personalizing breast cancer therapy - win 2018 … · personalizing breast cancer therapy: promise...
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Personalizing
Breast Cancer Therapy:Promise and Challenges
Funda Meric-Bernstam, M.D.
Chair, Department of
Investigational Cancer Therapeutics
(Phase I Program)
Medical Director,
Sheikh Khalifa Ben Zayed Al Nahyan
Institute for Personalized Cancer Therapy
Professor, Divisions of Cancer Medicine
and Surgery
Disclosures
Nature of Relevant Financial Relationship(include all of those that apply)
Commercial InterestProvide Name of Company/Companies
Grant or research support Novartis, AstraZeneca, Taiho, Genentech, Calithera, Debiopharma, Bayer
Paid consultant Genentech, Novartis, Roche, Celgene
Honoraria Genentech, Roche Diagnostics, Sysmex
Membership on advisory committees or review panels, board membership, etc. Inflection Biosciences, Genentech
Risk
Markers
Prognosis
Markers
Predictive
Markers
Response
Markers
Prevention Therapy Survivorship
Cancer Prevention
Cancer Genetics
Cancer Epidemiology
Pathology
Clinical Oncology
Pathology
Radiology
Molecular Diagnostics
Cancer Genetics
Immunology
Pathology
Molecular Diagnostics
Cancer Genetics
Investigational Therapeutics
Immunology
Outcomes Research
Comparative Effectiveness
Risk Reduction
Early
Diagnosis
Avoid Toxicity of Adjuvant Therapy
More Intensive Adjuvant Therapy
Least
Toxic
Therapy
Most
Efficaceous
Therapy
Lo
w R
isk
of
Re
cu
rre
nc
e
Hig
h-R
isk
Pa
tie
nts
Hig
h R
isk
of
Rec
urr
en
ce
Pre
dic
tors
of
To
xic
ity
Pre
dic
tors
of
Res
po
nse
/
Res
ista
nc
e
Chemotherapy
Targeted Therapy
Immunotherapy/Vaccines
Surgery
Radiation
Meric-Bernstam, JCO, 2013
Outline
• Cancer Risk
• Cancer Prognosis
• Predictors of Response/Resistance
• Neoadjuvant Therapy as a Research Platform
• Genomic Mechanisms of Acquired
Resistance and Genomic Evolution
• Liquid Biopsies
Couch Science 2014
Germline Genetics: Breast Cancer Risk
Mutations in highpenetrance genes (BRCA1/2, TP53, CDH1, LKB1, and PTEN)
Moderate-penetrance genes (e.g., CHEK2, ATM, and PALB2)
Common low-penetrance genetic variants
Frequency of likely Pathogenic Somatic and Germline Variants
F. Meric-Bernstam et al. Ann Oncol 2016
Outline
• Cancer Risk
• Cancer Prognosis
• Predictors of Response/Resistance
• Neoadjuvant Therapy as a Research Platform
• Genomic Mechanisms of Acquired
Resistance and Genomic Evolution
• Liquid Biopsies
TP53 Mutation is Associated with Worse Outcomes
Median 41 vs 95
months; P < 0.001
Median 22 vs 42
months; P < 0.001Median 26 vs 51
months; P < 0.001
Median 25 vs 43
months; P < 0.001
Basho ASCO 2015
CovariateUnivariate Multivariate
P Value HR(95% CI) P Value
Age
≤ 50
> 500.059
1
1.26 (0.97-1.64) 0.078
Subtype
HR-positive
HER2-positive
TNBC
<0.001
1
2.85 (1.66-4.89)
1.45 (1.01-2.08)
<0.001
0.043
Grade
I-II
III<0.001
1
1.28 (0.98-1.68) 0.071
Stage at Diagnosis
0-I
II
III
<0.001
1
1.61 (1.15-2.27)
2.01 (1.34-3.01)
<0.006
<0.01
Adjuvant Chemotherapy
No
Yes0.054
1
0.32 (0.17-0.59) <0.001
Surgery Type
Breast-Conserving
Mastectomy0.005
1
1.28 (0.95-1.74) 0.109
Mutation
AKT1
PIK3CA
TP53
0.88 (0.50-1.55)
0.77 (0.58-1.02)
1.58 (1.17-2.13)
0.645
0.072
0.003
Univariate and Multivariate Analysis for
