Stefano Iacobelli

Medical OncologyUniversity G. D’Annunzio, Chieti-Pescara

Prognostic and predictive markers and the role of genomics & proteomics

1. Prognostic & predictive biomarkers: General concepts

1. Novel technologies

Genomics(Proteomics)

3. Application in breast cancer

Human cancer: a series of genetic and epigenetic alterations that can be classified into 6 main classes and are responsable of the characteristics of the neoplastic

phenotype

Self-sufficiency ingrowth signals

Limitless replicative potential

Tissue invasion &metastasis

Sustained angiogenesis

Evasion of apoptosisInsensitivity to

anti-growthsignals

These genetic and/or functional alterations may play an important role as tumor

“biomarkers”

• Monitoring patients with established disease for- Recurrence/Prognosis assessment- Prediction of response to drugs

Biomarkers are important tools for cancer management

• Early detection of asymptomatic patients - Aiding in the diagnosis - Surveillance of individuals known to be at risk of cancer - Surrogate endpoint markers for primary prevention strategies (i.e. chemoprevention)

The identification of pts: at minimal risk of disease recurrence; at elevated risk, who may benefit from

systemic treatment; more likely to respond to specific

treatment

The idealtumor biomarker

It must give clinically useful information for:

The recognition of subgroups of pts who differ in disease outcome

BIOMARKER

Predictive NOT prognostic (bax and chemotherapy)

Prognostic NOT predictive (lymphnode status)

0

20

40

60

80

100

pos

Su

rviv

al (%

)

neg

treatment

control0

20

40

60

80

100

neg S

urv

ival (%

)pos

treatment

control

Adapted from Hayes et al., BCRT 52, 1998

Separates poor from favorablegroups independent of therapy

Outcome in the absence of therapy is the same regardless the marker is + or -

Prognostic AND Predictive

0

20

40

60

80

100

neg pos

Su

rviv

al (%

) treatment

control

ER and hormone therapy

low highS

urv

ival (%

)

treatment

control

0

20

40

60

80

100

TLI and CMF-based CT

Adapted from Hayes et al., BCRT 52, 1998

Separates groups to some extent but much more in the presence of specific treatment

0

10

20

30

40

50

60

70

80

90

100

Three categories of prognostic factors

strong

moderate weakD

isease r

ecu

rren

ce

(%)High risk

Low risk

medium risk

adapted from Hayes et al., BCRT 52, 1998

I Prognostic relevance Clinical stage (T,N,M)& proven clinical usefulness histologic grade

mitotic index, histotype, steroid hormone receptorsu

(uPA & PAI-1) II Many studies Proliferation indices

biological & clinical, Peritumoral invasionbut need of HER-2 /neu, p53statistical evaluation

III Prognostic relevance ploidy, neo-angiogenesis but not proven clinical

apoptosis (bcl-2), usefulness based on GF and their Rec, available information pS2, Cathepsin D

PROGNOSTIC factors in breast cancer

College of American Pathologists Consensus Statement 1999, Arch Pathol Lab Med, 2000

Follow-up (years)

Silvestrini et al., JCO 1995; CCR 1997

Estrogen Receptors Progesterone Receptors

1800 patients with N- breast cancer undergoing loco-regional treatment only

Incidence of distant metastases

Cell Proliferation (TLI)

Follow-up (years)

Silvestrini et al., J Clin Oncol 1995; CCR 1997

Incidence of distant metastases (1800 N-)

Response to treatment (281 N- TLI >3%)

CMF

None

1

DIS

EA

SE F

REE S

UR

VIV

AL

0.9

0.8

0.7

0.6

0.5

0.4

0

0 1 2 3 4 5 6 7 8

YEARS

HR=0.59 p=0.028

Amadori et al., J Clin Oncol 18, 2000

Coradini et al., Br J Cancer 2001

Disease recurrence(226 N- pts, surgery only)

0 12 24 36 48 60 720

20

40

60

80

100

rela

pse

-fre

e su

rviv

al (

%)

months

Angiogenesis (Intratumoral VEGF)

Response to treatment(212 N+/ER+ pts treated with Tamoxifen)

VEGF+

VEGF-

HR=2.46 (1.29-4.65), P=0.0059

0 12 24 36 48 60 720

25

50

75

100

log-rank p= 0.06

Relaps

e-fr

ee S

urviva

l (%

)

months

VEGF-

VEGF+

Coradini et al., Br J Cancer 2003

Prognostic relevance of uPA and PAI-14676 patients - Incidence of distant metastases

Look et al. JNCI, 2002

The score:

A comprehensive view that helps:

To identify patients:

at low risk of disease recurrence who do not need adjuvant treatment at high risk of disease recurrence who may

benefit from adjuvant systemic treatment

NOTTINGHAM PROGNOSTIC INDEX (NPI)  Tumor Size (cm) x 0.2 = pointsTumor Grade*: from 1 (better) to 3 (worse) = points Axillary Lymph Nodes: negative nodes = 1 point;

positive nodes, 1 to 3 positive = 2 points; positive nodes, >4 = 3 points

Size + grade + lymph-node = Total NPI points

80% OS @ 15 yrsif NPI

<3.4 sum

42% OS @ 15 yrsif NPI

3.4-5.4 sum

13% OS @ 15 yrsif NPI

>5.4 sum

Conclusions:as to need foradjuvant CT

need is doubtful chemo neededMAY BENEFITwith CT

Groups

*by whatever system

Combined score Patho-biologic features % in scoregroup

Relapse probability

0 T<1 cm/N-, ER+/PgR+ 1 0%

1 T<1 cm/1-3 N+ or T1-2 cm/N-, ER+/PgR+

T<1 cm/N-, ER- or PgR-8 9%

2 T<1 cm/N-, ER-/PgR-

T<1 cm/>3 N+ or T>2 cm /N-, ER+/PgR+

T1-2 cm/N- or T<1 cm/1-3 N+, ER-or PgR-

T<1 cm/>3N+ or T1-2 cm/1-3N+, ER+/PgR+

23 15%

3 T<1 cm/1-3N+ or T1-2 cm/N-, ER-/PgR-

T1-2 cm/>3N+ or T>2 cm/1-3N+, ER+/PgR+

T<1 cm/>3N+ or T1-2 cm/1-3N+ orT>2 cm/N-, ER- or PgR-

T>2 cm/>3N+, ER+/PgR+

29 26%

4 T1-2 cm/>3N+ or T>2 cm/1-3N+, ER-or PgR-

T<1 cm/>3N+ or T1-2 cm/1-3N+ orT>2 cm/N-, ER-/PgR-

28 42%

5 T>2 cm/>3N+, ER-or PgR-

T<1 cm/>3N+ or T1-2 cm/1-3N+ orT>2 cm/N-, ER-/PgR-

9 61%

6 T>2 cm/>3N+, ER-/PgR- 2 83%

Six-year recurrence rate as a function of bio-pathological score

Novel analytical toolsMicroarray technologyProteomics…….…….

From a “reductionistic” approach (gene by gene) to an “olistic” approach (global genomic analysis)

Molecular signature of cancer

The innovation

Proteomica

Genomica

Ricerca Clinica

Diagnostica

Farmacogenomica

Prevenzione

Genomics

• Gene Sequencing

• Conventional Karyotyping

• FISH (Fluorescent in Situ Hybridization)

• CISH (Chromogenic in Situ Hybridization)

• CGH (Comparative Genomic Hybridization)

• SKY (Spectral Karyotyping)

• Real Time RT-PCR

• cDNA Microarrays

Novel analytical tools

• 2D-PAGE

• MS (Mass Spectrometry)

• HPLC (High Performance Liquid Chromatography)

• CA (Capillary Array)

• MALDI (Matrix Associated Laser Desorption/Ionisation)

• MALDI-TOF – MS (Time of Flight)

• MALDI – ION TRAP- TOF – MS

• ESI (Electron Spray Ionisation) Tandem – MS

• Quadrupole

Functional proteomicsFunctional proteomics

• TWO YEAST– HYBRID SYSTEM

• PROTEIN MICROARRAY

• FRET (Fluorescence Resonance)

• SELDI (Surface-Enhanced Laser Desorption/Ionisation)

• TISSUE MICROARRAY

Proteomics

Multispot Arrays

Sonde

(DNA, oligonucleotidi, proteine, anticorpi)

Spots sulla superficie di un substrato solido

Deposito o sintesi

Gene chip, DNA chip, DNA array,

Protein chip…..

Sonde:• antigeni• anticorpi• cDNA• oligonucleotidi• prodotti di PCR• plasmidi• BACs (Bacterial Artificial Chromos.)• YACs (Yeast Artificial Chromos.)

Substrato:• vetro• nitrocellulosa• nylon• vetro rivestito di poliacrilammide• polipropilene• silicone• polistirene

Deposito • blotting• printing• elettrodipendente

Sintesi in situ• meccanica• fotolitografica• elettrodi• printing di precisione• deposito sulla

superficie tensione-dipendente

MICROARRAYS a cDNA o OLIGONUCLEOTIDI

Sistemi utilizzati per confrontare i livelli di espressione genica in due campioni diversi.

