43
copies of a gene in the DNA, typically doubling the prior copy number (hence
“duplication”)269. While “amplification” and “duplication” are often used
interchangeably, the term “duplication” is usually used to refer to an increase in the copy
number of a gene at the DNA level specifically, while the term “amplification” can be
used to refer to solely protein overexpression, mediated by a change outside the DNA,
and is thus not always explicitly considered a “mutation”270. Amplifications and
duplications are common in breast cancer, and are relatively well-studied, being some of
the oldest mutation types discovered271. For example, HER2 (ERBB2) is amplified in
approximately 20 % of all breast cancers272. EGFR, which like HER2 belongs to the
human epidermal growth factor receptor family of receptor tyrosine kinase, has also been
shown to be amplified and overexpressed in breast carcinoma273.
3.4.1.5.2 Large-Scale Deletions and Loss-of-Heterzygosities (LOHs)
Large-scale deletions, as their name would suggest, involve deletions of massive
chromosomal components, or sometimes of entire gene copies. Large-scale deletion
mutations in mitochondrial DNA have been found to be associated with breast cancer274.
Similarly, loss of heterozygosity (LOH) involves the loss of one of the two original
copies of a gene as well as its accompanying chromosomal region, resulting in a
hemizygous – and thus inevitably “homozygous” – locus in the modified organism’s
DNA. Loss of heterozygosity is often present in cancer, where it can cause an otherwise
functional tumor suppressor gene to lose function; given that one entire copy of a given
gene is rendered dysfunctional, LOH also often results in haploinsufficiency (i.e.,
insufficient quantities of a given protein)275. LOH commonly occurs in BRCA1 and
BRCA2 in breast cancer276, as well as in zinc finger genes, matrix metalloproteinases, and
the carcinoembryonic antigen-related cell adhesion molecule (CEACAM) family genes.
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3.4.1.5.3 Inversions
Inversions are mutations in which a component of a chromosome is reversed end-
to-end277. Inversions were found to be present in breast cancer in a study spanning 560
breast cancer whole-genome sequences278, in another study of ESR1 mutations in
hormone-resistant metastatic breast cancer279, and again in a third study, where inversion
resulted in a frame fusion gene of ETV6 and ITPR2280.
3.4.1.5.4 Translocations
Translocations are mutations involving chromosomal rearrangement across two
non-homologous chromosomes281. Translocations can be reciprocal or nonreciprocal, and
balanced or unbalanced282. If a translocation results in the joining of two otherwise-
separated genes, a fusion gene (i.e., a conjoining of the two aforementioned genes) can
arise283. Translocations are common in breast cancer: NRG1, for example, is translocated
in approximately 6 % of primary breast cancer cases284.
3.4.2 Functional Types
It is also important to be aware that mutations can be typed on the basis of their
phenotypic consequences. Under this classification scheme, there are gain-of-function
mutations and loss-of-function mutations.
3.4.2.1 Gain-of-Function Mutations
Gain-of-function mutations or activating mutations are mutations that introduce a
new function to a given gene product, or that lead to an altered pattern of gene
expression285. They typically act like dominant or semi-dominant alleles, and produce a
novel kind of phenotype286. Gain-of-function mutations are also sometimes referred to as
activating mutations, though the latter term can sometimes be used to refer specifically to
amino acid substitutions that confer new or higher activity upon a protein. Gain-of-
45
function or activating mutations are prevalent in breast cancer: for example, activating
mutations in HER2 have been identified even in human breast cancers that lack HER2
gene amplification287; activating mutations have also been uncovered in ESR1288,
PIK3CA289, and GATA3290.
3.4.2.2 Loss-of-Function Mutations
By contrast, loss-of-function mutations – also called inactivating mutations – are
those that result in a gene product’s having less or no function (i.e., being partially or
wholly inactivated)291. If a mutation results in complete loss of function (i.e., in a null
allele), then it is called an amorphic mutation292. Loss-of-function mutations are typically
recessive293, though some can be dominant. If loss-of-function mutations target a gene
that typically acts as a tumor suppressor, then malignancy is generally promoted. Loss-of-
function mutations are prevalent in many breast cancer genes, including GATA3294,
PALB2295, FANCM296, and PTEN297. Importantly, PTEN knockdown results in increased
resistance to trastuzumab-based therapy298, and can also result in hyperactivation of the
PI3K pathway: specifically, activating mutations in the gene encoding the p110-alpha
catalytic subunit of PI3K (PIK3CA) have been identified in 25 % of primary breast
cancers, potentially due to associated PTEN loss299.
3.5 Known Breast Cancer Susceptibility Genes
3.3.1 Breast cancer 1 (BRCA1) and breast cancer 2 (BRCA2)
3.3.1.1 Discovery
The notion that a DNA repair gene could be responsible for breast cancer
susceptibility was first advanced in a paper by Hall et al. in 1990300; this paper mapped
breast cancer susceptibility to Chromosome 17q21. Four years later, Miki et al. identified
breast cancer 1 (BRCA1) at the 17q locus via positional cloning, noting its candidacy for
46
breast and ovarian cancer susceptibility301. Breast cancer 1 thus became the first gene to
be associated with hereditary breast cancer; the discovery of breast cancer 2 (BRCA2),
localized to Chromosome 13q12-q13302, followed within the year in a paper by Wooster
et al. The discovery of these genes was anticipated to lead to increased understanding of
and ability to manage breast cancer303, and sparked attempts to find more genes.
3.3.1.2 Structure
BRCA1 is located on the long (q) arm of Chromosome 17, specifically at
17q21.31; it contains 81,188 base pairs, running from base pair 43,044,295 to base pair
43,125,483304,305. It contains 24 exons, 22 of which are coding exons306. In its protein
form, BRCA1 is composed of 1863 amino acids307. It contains four specialized domains:
the Zinc finger C3HC4-type RING domain, the BRCA1 serine domain, and two BRCA1
C-terminus (BRCT) domains308. The RING domain, located on the BRCA1 amino (N)
terminus309, interacts with BARD1 to form the BARD1 heterodimer310,311, which is
involved in transcriptional regulation to maintain genomic stability and DNA damage
repair312, including transcriptional control through modulation of chromatin
structure313,314,315 and functioning as a corepressor316. This domain is also integral to
protein ubiquitination via ubiquitin E3 ligases, which is an important factor in DNA
replication317. The BRCA1 serine cluster domain (SCD) spans exons 11-13, and
associates with BRCA2, PALB2, and ATM318. Finally, the BRCT domains, located on the
carboxy (C) terminus of BRCA1, associate with the histone deacetylases HDAC1 and
HDAC2319, and are involved in cell cycle control320; they are thought to be essential to
DNA repair, transcription regulation, and tumor suppression. BRCA1 also has an
unusually high density of Alu repetitive DNA sequences (41.5%)321,322.
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BRCA2 is located on the long (q) arm of Chromosome 13, specifically at
13q13.1323 324. It is sometimes also known as Fanconi anemia, complementation group
D1 (FANCD1)325 because of its association with the FANC protein complex. It is 84,192
base pairs long, running from base pair 32,315,480 to base pair 32,399,672326. It contains
27 exons327. In its protein form, BRCA2 contains 3,418 amino acids. BRCA2 contains
several special domains and motifs, including several copies of the 70-amino-acid-long
BRC motif, which mediates BRCA2-RAD51 recombinase binding and thus DNA
repair328.
BRCA1 and BRCA2 share several structural characteristics. Firstly, both have a
large exon 11, and translational start sites in exon 2. Both also have AT-rich coding
sequences. They also span approximately the same amount of genomic DNA (70 kb), and
are both expressed at high levels in several organs (e.g., the testis). Both have also been
found to contain components that biochemically and sequentially resemble members of
the granin protein family329, with BRCA1 exhibiting several characteristically granin
attributes (e.g., being localized to secretory vesicles, secreted by a regulatory pathway,
post-translationally glycosylated, and responsive to hormones).
3.3.1.3 Function
Both BRCA1 and BRCA2 are DNA repair genes. Specifically, both are involved in
the repair of double-strand breaks (DSBs) via homologous recombination repair (HRR).
Double-strand breaks are fractures of the DNA that, if inaccurately or insufficiently
repaired, can lead to mutations, large-scale genomic instability, and other tumorigenic or
apoptogenic changes330. As a result, the human body has developed highly-conserved
systems to detect and repair DSBs, of which BRCA1 and BRCA2 are members331. BRCA1
is involved in both homologous recombination repair and mismatch repair (MR) through
48
interactions with the mismatch repair protein MSH2. Because they prevent tumors from
developing when they are not mutated, BRCA1 and BRCA2 are known as tumor
suppressors. They accomplish this largely by participating in an “error-free” double
strand repair pathway involving MRE11, RAD50, NBS1, ATM, CHEK2, and FANCJ332;
when aspects of this pathway are mutated, DNA repair by lower-fidelity methods
sometimes occurs, increasing the risk of cancer333.
BRCA1 and BRCA2 accomplish their functions largely in conjunction with other
genes. BRCA1 is a member of several complexes. Firstly, it combines with several other
genes (i.e., MSH2, MSH6, MLH1, ATM, BLM, PMS2, the MRE11-RAD50-NBN protein
(MRN) complex, and RFC334) to form the BRCA1-associated genome surveillance
complex (BASC)335, which may have roles in transcription-coupled repair, recognition of
abnormal or damaged DNA, and DNA replication-associated repair, all of which would
implicate BRCA1 as playing a role in DNA repair and the maintenance of genomic
integrity336. Secondly, it acts as a component of the BRCA1-A complex, which is
composed of BRCA1, BARD1, UIMC1/RAP80, FAM175A/ABRAXAS, BRCC3/BRCC36,
BRE/BRCC45, and BABAM1/NBA1337. Thirdly, as part of a holoenzyme complex
containing BRCA1, BRCA2, BARD1, and RAD51, also known as the BRCA1- and
BRCA2-containing complex (BRCC), BRCA1 is needed for ubiquitin E3 ligase activity
that enhances cellular survival following DNA damage338. It also complexes with RBBP8
to regulate CHEK2 activation and control the G2/M checkpoint on DNA damage339, and
is thus also associated with regulating mitosis. It associates with a host of other genes and
enzymes, including RNA polymerase II340, SMC1A, inactive X (Xi)341, COBRA1,,
CHEK1, CHEK2, FANCD2342, FANCA, BAP1, BRCC3, AURKA, UBXN1, PCLAF,
49
ACACA, and EXD2, as well as with LMO4 and CCAR2, which repress its activation343.
BRCA1 also induces GADD45344, a DNA damage-inducible gene, and CHEK1345, and is
regulated by CHEK2346, CTIP, and ATM, the latter two of which specifically regulate
BRCA1 GADD45 induction347. Finally, as part of its work in homologous recombination
repair (HRR), BRCA1 interacts directly with PALB2 (and, in the presence of PALB2, with
BRCA2)348.
3.3.1.4 Breast Cancer Context: Prevalence, Mutations, Clinical
Consequences, and Ethnic Distribution
When BRCA1 and BRCA2 are mutated, they tend to lose their tumor suppressor
function, becoming breast cancer susceptibility genes. Jointly, they are believed to
account for the largest proportion of familial breast cancer cases: up to 5% of all breast
cancer cases349 and up to 20-25% of familial breast cancers are likely caused by
mutations or rearrangements within these genes350.
Breast-cancer related mutations in BRCA1 and BRCA2 vary. As early as 1994,
predisposing mutations in BRCA1 including deletions, insertions, truncating mutations,
and missense mutations were identified351. Shortly thereafter, more than 38 common
mutations were identified in a complete screen of BRCA1352, with 86% of them resulting
in a truncated BRCA1 protein353. Today, in 2017, we are aware of more than 1600 BRCA1
mutations, most of which are frameshifts and result in either an incorrect or a
dysfunctional protein354. The site of a given BRCA1 mutation partially determines
whether the carrier is more susceptible to ovarian cancer or breast cancer355: specifically,
mutant BRCA1 does not affect the growth of breast cancer cells, but mutations in the 3’
portion of BRCA1 inhibited ovarian cancer cell growth. Additionally, the domain affected
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matters: mutations in the 788-amino-acid RING finger domain of BRCA1 tend to be
associated with familial carcinomas356.
Like BRCA1, BRCA2 was initially linked to a relatively modest number of breast
cancer-specific germline mutations (namely, six), most of which disrupted translation by
leading to premature stop codons357. At present, however, more than 1800 mutations have
been found in BRCA2, spanning frameshift deletions, insertions, nonsense mutations,
splicing mutations, missense mutations, and also large rearrangements of one or more
exons, most of them again leading to premature termination and truncation. To this day,
most BRCA2 mutations tend to be frameshift mutations358. As with BRCA1, the region of
BRCA2 affected by a given mutation matters: mutations to the “ovarian cancer cluster
region” (OCCR), located in exon 11, tend to result in ovarian cancer359, whereas certain
other mutations – like the 999del5 mutation examined in an Icelandic population360 –
tend to result in breast cancer risk.
Mutations in BRCA1 and BRCA2 significantly impact breast cancer risk.