Recurrence Free Survival from Primary Cancer Diagnosis
Basho ASCO 2015
Outline
• Cancer Risk
• Cancer Prognosis
• Predictors of Response
• Genomic Predictors of Intrinsic Resistance
• Genomic Mechanisms of Acquired
Resistance
• Liquid Biopsies
Targetable Genomic Alterations in Breast Cancer
Arnedos et al, Nature Rev Clin Oncol, 2015
FGFs BGJ398, TAS-120
Akt AZD5363, MSC2363318A
Genotype/Biomarker-Selected Basket Trials in ICT
BRAFDabrafenib+Trametinib, Trametinib, Sorafenib,
Vemurafenib, BVD-523
PTCH Vismodegib, LY2940680
Erlotinib, KBP-5209, NeratinibEGFR
Crizotinib+Dasatinib, ABT-348CDKN2A
N-MYC GSK525762
OMP-52M51 NOTCH1
~80 alterations; 44 drugs, 47 trials
NTRK1/2/3 LOXO-101, RXDX-101
ROS1 Ceritinib, Crizotinib,RXDX-101
ALK Ceritinib, Crizotinib, RXDX-101, X-396SMO Vismodegib, LY2940680
KIT Imatinib
FGFR1/2/3 BGJ398, TAS-120, Debio1347
HER2KBP-5209, Neratinib,
Pertuzumab/Trastuzumab
HER3 KBP-5209, Neratinib
PIK3CA AZD536, GDC-0032, MSC2363318A
PTEN Buparlisib, MSC2363318A, Talazoparib
PIK3R1/2 MSC2363318A
ATM/ATR Talazoparib
MET Crizotinib+Dasatinib, INC280
BRCA1/2 Olaparib, Talazoparib
FANCs Talazoparib
EMSY Talazoparib
MRE11A Talazoparib
NBS1 Talazoparib
PALB2 TalazoparibRAD50/51
CTalazoparib
BVD-523 MAP2K1/3
KRAS CB-839, Selumetinib
Crizotinib+DasatinibDDR2
NUTM1 GSK525762
MK-3475 PD-L1
MRCA1 Talazoparib
MLL EPZ-5676
RNF43 LGK974
RSPO LGK974
DHH/IHH LY2940680
IDH1 IDH305, AG-221
TP53 MLN9708+Vorinostat, Pazopanib+Vorinostat
FGFR4 TAS-120
HER2 Mutations as a Novel Target
• 25 HER2 mutations among
1,499 patients
Bose R et al. SABCS 2012. Abstract S5-6.
Bose R et al. Cancer Discovery. 2013;3:224-37.
HER2 mutations as a Target
SUMMIT trial: Neratinib for HER2 mutant, HER2 nonamplified BC
ORR at 8 weeks :32%; Clinical benefit rate: 42%
Hyman, Piha Paul et al SABCS 2015
Neratinib + fulvestrant
Akt1 as a Target
Best percentage change in tumor size in patients with E17K-mutated
ER+ breast cancer treated with AZD5363
Hyman et al, EORTC, 2015
NTRK1 fusion Soft tissue Sarcoma
Hong et al, EORTC 2015
Secretary cancer rare
and potentially better
prognosis
Secretory Breast Carcinoma and NTRK fusions
Tognon et al , 2002
McAuliffe PF et al. Clin Breast Cancer. 2010;Suppl3:S59-65.
Targeting PI3K/Akt/mTOR
Greater PFS Benefit With EVE in Patients With Minimal Alterations in PIK3CA/PTEN/CCND1 or FGFR1/2
Presented By Gabriel Hortobagyi at 2013 ASCO Annual Meeting
Effect of Everolimus on PFS in HER2 + Breast Cancer
André et al. JCO 2016
PIK3CA wild type (WT) versus mutant (MT)
PTEN normal versus low/loss
PI3K pathway activity normal versus
hyperactive
Rationale for Neoadjuvant
Systemic Therapy Trials
Tumor response as an in vivo assay to determine
systemic therapy efficacy
• Test new therapy regimens
• Identify biomarkers
- Predictors of response
- Pharmacodynamic markers of response
• Modify therapy for non-responders
• Stratify for further therapy based on residual
cancer burden and molecular characteristics?
pCR is a Surrogate for Event-Free
Survival in All Subtypes
Cortazar SABCS 2012
Loibl S et al. SABCS 2013. Abstract S4-06.
pCR Rate According to PIK3CA Mutation
Status in the GeparSixto Study
Loibl S et al. SABCS 2013. Abstract S4-06.
