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Solid Tumors Reveal Their SecretsPredictive and Prognostic Evidence from Copy Number Analysis

Instructions for Viewers

Webinar Series

Brought to you by the Science/AAAS Custom Publishing Office

Sponsored by:

June 11, 2014

Participating Experts


Webinar Series

Paul C. Boutros, Ph.D.Ontario Institute for Cancer ResearchToronto, Canada

Ajay Pandita, D.V.M., Ph.D.Core DiagnosticsPalo Alto, CA

3

Copy-Number Based Biomarkers for Intermediate

Risk Prostate Cancer

Dr. Paul C. BoutrosOntario Institute for Cancer Research

June 12, 2014

4

Problem Formulation

CPC-GENE: A Prostate Cancer ICGC Intra-tumoural Heterogeneity Inter-tumoural Heterogeneity

Pathway

5

The Problem:Patient Outcome is Variable

Stage I NSCLC

6

A Potential Solution:Personalized Treatment

More

Less

7

The (Old) Hypothesis

There are a small number of distinct tumour subtypes.

Which each have distinct molecular profiles.

And distinct prognoses.

Pattern Discovery Techniques

8

Omic Prognosis Prediction: A “Classic” Question First Explored in Breast Cancer

Sorlie et al. PNAS Sept 11, 2001

Breast cancer was comprised of 5 subtypes

Each of which is characterized by some unique and some common genes

9

These Subtypes Have Different Prognoses

Sorlie et al. PNAS Sept 11, 2001

10

And Were Reproducible

Kapp et al. BMC Genomics, 2007

For example a 600-patient validation analysis by Rob Tibshirani’s group.

11

This Was Not True For Other Tumour Types

Beer et al. Nature Genetics, 2002

12

This Was Not True For Other Tumour Types

Potti et al. NEJM, 2006

13

Honest Evaluation Shows the Problem:Too Many Subtypes!!!

STX1A HIF1A CCT3 HLA_DPB1RNF5MAFK

Boutros et al. PNAS, 2009

14

This Rationalizes Power-Analyses

Sample Size

Ove

rlap

of B

iom

arke

rs

Ein-Dor et al. PNAS 2006

NSCLC

HCC

Breast

15

The (New) Hypothesis

There are a large number of distinct tumour subtypes.

Which have overlapping molecular profiles.

And distinct prognoses.

Machine Learning Techniques

16

Four years of research into machine-learning methods later…

Our mRNA-based markers reached theoretical maximums. Major

improvements are unlikely.

99.98%

Boutros et al. PNAS, 2009

17

Insufficient Accuracy

18

So It Was Time To Move Beyond mRNAWhat Do We Want In An Algorithm?

Accurate

Extensible

Fast

Clinically-Focused

Has to actually work!

Handles multiple datasetsHandles any type of dataHandles diverse problems

Incorporates known clinical features into modelingLinks to treatment options

Can run in a day

19

Problem Formulation


Pathway

2020

Dr. Robert Bristow Dr. John McPherson

Dr. Theodore van der Kwast

CPC-GENE: The People Involved

21

Incidence:> 25,000 / year

(1,515 per million)

Mortality:> 4,000 / year

(242 per million)

Risk factors: AgeFamily historyHigh-fat dietAfrican ancestry

Prostate Pathology: Epidemiology

2222

Prostate Cancer PrognosisDigital Rectal

Exam Imaging Biopsy Blood test

TNM stage Tumour grade PSA

Lowrisk

Intermediaterisk

Highrisk Metastatic

Localized cancers

23

Prostate Pathology: Gleason Scores

24

High

Low

% B

ioch

emic

al R

FR

Intermediate

Clinical stratification of localized cancer

D'Amico, et al. 2001. Urology.

Active Surveillance

Radiotherapy or Radical prostatectomy

Radiotherapy or Radical prostatectomy+ adjuvant treatment

25

Problem Formulation


Pathway

2626

The Landscape of Gleason 7 Prostate Cancer

OncoScan arrays

2727

Uniquely High-Resolution: Novel Findings!

