Discovery of Drug Sensitizing Genotypes in Discovery of Drug - - PowerPoint PPT Presentation

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Discovery of Drug Sensitizing Genotypes in Discovery of Drug - - PowerPoint PPT Presentation

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Discovery of Drug Sensitizing Genotypes in Discovery of Drug Sensitizing Genotypes in Cancer Cells Mathew Garnett NCT Conference Heidelberg, Sept 2013 Precision Cancer Medicine Precision Cancer Medicine Using targeted drugs to exploit specific

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Discovery of Drug Sensitizing Genotypes in Discovery of Drug Sensitizing Genotypes in Cancer Cells

Mathew Garnett NCT Conference Heidelberg, Sept 2013

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Precision Cancer Medicine Precision Cancer Medicine

  • Using targeted drugs to exploit specific

vulnerabilities and dependencies within cancer cells.

  • Genomic alterations can be used as biomarkers to

Genomic alterations can be used as biomarkers to identify patients most likely to benefit from treatment treatment.

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SLIDE 3

Combined BRAF and MEK inhibitors for the treatment of BRAF mutant l melanoma

Flaherty et al, NEJM 2012

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Targeted Molecular Therapies

Mutated cancer genes as biomarkers of drug response:

Targeted Molecular Therapies

Mutated cancer genes as biomarkers of drug response:

FDA‐approved targeted therapies

M l l bi k FDA d d Cli i l i di ti ( ) Th ti t t Molecular biomarker FDA-approved drug Clinical indication(s) Therapeutic target BCR-ABL Imatinib, Dasatinib, Nilotinib CML, AML ABL1 KIT, PDGFR Imatinib Gastrointestinal stromal tumour KIT, PDGFRA EGFR Gefitinib, Erlotinib Non-small cell lung cancer, pancreatic EGFR ERBB2/HER2 T t b L ti ib HER b t HER2 ERBB2/HER2 Trastuzumab, Lapatinib HER+ breast cancer HER2 BRAF Vemurafinib melanoma BRAF EML4-ALK Crizotinib Non-small cell lung cancer ALK ER+ Tamoxifen ER+ breast cancer ER

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Preclinical Biomarker Discovery Preclinical Biomarker Discovery

  • To systematically explore pre‐clinically the diversity

To systematically explore pre clinically the diversity

  • f cancer for biomarkers that predict drug

sensitivity sensitivity.

  • To understand the landscape of drug response in

relation to cancer genes. g T id tif ff ti bi t i l th i t

  • To identify effective combinatorial therapies to

circumvent resistance.

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SLIDE 6

High‐throughput Drug Screening in Cancer Cells High throughput Drug Screening in Cancer Cells

a b c

[drug] [drug]

IC50

72 hour drug treatment Fluorescence based viability assay

concentration (uM)

Fluorescence based viability assay

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SLIDE 7

High‐throughput Drug Screening in Cancer Cells High throughput Drug Screening in Cancer Cells

Cancer cell lines Cancer cell lines Single drug screens Cancer cell lines Patient‐derived cultures Cancer organoids Drug resistant clones Cancer cell lines Patient‐derived cultures Cancer organoids Drug resistant clones Single drug screens Combinatorial screens siRNA +/‐ drug

a b c

Drug resistant clones Drug resistant clones

[drug] [drug]

IC50

72 hour drug treatment Fluorescence based viability assay

concentration (uM)

Fluorescence based viability assay Link drug response with genomic features

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SLIDE 8

Screening compounds are selected to t t th target cancer pathways

Energy/ATP Synthesis Ser/Thr Kinase (mTOR) Translation Amino Acid Synthesis Nucleotide Synthesis Gene Expression Tyrosine Kinase (EGFR) Gene Expression Chromatin (EZH2) Differentiation (Wnt) Cell Cycle (Aurora) Tyrosine Kinase (EGFR) Ser/Thr Kinase (BRAF) DNA Damage ER Stress Senescence Autophagy Senescence Apoptosis (Bcl2, IAP) Other Cell Death

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SLIDE 9

The GDSC1000 Cell Line Collection

Soft tissue Testis Bone Hodgkin lymphoma Burkitt lymphoma Other Other Cell lines are grouped according to the TCGA classification system

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Pharmacogenomic Characterisation of h GDSC1000 the GDSC1000

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Cancer Cell Lines as an Experimental Model

Experimental tractability Biological relevance

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Systematic Analysis of Drug Sensitivity Systematic Analysis of Drug Sensitivity

IC50 value heatmap

  • 551 anti‐cancer drugs
  • Mean of 432 cell lines screened per drug (range 7 – 672)
  • 238,000 drug‐cell line combinations

g

  • Correlated drug response with coding mutation,

amplification and deletion in 71 frequently mutated cancer genes.

