Integative Genomic Approaches to Personalized Cancer Therapy

Patrick Tan, MD PhD

International Conference on Bioinformatics Singapore, Sept 09 2009

Genomic Oncology in Singapore : TranslatingInformation into Knowledge

Clinical Biomarkers

Disease Genes

Cancer Pathways

3) Lung Cancer Outcome

- Integrative Genomics

1) Metastasis Genes

- Network Structures

Basic Science to Translation

2) Cancer Classification

- Pathway Signatures

Biological Networks – Robust Yet Fragile

Edge Gene Hub Gene

Tolerant

Wide Variation

Ultrasensitive

Low Variation

Can we infer ‘hub-like’ genes in cancer?Yu Kun

Identifying Precisely Controlled Genes in Cancer

Lung Thyroid Liver Esophagus Breast

270 Tumors

Large Variation

RestrictedVariation

Restricted Variation Only in Cancers

48 Precisely Controlled Genes in Cancers

Cancer

Non-malignant

Tumor

Gastric, NPC (99)

Breast (286) Lung (118)

Ovarian (146)

Breast (189)

Glioma(77)

Colon (100)

The PGC is Precisely Controlled in Many Solid Tumors

Significance

0 1 2 3 4 5 6 7 8 9 10 11 12 13

Normal

Novartis (158)

Ge et al (36)

The PGC is NOT Precisely Controlled in Normal Tissues

Significance

0 1 2 3 4 5 6 7 8 9 10 11 12 13

PGC Genes are Enriched in the Integrin Signaling Pathway

Growth Factor Regulation

RAS/MAPK Signaling

PI3K Signaling

JNK/SAPK Signaling

Cytoskeletal Interactions

Cell Motility

Implications of Precise PGC Regulation

Dedicated Cellular Mechanisms to Ensure Accurate Expression

A Functional Requirement for Tight PGC Control in Tumors?

Are Tumors Ultrasensitive to PGC Activity?

PGC Expression in Breast Cancer Cell Lines

30 Breast Cancer Cell Lines

PGC

Non-invasive Invasive

P=0.01

HCT116Tumor Cells

SplenicInjection

LiverMetastases

Adapted from Clark et al (2000)

PGC Expression in Experimental Metastasis

Reduced PGC Expression Correlates with Metastatic Potential

P=0.022

siRNA Knockdown of PGC Genes Enhances Metastasis

p53CSV siRNA qRT-PCR

PGC Expression in Primary Tumors

Reduced PGC Expression Predicts Clinical Prognosis

Elevated PGC

Decreased PGC

Are Low-Variance Genes True Hubs? (Lessons from Yeast)

mRNA variance overlaid on a protein-protein network

Black nodes = missing data.

A: proteasome regulatory lidB: mediator complex C: SAGA complexD: SWR1 complex

Slide Courtesy of Marc Wilkins

Goel and Wilkins, unpublished.

Take Home Messages

- A General Strategy for Identifying Tightly RegulatedGenes

- A Precisely Regulated Expression Cassette in Cancer

- Fine-scale alterations potently modulate tumor behaviourand clinical outcome

-Not discernible by conventional microarray analysismethods

Yu et al (2008) PLOS Genetics

3) Lung Cancer Outcome

- Integrative Genomics

1) Metastasis Genes

- Network Structures

Basic Science to Translation

2) Cancer Classification

- Pathway Sigantures

From The Scientist, Sep 22, 2003

High Prevalence of Gastric Cancer in Asia

Global Cancer Mortality

Lung (1.3 million deaths/year)Stomach (1 million deaths/year) Liver (662,000 deaths/year) Colon (655,000 deaths/year)Breast (502,000 deaths/year)

- WHO, 2005

Tumor Heterogeneity Impacts Response

CML “One Disease”

Imatinib

100% Response

Gastric Cancer“Many Diseases”

5-FU

20% Response

Pre-Selecting Patients for Optimal Therapy

Gastric Cancer

Subtype A Subtype B Subtype C Subtype D

Subtype E Subtype F

Rx 1 Rx 2 Rx 3 Rx 4 Rx 5 Rx 6

Expression Signatures as Cancer Phenotypes

Tumor Type A(“State A”)