Specifically, women with BRCA1 or BRCA2 mutations have a lifetime risk of breast
cancer of up to 80%361, compared to the 12% lifetime risk that the general population
faces362. Mutations in genes within the BRCA pathway also increase the risk of certain
leukemias and lymphomas up to 2000-fold363, and are integral to BRCA1- and BRCA2-
associated hereditary breast and ovarian cancer syndrome (HBOC), which is
characterized by increased risk of female and male breast cancer, ovarian cancer, prostate
cancer, pancreatic cancer, and, in individuals with a BRCA2 pathogenic mutation,
melanoma364. BRCA1 mutation carriers have a higher risk of associated ovarian cancer
than BRCA2 mutation carriers365.
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Each of mutated BRCA1 and BRCA2 is associated with somewhat different
cancer-related presentations. For example, BRCA1 mRNA levels were associated with the
transition from in situ to invasive breast cancer in sporadic breast cancer366, suggesting
that BRCA1 normally negatively regulates breast epithelial cell growth. However, it isn’t
solely a decrease in BRCA1 levels that results in breast cancer: aberrant location of
BRCA1 rather than its absence has also been correlated with breast cancer367. When
BRCA2 is lost, on the other hand, replicative failure, mutagen sensitivity, and genomic
instability occur, creating symptoms such as those seen in Bloom syndrome and Fanconi
anemia368.
BRCA1 and BRCA2 differ histologically in the context of breast cancers. Tumors
with BRCA1 mutations tend to be triple-negative (i.e., lacking the estrogen and
progesterone receptors as well as HER2 overexpression), resembling basal-like and
weakly luminal-like cancers, while tumors with BRCA2 tend to be hormone-receptor-
positive369, and tend to resemble sporadic cancers370 371 372. A BRCA1 or BRCA2 mutation
can also increase the risk of a second primary breast cancer occurrence, usually
contralateral to the first: specifically, for carriers, the chance of contralateral breast cancer
occurring within ten years of the first diagnosis is 10-30%, compared to the 5-10%
contralateral breast cancer risk that non-carrier survivors have373; the lifetime risk of a
second primary contralateral breast cancer for carriers is even higher, at about 40-65%374.
Reports concerning whether the risk of ipsilateral recurrence is higher in BRCA mutation
carriers are inconclusive375,376.
BRCA1- and BRCA2-mutated tumors also differ from other tumors where
monitoring and treatment are concerned. For BRCA mutation carriers, annual monitoring
52
via mammography or MRI is recommended from age 30 onwards377. In many cases,
prophylactic surgeries, including salpingo-oophorectomy and bilateral mastectomy, are
recommended to reduce mortality378 379; specifically, oophorectomy is thought to both
prevent breast and ovarian cancers in BRCA1 and BRCA2 carriers without prior breast
cancer, and to reduce mortality in BRCA mutation carriers380. Prophylactic measures
involving chemotherapy rather than surgical intervention (e.g., prophylactic tamoxifen)
may also be administered381. Radiation, however, may have problematic consequences:
there is some evidence that radiation due to mammography before the age of 30 may
increase breast cancer risk382.
Treatment of BRCA1-mutated tumors differs from treatment of BRCA2-mutated
tumors. Given that they are likely to be triple-negative (i.e., lacking the estrogen receptor,
progesterone receptor, and HER2 amplification), BRCA1-mutated tumors can be difficult
to treat with conventional methods: for example, BRCA1-mutated hormone receptor-
negative tumors respond less well to taxanes (e.g., docetaxel, paclitaxel) than non-
BRCA1-mutated hormone receptor-negative tumors383. However, relatively recent work
has found that poly-ADP ribose polymerase (PARP) inhibitors can effectively treat
BRCA1- and BRCA2-mutated tumors384385. Women with BRCA1 mutations may also
benefit from olaparib or cis-platinum administration386. By contrast, women with BRCA2
mutations primarily have ER-positive tumors387, which can be treated with hormone
therapy more readily than BRCA1-mutated tumors.
BRCA mutation frequencies differ from population to population. In the general
population, these mutations are rare, occurring in approximately 0.1-0.5% of the general
population, translating to a 3-7% population-attributable risk. However, certain
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populations have elevated BRCA mutation frequencies. For example, BRCA mutations are
about ten-fold more common in Ashkenazi Jews (and especially the mutations BRCA1
185deAG, BRCA1 5382insC, and BRCA2 6174delT), with 2% of this population carrying
a deleterious mutation, translating to a much larger 15-30% population-attributable risk.
In the United States, BRCA1/2 mutations are especially prevalent in Ashkenazi Jews,
with BRCA1 mutations presenting in 8-10 % of Ashkenazi Jewish-American breast
cancer patients, and BRCA2 mutations presenting in 1 % of Ashkenazi Jewish-American
breast cancer patients. Hispanic Americans, African Americans, and Caucasian
Americans also have somewhat elevated mutation prevalence relative to the general
population for one or both BRCA genes: Hispanic Americans with breast cancer have a
4% population-specific prevalence of BRCA1 mutations (data concerning BRCA2
mutations in this population is not as available), African-Americans with breast cancer
have a 3% population-specific prevalence of BRCA2 mutations, and Caucasians with
breast cancer have an approximately 2-3% population-specific prevalence of both BRCA1
and BRCA2 mutations388. Overall, BRCA1 and BRCA2 heterozygote frequency appears to
be highest in European and European American populations, followed by African-
American, Latino, South Asian, East Asian, African, and European Finnish
populations389. Germline mutations of BRCA1 and BRCA2 relevant to breast cancer have
also been examined in a range of smaller populations, including groups from Puerto
Rico390, the UK391, and Finland392.
Continued examination of individual populations in the context of germline BRCA
mutations is critical, because mutations that are recurrent in one region may not be
prevalent – and thus, clinically relevant – in another. These regionally-specialized
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mutations are known as founder mutations, because they tend to arise in small
populations that were once “founded” primarily as a result of migration, and that are thus
relatively genetically homogeneous. Examples of BRCA1 and BRCA2 founder mutations
include BRCA1 185delAG and 5382insC in Hungarian and Russian Ashkenazim, BRCA2
6174delT in Hungarian and Russian Ashkenazim, BRCA1 G5193A and BRCA2 999del5
in an Icelandic population, BRCA1 IVS1-2A>G in a Finnish population, large deletions
of BRCA1 in a Dutch population, BRCA1 1675delA and 1135insA in a Norwegian
population, and BRCA1 2800delAA in a Scottish and Irish population393.
3.3.2 Tumor protein 53 (TP53, p53)
Tumor protein 53 (TP53) is another tumor suppressor gene394. Like BRCA1, it is
located on chromosome 17. However, unlike BRCA1, it is located on the short (p) arm of
the chromosome, specifically at 17p13.1. TP53 is 25,771 base pairs long, running from
base pair 7,661,779 to base pair 7,687,550. It has 11 exons, but the first exon is non-
coding395. Its protein contains 393 amino acids. Like BRCA1 and BRCA2, TP53 has
several highly-conserved domains that are considered essential to its function396.
Functionally, TP53 is involved in the control of the G1/S and G2/M phase
transitions, transcription, DNA repair, senescence, apoptosis, autophagy, mitotic
catastrophe, and angiogenesis397. Specifically, TP53 produces a protein that stimulates
the production of p21, a protein which binds with another protein – cdk2 – to stop cell
division398. If TP53 is mutated, then p21 is not synthesized, and cell division cannot be
stopped, which is why TP53 mutations are typically associated with cancer. TP53 also
interacts with many of the same genes that BRCA1 interacts with: it induces the
expression of GADD45, and is controlled by ATM/ATR and CHEK1/CHEK2.
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In a somatic cancer context, p53 – the protein produced by TP53 – is among the
most commonly mutated in most human cancers, with mutations occurring in about half
of all cancers399. Where breast cancer is concerned, TP53 somatic mutations are found in
the majority (70-80%) of BRCA1-mutated cancers400, and in approximately 20% of breast
cancer cases overall401. Germline TP53 mutations, however, are extremely rare: while
BRCA1 and BRCA2 germline mutations have an estimated population frequency of 0.5%,
TP53 germline mutations have an estimated population frequency of 0.025%. However,
when they do occur, TP53 germline mutations are associated with an 18- to 60-fold
increased risk for early onset breast cancer compared to the general population402.
As with BRCA1 and BRCA2, the region of TP53 that is mutated matters. The
majority of TP53 are missense point mutations, mostly located in the DNA binding
domain; some sources have stipulated that only such mutations influence breast
carcinoma outcomes at all, with mutations in other regions – like the oligomerization and
transactivation domains – failing to affect disease-free and overall survival403.
TP53 germline mutations are more common in some forms of breast cancer than
in others. While the literature on TP53 germline mutation prevalence is relatively sparse,
it appears that most in situ and invasive ductal breast carcinomas with such mutations are
hormone-receptor-positive (HR+) or HER2-positive (HER2+)404. As in sporadic cancer,
tumors with germline TP53 mutations also tend to cluster with BRCA1/2-germline-
mutation-associated tumors405. Finally, sporadic TP53 mutations often occur in triple-
negative breast cancers (TNBCs), which dominantly fall under the basal-like molecular
class of breast cancer, and account for 15% of all breast cancers. These cancers, as their
name suggests, are negative for both hormone receptor and HER2 expression.
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Characteristically, TNBCs have earlier onset and poorer outcomes, but poorer prognosis
is especially like when TP53 is mutated. Overall, then, TP53 mutations tend to correlate
with aggressive cancers, poor prognoses, invasion, metastasis, and expression of vascular
endothelial growth factor (VEGF), which aids tumors in vascularizing – and, thus, in
surviving and growing406. While this does not at first seem particularly important in the
context of hereditary cancer, TP53 and TNBC status become important in view of the
fact that at least one third of BRCA1 mutation carriers have triple-negative breast
cancer407.
Certain treatments work better on TP53-mutated cancers than others, whether
these cancers contain germline or somatic TP53 mutations. TP53-mutated cancers are
associated with resistance to doxorubicin, anthracyclines, tamoxifen post-chemotherapy
in pre-menopausal women408, and radiation409. Radiation should especially be avoided in
patients with Li-Fraumeni syndrome, given that they are prone to have increased
secondary tumor risk in response to radiation therapy410. However, TP53-mutated tumors
seem to respond well to paclitaxel411, cyclophosphamide412, and chemotherapy413. TP53
status is not a predictor of the effectiveness of trastuzumab414.
Relatively little has been written about TP53 germline mutations’ distribution
across distinct populations. However, TP53 germline mutations are dominantly
associated with one demographic: Li-Fraumeni syndrome, which in itself results in
increased breast cancer risk415. While germline TP53 mutations are very rare in normal
breast cancer populations, existing in <1% of families, there is a very high incidence of
early-onset breast cancer among Li-Fraumeni syndrome416, suggesting that TP53
mutations are strong hereditary breast cancer susceptibility genes where Li-Fraumeni
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syndrome patients are concerned. Germline mutations have also been isolated in certain
founder populations, including in Brazil (p.R337H)417 and in Quebec (c.844C>T and
c.685T>C)418.
3.3.3 Phosphatase and tensin homolog (PTEN)
Phosphatase and tension homolog (PTEN) is located on the long (q) arm of
chromosome 10, specifically at 10q23.31419. It spans 108,492 base pairs; it runs from
base pair 87,863,438 to base pair 87,971,930. It contains nine exons420, and 403 amino
acids421. It has several domains, including a phosphatase-coding domain in exon 5 that,
when mutated, leads to a loss of tumor suppressor activity422.
PTEN functions to inhibit cell proliferation, survival, and growth through
inactivating PI3K-dependent signaling, acting as a tumor suppressor when it is not
mutated423. PTEN also plays a critical role in the DNA damage response, cell signaling,
and genomic stability424. PTEN is thought to interact with BRCA1 where transcription,
protein modulation, and protein stability are concerned, and to regulate TP53 levels
through assorted mechanisms425.
Certain breast cancer types are more commonly associated with PTEN mutations
than others. Specifically, the basal-like cancer subtype in hereditary BRCA1-mutated
breast cancer is more likely to see a loss of PTEN expression426.
PTEN mutations are associated with Cowden syndrome, which, in female
patients, is associated with a lifetime breast cancer risk of up to 50%427. Up to 80% of
families affected with Cowden syndrome have germline PTEN mutations428, making
PTEN a significant breast cancer susceptibility gene in the context of Cowden syndrome.
PTEN germline mutations have also been explored in the context of several populations.
One study found that PTEN germline mutations are unlikely to contribute to familial
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breast cancer risk in a French-Canadian population429. Another study found that PTEN
also did not play a role in predisposing Israeli women to hereditary breast cancer430.
3.3.4 Other high-penetrance genes
While BRCA1, BRCA2, TP53, and PTEN are perhaps the most thoroughly
discussed genes in the literature, there are two other high-penetrance breast cancer genes:
cadherin-1 (CDH1) and serine/threonine kinase 11 (STK11).
Cadherin-1 (CDH1) is located on the long (q) arm of chromosome 16, specifically
at 16q22.1431. It is 98,323 base pairs long; it runs from base pair 68,737,225 to base pair
68,835,548. It contains 16 exons432, and 566 amino acids. CDH1 normally works to
control cell division during telophase and in G1. CDH1 mutations are associated with
invasive lobular breast cancer even in families without hereditary diffuse gastric
cancer433, and CDH1 mutation carriers have an up to 40-54% lifetime risk of developing
lobular breast cancer434. As such, increased surveillance of CDH1 in at-risk populations
has been recommended.