• Estrazione RNA cellulare

• Trasformazione in cDNA

• Marcatura del cDNA

• Ibridazione (DNA/nucleotidi)

• Lettura laser

• Analisi datiDe Risi et al Science 278:680 (1997)Heller et al PNAS 94:2150 (1997)

Un microarray è costituito da una superficie sulla quale sono depositate migliaia di sequenze specifiche di nucleotidi, ciascuna delle quali identifica un particolare gene.

Le diverse migliaia di cDNA sono poste in spot separati. Ciascuno spot rappresenta un gene, in quanto contiene numerose copie di un cDNA corrispondente a tale gene.

500.000 spot

GeneChip array

Milioni di catene di DNA in ciascuno spot

25 basi in ogni catena

1.28 cm

1.28 cm

Ibridando tale superficie con cDNA ottenuti dalla retro-trascrizione dell’RNA estratto da due campioni diversi è possibile determinare il livello di espressione dei singoli geni per confronto diretto tra l’abbondanza relativa di RNA prodotto.

RNA del tumore

Plot multidimensionale

RNA normale

cDNA del tumorecDNA normale

Analisi statistica

Ibridazione

Per effettuare tale confronto, i cDNA corrispondenti ai due differenti campioni vengono marcati con sostanze fluorescenti diverse e, ad ibridazione avvenuta, il microarray viene esposto ad una sorgente di luce laser.

Gli spettri di emissione vengono quindi raccolti da uno scanner e le immagini monocromatiche indicanti i livelli diversi di espressione genica vengono pseudocolorate da un software di acquisizione d’immagine.

De Risi J.L. et al Science 1997; 278:680-686.Heller R.A. et al PNAS 1997; 94:2150-2155.

Utilizzo dei microarrays

Fattori Prognostici e predittivi

Markers Diagnostici

Targets per farmaci

Attività farmaci

Per lo studio di:

Tumori

Patologie su base genetica

Malattie infettive

……

I cDNA MICROARRAYS nel 2004

Agilent's microarray, con 36.000 geni e transcritti su un singolo vetrino 1 x 3". I probes sono sintetizzati in situ usando la tecnologia ink-jet

Tre ditte stanno lanciando dei chip per l’intero genoma umano:

•Applied Biosystems, 30.000 geni tecnologia chemiluminescenza

•NimbleGenSystems, 190.540 probes con una media di 5 pobes per gene tecnologia fotolitografica

•Agilent Technologies, 44000 probes per 36.000 geni e trascritti tecnologia injk-jet

Limiti dei Microarrays

• Disponibilità di tessuto “fresco”

• Esame dell’espressione genica limitato alla valutazione della presenza di mRNA

• Riproducibilità dei dati

• Eterogeneità delle cellule presenti nel campione

Campione eterogeneo

Campione A

Campione BMicrodissezione

Microdissezione

Popolazione eterogenea

Popolazione eterogenea

Laser capture

microdissection

(LCM)

Popolazione omogeneaPopolazione

omogenea

ANALISI PROTEOMICA

Possibilità di individuare markers molecolari di tumori (o altre patologie)

Siero Proteine

Frazionamento Digestione con enzimi proteolitici

Peptidi

Cromatografia o 2D-PAGE

Spettrometria di massa

Analisi con algoritmi specifici

Sidransky D. Emerging molecular markers of cancer. Nature Cancer Rev 2002; 2:210-9.

PROTEIN MICROARRAYS (ProteinChip)

Sono utilizzati per esaminare:

• i livelli di espressione delle proteine

• le interazioni proteina-proteina

• le interazioni proteina-piccole molecole (farmaci, etc)

• le attività enzimatiche

Page, M. J. et al. Proteomic definition of normal human luminal and myoepithelial breast cells purified from reduction mammoplasties. PNAS 1999; 96:12589–12594.

PROTEIN MICROARRAYS

Esistono due tipi principali di chip:

• antibody arrays

Ab Microarray 500™ - BD Biosciences' Clontech division, Palo Alto, CA

> 500 anticorpi per quantificare proteine in lisati cellulari o altri campioni biologici

TranSignal Human Cytokine Antibody Array 2.0 (Redwood City, CA)

> 21 anticorpi per misurare citochine

• general protein arrays

Yeast ProtoArray™ from Protometrix, Branford, CT, con circa 5.000 polipeptidi da Saccharomyces cervisiae

per monitorare le interazioni proteina-proteina e proteina-piccole molecole (farmaci….)