Multivariable Analysis for Prediction of pCR
in HER2+ Breast Cancer in GeparSixto
Adjusted for therapy, age, tumor and nodal status, histotype and grading
Odds ratio 95% CI P-value
Hormone
receptor status
Negative 1.00 0.006
Positive 0.44 0.24-0.79
PIK3CA WT 1.00 0.007
Mutant 0.29 0.12-0.71
Role for combinations with PI3K inhibitors
Potential role for PIK3CA for stratification/therapy selection (? TDM1)
Stacy Moulder
ARTEMIS: A Randomized TNBC Enrolling trial to
confirm Molecular profiling Improves Survival
Targeting PD-L1 in TNBC
PIs: Litton and Mittendorf
adriamycin
cyclophosphamide
q3wk x 4
surgery
enroll
blo
od
tissu
e
bio
psy
blo
od
su
rgic
al
sp
ecim
en
blo
od
nab-Paclitaxel
q3wk
Atezolizumab
Atezolizumab
to complete 6 mo
of treatment
blo
od
Neoadjuvant Therapy gives Unique
Insights into Genomic Evolution
• Loss of HER2 in patients treated with HER2 targeted
neoadjuvant therapy
• Molecular evolution with treatment and progression
Mittendorf Clin Cancer Res 2009
HER2 in trastuzumab-resistant
breast cancer
Pre Post
Genomic Characterization of Residual Disease
No
. o
f sa
mp
les
wit
h a
ber
rati
on
s
PI3K/mTOR
inhibitors
DNA repair-
targeting
agents
RAF/MEK
inhibitors
Cell
cycle/mitotic
spindle
inhibitors
Targeted RTK
inhibitors
Balko J et al. SABCS 2012. Abstract S3-6.
TNBC after adjuvant chemotherapy is heterogeneous and has multiple
alterations that are targetable with existing drugs in development
Development of PDXs for Study of
Genotype-Phenotype Correlation
LAB07-0950
McAuliffe and Evans, Meric-Bernstam Plos One, 2015
Figure 2: Kaplan-Meier curves of DRFS by BCX or noBCX
Distant Recurrence-Free
Survival
Recurrence Free SurvivalOverall Survival
Survival Outcomes in Patients whose Tumors Develop
a Patient-Derived Breast Cancer Xenograft vs Not
McAuliffe and Evans et al Plos One, 2015
Source ER (%) PR (%) HER2 NeoCT Alive
Distant
Relapse
Follow-up
(months)
BCX.006a Primary 0 0 0 Yes No Yes 9.6
BCX.009 cALN 1 1 0 Yes Yes Yes 50.0
BCX.010a Primary 0 0 0 Yes No NA 1.0
BCX.011a Primary 0 0 0 Yes No Yes 4.2
BCX.017a Primary 25 0 1+ Yes Yes No 42.6
BCX.022 Primary 10 0 0 Yes Yes No 41.2
BCX.024 Primary 10 3 0 No Yes No 36.3
BCX.042 Primary 0 0 0 No Yes No 33.0
BCX.051 LRR 0 0 0 Yes No Yes 10.0
BCX.055 Primary 1 0 0 Yes Yes No 23.5