Serine/threonine kinase 11 (STK11) is located on the short (p) arm of
chromosome 19, specifically at 19p13.3. It spans 50,877 base pairs; it runs from base pair
1,177,558 to base pair 1,228,435435. It contains nine exons encoding a 433 amino acid
protein436. STK11 is normally involved with cell cycle regulation and apoptosis control,
and, like BRCA1, BRCA2, and TP53, is a tumor suppressor gene. STK11 mutations are
associated with Peutz-Jeghers syndrome, which is in itself associated with increased risk
of cancer, including breast cancer437. Annual breast MRI is recommended for Peutz-
Jeghers patients from the age of 25-30 onwards438.
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3.3.5 Moderate-penetrance genes
3.3.5.1 Ataxia telangiectasia mutated (ATM)
Ataxia telangiectasia mutated (ATM) is located on the long (q) arm of
chromosome 11, specifically at 11q22.3439. It spans 146,648 base pairs, running from
base pair 108,222,454 to base pair 108,369,102. It contains 66 exons, and 3056 amino
acids440. ATM also contains a C-terminus FAT (FRAP, ATM, TRAPP) domain with a
highly-conserved residue (the FATC domain), which is important for regulating ATM
activity. The N-terminus domains of ATM may also be critical to interactions with
substrates and other proteins.
When it functions normally, ATM is a kinase that predominantly monitors
checkpoints during DNA repair, alongside BRCA1, TP53, and CHEK2. It is especially
involved in the DNA damage response to DNA double-strand breaks (DSBs)441. It tends
to initiate the signaling cascade containing TP53, BRCA1, and CHEK2 through
phosphorylation.
Heterozygous mutations of ATM lead to a two- to five-fold increased risk of
breast cancer442, depending upon the age of the carrier. Many ATM mutations are
missense mutations; it has even been hypothesized that some ATM missense mutations
confer higher breast cancer risk than truncating mutations. One of the most common ATM
pathogenic variants is c.7271T>G 443; this mutation has been isolated in Canadian,
Australian, and Northern California populations. Some studies suggest that there is an
increased frequency of lobular breast cancers in ATM, while other studies dispute this. On
the basis of ATM mutations’ response to PARP inhibition, it has been suggested that
PARP1 inhibitors could be used to treat ATM carriers444.
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3.3.5.2 Checkpoint kinase 2 (CHEK2)
Checkpoint kinase 2 (CHEK2) is located on the long (q) arm of chromosome 22,
specifically at 22q12.1445. It spans 94,123 base pairs, running from base pair 28,687,743
to base pair 28,741,866. It contains 14 exons, and 543 amino acids.
CHEK2 is a serine threonine kinase that primarily acts as a cell cycle checkpoint
controller, and is thus part of the DNA damage response. It binds and regulates BRCA1,
and, along with CHEK1, acts upstream of TP53 in the DNA damage response446. It thus
acts as an important conveyor of DNA damage signals to downstream repair proteins.
Two breast-cancer-linked CHEK2 mutations that are particularly prevalent in
European populations are c.1100delC447 and I157T448. The former has been shown to lead
to a two-fold increased risk of breast cancer. c.1100delC is particularly common in north
European populations (e.g., the Netherlands, Finland), and is associated with an increased
risk of both bilateral and male breast cancer. Carrying more than one CHEK2 mutation
also increases risk449: in women homozygous for this mutation, breast cancer risk is six-
fold compared to heterozygotes. c.1100delC is also one of three CHEK2 truncating
mutations (i.e., c.1100delC, IVS2 + 1G > A, and a 5,395-base-pair founder deletion)
jointly responsible for over 2% of all breast cancers in Poland450. c.1100delC has
important functional consequences: a truncating mutation, it induces premature
termination of CHEK2’s C-terminal kinase domain, leading to decreased CHEK2 mRNA.
c.1100delC is also associated with specific forms of breast cancer: namely, its carriers
tend to exhibit bilateral, estrogen-receptor-positive tumors451 452 453of the ductal
histological type454.
I157T is a missense mutation conferring a 1.4-fold elevated risk of breast
cancer455, and is most common in Finland and Poland456. This mutation tends to interfere
61
with the CHEK2 homodimerization necessary for CHEK2 activation and function. Like
c.1100delC carriers, I157T carriers tend to have estrogen-receptor-positive tumors457,
though their tumors, unlike those of c.1100delC carriers, tend to be of the lobular type458.
3.3.5.3 Partner and localizer of BRCA2 (PALB2)
Partner and localizer of BRCA2 (PALB2) is located on the short (p) arm of
chromosome 16, specifically at 16p12.2459. It contains 38,197 base pairs, running from
base pair 23,603,160 to base pair 23,641,357. It contains 13 exons, and 1186 amino acids.
It is sometimes also known as FANCN; like the BRCA genes, it is associated with the
Fanconi anemia family of genes.
As its name suggests, PALB2 interacts with and manipulates BRCA2 during
double-strand break (DSB) repair and homologous recombination460.
PALB2 mutations confer up to a 5.3-fold increase in breast cancer risk461.
Specifically, women with an abnormal PALB2 gene have a 35% risk of developing breast
cancer by age 70462, compared to the 80% risk associated with BRCA1 and BRCA2463.
PALB2 breast-cancer-associated mutations are often loss-of-function mutations. Certain
specific PALB2 founder mutations have also been located. For example, a PALB2
mutation in a Polish founder population has been estimated to potentially increase breast
cancer risk464. Polish PALB2 founder mutations are reported to be associated with lower
ten-year survival compared to non-carriers (48 % versus 74.7 %)465. Founder mutations
have also been identified in Finland (c.1592delT)466 and in Canada (c.2323C>T)467.
3.3.6 Plausible and former candidate genes
There are many genes that are not validated in their susceptibility status, but are
being investigated. For example, RAD51C, RAD51D, XRCC2, BARD1, ABRAXAS, NBS1,
RAD50, and MRE11 are all genes that are presently being investigated in terms of their
62
breast cancer susceptibility value. RAD51C and RAD51D seem to be mostly ovarian
cancer susceptibility genes, not breast cancer susceptibility genes, but some of the other
aforementioned genes merit further investigation.
One such gene is XRCC2, which plays a significant role in homologous
recombination468. Two studies identified two and ten germline XRCC2 mutations in
breast cancer families469, while another study found that certain XRCC2 SNPs – in
particular, the missense mutation p.R188H – were associated with poor survival.
However, XRCC2’s relevance to breast cancer susceptibility has been questioned, given
that some studies have failed to identify mutations present in solely breast cancer
patients. Another is BRCA1-associated RING domain (BARD1), which interacts with
BRCA1 during double-strand break (DSB) repair and apoptosis470. BARD1 germline
mutations have been found in high-risk breast cancer families471, though further, large-
scale work is needed to confirm that BARD1 may be a breast cancer susceptibility gene.
The NBS1472, RAD50473, and MRE11474 gene trio, responsible for the MRE11-RAD50-
NBS1 (MRN) protein complex, is another group of genes involving in double-strand
break (DSB) detection, DNA repair, and checkpoint control via ATM, BRCA1, and
CHEK2. While mutations have been seen in relatively low frequencies, the RAD50
687delT founder deletion, the RAD50 IVS3-1G>A splicing variant, and the NBS1
Leu150Phe missense mutation were three mutations found in patients from Sweden,
Norway, Iceland, and Finland475. Finally, ABRAXAS, also known as ABRA1, CCDC98,
and FAM175A476, binds to BRCA1 BRCT motifs, and acts in conjunction with BRCA1,
RAP80, BRCC36, BRCC45, and MERIT40/NBA1 to form the BRCA1 holoenzyme
complex, control DNA damage checkpoints, and respond to double-strand breaks
63
(DSBs). An ABRAXAS missense mutation, p.R361Q, has been found to result in
abnormal DNA responses, and has been isolated in three of 125 Finnish breast cancer
families, one of 991 unselected breast cancer cases, and no healthy controls477.
There are also genes that, once thought to increase breast cancer susceptibility, are
now known not to do so. One example is BRIP1, which encodes a protein associated with
BRCA1; while it was found to contribute to breast cancer susceptibility478, more recent
reports indicate that BRIP1 does not generally account for increased breast cancer risk479,
and is more an ovarian cancer susceptibility gene480 481.
3.3.7 Low-penetrant loci
While the aforementioned high- and moderate-penetrance genes account for a
significant proportion of breast cancer risk, it has been estimated that a small percentage
of breast cancer cases are attributable to a number of comparatively common breast
cancer susceptibility loci that, operating polygenically482 or in conjunction with
environmental factors, can cause elevated risk of breast cancer. These loci have been
traced dominantly via genome-wide association studies (GWAS). Examples of such loci
include single-nucleotide polymorphisms (SNPs) in the following genes and no-known-
gene loci: MAP3K1, FGFR2, LSP1, TNRC19, TOX3, TGFB1, H19, 2q35, and 8q24483.
Chapter 4
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Screening RECQL Gene Among Breast Cancer Patients in Ontario, Canada
4.1 RECQL: Introduction
4.2.1 Introduction
RecQ-like helicase (RECQL) is a human gene that encodes the enzyme ATP-
dependent DNA helicase Q1484. It is a member of the RecQ family of DNA helicases. It
is also sometimes termed RECQL1 or RecQ1. It has now been identified as a potential
breast cancer susceptibility gene by both our lab485 and a Chinese group486.
DNA helicases, discovered in E. coli in 1976487 and in humans in 1990488, are
enzymes whose main function is to separate the annealed strands of a DNA double helix
by moving unidirectionally along the DNA duplex, and breaking the hydrogen bonds
between annealed nucleotide bases using energy from ATP hydrolysis. Because cellular
processes like replication, transcription, translation, recombination, and DNA repair all
necessitate the catalysis of nucleic acid strand separation, up to one % of all eukaryotic
genes are helicases489.
4.2.2 Structure
The RecQ family of helicases is included in helicase Superfamily 2 (SF2)490: the
superfamily containing perhaps the largest group of helicases. Like Superfamily 1, this
superfamily of helicases does not form a ring structure. It may be referred to as a beta
helicase family because it works with double-stranded DNA. Even more specifically, SF2
is characterized by a specific set of motifs – Q, I, Ia, Ib, and II through VI – in its
structure491. Finally, RecQ helicases, as their name suggests, are structurally defined by
sequences similar to those found in the catalytic domain of E. coli RecQ. Of the five
RecQ helicases, RECQL is the smallest492.
65
Mutations in certain components of RECQL can be more deleterious to gene
function than mutations in other components of RECQL493. Specifically, mutations found
in the helicase’s highly conserved core residues as well as in the RecQ C-terminal
domain of RECQL tend to affect strand separation, ATP hydrolysis, dimer formation, and
protein stability more so than other mutations.
4.2.3 Function
RECQL belongs to a class of DExH-containing DNA helicases called the RecQ
helicases494. These helicases, which unwind double stranded DNA (dsDNA), are
involved in important cellular functions, including DNA repair, replication,
recombination, and gene transcription. Like other Superfamily 2 helicases, all five RecQ
helicases help maintain genomic stability. Three of them, when mutated, are associated
with genetic disorders that cause premature aging, developmental abnormalities, and
cancer predisposition495. For example, mutations in BLM result in Bloom syndrome
(BS); mutations in RECQL4 result Rothmund-Thomson syndrome (RTS); and mutations
in WRN result in Werner syndrome (WS). Incidentally, all of these syndromes are
associated with a predisposition to cancer496, as we suspect RECQL may also be.
Furthermore, just like cells containing RECQL mutations, cells containing mutations of
BLM, RECQL4, and WRN are characterized by chromosomal damage, including breaks,
translocations, rearrangements, and large deletions of chromosomal components. Cells
deficient in RecQ helicase products also exhibit problematic genetic recombination and
replication in both human and mouse models, resulting in chromosomal instability.
While the function of RECQL has yet to be conclusively determined, several
possible roles have been proposed for the gene497. Perhaps most probably, RECQL may
66
resolve stalled replication forks, thus preventing double-strand DNA (dsDNA) breaks,
maintaining genome integrity, and suppressing tumor growth.
While double-strand DNA breaks are common eukaryotic events, they must be
repaired if cells are to continue functioning. Thus, two major pathways exist for repairing
dsDNA breaks: homologous recombination and non-homologous DNA end joining
(NHEJ). Several genes known to be breast cancer susceptibility genes are involved in
repairing dsDNA breaks (e.g., BRCA1, BRCA2, PALB2)498, suggesting that RECQL’s
core proposed function (i.e., the prevention of these breaks) complements the function of
known breast cancer susceptibility genes, and is thus functionally likely to also be a
breast cancer susceptibility gene. RECQL may even play a role not solely in damage
prevention, but also in homologous recombination repair. It is important to note that
RECQL’s association with DNA repair makes it a functionally convincing candidate
susceptibility gene, given that many already-known breast cancer susceptibility genes
affect either the DNA damage response or some other facet of DNA repair: BRCA1 is
involved in double-strand break (DSB) recognition499; RAD50, MRE11, and NBS1 are
involved in DNA end-processing500; both BRCA genes and PALB2 are involved in strand
invasion501 502 503; RECQL appears to be involved in ATP-dependent DNA unwinding504
505; ATM506 and CHK2507 are involved in signal transduction, and p53, the product of
TP53508, is involved in regulation of gene expression and HR branch-migration. RECQL-
deficient mice, for example, are prone to aneuploidy, spontaneous chromosomal
breakage, translocations, spontaneous sister chromatid exchange, increased sensitivity to
ionizing radiation, a high load of dsDNA breaks, and decreased cell growth, all of which
67
suggest that RECQL may normally play a role in genome integrity as a tumor suppressor,
similar to the BRCA genes509 510.