Yeast ProtoArray™

PROTEIN MICROARRAYS

Conrads TM et al. Cancer diagnosis using proteomic patterns.Expert Rev Mol Diagn 3:411-20 (2003)

IndividuazioneIndividuazionenuovi nuovi

biomarkersbiomarkers

SieroProteine

Bio-chip

SELDI-TOF MS

m/zPattern proteico Riconoscimento

del pattern

Nuovi Biomarkers individuati con il ProteinChip e tecnologia SELDI-TOF-MS

Tumore kDa Nome Autore

Pancreas 16.57 HIP/PAP-1 Rosty et al.

Cancer Res 2002; 62:1868-1875

Vescica 3.4 Alpha-Defensina Vlahou et al. Am J. Pathol 2002; 158:1491-1501

Nasofaringe 11.6

11.8

Serum Amyloid A (SAA) isoform

Yip et al - AACR 2002

Prostata 100 PSMA Wang et al. Int J. Cancer 2002; 92:871-876

Ovaio

9.2

79

54

Frammento di Aptoglobina

Transferrina

Catena pesante Ig

Ye et al. Poster 3687 - AACR 2002

Rai et al. Arch. Pathol. Lab. Med 2002; 126:1518-1526

“ “ “

“ “ “

Gene profiling of breast cancer

Sorlie et al., PNAS 98, 10869-10874, 2001

Hierarchical clustering of 78 primary breast cancers and 4 normal breast tissue

Dendrogramma

“Alberi di unionetra i vari casi chesi assomigliano” (i.e: intensità di colore relativo ad un gene o a gruppi di geni)

5 differenti fenotipi

ER- ER+

Cluster analysis

Sorlie et al., PNAS, 98, 2001

Van ‘t Veer et al.Nature 415, 530, January 2002

~~5000 genes5000 genessignificantly regulated significantly regulated ((in > 3 tumors)in > 3 tumors)

231 genes correlated w disease outcome231 genes correlated w disease outcome

70 genes70 genes= = Poor/Good prognostic signaturePoor/Good prognostic signature

correctly predicted disease outcome in 65/78 correctly predicted disease outcome in 65/78 sporadic tumorssporadic tumors

Unsupervised hierarchical clustering

78 sporadic BC (T<5cm, N-) + 20 BRCA1/2+ BC

34 pts w metastases <5 y 44 pts NED >5 y

25,000 genes of microarray

Supervised hierarchical classification

Rank-ordered based on p-value

Tumori clinici: studio pilota

Tumori clinici: serie di validazione (N=19)

Van’t Veer et al., Nature 415, 2002

Good prognosissignature

Poor prognosissignature

Van de Vijver et al., NEJM, 347, 25, 2002

295 carcinomi mammari sporadici T<5 cm, N-/N+ < 53 anni:

Decorso clinico in base al profilo di espressione genica(70-gene prognosis signature)

Van de Vijver et al., N Engl J Med, 2002

295 sporadic breast cancers:

Clinical course according to gene expression profile

Van de Vijver et al., N Engl J Med, 2002

151 N- patients

144 N+ patients

Van de Vijver et al., N Engl J Med, 2002

Van de Vijver et al., N Engl J Med, 2002

151 N- patients:

Clinical course according to molecular signature (A) or clinico-patological classification (B, C)

Therapeutic benefit

According to usual selection criteria(EBCTCG) over 100 pts N- pre-menopausal pts receiving adjuvant chemotherapy, 83.5 are alive even without chemotherapy and 13.5 die despite chemotherapy at 5 years FU.

Using gene expression profile, only 22.5% of pts will be over-treated

Clin Cancer Res 10: 2272-83, 2004

Clin Cancer Res 9: 6326-34, 2003

BM, Ab to CKPE, Ab to CK &HER-2 FISH

BM, Ab to CK & nuclear Counterstaining w d-p-indole

Same as in C Ab to uPAI-R

AdnaGen CancerSelectGenzyme Virotech GmbH

Test system for the early detection of disseminated cells in blood for a better diagnostic and monitoring of

colon and breast cancer patients

Will the new molecular knowledge be applied to “bedside”?

The first large-scale independent trial to prospectively validate the 70-gene expression signature (MammaPrint®) in breast cancer.