2828

We Didn’t Believe, So We RT-PCR Validated

2929

L-Myc: Associated With Genomic Instability

3030

L-Myc: But In An Unusual Way

3131

And L-Myc Is Weakly Prognostic

32

And Associates With A Unique mRNA Profile

3333

CPC‐GENE Pilot Study – Sample Workflow

3434

Pilot Study Pathology Example

3636

Immense Intra-Prostatic Diversity…

3737

…At All Levels of Investigation

3838

Is This Heterogeneity Clinically Relevant?

3+4 3+4 3+4 3+43+4 3+44+3 4+3 4+3

3939

Prognostic Markers Also Affected

4040

Multi-Clonality

41

Problem Formulation


Pathway

42

Emilie Lalonde

Dr. Robert Bristow

43

Study design

126 intermediate-risk patients

129 low and intermediate-risk patients

Median follow up: 7.8 years

Median follow up: 4.6 years

Radiotherapy Cohort Surgery Cohort

44

Univariate prognostic biomarkers

These account for only a fraction of patients

MYC NKX3-1

45

Unsupervised analysis

Supervised analysis

Multivariate gene signature

Cluster trainingpatients

Select clusters

Add validation patients to

clustersSurvival

associations

Pt1

Pt2

…

46

MYC

NKX3-1RB1

Training Cohort

47

Cluster CNA profiles

48

Clusters are associated with outcome

49

Genomic Instability: PGA As A Proxy

PGA = CNA bp / total bp x 100

50

PGA as a biomarker

51

Multivariate gene signature

Naïve feature selection

Find signature (cross

validation)

Build model (random forest)

Train and test

p < 0.05

Pt1

Pt2

…

• Unsupervised analysis

• Supervised analysis

52

Classifier evaluation

PredictionsAccuracy 77.5%

Sensitivity 50.0%Specificity

AUC83.8%0.658

100 genomic regions

or 245 genes

53

This Is the Best Performing Signature

54

Feature importance

GFRA2

MGMTEBF3

TUBGCP3IRX1

NKX3-1p53

55

Signature Genes Have Interesting Biology

Homo sapiens SET binding factor 1(SBF1)

56

SBF1

57

Intra-tumoural Heterogeneity Prostate cancer can be multi-clonal

Inter-tumoural Heterogeneity CNAs alone are a promising biomarker

Summary

5858

Dr. Robert Bristow Dr. John McPhersonDr. Theodore van der Kwast

CPC-GENE: The People Involved

Boutros LabRichard de BorjaNicholas HardingPablo Hennings-YeomansEmilie LalondeAmin ZiaJianxin WangFrancis NguyenNatalie FoxMichelle Chan-Seng-YueMaud StarmansTakafumi YamaguchiVeronica Sabelnykova

InformaticsTimothy BeckFouad YousifRobert DenrocheXuemei Luo

GenomicsTaryne ChongAndrew BrownMichelle SamJeremy JohnsLee TimmsNicholas BuchnerAda Wong

Clinico-MolecularDominique TrudelAlice MengGaetano Zafarana

PIs & PMsMichael FraserMelania PintilieNeil FleshnerLakshmi MuthuswamyColin CollinsThomas HudsonLincoln Stein


Sponsored by:

June 11, 2014



Webinar Series



Copy Number Alterations: From Bench to Bed

Ajay PanditaCore DiagnosticsJune 11, 2014

Normal Karyotype

Cancer Patient’s Karyotype

Trisomy 21 (Down’s Syndrome)

Exhibit a number ofdevelopmental disabilities and have a significantly reduced life‐span

Normalcell

Tumorcell

Epigenetic Mutations

Numerical & structuralalterations

Dx positive

Dx negative

Genetic events in cancer & Dx

“Growth inhibiting & growth promoting chromosomes results in unlimited multiplication” and suggested that malignant tumors might be a result of “abnormal conditions of the chromosomes”

Theodor Boveri1902

Venter et al. Science, 2001

Human Genome Project

Pathology and Cancer• Pathologists are good at

– diagnosis of cancer– grading, mapping extent of disease

Diagnosis of Typical Cancers

Histology – H&E Immunohistochemistry

However….