It is also noteworthy that RECQL is not solely the most frequently expressed of
the RecQ helicases where normal cells are concerned: it also has unusually high
expression in cancer cells511. This high RECQL expression in cancer cells seems
paradoxical, given the cancer-countering functions that we attributed to RECQL above.
However, as members of our lab have already suggested in a 2015 paper, it is plausible
that high expression of RECQL in cancer cells only occurs after a tumor phenotype has
been acquired in these cells, potentially to keep abreast of the high DNA replication rate
in cancer cells, or to maintain telomere length through alternative pathways.
RECQL interacts with several other genes, including KPNA4512 and karyopherin
alpha 2. It is also probable that RECQL works with some of the genes listed above – i.e.,
BRCA1, BRCA2, RAD50, MRE11, NBS1, PALB2, ATM, CHK2, and TP53 – where repair
is concerned. Finally, there may be a direct interaction between RECQL and RAD51 that
affects stalled replication forks.
4.3 RECQL: Breast Cancer Susceptibility Gene Candidacy in Selected
Populations
4.3.1 Our Lab’s Prior Paper
Our 2015 Nature Genetics letter identified RECQL as a potential breast cancer
susceptibility gene513. Our lab used whole-exome sequencing to investigate a total of 195
familial breast cancer patients (both high-risk and unselected) from Polish and Quebecois
populations in a discovery phase, both of which are populations with founder mutations
in known susceptibility genes. We initially surveyed a range of genes in order to identify
genes within which a common mutant allele was present, and then examined certain
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genes in large patient sets. Genes were prioritized on the basis of possessing a truncating
mutation (i.e., a nonsense mutation, essential splice-site mutation, frameshift insertion or
deletion, or start codon mutation). Only variants with minor allele frequency (MAF)
lesser than or equal to one % were included. While many genes harbored a mutation, only
RECQL harbored multiple rare truncating mutations in both populations and was
functionally similar to many existing breast cancer susceptibility genes. Specifically, two
rare recurrent RECQL mutations were identified, one in each population, with each being
more significantly frequent among the breast cancer patients than among the controls.
Seven of 1,013 high-risk breast cancer cases compared to only one of 7,136 controls
carried the c.643C>T (p.Arg215*) variant in Quebec (p < 10-4), and 30 of 13,136
unselected breast cancer cases compared to only two of 4,702 controls carried the
c.1667_1667+3delAGTA (p.K555delinsMYKLIHYSFR) variant in Poland (p = 0.008).
In other words, RECQL seemed to be associated with an odds ratio of 5.4 for
breast cancer development among unselected Polish patients with RECQL mutations,
suggesting a lifetime risk of approximately 50 %. If this risk is found to be present in
further populations, then prophylactic measures like double mastectomy may be
beneficial for RECQL mutation carriers. Further, one in 400 patients carried a particular
RECQL truncating mutation that seemed to be particularly functionally significant.
Finally, mutations increased the risk of breast cancer by sixteen-fold among higher-risk
cases in Quebec. Our study also located fourteen RECQL missense mutations, all of
which had a minor allele frequency lower than 0.5 %; given that rare missense mutations
tend to be more likely to be pathogenic, these mutations merit further exploration where
69
potential pathogenicity is concerned, both via further sequencing studies and via
functional assays to investigate their protein expression consequences.
However, our study involved several limitations. Firstly, it targeted two founder
populations (the Quebecois population and the Ashkenazi Jewish population in Poland).
Founder populations are derivative populations which arise when a group of individuals
relocate to a new region and establish a community that practices some degree of
endogamy, resulting in a genetically unique population that nevertheless contains a
higher-than-usual frequency of certain alleles. Because such populations are prone to
containing mutations distinct from those found in the general population (i.e., founder
mutations), it would be wise to study a more generalized population if we wise to
conclude that RECQL mutations are relevant to other ethnicities, particularly in view of
the fact that the highest incidence of breast cancer exists in countries like the United
States and Canada.
Secondly, it was centered on a single recurrent truncating mutation. However,
there may be other mutations in RECQL that are pathogenic, and it is thus necessary to
study the entire coding region of RECQL if we are to truly gauge its significance as a
breast cancer susceptibility gene. Finally, while our study showed that RECQL may well
be a susceptibility gene, the existence of a susceptibility gene is not in itself an actionable
clinical development. In order to make RECQL knowledge relevant to breast cancer
management, it becomes important to calculate the breast cancer lifetime risk and
survival associated with carrying a germline RECQL mutation. Should the risk be found
to be high in more generalized populations of breast cancer patients, recommendations
70
involving prophylactic procedures such as double mastectomy can subsequently be
generated.
4.3.2 Other Papers
A 2015 paper by Sun et al. also associated mutations in RECQL with a
predisposition to breast cancer514. In this study, 448 unrelated familial breast cancer
patients were screened for RECQL mutations. The study found three nonsense mutations
leading to a truncated protein (p.L128X, p.W172X, and p.Q266X), one mRNA splicing
mutation (c.395-2A>G), and five missense mutations disrupting helicase activity
(p.A195S, p.R215Q, p.R455C, p.M458K, and p.T562I). Nine of the 448 BRCA-negative
families examined carried a pathogenic RECQL mutation compared to one of 1588
controls (p = 9.14 x 10-6), suggesting that, as per our paper, RECQL is a potential breast
cancer susceptibility gene.
A 2016 paper by Kwong et al. again associated germline RECQL mutations with
high-risk breast cancer in a Chinese population515. In this study, 1110 breast cancer
patients negative for BRCA1, BRCA2, TP53, and PTEN gene mutations were screened
for RECQL mutations. Four different pathogenic RECQL mutations were identified in six
of the 1110 patients tested (0.54 %). These mutations included one frame-shift deletion
(c.974_977delAAGA), two splice site mutations (c.394+1G>A, c.867+1G>T), and one
nonsense mutation (c.796C>T, p.Gln266Ter). Two of these mutations were seen in more
than one patient, suggesting that they may have a recurrent role in breast cancer
susceptibility. Because it controls for mutations in common breast cancer susceptibility
genes that we did not account for, this paper goes a step further in validating RECQL as a
potential breast cancer susceptibility gene by eliminating the possibility that the elevated
risk witnessed could be due to mutations in genes like TP53 and PTEN. The study also
71
underscores the significance of RECQL as a universal susceptibility gene by examining a
population that has not as yet been considered in RECQL research (i.e., that of Southern
China).
Bogdanova et al. are another group that examined the frequency of the Polish
RECQL mutation – c.1667_1667 + 3delAGTA – in the context of two Central European
(Belarusian and German) populations516. They tested 2,596 breast cancer patients and
2,132 healthy females. The c.1667_1667+3delAGTA mutation was found in 9 of 2,596
cases (0.35%) and 6 of 2,132 (0.28%) controls. Our study on the other hand, reported 30
carriers among 13,136 cases (0.23%), and 2 carriers among 4,702 controls (0.04%).
While the mutation frequency among the cases is close in both of the studies, the
mutation frequency among the controls is quite high in the Bogdanova et al. study
relative to what our study reported for the controls. It is also pertinent to note that the
mutation frequency of this mutation in the European population as reported by the ExAC
Browser after excluding the cancer genome atlas data is 0.12% (37/30,180), which is
between what we reported in our study and what has been reported by Bogdanova et al.
Based on the meta-analysis of the mutation frequencies among cases and controls from
our study, Bogdanova et al. study and also the controls from ExAC Browser, we have 39
carriers among 15,732 cases (0.24%), and 45 carriers among 37,014 controls (0.12%)
(p=1.3 x 10-3). However, since ExAC Browser reports mutation frequencies from a
mixture of European populations, it is still inconclusive based on the available data about
the true value of the mutation frequency among the controls, and future large-scale
studies from other European populations with matched cases and controls will be able to
72
shed more light on proving or disproving the association of RECQL
c.1667_1667+3delAGTA mutation with breast cancer risk.
In a 2017 paper517, Sun et al. examined the prevalence of cancer susceptibility
gene mutations among unselected breast cancer patients in a Chinese population. 8,085
unselected Chinese breast cancer patients were enrolled, and a 62-gene panel was tested.
46 cancer susceptibility genes were found to contain germline mutations. Specifically, 30
pathogenic RECQL mutations were detected among 8,085 unselected cases.
Unfortunately, however, the Sun paper lacked controls. Looking at the 2015 Sun study’s
1,588 controls and the ExAC Browser’s 3,927 East Asian non-TCGA samples, we see
that while there were 30 mutations in 8,085 unselected cases in the 2017 Sun study, there
was only 1 carrier in 1,588 controls in the 2015 Sun study, and only 4 carriers in the
3,927 non-TCGA samples. Using these two control groups as a control for the 2017 Sun
study’s 8,085 selected cases, we can say that we would have 5 carriers in 5,515 controls
if we were to add these all to one another, and 30 carriers in 8,085 samples. Effecting a
simple case-control analysis, we see that there is a significant difference between the
number of mutations in the unselected patients and the number of mutations in the
controls (p-value: 0.0035). The associated odds ratio (OR) was 4.10.
4.4 Our Project: Screening RECQL in Ontario Population
4.4.1 Rationale
In on our lab’s 2015 Nature Genetics publication, it was suggested that RECQL
mutations are associated with breast cancer susceptibility in Polish and Quebecois
founder populations518. Specifically, for one recurrent truncating mutation
(c.1667_1667+3delAGTA) in unselected Polish patients, an odds ratio of 5 for
developing breast cancer was observed, representing a life-time risk of about 50 %. This
73
suggests that RECQL could be a high-penetrance breast cancer susceptibility gene. Like
other high-penetrance breast cancer susceptibility genes, RECQL is also rarely mutated:
only about 1 in 400 cancer patients carried the aforementioned truncating mutation. But
this mutation penetrance and frequency are based on a single mutation and a single
population, and are thus not generalizable to the general population.
To understand whether RECQL can be said to be an actionable susceptibility gene
at the level of a heterogeneous Canadian population, we needed to arrive at a better
estimate of RECQL mutation frequency and associated breast cancer risk in the general
population. We thus assessed RECQL mutation frequency and carrier risk in a more
generalized population. We also utilized a larger population, and studied mutations across
the entire coding region of RECQL: specifically, we screened the DNA of 2859 familial
breast cancer patients, as well as 929 healthy controls. In order to ensure that the patients
in our study were representative of hereditary breast cancer patients in Ontario,
participants were recruited from Mount Sinai Hospital and Women’s College Hospital,
two major genetic testing referral centers in Ontario.
Ultimately, with the data obtained from this study, we will have an improved
estimate of the mutation frequency and life-time risk associated with RECQL in a
Canadian breast cancer context, which will be useful for establishing screening strategies
for RECQL in breast cancer patients, as well as prevention and treatment strategies for
mutation carriers. If, for example, the 50 % life-time risk of breast cancer among RECQL
mutation carriers in our lab’s original study is consistent with the data from this research,
then prophylactic measures such as double mastectomy could potentially be
recommended for RECQL mutation carriers. If, on the other hand, the life-time risk of
74
breast cancer associated with RECQL mutations is not as significant in our research, then
RECQL may be found to be a breast cancer susceptibility gene solely relevant to certain
founder populations rather than to a general population context: a finding that would
focus future directions for RECQL research in the contexts of the aforementioned founder
populations.
4.4.2 Objective
Objective: We sought to examine the frequency of RECQL mutations and
associated life-time breast cancer risk in familial breast cancer patients and controls.
4.4.3 Methodology
Overall, we screened germline DNA for the entire coding region plus 20 base
pairs of the intronic regions at each end of the exons of RECQL in 3788 participants
(2859 familial breast cancer patients and 929 controls), all of European ancestry to locate
RECQL mutation carriers. We then estimated the life-time risk of breast cancer
associated with carrying a RECQL mutation among women in Ontario.
4.4.3.1 Selection
Participants were recruited from Mount Sinai Hospital and Women’s College
Hospital, two major genetic testing referral centers in Ontario. All women provided
consent to participate in this study; the study protocol was approved by an ethics board.
All included participants were also negative for mutations in both BRCA1 and BRCA2.
All controls were forty or older at the time of the study, and were required to have no
personal history of cancer. All samples were made available in biobanks at Women’s
College Hospital and Mount Sinai, where they were subsequently processed. Blood
samples, medical history, and family history were collected from each participant, and
several characteristics were noted, namely: current age and ethnicity. Patients recruited
75
from Mount Sinai will hereafter be referred to as Mount Sinai Hospital (MSH) samples,
and patients recruited from Women’s College Hospital will hereafter be referred to as
Women's College Hospital (WCH) samples.
4.4.3.2 Laboratory Protocol
Laboratory work was conducted at Women’s College Research Institute in
Toronto, Canada.