BCX.060a Primary 5 0 0 No Yes No 22.4
BCX.065a cALN 10 0 0 Yes Yes No 19.6
BCX.066 LRR 0 0 0 Yes Yes Yes 19.7
BCX.070 Primary 0 0 1+ Yes Yes No 17.5
BCX.080 Primary 10 0 1+ Yes Yes No 9.0
BCX.084a Primary 0 0 0 Yes No Yes 10.9
BCX.087 Primary 2 0 1+ Yes No Yes 3.8
BCX.092a Primary 0 0 0 No Yes No 9.8
BCX.094a Primary 0 0 0 Yes Yes Yes 11.6
BCX.095a LRR 5 0 0 Yes Yes Yes 10.3
BCX.096 LRR 20 0 0 Yes Yes Yes 10.2
BCX.099 LRR 20 0 0 Yes Yes NA 9.3
BCX.100a Primary 20 0 0 Yes Yes No 6.5
BCX.102a LRR 0 0 2+b Yes Yes Yes 7.2
BCX.104 LRR 40 40 0 Yes Yes Yes 7.6
BCX.105 Primary 5 0 1+ No Yes Yes 7.0
PI3
CK
A
AK
T
PT
EN
4E-
BP
1_p
S65
4E-
BP
1_p
T37
_T4
6
Akt
_pS4
73
Akt
_pT3
08
PR
AS4
0_p
T24
6
mTO
R
S6_p
S23
5_S
23
6
S6_p
S24
0_S
24
4
PI3
K S
co
re
MA
PK
_p
T202_Y
204
ME
K1
_p
S217_S
221
ME
K/E
rkT
ota
l
BCX.087 1.02 1.23 6.6 4.6 1.2 1.1 1.3 1.4 18.4 1.0 0.9 1.9
BCX.010 1.02 1.25 3.8 3.0 0.5 0.9 1.7 1.5 13.9 0.9 0.9 1.8
BCX.006 1.01 1.2 3.0 1.7 1.5 1.0 1.9 1.6 12.9 1.3 1.0 2.3
BCX.070 0.93 0.96 4.2 1.7 1.0 0.8 1.3 1.3 12.4 1.3 1.0 2.3
BCX.024 0.78 0.74 3.0 2.3 1.0 1.0 1.2 1.2 11.2 1.1 0.9 2.0
BCX.009 1.03 1.68 0.8 0.7 1.8 1.3 0.8 0.9 8.8 1.0 0.9 1.9
BCX.100 0.64 0.85 2.5 1.6 0.3 0.9 1.1 1.0 8.8 0.9 0.8 1.7
BCX.022 0.91 0.95 1.5 1.0 0.8 0.9 1.2 1.1 8.4 1.1 1.0 2.0
BCX.017 0.81 0.99 0.7 0.6 1.6 0.8 0.9 1.0 7.7 1.6 1.0 2.6
BCX.051 1.12 1.45 0.7 0.7 0.8 0.9 1.0 1.1 7.7 1.1 0.9 2.0
BCX.055 0.89 1.07 1.5 1.2 0.9 0.9 0.7 0.7 7.7 0.9 0.9 1.8
BCX.011 0.99 1.05 0.5 0.6 1.0 1.1 0.9 0.9 7.1 0.9 0.9 1.8
BCX.096 0.91 0.95 0.5 0.6 1.0 1.3 0.8 0.8 6.6 1.1 1.0 2.1
BCX.060 0.91 0.84 0.7 0.7 0.7 1.0 0.7 0.9 6.5 1.2 1.0 2.3
BCX.066 0.92 0.75 0.5 0.7 1.0 1.3 0.8 0.9 6.5 1.0 1.0 2.0
BCX.102 0.96 0.74 0.6 0.7 1.2 1.1 0.6 0.5 6.5 1.2 1.0 2.2
BCX.095 1.01 0.94 0.6 0.9 0.5 1.0 0.6 0.7 6.1 1.2 1.2 2.4
BCX.080 0.9 0.58 0.6 0.7 0.8 1.1 0.8 0.7 6.0 1.2 1.0 2.2
BCX.094 0.92 0.75 0.4 0.6 0.8 1.2 0.7 0.7 5.9 1.1 1.1 2.2
BCX.042 0.78 0.83 0.7 0.7 0.4 1.2 0.4 0.5 5.2 1.1 1.0 2.1
A
B
A
B C
PARP Inhibitor Sensitivity
Pe
rce
nta
ge
of
Alt
era
tio
ns
Somatic Genomic Alterations in Metastatic Breast Cancer
• Mutations in PIK3CA, TP53, ARID1A, PTEN, AKT1, NF1, FBXW7 and FGFR3
• Amplifications in MCL1, CCND1, FGFR1, MYC, IGF1R, MDM2, MDM4, AKT3,
CDK4, AKT2.