Tumor Type B(“State B”)

Genes

BA

Tay et al., Cancer Research (2003)

Expression Signatures

Capture Heterogeneity

Using Pathway Signatures to Guide Targeted Therapies

Pathway A

-1.5

-1

-0.5

0

0.5

1

1.5

-1.5 -1 -0.5 0 0.5 1 1.5

Experimental System

Tumor Profiles

Pathway A

Chia Huey Ooi

Mapping Pathway Signatures to Tumor Profiles

Pathway A

-1.5

-1

-0.5

0

0.5

1

1.5

-1.5 -1 -0.5 0 0.5 1 1.5

Tumor Profiles

Pathway B

-1.5

-1

-0.5

0

0.5

1

1.5

-1.5 -1 -0.5 0 0.5 1 1.5

Pathway D

-1.5

-1

-0.5

0

0.5

1

1.5

-1.5 -1 -0.5 0 0.5 1 1.5

Pathway E

-1.5

-1

-0.5

0

0.5

1

1.5

-1.5 -1 -0.5 0 0.5 1 1.5

Pathway C

-1.5

-1

-0.5

0

0.5

1

1.5

-1.5 -1 -0.5 0 0.5 1 1.5

A

B

C

D

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1

Predominant Oncogenic Pathways in Gastric Cancer

200 primary gastric tumors

Proliferation/stem cell pathways activated

-catenin pathway

activation

p53 pathway

activation

Onc

ogen

ic P

athw

ays

BRCA1p53 (b)HDACBRCA1RasSrc-cateninHDACp53 (a)NF-kB (b)WntNF-kB (a)Myc (b)Stem cell (c)Myc (a)Stem cell (b)Stem cell (a)E2F (b)E2F (a)P21

Activation score

Validating Oncogenic Pathway Predictions

GC cell lines

Path

ways

Path

ways

NFKBWntProliferation

Proliferative capacity vs. combined E2F+Myc+Stemcell activation score for 22 GC cell lines

1

1.5

2

2.5

3

3.5

4

0.2 0.4 0.6 0.8 1

R = 0.5051 p = 0.0165

Summarized activation score of the proliferation/stem cell cluster

Pro

life

rati

ve

cap

acit

y

High Proliferation Scores are Associated with Rapid Growth

-0.4

-0.2

0

0.2

0.4

0.6

AG

S

YC

C3

Kato

III

NC

I-N

87

SN

U1

SN

U5

SN

U16

In-s

ilico

pre

dic

tion o

f

-ca

tenin

path

way

act

ivation High Wnt Scores are Associated with Wnt Activity

Relative constitutive TCF7L2 activity

TCF7

L2 a

ctiv

ity

(fol

ds)

TCF7L2:

Relative constitutive TCF7L2 activity

TCF7

L2 a

ctiv

ity

(fol

ds)

TCF7L2:

Oncogenic Pathways in Gastric Cancer are Functionally Significant

Path

ways

Path

ways

NFKB

Wnt

Cell Lines

Annexin +ve cells

0

10

20

30

40

50

60

Neg siRNA B-Catenin siRNANeg siRNA -catenin siRNA%

ap

op

toti

c ce

lls

Cell DeathAssay

GC cell lines

Neg siRNA-catenin siRNA-catenin (WB)

Actin (WB)

Control shRNA

p65 shRNA0

1

2

3

4

5

T72/T0

Pro

life

rati

ve

cap

acit

y

p=4.549106

Proliferation

NFKB

Wnt

Single Pathways

Pathway Interactions Influence Survival Pathway

Combinations

NFKB +

Prolif.

Wnt +

Prolif.