4.4.3.2.1 Primer Design
Forty-two primer sets were designed for RECQL using the Primer3Plus
program519 using the following GenBank sequences: NM_002907.3.
4.4.3.2.2 Polymerase Chain Reaction (PCR) Amplification
Testing was performed on DNA samples extracted from white blood cells. DNA
was quantified using the NanoDrop ND-1000 spectrophotometer (Thermo Scientific Inc.,
Wilmington, DE, USA).
All 14 coding exons of RECQL (NM_002907) plus 20 base pairs from the exon
boundaries were amplified in 42 amplicons between 251 and 415 in size using WaferGen
SmartChip technology (WaferGen Biosystems Inc., CA, USA). First, 384-well sample
plates containing the PCR reagent, genomic DNA samples, and barcoding primers were
prepared. Then, assay plates containing more PCR reagent and target-specific primers
were prepared. Both sets of plates were subsequently dispensed into the SmartChip using
the MultiSample NanoDispenser (MSND) (WaferGen Biosystems Inc., CA, USA). Next,
the SmartChip was loaded into a thermal cycler for completing the amplification and
incorporating sample specific barcodes and Illumina adaptors. The amplicon library was
then extracted, purified, and quantitated following the operational directions outlined in
the Seq-ReadyTM TE (FLEX) manual provided by WaferGen.
76
4.4.3.2.3 Sequencing
Sequencing was conducted at the Research Molecular Genetics Laboratory of
Women’s College Research Institute (WCRI) in Toronto, Canada. Two rounds of
sequencing were performed. Initially, Illumina next-generation sequencing (NGS) was
performed; subsequently, Sanger sequencing was used to validate the results.
4.4.3.2.3.1 Next-Generation Sequencing
Paired-end sequencing was performed using the Illumina MiSeq sequencing
system (Illumina Inc., San Diego, CA, USA). Six chips were run in parallel, with each
chip containing 42 samples, at 500 cycles of 250-base-pair paired-end sequencing. The
mean depth of coverage was 1959x (range: 1114 to 3229). On average, 99.7 % (range:
98.6 % to 100 %) of the coding exons of RECQL were covered at an 50x depth of
coverage or higher (used for variant calling).
4.4.3.2.3.2 Sanger Sequencing
All pathogenic variants with potential clinical significance recovered via NGS
were confirmed using Sanger sequencing. Sequencing reactions were performed using a
BigDye Terminator v3.1 Cycle Sequencing Kit (Fisher Scientific International Inc.,
Pittsburgh, PA, USA) according to the manufacturer’s protocol on the ABI prism
3500XL Genetic Analyzer (Fisher Scientific International Inc., Pittsburgh, PA, USA),
and were then analyzed for variant detection using Mutation Surveyor software
(SoftGenetics LLC, State College, PA, USA).
4.4.3.2.4 Analysis
4.4.3.2.4.1 General Analysis Protocol
Initially, the pooled sequencing reads were demultiplexed, generating two
FASTQ files for each sample. Next, primer sequences were trimmed from the ends of
reads, so that only the relevant DNA was preserved. Then, DNA sequences were mapped
77
to the hg19 – the whole human genome reference sequence – using the Burrows-Wheeler
Alignment (BWA) algorithm on the Linux operating system. Following alignment,
sequencing reads were stored as SAM files, which were then converted to BAM files
using the Picard package. The BAM files were sorted and indexed. The files were then
realigned again in order to be used in analysis of insertions/deletions (indels) and single-
nucleotide polymorphisms using the Genome Analysis Toolkit (GATK). Variants were
then called using UnifiedGenotyper command of the GATK. Variants were included in
the analysis if they represented a nucleotide different from the RECQL reference
sequence in at least 25 % of reads, if they were of at least 50x depth, and if they were rare
variants (minor allele frequency (MAF) ≤ 1). All truncating variants (frameshift indels,
stop codon gains, essential splice site mutations) were considered pathogenic mutations,
and were included. Finally, called variants were annotated using SNP & Variation Suites
from Golden Helix Inc. (Bozeman, MT, USA).
4.4.3.2.4.2 Read Mapping (Alignment) Commands
Reads were aligned to the hg19 whole human genome reference sequence using
the Burrows-Wheeler Aligner (BWA) algorithm on the Linux kernel. Our sample
sequence files, which were initially in FASTQ format (i.e., the de facto standard format
for storing the output of instruments like the Illumina Genome Analyzer), had to be
converted to the standard Sequence Alignment/Map (SAM) format during alignment. The
following command was used to convert two fastq.gz files (one for each of the two paired
reads) to SAM while aligning them both to the hg19 reference sequence:
bwa mem -Map ucsc.hg19.fasta 1_1.fastq.gz 1_2.fastq.gz > 1.sam
The output of this command results in a SAM file, which can be directly
manipulated using the Picard package was used to clean SAM files, and convert them to
78
binary versions (referred to as BAM files). BAM files were subsequently further sorted,
and read groups were added. A final index of all BAM files was then generated. The
following commands were used to clean, convert, and sort BAM files, and then to add
read groups and build a BAM index respectively:
java -jar picard.jar CleanSam \
I= Sample_ID.sam \
O=Sample_ID_cleaned.sam
java -jar picard.jar SamFormatConverter \
I=Sample_ID_cleaned.sam \
O= Sample_ID _cleaned.bam
java -jar picard.jar SortSam \
I= Sample_ID _cleaned.bam \
O= Sample_ID _cleanedsorted.bam \
SORT_ORDER=coordinate
java -jar picard.jar AddOrReplaceReadGroups \
I= Sample_ID _cleanedsorted.bam \
O= Sample_ID _cleanedsortedRG.bam \
RGLB=RECQL \
RGPL=Illumina \
RGPU=hum1 \
RGSM=Sample_ID
java -jar picard.jar BuildBamIndex \
I=Sample_ID_cleanedsortedRG.bam
The indexed BAM was then subjected to further analysis via the Genome
Analysis Toolkit (GATK). First, filtration was carried out to remove poor-quality reads;
after filtration, reads were printed to a new BAM file. The commands used for this were
as follows:
java -jar GenomeAnalysisTK.jar \
-T PrintReads \
-R ucsc.hg19.fasta \
-I Sample_ID cleanedsortedRG.bam \
--read_filter MappingQualityZero \
--read_filter BadMate \
79
--read_filter MappingQualityUnavailable \
--read_filter UnmappedRead \
--read_filter NotPrimaryAlignment \
--read_filter FailsVendorQualityCheck \
-o Sample_ID_cleaned_sorted_Filtered.bam
Once the BAM files were filtered, the RealignerTargetCreator was used to identify
suspicious intervals within the sequence that were likely to be in need of realignment:
java -jar GenomeAnalysisTK.jar \
-T RealignerTargetCreator \
-R ucsc.hg19.fasta \
-I Sample_ID_cleaned_sorted_Filtered.bam \
--known 1000G_phase1.indels.hg19.vcf \ --known
Mills_and_1000G_gold_standard.indels.hg19.vcf \
-L RECQL.intervals \
-o 1.intervals
These selected intervals were then realigned using IndelRealigner, generating a BAM file
of cleaned, sorted, filtered, and realigned samples.
java -jar GenomeAnalysisTK.jar \
-T IndelRealigner \
-R ucsc.hg19.fasta \
-I Sample_ID_cleaned_sorted_Filtered.bam \
-known 1000G_phase1.indels.hg19.vcf \ -known
Mills_and_1000G_gold_standard.indels.hg19.vcf \
-targetIntervals 1.intervals \
-o Sample_ID_cleaned_sorted_Filtered_realigned.bam \
--consensusDeterminationModel USE_READS \
-LOD 5.0
Variants were then called using UnifiedGenotyper, another Genome Analysis Toolkit
feature for every 100 samples. The UnifiedGenotyper is capable of calling SNPs and
insertions/deletions (indels) simultaneously, as per this command:
java -jar GenomeAnalysisTK.jar \
-T UnifiedGenotyper \
-R hg19.fa \
-I Sample_ID _cleaned_sorted_Filtered_realigned.bam [-I sample2.bam ...] \
-- dbsnp_138.hg19.vcf \
-dt NONE \
-stand_call_conf [30.0] \
-stand_emit_conf 10.0 \
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-L RECQL.intervals
-glm BOTH
-o snps.raw.vcf \
4.4.4 Results
4.4.4.1 Overall Patient Characteristics
The mean current age of hereditary Mount Sinai Hospital breast cancer patients was
53.74 years (range: 16 – 93). The mean current age of hereditary Women’s College
Hospital patients was 47.80 years (range: 23-91).
Table 1: Characteristics of Hereditary Mount Sinai Hospital Patients
n = 2221 (Total = 2225) Number Proportion
Mean age 53.74 N/A
Age range 16-93 N/A
Ethnicity (based on 76.13% of patients with available data)
English
French
Italian
Scottish
Chinese
237
190
148
119
82
0.145
0.116
0.089
0.073
0.05
Table 2. Characteristics of Hereditary Women’s College Hospital Patients
81
n = 632 (Total = 634) Number Proportion
Mean age 47.80
Age range 23-91
Ethnicity (based on 98.26% of patients with available data)
British:
Ashkenazi Jews
Chinese
Filipino
Jamaican
84
57
44
30
17
0.133
0.090
0.070
0.047
0.027
4.4.4.2 Mutations and Carrier Characteristics
Overall, 8 of 2859 hereditary breast cancer cases sampled had a mutation in
RECQL (0.28 %), compared to only 1 of 929 controls (0.11 %) (p-value: 0.3672). One
mutation (c.1476C>G, or p.Tyr492Ter) was seen twice: once in a breast cancer patient
(Patient 64485) and once in a control (Patient 15385). The affected exons were 3, 6, 7, 9,
11, and 13. Six of eight breast cancer patient mutations were loss-of-function mutations;
of these, one was a splice acceptor variant, two were frameshift insertion/deletions, and
three were nonsense mutations (i.e., gains of stop codons). The remaining two mutations
were an in-frame deletion that resulted in deletion of one amino acid from the protein,
and a splice region variant. The one mutation found in the control set was a loss-of-
function nonsense mutation in exon 13, resulting in a gained stop codon; it involved the
DNA change c.1476C>G, resulting in the protein change p.Tyr492Ter. This was the one
mutation shared with one of our cases.
82
For the intronic mutation characterized as a splice region variant (c.867+3A>T)
we ascertained its pathogenicity by utilizing standard computational tools, such as
Random Forest (RF) scores, and Adaptive Boosting (AdaBoost) scores. Both scores have
a range of 0 to 1, where a 0 indicates no pathogenicity, 0.6 is the threshold of possible
pathogenicity, and 1 indicates a very high probability of pathogenicity520. The intronic
mutation we located had an RF value of 0.998, indicating a very high chance of
pathogenicity, and an AdaBoost score of 0.748, indicating a moderately high chance of
pathogenicity. Also, for the in-frame deletion (c.633_635delGAG), we found the
mutation to be disruptive (affecting one nucleotide of codon #211, and two nucleotides of
codon #212), causing the deletion of arginine at position 212, which is part of RECQL’s
ATP binding domain. It is also important to note that disruption of this helicase activity
via a mutation within this ATP-binding domain has been documented previously. More
specifically, Sun et al. found five missense mutations – one of them being p.R215Q – and
confirmed the disruption of RECQL’s helicase activity via an in vitro helicase assay. This
significantly suggests that this mutation is pathogenic.
The mean current age of breast cancer patients with 8 mutations was 46.8 years
(range: 38 – 62), while the mean age of the non-carriers without a mutation was 52.45
years (range: 16-93; SD: 12.44). The difference in mean ages between carriers and non-
carriers was not significant (p-value 0.1995). No ethnicity prevailed among the eight
carriers with breast cancer: one patient each was Italian, Romanian, Irish, Scottish,
German, South Asian and Jamaican, and one patient did not have ethnicity-related data
available. Similarly, the overall cohort studied across both controls and breast cancer
patients was diverse, and did not exhibit a prevailing ethnicity.
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Table 3: Characteristics of RECQL mutations found among breast cancer patients
Carrier
ID
Exon cDNA change Protein change Age
Gene Region
(Combined)
Effect
(Combined)
Sequence
Ontology
(Combined)
321 6
NM_002907.3:c.633_6
35delGAG
NP_002898.2:p.Ar
g212del
62 Exon Missense
inframe deletion
563 8-9
NM_002907.3:c.950-
2A>C
- 52 Intron LoF
Splice acceptor
variant
809 7
NM_002907.3:c.825de
lT
NP_002898.2:p.Phe
275fs
38 Exon LoF
Frameshift
variant
1305 11
NM_002907.3:c.1219
C>T
NP_002898.2:p.Ar
g407Ter
- Exon LoF
Stop gained
2852 7-8
NM_002907.3:c.867+
3A>T
- - Intron Splicing
Splice region
variant
3022 9
NM_002907.3:c.1012_
1019delGGAATTCA
NP_002898.2:p.Gly
338fs
- Exon LoF
Frameshift
variant
84
64485 13
NM_002907.3:c.1476
C>G
NP_002898.2:p.Tyr
492Ter
40 Exon
LoF Stop gained
58889 3
NM_002907.3:c.132_1
35delGAAA
NP_002898.2:p.Lys
44delinsLysTer
42 Exon
LoF Stop gained
85
4.4.4.3 Life-Time Risk Calculation
After all pathogenic truncating variants were discovered, the frequencies of
pathogenic mutations in hereditary breast cancer patients and controls were calculated.