Meric-Bernstam Mol Can Ther 2014
• 33 matched primary and recurrent
tumors
• Somatic mutations
• 97 of 112 (86.6%) somatic
mutations were concordant
• Copy number alterations
• 136 of 159 (85.5%) were
concordant
• 37(23.3%) were concordant,
but below the reporting
threshold in one of the
matched samples
• 23 (14.5%) discordant
Meric-Bernstam, Mol Can Ther 2014
Concordance of Genomic Alterations
in Primary vs Metastatic Tumors
• Alterations potentially targetable with established or
investigational therapeutics were considered “actionable”
• 40 of 43 (93%) patients had actionable alterations that
could inform targeted treatment options.
• There were both losses and gains of actionable
alterations
Concordance of Genomic Alterations
in Primary vs Metastatic Tumors
Meric-Bernstam, Mol Cancer Ther 2014
Jeselsohn etc al, Clin Can Res 2014
Acquisition of a Constitutively Active ESR1 Mutation
Unusual Responder Program
Liquid Biopsies
Bettegowda C et al. Sci Transl Med 2014
Olsson E, EMBO Mol Med, 2015
Early Recurrence Detection
• Serial monitoring of ctDNA 20 patients diagnosed
with primary breast cancer
• ctDNA monitoring discriminated between patients
with (93%) and without (100%) eventual clinically
detected recurrence
• ctDNA-based detection preceded clinical detection of
metastasis in 86% of patients with an average lead
time of 11 months (range 0-37 months)
• Patients with long-term disease-free survival had
undetectable ctDNA postoperatively
• ctDNA quantity was predictive of poor survival
Olsson E, EMBO Mol Med, 2015
Liquid Biopsy Opportunities
• Early detection
• Monitoring of response in neoadjuvant
therapy
• Determination of pathologic complete
response after neoadjuvant therapy
• Monitoring response in metastatic breast
cancer
• Monitoring for acquired resistance
mechanisms
Novel Breast Targets
• Antibody drug conjugates
• DNA Damage repair
– PARP, Wee1, ATR
• Cell signaling
– PI3K/mTOR, PI3Kbeta, FGFR, MEK, Mnk
• Cell metabolism
• Epigenetics
• Other novel targets (eg. MDM2, XPO1)
Summary
• Genomics is increasingly available
• None of the somatic genomic markers are yet
linked to approved therapy in breast cancer
• Novel targets in development
• Expect major advances with integrated
multianalyte analysis
AcknowledgementsBioinformatics/Stats
•Ken Chen
•Hao Zhao
•Yuan Qi
•Xiaoping Su
•Han Liang
•Li Zhang
•Roel Verhaak
Oncology Champions
•Debu Tripathy, Stacy Moulder and
Naoto Ueno, Senthil Damadoran- breast
•Cathy Eng and Scott Kopetz- CRC
•Mike Davies-melanoma
•John de Groot- neurooncology
•Ravi Vinod sarcoma
•John Heymach- lung
•Faye Johnson
•and William William-head and neck
•Shannon Westin /Rob Coleman- gyn onc
Molecular Diagnostic Lab
• Stan Hamilton
• Raja Luthra
• Russel lBroaddus
• Mark Routbort
• Keyur Patel
• Sinchita Roy-Chowduri
Path
• Aysegul Sahin
• Dipen Maru
• Alex Lazar
• Victor Prieto
• Coya Tapia
Clinical Cancer Genetics
• Molly Daniels
• Louise Strong
• Karen Lu
• Banu Arun
• Jenniifer Litton
Funding:
Bosarge Foundation,
Khalifa Foundation,
U01 CA180964,
NCATS UL1 TR000371;
CCSG P30 CA 016672
CPRIT
IPCT Admin Team
Gordon Mills
John Mendelsohn
Kenna Shaw
Decision Support Team
Amber Johnson
Ann Bailey
Jia Zeng
Vijay Holla
Beate LitzenburgerNora Sanchez
Yekarterina Khotskoya
Univ Texas School of
Biomedical Informatics
• Elmer Bernstam
• Trevor Cohen
• Hua Xu
• Jim Chang