Clinical Validation of Pathway Combinations

Proliferation and NKFB

Proliferation and Wnt

Singapore (200) Australia (90)

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1

Oncogenic Pathways in Gastric Cancer May Guide Therapy

200 primary gastric tumors Potential Therapies

HLM006474

CX-3543

RTA-402

PXD-101

KX2-391Salirasib

pifithrin-

Onc

ogen

ic P

athw

ays

BRCA1p53 (b)HDACBRCA1RasSrc-cateninHDACp53 (a)NF-kB (b)WntNF-kB (a)Myc (b)Stem cell (c)Myc (a)Stem cell (b)Stem cell (a)E2F (b)E2F (a)P21

Activation score

Take Home Messages

• A framework for mapping defined pathway signatures into complex tumor profiles

• Signatures are transportable (in vitro to in vivo)

• Gastric cancers can be subdivided by pathway activity into biologically and clinically relevant subgroups

• “High-throughput pathway profiling” highlights the role of oncogenic pathway combinations in clinical behavior

Ooi et al (2009) PLOS Genetics

3) Lung Cancer Outcome

- Integrative Genomics

1) Metastasis Genes

- Network Structures

Basic Science to Translation

2) Cancer Classification

- Pathway Biology

Genomic Classification of Early StageLung Cancer

Philippe and Sophine BroetINSERM U472, Faculté de Médecine Paris-Sud

Lance MillerWake Forest University, USA

Broet et al., (2009) Cancer Research

Adjuvant Chemotherapy in Early-Stage NSCLC

Surgery

Observation(Watch and Wait)

Chemotherapy?

40-50% 5-yr Survival

Stage I, II

Clinical questions Can we use genomics to discriminate between low risk (pseudo-stage I) & high risk (pseudo-stage II) groups?

Study Questions

Previous studies on NSCLC prognosis have been transcriptome centered, not incorporating genomic alterations

An Integrated Genomic Strategy to Identify “Poor Prognosis” NSCLC

Cases

Stage IBNSLCLCs

(Training Set)

Array-CGH

Recurrent AmplificationsAnd Deletions

Gene Expression Profiling

Highly RegulatedGenes

Recurrent Genomic Alterations in NSCLC

1q31 5p13 8q24 11q13

CyclinD1 WWOX

Survival associations – “Survival CNAs”

Genomic Regions Associated with Outcome

Survival CNAs

201408_at

204322_at

2113458_at

201699_at201698_at

205564_at203342_at

203343_at

203301_at202988_at

Gene Expression Associated with Survival-CNAs

Gene Expression

Copy Number Driven Expression

Predicting Prognosis in Stage IB NSCLC

Integrated Signature

103 genes (Chr. 7, 16, 20, 22)

Good Prognosis

Poor Prognosis

P=0.002

Training Cohort

Michigan Series: 73 Stage I A&B NSCLCs

Validation of the Integrated Signature

Good Prognosis

Poor PrognosisP=0.025

Another Validation of the Integrated Signature

Duke Series: 31 Stage I A&B NSCLCs

Good Prognosis

PoorPrognosis

P=0.003

Candidates forChemotherapy?

Implications for Chemotherapy Selection

Poor PrognosisStage IB

Stage IINSCLC

Poor Prognosis Ib PatientsAre Comparable to Stage IIPatients

A Genomic Approach to Guide Chemotherapy in Early-Stage NSCLC

Surgery

Stage IbNSCLC

Genomic Predictor

Good Prognosis

(“Stage Ia-like”)

Poor Prognosis

(“Stage II-like”)

Observation

AdjuvantChemotherapy

AcknowledgementsPhilippe Broet (Paris)Sophine Broet (Paris)Lance Miller (GIS)Elaine Lim (NUH)

Wei Chia Lin (GIS)Hooi Shing Chuan (NUS)

Alex Boussioutas (Peter Mac, AU)David Bowtell (Peter Mac, AU)Sun Yong Rha (S. Korea)Heike Grabsch (Leeds)

Support : French-Singapore MERLION program Singapore Cancer SyndicateBiomedical Research CouncilNational Medical Research Council

Kun YuKumaresan Ganesan

Ooi Chia HueyTatiana Ivanova

Shenli ZhangWu Yonghui

Lai Ling ChengVeena GopalakrishnanJun Hao KooJulian LeeMing Hui Lee

Iain TanAngie TanJiong TaoJeanie WuYansong Zhu