Specifically, the frequency of pathogenic mutations in hereditary breast cancer patients
was 0.28 %; by contrast, the frequency of pathogenic mutations in controls was 0.11 %.
On this basis, an odds ratio (OR) was calculated. An odds ratio is a measure of the
association between an exposure (an event) and a given outcome (the presumed
consequence of that event). The odds ratio represents the chances that the consequence
will occur given a certain event, compared to the chances that the consequence will occur
without that event, and is frequently used in case-control studies, which already
inherently compare cases (i.e., outcomes that have been exposed) to controls (i.e.,
outcomes that have not been exposed). An odds ratio is typically calculated as such:
𝑂𝑅 =
𝑁𝑒𝑥𝑝𝑜𝑠𝑒𝑑 𝑐𝑎𝑠𝑒𝑠
𝑁𝑢𝑛𝑒𝑥𝑝𝑜𝑠𝑒𝑑 𝑐𝑎𝑠𝑒𝑠
𝑁𝑒𝑥𝑝𝑜𝑠𝑒𝑑 𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑠
𝑁𝑢𝑛𝑒𝑥𝑝𝑜𝑠𝑒𝑑 𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑠
where OR < 1 indicates that the exposure is associated with lower odds of the outcome,
OR = 1 indicates that the exposure does not affect the odds of the outcome, and OR > 1
indicates that the exposure is associated with higher odds of the outcome.
Specifically, in our case, the odds ratio was calculated utilizing the following
equation:
𝑂𝑅 =
𝑁𝑅𝐸𝐶𝑄𝐿 𝑚𝑢𝑡𝑎𝑡𝑖𝑜𝑛 𝑐𝑎𝑟𝑟𝑖𝑒𝑟𝑠 𝑤𝑖𝑡ℎ 𝑏𝑟𝑒𝑎𝑠𝑡 𝑐𝑎𝑛𝑐𝑒𝑟
𝑁𝑅𝐸𝐶𝑄𝐿 𝑚𝑢𝑡𝑎𝑡𝑖𝑜𝑛 𝑛𝑜𝑛−𝑐𝑎𝑟𝑟𝑖𝑒𝑟𝑠 𝑤𝑖𝑡ℎ 𝑏𝑟𝑒𝑎𝑠𝑡 𝑐𝑎𝑛𝑐𝑒𝑟
𝑁𝑅𝐸𝐶𝑄𝐿 𝑚𝑢𝑡𝑎𝑡𝑖𝑜𝑛 𝑐𝑎𝑟𝑟𝑖𝑒𝑟𝑠 𝑤𝑖𝑡ℎ𝑜𝑢𝑡 𝑏𝑟𝑒𝑎𝑠𝑡 𝑐𝑎𝑛𝑐𝑒𝑟
𝑁𝑅𝐸𝐶𝑄𝐿 𝑚𝑢𝑡𝑎𝑡𝑖𝑜𝑛 𝑛𝑜𝑛−𝑐𝑎𝑟𝑟𝑖𝑒𝑟𝑠 𝑤𝑖𝑡ℎ𝑜𝑢𝑡 𝑏𝑟𝑒𝑎𝑠𝑡 𝑐𝑎𝑛𝑐𝑒𝑟
86
The OR was found to be 2.60. Based on the life-time risk associated with
developing breast cancer in the general population, which is 10 %, the life-time risk
associated with RECQL mutation carriers was estimated to be 26 %. However, this life-
time risk was insignificant, given that our p-value was 0.3672, and can thus not reliably
be said to be indicative of RECQL-associated breast cancer risk in a Canadian population.
4.4.5 Discussion
4.4.5.1 Trends
There was no statistically significant difference between the frequency of RECQL
mutations in breast cancer patients (8/2859, 0.28%) and controls (1/929, 0.11%; p =
0.3672). Based on the odds ratio of 2.60, and 10 % life-time risk associated with
developing breast cancer in general population, RECQL mutation carriers appear to have
a lifetime breast cancer risk of 26 %.
However, the difference between mutation frequencies in patients and controls
was not as significant in this study as it was in the paper within which we initially
proposed RECQL’s breast cancer susceptibility candidacy. Furthermore, the odds ratio
also decreased compared to our initial study based on Polish samples. This could due to
the smaller number of cases and controls we have sequenced here. Future studies with an
increased number of cases and controls will be better able to evaluate RECQL’s role as a
breast cancer susceptibility gene.
The RECQL mutations uncovered in this study varied in terms of their
characteristics. Firstly, three of the eight mutations (namely, those associated with Carrier
IDs 321, 563, and 3022) do not appear to have previously been found, while the other
five had previously been described within either our 2015 study or online resources (i.e.,
87
the ExAC Browser, Reference SNP, and Nature’s full mutation list). Mutations which
have not previously been charted do not often have well-documented functional
consequences; as such, depending upon their eventual functional consequences, they
could be either significant or insignificant to RECQL’s breast cancer susceptibility gene
potential. Secondly, the eight mutations had distinct effects: six were loss-of-function
(LoF) mutations, one was a in-frame deletion, and one was a splicing mutation;
specifically, in terms of sequence ontology, three involved the gaining of a stop codon,
two were splice region or acceptor variants, two were frameshift variants, and one was an
in-frame deletion. Finally, while six of the eight mutations were exonic, two were
intronic.
In summary, our results suggest a statistically non-significant association of
RECQL with breast cancer. Several large studies are under way by our team and others;
their results could help clarify the association of RECQL with breast cancer. Until more
information is gathered, nothing definite can be said in the way of lifetime risk; for that
reason, no prophylactic or national screening measures can be recommended.
4.4.5.2 Limitations
There are two major limitations in this study. Firstly, due to lack of large number
of cases and controls, we were unable to validate the role of RECQL as a potential breast
cancer susceptibility gene. Instead, we were only able to screen the entire coding regions
of the gene, and report the relevant mutation frequency. In order to make a definitive
statement on the role of RECQL in breast cancer, we need to conduct large-scale case-
control studies.
88
Secondly, and more importantly, in our current study, we only looked at the role
of truncating variants in RECQL. We found 13 missense mutations, but we did not
investigate their roles further. However, RECQL can only thoroughly be investigated if
the missense variants from this study and the original study by Cybulski et al. are
analyzed. There are several indications that missense mutations may be critical to future
studies involving RECQL. First, the study conducted by Sun et al. in 2015, which
independently found RECQL to be a breast cancer susceptibility gene, found nine
mutations. The majority of them – five – were missense, and these missense mutations
were confirmed to be pathogenic via an in-vitro functional assay. Second, Sun et al.
conducted a follow-up study in 2017 examining 8085 unselected breast cancer patients
which retrieved 30 mutations in RECQL. Once again, 20 out of the 30 mutations were
missense mutations that had previously been confirmed to be pathogenic. Third and most
recently, Tervasmäki et al. found a rare founder missense variant in a Finnish population,
and no mutations in their controls. The mutation was found in the helicase domain of
RECQL, which was also found to be disrupted by an in-frame deletion mutation in our
study, and a missense mutation in the 2015 Sun et al. study. Fourth, in the discovery
paper of RECQL by Cybulski et al., there were 14 missense variants found in 1,145
patients, and all of them had a minor allele frequency (MAF) of less than 1%. Though no
functional analysis has thus far been done on these missense variants, the fact that these
variants have an MAF of less than 1% indicates a strong likelihood of pathogenicity.
Fifth, there are several other genes in which the role of missense mutations has been
studied extensively. These include TP53, ATM, and CHEK2, all of which are breast
cancer susceptibility genes, and are involved in some facet of DNA repair or DNA
89
damage signaling response. Finally, given that RECQL is a DNA helicase, it should not
be surprising to see that missense mutations might play a larger role than truncating
variants, as has often been the case with other breast cancer susceptibility genes. The beta
hairpin secondary structure of RECQL, which is used for unwinding double-stranded
DNA, has in fact been shown to be easily disrupted by both a missense variant (found in
Sun et al. study), and an in-frame indel (found in our lab’s original RECQL discovery
study, Cybulski et al.). Due to the six reasons mentioned above, it seems quite plausible
that by conducting in-vitro functional assays for the 13 missense variants from our
current study, and 14 missense variants from the Cybulski et al. study, we will find a
clearer picture of the role of RECQL in breast cancer susceptibility.
Given that missense mutations may play a much stronger role than truncating
mutations, it is premature to make a definitive statement about the role of RECQL in
breast cancer on the basis of the results and odds ratio found in our current study. Not
only is the number of cases and controls lower, but in failing to consider the role of
missense mutations, we may thus far be unaware of the mutation frequency and general
hereditary mutation spectrum of RECQL. Further, the findings of in-vitro functional
assays in follow-up studies will shed more light on RECQL mutations in a heterogeneous
population like that of Ontario.
90
Chapter 5
Old Genes, New Context: BRCA1, BRCA2, and PALB2 in the Caribbean
5.1 Cancer in Selected Populations
In the RECQL-related component of our laboratory work, we focused on
screening a breast cancer susceptibility gene initially discovered in two founder
populations in a generalized, heterogeneous population context.
The same is true of populations in developing countries. The problem of cancer in
developing countries was recently designated a high-priority issue for the International
Atomic Energy Agency (IAEA), which has launched a Joint Program on Cancer Control
in collaboration with the World Health Organization (WHO). According to 2010 WHO
statistics, approximately 70 % of the world’s cancer deaths are currently occurring in the
developing world, in large part because 70 % of cancer patients in developing countries
are diagnosed at a very late stage of illness, when treatment is no longer effective. As of
2010, approximately 30 developing countries did not even possess a single radiation
therapy machine. Thus, cancers that are highly curable in some parts of the world (e.g.,
cervical cancer) have a survival rate of approximately 20 % in certain developing regions.
In view of the fact that one in six cancers worldwide are caused by infections, and most
infection-attributable cancers occur in less developing countries, the fact that the burden
of cancer-related health problems has shifted to less developed countries while the gap in
cancer-related health outcomes between developing and developed countries is widening
is a critical problem.
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5.2 Breast Cancer in the Caribbean
The Caribbean is classified as a developing region by the United Nations521, and
is heavily burdened by both cancer in general, with cancer being the second leading cause
of death in the Caribbean522, and by breast cancer specifically, with breast cancer being
the leading cause of cancer-related death among Caribbean women523. In a study
conducted by the Caribbean Public Health Agency (CARPHA) and the United States
Center for Disease Control and Prevention (CDC), it was discovered that the rates of
death from cervical, breast, prostate, and colon cancers are two to nine times higher in the
Caribbean than in the United States524. Sixty-three % of Caribbean women with breast
cancer are also pre-menopausal525, indicating increased prevalence of early-onset cancers
in the region. Further, an alarming percentage of women present with late-stage cancers,
indicating that early diagnosis, prevention, and treatment are likely not being practiced.
The prevalence of breast cancer death in the Caribbean is largely due to
situational factors. Epidemiologically, the Caribbean is presently transitioning from a
developing- to a more developed-country in terms of fertility pattern526. Because of this,
the age at menarche is decreasing, age at menopause is increasing, the number of children
women have is decreasing, and the age at first pregnancy is increasing; all of these factors
modify breast cancer risk, as is further discussed in Chapter Two. Furthermore, breast
cancer prevention and management is not well-orchestrated in the Caribbean, and
awareness is not normalized: tests for ER and HER2 are not carried out in many
countries, screening mammography is often not feasible, and breast self-examination is
culturally stigmatized. This means that late-stage breast cancer predominates, as self-
examination – the diagnostic method responsible for detecting 86 % of breast cancers in
Caribbean women – is only carried out when tumors are noticeable527.
92
One of the important components of ameliorating screening, prevention, and
treatment in the Caribbean is understanding the breast cancer genetic landscape to see if
existing genetic testing can theoretically be implemented, and if screening
recommendations can be drafted. To this end, we undertook two projects focused on
Caribbean populations.
5.3 Project One: A survey of BRCA1, BRCA2, and PALB2 mutations in women
with breast cancer in Trinidad and Tobago
5.3.1 Rationale
Trinidad and Tobago has one of the highest breast cancer incidence rates in the
Caribbean528, but the country’s hereditary breast cancer burden has not been studied529.
While access to screening and treatment is free, breast cancer is often not diagnosed at
advanced stages530 531 532 533 , indicating the possibility that hereditary breast cancer
susceptibility is not adequately pre-empted. Island nations also often have unique genetic
characteristics due to their immigration patterns and reproductive isolation, and our
laboratory has already verified the contribution of mutations in BRCA1/2 to breast cancer
in the Bahamas, another Caribbean nation534. BRCA1/2 also tend to have a higher
mutation rate in non-Caucasian populations, and Trinidad and Tobago’s population is
largely (approximately 80 %) African and Indian. BRCA1/2 are also highly penetrant
genes, with mutated genes conferring up to an 80 % life-time risk of breast cancer535.
Mutations in moderate penetrance genes, such as PALB2, also result in a moderate-to-
high (33 to 58 %) life-time risk of breast cancer536. If the BRCA genes and PALB2 are
found to be significantly prevalent in an unselected population, then offering genetic
testing for these three genes to all breast cancer patients in Trinidad and Tobago could be
merited.