EORTC/TRANSBIG MINDACT TRIAL

MammaPrint® has reached level 3 in Evidence Based Medicine

Adequate Processed Core BiopsyPrognostic Risk Evaluation

Randomize

Clinico-pathological Microarray

Low Risk Low Risk

Average/High Risk

ChemotherapyPossible further randomization

Endocrine therapyPossible further randomization

Other ongoing trials incorporating translational research in BC

Evaluating predictive factors for response:

BIG p53 (EORTC 10994): pts with LABC Tax vs Non-Tax CT (Neoadjuvant)

Evaluating prognostic factors (uPA/PAI-1)

EORTC-RBG: High-Risk, Node-negative(NNBC-3) FEC vs FEC Docetaxel

ADEBAR: 4+ lymph nodes Adjuvant Epirubicin Docetaxel (Wilex's uPA inhibitor WX-UK1)

Affymetrix Launches ENCODE Array to Uncover Hidden Function of Human GenomeOctober 22, 2004

BioTrove Announces OpenArray™ Transcription Analysis SystemDate: September 20, 2004

Expression Analysis Launches Affymetrix Microarray-Based Genotyping ServicesDate: September 14, 2004

Agilent Partners with TGen to Develop CGH Arrays for Cancer ResearchJune 8

Gene Logic Provides Data from GeneExpress System to FDAMay 13, 2004

Toray Develops Ultra Sensitive, Quick DNA ChipDate : September 20, 2004

SIRS-Lab releases new biochipDate: September 22nd, 2004

Agilent Acquire Silicon GeneticsAugust 29, 2004

Velcura to use custom Affymetrix technologyAugust 03, 2004

Toshiba to Develop DNA Chip with Osaka University July 20, 2004

Affymetrix and Immusol to Collaborate on Cancer Drug DiscoveryJune 22, 2004

Predicting cancer patient survival with gene- expression dataDate: May 06, 2004

Setting The Gene Expression Base-Line For Breast Cancer ResearchDate: May 05, 2004

Illumina Announces 100,000 SNPs on a Single BeadChipDate: April 21, 2004

NCI awards grant for gene expression researchDATE: Thursday, April 15, 2004

Can the novel technologies be used topredict the therapeutic response?

Nature Clin Pract Oncol 1; 44-50, 2004

New Engl J. Med. 351: 2817-2826 (2004)

Early BC

Panel of the 21 genes and the Recurrence Score Algorithm

Oncotype DX from Genomic Health

J. Clin. Oncol. 23: 732-740 (2005)

Advanced BC

Anthracyclines Topoisomerase II, MDR, MRP, ErbB-2

5-FU, Capecitabine Cyclin D1, Thymidilate synthase, Thymidine phosphorylase, NFkB, p53, Bax/Bcl2

Gemcitabine Ribonucleotide reductase, 5-nucleotidase, -tubilin III deoxycytidine-kinase

Vinca Alkaloids MAP4, Topoisomerase I

Taxol -tubulin III, MDR, MAP4, survivin

Platin compounds ERCC1, MDR1, -tubulin III, XPA, XPD, cJunXPG, p53, cyclinD1, GSTpi, MLH1, MSH6

Irinotecan, Topotecan Topoisomerase I, p14ARF, carboxylesterase, MDR

Small TRK inhibitors Akt, MAPK, ecc

GENES & GENE PRODUCTS INVOLVED IN DRUG RESISTANCE/SENSITIVITY(Cancer literature)

Breast cancer

1. Anthracyclines

2. Fluoropyrimidines 

Topo II MDR11.

TS Cyclin D12.

p14ARFTopo I3. 3. Taxanes

 

1.

TS Cyclin D12.

ERCC1 p533.

2. 5-FU, capecitabine

3. Platin compoundscJun

“Smart Chip” (antibody array)A CINBO’s Project

Colorectal cancer

    

    

1. Irinotecan

         

    

  

ErbB-2

Bcl-2/bax

Topo I p14ARF

Bcl-2/bax

++

FFIA (Fluorescent Immuno FFIA (Fluorescent Immuno Assay)Assay)

1. Biopsia del 1. Biopsia del pazientepaziente(Biomarkers)(Biomarkers)

2. Rh-Fusion-GFP proteins

Biotin-Antibody coated chipFl

uore

scence

inte

nsi

ty

Amount of Biomarker

“Smart Chip” (antibody array)

A CINBO’s Project

Conclusion

The emerging fields of genomics (and proteomics) offer the ability to precisely analyze the molecular portrait of a particular patient’ tumor;

These approaches appear extremely useful for defining individual patient’s prognosis and assessing responivenessto anti-cancer therapy;

A new era will come soon, wherein we will treat each patient with a “prescription” based on the molecular profile of its tumorresulting in more rationale use of the therapy

Good prognostic signature

Poor prognostic

signature

White = ED ptsBlack = NED pts