• Conventional pathology not good at

–diagnosing rare tumors

–determining which tumors will be the “good” or “bad” players

–predicting which therapy is most effective for early or advanced tumors

BiomarkersA biomarker is anything that can be used to measure any biological process

Biomarkers

Predictive biomarker: a test that can be done before treatment to predict whether a particular treatment is likely to be beneficial

Prognostic biomarkers correlate with disease outcome. They improve our ability to design informative trials and to interpret them confidently.

Resistance markers correlate with resistance to treatment. They improve our ability to understand resistance mechanisms to treatment.

Diagnostic biomarker: a test that can be done to confirm the diagnosis

Ajay Pandita

Prognostic value of biomarkers• Early detection of cancer• Important to determine

– indolent tumor• “wait and watch” with periodic monitoring

– Progressive tumor• aggressive treatment regimen• surgery• quality of life

Confidential 73

HER2: Prognostic Factor

Predictive biomarkers (Companion Diagnostics)

• A test that can be done before treatment to predict whether a particular treatment is likely to be beneficial

• bcr‐abl for imatinib• hormone receptors for tamoxifen• HER2 for trastuzumab

Predictive Biomarker (HER2 and trastuzumab)

Effect of trastuzumab in all patients (no selection)

Effect of trastuzumab in HER2 positive patients (selection)

Median time to progression: 3.8 monthsMedian time to progression: HER2 + patients: 4.5 monthsHER2 – patients: 1.7 months

Biomarker and Matching Drugs

Ajay Pandita

Drug discovery: Past, Present & Future…

BasicResearch

Prototype design/Discovery

Preclinicaldevelopment

Phase I Phase II Phase III FDA filing, approval& launch

Label considerations basedon marker status

Label considerations basedon trial results

Marker assayvalidation

Analytical validationdiagnostic kit

Clinical validation diagnostickit; final platform determined

Targetselection

Targetvalidation

Identification of stratificationmarkers

Clinical utility forstratification markers

Clinical validation forstratification markers

Analytical validation

Pre-clinical feasibility

Clinical validation

Clinical utility

TMN Staging

Molecular Biomarkers• PD biomarker• Predictive• Prognostic• Resistance

BasicResearch

Non‐small cell lung cancer (NSCLC)

• ~ 120,000 new patients a year• Molecular stratification

– KRAS mutation– ALK fusion– EGFR (mutation, amplification)

• MET over‐expression

Confidential 80

MET• Evidence of underlying genetic events responsible for oncogene activation

• Expressed in human tumors primarily in epithelial tumors

• Transgenic mice develop tumors

• Prognostic factor

C‐Met expression: poor prognostic marker

Masuya et al, Br J Cancer. 2004 Apr 19;90(8):1555‐62

MET Expression in Various Tumor TypesTumor Type Incidence/Comments

Bladder 38%; MET expression correlates with aggressive histology (nodular pattern muscle invasion); n=329

Esophagus 86%; 2of 3 studies (n=85) look at adenocarcinoma only; n=95

Mesothelioma 80%; single study (Salgia) with 66 cases

Melanoma 80%; 132 cases are uveal cases only; one study observed increased incidence of membranous staining in metastatic cases; n=231

Breast The largest study (n=>300 patients) by Rimm (well respected pathologist) shows 28% incidence and correlation with high nuclear grade and poor clinical outcome

Thyroid Papillary subtype only (most common form); approx. 60%; low or no staining correlates with metastatic or poorly differentiated disease; n=162