93
5.3.2 Objectives
We sought to examine the frequency of BRCA1/2 and PALB2 mutations in
unselected Trinidad and Tobago breast cancer cases to determine whether genetic
screening for all three genes for all breast cancer patients should be implemented in
Trinidad and Tobago.
5.3.3 Methods
Participants were recruited from the largest breast cancer referral center in Port of
Spain, Trinidad and Tobago, and in the breast clinic of the St. James Medical Complex,
which is a part of the North West Regional Health Authority in Trinidad and Tobago. All
women consented to participate in this study in writing, and the University of Miami
Institutional Review Board (IRB) and the ethics committee of the North West Regional
Health Authority of Trinidad and Tobago approved our protocol. Women were eligible to
participate if they had been diagnosed with invasive primary breast cancer or ductal
carcinoma in situ (DCIS) at any age, in any year, and if at least one of their grandparents
was born in Trinidad and Tobago. A total of 268 women participated. Saliva samples,
medical history, and family history were collected, reviewed, and subsequently verified
via medical record reviews or surgical pathology reports if available. Several breast
cancer-related characteristics – namely, age at diagnosis, laterality, stage, grade,
histology, estrogen receptor (ER) status, HER2/neu (HER2) status, age at menarche, age
at first pregnancy, parity, birth order, race, ethnic group, and age at menopause – were
also collected.
Saliva was collected using the Oragene ® DNA sample collection kit (OG-250
format, DNA Genotek, Kanata, ON, Canada), and extracted following the manufacturer’s
94
instructions. DNA was then quantified using the NanoDrop ND-1000 Spectrophotometer
(Thermo Scientific Inc., Wilmington, DE, USA).
All 22 coding exons of BRCA1 (NM_007294.3), 26 coding exons of BRCA2
(NM_000059.3), and the first 13 coding exons of PALB2 (NM_024675.3) plus 20 base
pairs from the exon boundaries were amplified in 81, 134, and 54 amplicons respectively
using Wafergen SmartChip technology (Wafergene Inc., CA, USA). Following
incorporation of adaptors and sample-specific DNA barcodes via PCR, the prepared
DNA library was pooled and paired-end-sequenced at 2 x 250 cycles using an Illumina
MiSeq sequencer.
Reads were aligned to the reference sequences of the three genes using the
Burrows-Wheeler Aligner (BWA) algorithm, and subsequently converted from SAM file
format to BAM files, which could then be sorted and indexed. Reads that remained
unmapped, exhibited low mapping quality, or mapped to more than one region were
filtered using the Genome Analysis Tool Kit (GATK). SNPs, insertions, and deletions
were subsequently called using the GATK’s UnifiedGenotyper module. In order to be
utilized in calling variants, regions had to have at least 20-fold depth of coverage; in
order to be considered a variant, a nucleotide had to differ from the reference sequence in
at least 25 % of reads aligned to a given position. After variants were called, they were
annotated using the SNP and Variation Suite (GoldenHelix Inc., Bozeman, MT, USA).
Sanger sequencing was then used to validate all truncating and missense variants
of clinical significance; specifically, sequencing reactions were performed using the
BigDye Terminator v3.1 Cycle Sequencing Kit (Fisher Scientific International Inc.,
Pittsburgh, PA, USA) according to the manufacturer’s protocol on the ABI prism
95
3500XL Genetic Analyzer (Fisher Scientific International Inc., Pittsburgh, PA, USA),
and were analyzed for variant detection using Mutation Surveyor (SoftGenetics LLC,
State College, PA, USA).
The mean depth of coverage of all samples was 1927x (range: 907-4967x). On
average, 95.2 % (range: 87.4-100 %) of the coding exons were covered at a 20x depth of
coverage or higher.
5.3.4 Results
The mean age of diagnosis was 42.9 years (range: 19-80). The majority of
patients (84.7 %) were pre-menopausal at diagnosis. Breast cancer was also self-detected
in the majority of patients (91.6 %). Eleven patients had bilateral breast cancer (4.1 %).
115 of 231 patients (49.8 %) had ER-positive breast cancers, 42 of 223 patients (18.8 %)
had HER2-positive cancers, and 91 of 235 patients (38.7 %) had ER- or HER-negative
cancers.
28 of 268 patients (10.4 %) had a mutation in BRCA1, BRCA2, or PALB2. Of
these individuals, 15 (53.6 %) had BRCA1 mutations, 10 (35.7 %) had BRCA2 mutations,
2 (7.1 %) had PALB2 mutations, and 1 (3.6 %) had both a BRCA2 and a PALB2 mutation.
Of the 29 mutations seen in total, four mutations were seen twice in unrelated patients.
The mean age at diagnosis of the 28 mutation carriers was 39.8 years (range: 20-
48), and the mean age of diagnosis of the 239 non-carriers was 43.3 years (range: 19-80;
p-value: 0.052). The mean age at diagnosis was 37.3 years for BRCA1 mutation carriers,
42.2 years for BRCA2 mutation carriers, 43.3 years for non-carriers, and 46 years for
PALB2 mutation carriers. The prevalence of mutations was 33.3 % (4/12) in women
diagnosed between the ages of 20 and 29, 8.4 % (7/83) in women diagnosed between the
ages of 30 and 39, 13.2 % (18/136) in women diagnosed between the ages of 40 and 49,
96
and 0 % (0/35) in women diagnosed at age 50 or older. For women aged 49 or younger,
the rate of mutation was 12.5 % (29/231).
None of the eight participants with DCIS carried a mutation. Mutations were
found in 17.6 % (16/91) of ER-negative, HER2-negative breast cancers, and in 6.1 %
(7/115) ER-positive breast cancers. None of the BRCA1/2 carriers were HER2-positive,
but one PALB2 carrier was HER2-positive.
The most commonly-reported ethnicities were African, Indian, and Caucasian.
African heritage was higher in carriers (85.7 %, 24/28) than in non-carriers (57.1 %,
137/240; p-value: 0.003).
17.2 % (46/268) of patients had a first-degree breast cancer relative, and 34 %
(91/268) of patients had a first- or second-degree breast cancer relative. 12.3 % (33/268)
patients had a first- or second-degree ovarian cancer relative. The prevalence of
mutations was 31.8 % (7/22) among women with two or more first- or second-degree
breast cancer relatives, 14.9 % (10/67) among women with one first- or second-degree
breast cancer relatives, and 6.4 % (11/172) among women with no first- or second-degree
breast cancer relatives. 73.3 % of BRCA1 mutation carriers and 50 % of BRCA2 mutation
carriers had a first- or second-degree breast cancer relative compared to 30.8 % of non-
carriers (p-value: 0.001). 64.3 % (18/28) carriers had a first- or second-degree history of
breast or ovarian cancer compared to 38.3 % of non-carriers (p-value: 0.008).
97
Table 4. Characteristics of the 28 Trinidadian breast cancer patients who carry a pathogenic mutation in BRCA1, BRCA2 and
PALB2 genes
Carrier
ID
Gene Exon cDNA change
Protein
change
Age
at Dx
Stage Grade
Hormone
status
Ethnicity
Family history - 1st
or 2nd degree
relatives with
breast or ovarian
cancer
TNT007 BRCA2 11
c.6373_6375delA
CCinsG
44 I II
ER+,
HER-
African, Indian,
Caucasian.
No
TNT012 BRCA1 11 c.3108delT 42 IIa II
ER-
HER-
Indian
No
TNT013 BRCA1 11
c.1636_1654delA
TGAATATTACT
AATAGTG
28 III III
ER-
HER-
African
Yes
TNT021 BRCA1 21
c.5324T>G
p.Met1775Ar
g
20
T3N0
M0
III
ER-
HER-
African
No
98
TNT023 BRCA1 24
c.5510G>A
p.Trp1837Ter
37
T2N0
M0
N/A
Unknown
Unknown
Chinese,
Caucasian,
African.
Yes
TNT026 PALB2 4
c.1571C>G
p.Ser524Ter
43
T2N0
M0
N/A
ER-
HER+
African
Syrian
Yes
TNT027 BRCA1 11
c.3331_3334delC
AAG
42 IV II
ER-
HER-
Mixed
Yes
TNT032 BRCA1 13
c.4327C>T
p.Arg1443Ter
48 ?? N/A
ER-
HER-
African,
Caucasian.
Yes
TNT033 BRCA1 11
c.2389_2390delG
A
44
T2N0
M0
N/A
ER-
HER-
African, Indian
No
TNT034 BRCA2 10
c.1763_1766delA
TAA
48
T3N1
M0
N/A
ER-
HER-
African
Yes
99
TNT036 BRCA1 19
c.5177_5180delG
AAA
44
T2N0
M0
N/A
ER-
HER-
African
Yes
TNT038 BRCA2 10
c.1308_1309delG
A
41
T3N1
M0
III
ER-
HER-
African, Indian
No
TNT042 BRCA2 25
c.9382C>T
p.Arg3128Ter 37
T2N0
M0
N/A
Unknown
Unknown
African
Yes
TNT046 BRCA1 11
c.2766delA
44
T3N0
M0
III
ER-
HER-
Indian
Yes
TNT067 BRCA2 11
c.5909C>A
p.Ser1970Ter 39
T1N2
M0
III
ER+
HER-
Chinese, African,
Caucasian,
Caribbean.
Yes
TNT076 BRCA2 15
c.7558C>T
p.Arg2520Ter 38
T4dN
2M0
III
ER+
HER-
African,
Caucasian,
Caribbean
Yes
100
TNT084 BRCA2 18
c.7977-1G>A
47
T1N0
M0
II
ER+
HER-
African,
Caucasian
Yes
TNT088 BRCA1 11
c.2138C>A
p.Ser713Ter
40
T2N1
M0
III
Unknown
Unknown
Indian
Yes
TNT130 BRCA1 11
c.2686_2687insA
26
T2N0
M0
III
ER-
HER-
Indian, African
Yes
TNT132 PALB2 4
c.1571C>G
p.Ser524Ter
47
T2N0
M0
N/A
ER+
HER-
African, Indian,
Caucasian.
Yes
TNT141 BRCA2 11
c.6437_6440delA
TCA
48
T2N3
M0
N/A
ER-
HER-
Indian, African,
Caucasian. Yes
TNT141 PALB2 1
c.12_13insT
48
T2N0
M0
N/A
ER-
HER-
Indian, African,
Caucasian.
Yes
TNT147 BRCA2 25
c.9382C>T
p.Arg3128Ter
35
T3N1
M0
III
ER-
HER-
African
No
TNT151 BRCA1 11
c.3365_3366delC
A
28
T2N1
M0
III
ER+
Unknown
African
Yes
101
TNT190 BRCA2 10
c.1310_1313delA
AGA
40
T2N2
M0
II
ER+
HER-
African
Yes
TNT241 BRCA1 16
c.4945_4947delA
GAinsTTTT
33
T3N1
M0
III
ER-
HER-
African
Yes
TNT249 BRCA2 15
c.7558C>T
p.Arg2520Ter
47 III N/A
ER+
HER-
Indian
Spanish
No
TNT253 BRCA1 11
c.3764_3765insA
48
T2Nx
M0
III ER-HER-
Indian
African
Yes
TNT267 BRCA1 16
c.4945_4947delA
GAinsTTTT
36
T2N0
M0
N/A
ER-
HER-
Caribbean
Spanish
Indian
Yes
102
5.3.5 Discussion
The demographic insights we derived from this study are of relevance. Trinidad
and Tobago has an ethnically diverse population due to its colonial history: its published
ethnic composition is 39.5 % African, 40.3 % East Indian, 18.4 % mixed, 1.2 %
Caucasian, and 0.6 % Chinese, along with a small percentage of other groups537. The
distribution of ethnic groups self-reported in our study was similar to this composition,
suggesting that our study results could be generalizable. Notably, as in past studies, a
significant number of mutation carriers reported either solely African ancestry (10/28,
35.7 %) or African and mixed African ancestry (24/28, 85.7 %). Several of our mutations
had previously been found in our other Caribbean cohorts in the Bahamas and
Barbados538, and reported in Afro-Caribbean539, African-American540, and Nigerian
breast cancer patients541 previously. This suggests that African ancestry is inherently
connected to certain BRCA1/2 mutations, knowledge of which could ameliorate targeted
screening programs. Twelve mutation carriers also reported either Indian-only or mixed
Indian ancestry, and two of the mutations found in our Indian patients had previously
been reported in an East-Indian patient and two Muslim families in Israel542, suggesting
that these two mutations may be founder mutations associated with Indian ancestry.
Where carrier status is concerned, 52.7 % of patients diagnosed at age 45 or under
tested negative for mutations, but 36 % of mutation-negative patients reported a family
history of first- or second-degree breast or ovarian cancer. This suggests that there may
be other genes which account for the susceptibility of these additional cases; Churpek et
al. found that 20 % of mutations in a high-risk African-American breast cancer
population were attributable to PALB2, CHEK2, BARD1, ATM, PTEN, and TP53, and
Desmond later found that an mutation detection increased by 3.8 % when these additional
103
genes were added543. However, the interpretability of low- and moderate-penetrance gene
results in multigene panels is uncertain.