GBM 33% stain 2+ and 3+ for MET, 10 cases are 1+; n=15

CRC 80% by immunoblot (expression higher in cancer compared to matched normal mucosa); confirmed in a smaller subset of patients by IHC; correlation with blood vessel invasion; n=130

H&N Approx. 60%; correlates with lower response rate and worse outcome; n=246

Osteosarcoma

25%; incidence possibly higher in metastatic cases; n=23

Prostate 50%; incidence increases from PIN to invasive cancer to metastatic foci; n=110

Initiate a MET drug development program

Companion Diagnostic Assays• IHC for over‐expression

– IHC score of 2+, 3+ or H score >200; Dx positive• MET FISH assay for gain/amplification

– >5 copies of MET/cell; Dx positive• MET mRNA expression• HGF expression• MET mutation• Plasma MET levels

Spigel et al. JCO, 2013; Koeppen et al. CCR, 2014

Diagnostic hypothesis & strategyDx Hypothesis

• No clear predictive diagnostic hypothesis from preclinical studies• Target (c‐Met) expression may enrich population likely to benefit

• Elevated c‐Met expression is a poor prognosis factor• Preclinical activity in high c‐Met expressing models

Dx Strategy• A companion Dx test to measure c‐Met protein on tumor cells was developed for potential patient stratification in a pivotal study

• Phase II study sized to evaluate cMet expression and incorporated as primary endpoint

• Exploratory objectives incorporated to evaluate additional markers that may influence clinical benefit to MetMab

• EGFR/KRAS mutation status, MET copy number, MET/EGFR pathway gene expression


IHC based Patient Selection


FISH based Patient Selection

OS: HR – 0.30 (p=0.06)PFS: HR – 0.58 (p=0.38)


Conclusions

• Anti‐met Ab is a potent and selective antibody targeting MET

• MET IHC and FISH are independent predictors for MET based targeted therapy

• MET IHC seems to be a better predictive assay

Confidential 87

Resistance biomarkers

• A test that correlates with resistance to treatment. They improve our ability to understand resistance mechanisms to treatment.

• Mutation in abl for imatinib• T790M for anti‐EGFR therapy• BRAF amplifications for MEK inhibitors

Resistance in Targeted Therapy

• Clinical resistance is likely to reduce the clinical benefit of targeted therapy agents

• Understanding the range of mechanisms contributing to targeted therapy resistance pre‐clinically provides hypothesis for clinical studies

Mechanism of Resistance

• HER2 positive PIK3CA mutant cell line (KPL4)– Resistance to HER2 inhibitors due to PIK3CA mutation

– Sensitive to PI3K inhibitors• Develop KPL4 resistance to PI3K inhibitors• Mechanism of resistance?• OncoScan (Affymetrix) Kit

– Based on molecular inversion probes (MIB)– http://www.affymetrix.com/estore/promotions/mip/index.affx

Huw et al. Oncogenesis, 2013

OncoScan Analysis


Amplification of mutant PIK3CA imparts resistance?

Novel mechanism of PIK3CA resistance with activation of the target itself


Conclusions

• New technologies aid in discovering novel biomarkers/signatures

• Patient stratification based on these Biomarkers open doors to better clinical management

Confidential 93

Ajay Pandita

Acknowledgements• GNE: Bob Yauch, Sankar Mohan, Jiping Zha, Rajiv Raja, Lukas Amler, Garret Hampton, Wei Yu, Premal Patel, Amy Peterson, Ling Huw, Jill Sproeke, Mark Lackner

• Clinicians: David Spigel, Thomas Ervin, Rodryg Ramlau, Davey Daniel, Jerome Goldschmidt Jr., George Blumenschein Jr., Maciej Krzakowski, Gilles Robinet, Christelle Clement‐Duchene, Fabrice Barlesi, Ramaswamy Govindan, Taral Patel, Sergey Orlov, Michael Wertheim

• Core Diagnostics: Sankar Mohan, Rob Monroe• Patients & families of the patients

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