Two other factors may have influenced the generalizability of our study. Firstly,
the average age of our participants was 43, while the average age of women diagnosed
with breast cancer in Trinidad and Tobago is considerably higher, at 56.5544. Self-
selection may have resulted in a younger population’s being overrepresented. Secondly,
we enrolled women in the North West Regional Health District, which has the highest
incidence of breast cancer and the highest mortality rate in Trinidad and Tobago, which
could have resulted in somewhat poorly generalizable results.
Finally, in the way of overarching insights, this is the first survey of hereditary
breast cancer in Trinidad and Tobago, indicating a need for further research into breast
cancer susceptibility and genetic testing. Overall, we detected BRCA1/2 mutations in 9.5
% of cases, and PALB2 mutations in 1.1 % of cases, and showed that mutations in
BRCA1, BRCA2, and PALB2 account for more than 10 % of breast cancer in Trinidad and
Tobago. This suggests that a national screening program would potentially aid in guiding
prevention and therapy.
5.4 Project Two: A high frequency of PALB2 mutations in Jamaican patients
with breast cancer
5.4.1 Rationale
Jamaica has one of the highest breast cancer incidence rates in the Caribbean545,
but the country’s breast cancer genetic landscape has not been explored, and while
genetic testing and counselling is offered to women with a family history of breast and
ovarian cancer, unselected women are not yet encouraged to undergo testing. Further,
while BRCA1 and BRCA2 are known contributors to hereditary breast cancer in Jamaica,
104
PALB2 has not yet been examined as a potential addition to genetic testing panels. If the
BRCA genes and PALB2 are found to be significantly prevalent in an unselected
population, then instituting a formal national screening policy, as has been advocated for
by several prior studies, could be merited, and could foster cancer risk reduction and
survival improvements.
5.4.2 Objectives
We sought to examine the frequency of BRCA1/2 mutations in unselected
Jamaican breast cancer cases to determine whether genetic screening for all breast cancer
patients should be implemented in Jamaica. We also examined the frequency of PALB2
mutations in unselected patients in order to determine whether PALB2 should potentially
be added to genetic testing panels.
5.4.3 Methods
Participants were recruited from the University of West Indies, MONA, Jamaica.
All women consented to participate in this study in writing, and the UHWI/UWI/FMS
Ethics Committee of the University of the West Indies approved our protocol. Women
were eligible to participate if they had at least on Jamaican-born grandparent. A total of
179 women participated. Saliva samples, medical history, and family history were
collected and reviewed, and several breast cancer-related characteristics – namely, age at
diagnosis, presence of second primary cancers, tumor size, nodal status, histology, grade,
estrogen receptor status, and HER2 receptor status – were noted.
The remainder of our methodology for this study is identical to the methodology
employed in the previous Trinidad and Tobago study – for a more detailed overview of
our methods, please refer to Section 5.3.3.
105
The mean depth of coverage of all samples was 2948x (range: 1055-6550x). On
average, 98.2 % (range: 92.4-100 %) of the coding exons were covered at a 20x depth of
coverage or higher.
5.4.4 Results
The mean age of diagnosis was 49 years (range: 26-76). 94 of 177 patients (53.1
%) were pre-menopausal at diagnosis. 91 of 141 patients (64.5 %) had ER-positive breast
cancers, 33 of 136 patients (24.3 %) had HER2-positive cancers, and 38 of 146 (26 %)
had ER- or HER-negative cancers.
82 of 175 patients (46.9 %) reported a female relative with breast cancer, and 12
of 177 (6.8 %) reported a female relative with ovarian cancer.
8 of 179 patients (4.5 %) had a mutation in BRCA1, BRCA2, or PALB2. Of these
individuals, 1 (12.5 %) had a BRCA1 mutation, 2 (25 %) had BRCA2 mutations, and 5
(62.5 %) had PALB2 mutations. No mutation was seen in more than one patient.
The mean age at diagnosis of the eight mutation carriers was 50 (range: 41-60),
which was similar to the mean age of diagnosis of the 171 non-carriers (49 years; range:
26-76; p-value: 0.790). The prevalence of mutations was 0 % (0/4) in women diagnosed
at age 39 or younger, 6 % (3/50) in women diagnosed between the ages of 40 and 49, and
5.7 % (5/88) in women diagnosed at age 50 or older.
A mutation was found in 0 % (0/38) ER-negative and HER2-negative breast
cancers, and in 7.7 % (7/91) of ER-positive breast cancers. There were no HER2-positive
carriers.
The prevalence of mutations was 8.5 % (7/82) in women with one or more breast
cancer relatives, and 1.1 % (1/93) in women with no breast cancer relatives (p-value:
0.03). 87.5 % (7/8) carriers had a family history of breast or ovarian cancer compared to
106
46.8 % (80/171) non-carriers (p-value: 0.02). All three BRCA1 and BRCA2 carriers had a
breast cancer relative, while only three of five (60 %) of PALB2 carriers had a breast
cancer relative. No BRCA1 or BRCA2 carriers had an ovarian cancer relative, but one of
five (20 %) of PALB2 carriers had an ovarian cancer relative.
107
Table 5. Characteristics of the 8 Jamaican breast cancer patients who carry a pathogenic mutation in BRCA1, BRCA2 and
PALB2 genes.
Carrier
ID
Gene Exon
cDNA
change
Protein
change
Age
at Dx
Stage Grade
Hormone
status
Ethnicity
Family history
(1+ relatives
with breast or
ovarian cancer)
JAM 039 BRCA1 10
c.1953_1956
delGAAA
p.Lys653fs
47 - -
ER +
HER2 -
African
Yes
JAM 051 BRCA2 14
c.7386_7389
delCAAT
p.Asn2463fs
46 - - -
African
Yes
JAM 074 PALB2 5 c.2052delC
p.Arg686fs
52 - -
ER +
HER2 -
African,
Chinese,
Indian
Yes
108
* This is a nucleotide change located at the 5' end of exon 3 of PALB2 gene. This mutation is predicted to affect the splicing of the
PALB2 mRNA according to dbscSNV database with an Ada and RF scores of 0.93 and 0.70 respectively546.
JAM 089 PALB2 3 c.109C>A
Probably
affecting
splicing*
60 - -
ER +
HER2 -
African
No
JAM 139 PALB2 1 c.43G>T
p.Glu15Ter
41 II -
ER +
HER2 -
Native
American,
African
Yes
JAM 150 BRCA2 15 c.7558C>T
p.Arg2520Ter
54 IV -
ER +
HER2 -
African
Yes
JAM 156 PALB2 11 c.3166C>T
p.Gln1056Ter
52 IIIC -
ER +
HER2 -
-
Yes
JAM 177 PALB2 4
c.758_759ins
T
p.Ser254fs
50 - - ER +
African,
Indian
Yes
109
5.4.5 Discussion
Where BRCA1/2 mutations are concerned, our study’s mutation rate (1.7 %) was
lower than expected547. In most Caucasian non-founder populations, the prevalence of
BRCA mutations in unselected patients is higher (3-4 %) than the frequency we found. In
Trinidad and Tobago and the Bahamas, two countries with African ancestry comparable
to Jamaica’s, we identified considerably higher BRCA1/2 mutation rates at 9.5 % and 24
% respectively; a previous study of unselected patients also found an 11 % BRCA1/2
mutation rate in Nigeria548, another country with predominantly African ancestry. In
general, this suggests that the BRCA1/2 mutation rate of Jamaica may not have been
thoroughly detected because of the size of our sample, and that further studies examining
BRCA1/2 mutations in the context of the Jamaican population may be merited.
In terms of PALB2, we found a relatively high frequency of mutation carriers
among Jamaican breast cancer patients (5/179, 2.8 % compared to 3/179, 1.7 % for
BRCA1/2). This frequency of PALB2 mutations among unselected breast cancer patients
is the highest to be reported to date: in Finland, a single relevant PALB2 mutation was
identified in 1 % of unselected breast cancer patients549, and in Trinidad and Tobago, 1.1
% (3/268) of unselected breast cancer patients had a PALB2 mutation. Further, PALB2
mutation carriers have a significant life-time risk of breast cancer (between 33 and 58
%)550. Our study results also appeared to be generalizable to the broader Jamaican
population, which is approximately 90.9 % African551, as 99 % of our patients reported at
least some African ancestry. In light of all this, PALB2 may be a gene that is particularly
relevant to Jamaican breast cancer susceptibility. Incorporation of PALB2 into Jamaican
gene panels could potentially improve mutation detection rates in Jamaica.
110
Finally, as has already been noted, Jamaica has a high prevalence of breast cancer,
but relatively few studies examining breast cancer in Jamaica specifically have been
conducted, and though several studies have called for urgent institution of a formal
national screening policy since 2008552 553 554, no such policy exists555. As such, we
would encourage the development of governmental programs geared at promoting
genetic counseling, testing, and the investigation of further risk management and testing
for at-risk family members. Such programs, if implemented, could potentially ameliorate
risk-reducing technique implementation, leading to cancer risk reduction and survival
improvement.
111
Chapter 6
Future Directions
6.1 RECQL
RECQL was first proposed as a potential breast cancer susceptibility gene by our
team, and independently by a Chinese group, in 2015. Since the work previously done
was localized to founder populations, our team initially decided to validate these findings
in a more heterogeneous population. However, since the number of cases in this project
was not sufficient to perform validation, we resorted to screening RECQL in a more
heterogeneous population.
Our lab’s next step would be to further expand our understanding of RECQL
using genetic epidemiology and molecular genetics approaches. More specifically, our
lab intends to first determine the frequency of pathogenic RECQL mutations, including
truncating and missense mutations, among 6000 hereditary breast cancer patients,
unselected breast cancer patients, and healthy women in non-founder populations. Our
lab’s goal from this work would be to build on the work of this current project, and to
validate RECQL’s role as a potential breast cancer susceptibility gene.
Next, based on the missense mutations obtained (both from our original study,
and our follow-up studies), our lab intends to perform functional assays to ascertain the
pathogenicity of RECQL missense mutations. Our lab will specifically look for DNA
damage foci formation, and DNA repair pathways in vivo. As part of this project, our lab
and our collaborators will also knock-down expression of RECQL using RNA
interference techniques such as small interfering RNA (siRNA) and short hairpin RNA
(shRNA). Our lab will conduct assays to monitor genomic instability and to see if
missense mutations affect H2AX and 53BP1 levels. The two major DNA repair pathways
112
– homologous recombination repair and non-homologous end-joining repair – will be
assayed in vivo to study the role of RECQL in DNA repair. Finally, our lab will also test
how RECQL missense mutations affect cell survival post-damage in order to understand
whether RECQL mutations are resistant in the presence of genotoxic environments.
Additionally, in order to establish a causal link between RECQL mutations and
breast cancer, our lab will conduct knock-out (KO) mice studies looking at wild-type
RECQL heterozygous (+/-), and RECQL homozygous (-/-) mice in the presence of
mammary carcinogen DMBA (7, 12-dimethylbenz(alpha)anthracene) to observe
mammary tumor incidence. These studies will help our lab answer the question of
whether RECQL knock-out (KO) mice have a higher risk of developing mammary tumors
compared to wild-type mice, demonstrating RECQL’s pathogenicity in vivo.
Finally, our lab will perform gene expression profiling to identify genes that are
specifically upregulated or downregulated in RECQL-deficient tumour cells, and to
measure expression levels of RECQL in RECQL mutation carriers and non-carriers
among tumour cells in order to better understand the genetic signatures associated with
RECQL mutation carriers for screening purposes. To do this, our lab will conduct whole-
mRNA sequencing of breast tumour cells in 40 patients with germline RECQL mutations
and 40 controls. These controls will be matched for histology, grade, and hormone status
[ER, PR and HER2] of tumours, and for menopause status of the patients. Patients who
have received neoadjuvant therapy will be excluded. Knowing the particular gene
expression patterns will ameliorate the development of targeted treatments for breast
cancer patients with germline RECQL mutations.
113
6.2 BRCA1, BRCA2, & PALB2 in the Caribbean
Overall, it is clear that hereditary breast cancer is a significant concern across the
Caribbean, and merits implementation of national screening policies. However, there are
also regional differences in hereditary breast cancer trends from island to island. In
Jamaica, while the mutation rate of BRCA1/2 was lower than expected, the mutation rate
of PALB2 among unselected patients was the highest reported to date, suggesting that the
BRCA1/2 mutation rate may need to be investigated by further, larger studies, and that
PALB2 is a particularly significant breast cancer susceptibility gene in a Jamaican
context. Conversely, in Trinidad and Tobago, BRCA1/2 mutations were detected at a
much higher rate than PALB2 mutations. However, overall, national screening policies
incorporating BRCA1, BRCA2, and PALB2 were recommended for both Caribbean
regions studied.
A national screening program for both Trinidad and Tobago and Jamaica could
begin to be implemented by finding potential carriers from the families of individuals
who are diagnosed with breast cancer. If a mutation is detected in one of the three breast
cancer susceptibility genes, its pathogenicity can be confirmed by co-segregation
analysis. Co-segregation analysis involves calculating the likelihood ratio of a sequence
variant to be deleterious. If the variant is found to co-segregate with the disease
phenotype in pedigrees, then that makes a strong case for its pathogenicity. This would be
the first step in a national screening program that would provide effective early detection
measures helpful in reducing late-stand cancers in the Caribbean, thus ameliorating the
burden of hereditary breast cancer mortality in the region.
114
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