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SLIDE 13

BRAF Mutations Confer Sensitivity to BRAF and MEK inhibitor combo

C ll li IC50 t bi ti f BRAF d MEK i hibit Cell line IC50s to combination of BRAF and MEK inhibitor

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Drug response associated with BRAF mutational status

Dabrafenib + Trametinib ‐vlaue) Multiple MEK and BRAF inhibitors ficance (p Multiple MEK and BRAF inhibitors Signif sensitivity resistance

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SLIDE 15

Landscape of drug response in relation to cancer genes

drug Target(s)

9e−50 1e−45 1e−50 1e−55

PLX4720BRAF SB590885BRAF (BRAF) NilotinibABL (BCR‐ABL) g gene

30 9e−40 1e−30 1e−35 1e−40

SC

vlaue)

PLX4720BRAF (BRAF) g

9e−20 9e− 1e−20 1e−25 1e 30

GDSC

cance (p‐v

9e−10 1e−05 1e−10 1e−15

G

0 05 20% fdr = 1 18e−02

Signifi

1e−07 1e−06 1e−05 1e−04 1e−03 1e−02 1e−01 1e+00 1e+01 1e+02 1e+03 1e+00 1e+00 p = 0.05 20% fdr = 1.18e 02

sensitivity resistance 1924 significant gene drug interactions (p<0.05, 20% FDR)

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Cell line models capture clinical k f d i i i markers of drug sensitivity

FDA‐approved targeted therapies pp g p

Molecular biomarker FDA-approved drug Clinical indication(s) Therapeutic target BCR-ABL Imatinib, Dasatinib, Nilotinib CML, AML ABL1 KIT, PDGFR Imatinib Gastrointestinal stromal tumour KIT, PDGFRA EGFR G fiti ib E l ti ib N ll ll l ti EGFR

✔

ND

✔

EGFR Gefitinib, Erlotinib Non-small cell lung cancer, pancreatic EGFR ERBB2/HER2 Trastuzumab, Lapatinib HER+ breast cancer HER2 BRAF Vemurafinib melanoma BRAF EML4-ALK Crizotinib Non-small cell lung cancer ALK ER T if ER b t ER

✔ ✔ ✔

ND

✔

ER+ Tamoxifen ER+ breast cancer ER Molecular biomarker Drugs in clinical development Clinical indication(s) Therapeutic target

Targeted therapies in clinical development ✔

Molecular biomarker Drugs in clinical development Clinical indication(s) Therapeutic target BRAF e.g. PD0325907 melanoma, NSCLC MEK KRAS e.g. PD0325908 NSCLC MEK NRAS e.g. PD0325909 melanoma MEK FGFR2 e.g. PD173074 FGFR

✔ ✔ ✔ ✔

g PIK3CA e.g. AZD6482 PI3K PIK3CA e.g. AKT inhibitor VIII AKT FLT3 e.g. sunitinib FLT3 BRCA1/2 e.g. Olaparib Breast, ovarian PARP

✔ ✔ ✔ ✔ ✔

g p ,

Many novel association identified some of which may represent new therapeutic avenues

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The Majority of Cancer Genes are Correlated With Drug Response

CDKN2A

200 150

  • rrelations

APC

100

iti it significant co

50

sensitivity resistance Number of s

median

N

1 11 21 31 41 51 61 71

Cancer Genes

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The Activity of Most Drugs is Correlated with Cancer Genes

25 20

tions

15

cant correla

10

Sensitivity Resistance er of signific

5 median

Numbe

1 51 101 151 201 251 301 351 401 451 501 1 51 101 151 201 251 301 351 401 451 501

Drugs

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EWS-FLI1 Mutated Cells are Sensitive to PARP Inhibitors

Olaparib (PARP1/2) AG‐014699 (PARP1/2)

C50 (uM)

50 (uM)

IC IC

n = 13 n = 467 n = 14 n = 544

Mutations of BRCA1 or BRCA2 are not present in these EWS‐FLI1 mutated cell lines

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EWS FLI1 EWS‐FLI1

  • Characteristic of Ewing’s sarcoma, a malignant bone tumour that affects

g , g children.

  • A chromosomal translocation (11:22)(q24;q12) fusing the EWSR1 gene to the

A chromosomal translocation (11:22)(q24;q12) fusing the EWSR1 gene to the FLI1 gene.

  • Fusion proteins act as aberrant transcription factors that bind DNA through
  • Fusion proteins act as aberrant transcription factors that bind DNA through

their ETS DNA binding domain. C t t t t i i h th d di th

  • Current treatment is aggressive chemotherapy, surgery and radiotherapy.
  • Poor prognosis in the 15‐25% of patients with metastatic or recurrent disease.
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Olaparib Selectively Induces Apoptosis p y p p in Ewing’s Cells

B$ 7 M 4L 7 FN " L 6 3 / 9 &' 9 2 3 4546 7 8 #9 &' 9 2 3 4546 7 8 / # H @ ,J9 4K L 7 M #9 &' 9 2 3 4546 7 8 / $ @ I 9 " 4H 54 # EF6 G43 7 H @ ? @ 3 4 +

  • A

, B , 2 + ? ? , ) + , / ) + C ) + D $ E ? A ' ?

Ewing’s

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PARP Inhibitors Induce DNA Damage in Ewing’ Cells

Nuclei H2AX 8h

5uM AZD2281

Ctrl

5uM AZD2281

15

ES8 sponders

5 10

ncrease in res

Olaparib

  • 2

4 8 2 4

Time (hours) Fold in Time (hours)

H2AX – Marker of DSBs H2AX – Marker of DSBs

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Is PARP inhibitor sensitivity is dependent on the y p EWS-FLI1 translocation?

1.2

e

mouse mesenchymal cells

0.8 1.0 ability 0 4 0.6 elative via 0.2 0.4 R EWS-FLI1 FUS-CHOP SKNMC . . 3 9 . 7 8 1 . 5 6 3 . 1 3 6 . 2 5 1 2 . 5 0.0 [Olaparib] (uM) [Olaparib] (uM)

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PARP Inhibitors Trials in Ewing’s Patients

N i l t ti it ? Wh t? No single agent activity? Why not?

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Modeling Drug Resistance Modeling Drug Resistance

1000 cell line drug sensitivity screen 1000 cell line drug sensitivity screen Intrinsic resistance Sensitive Acquired resistance

  • drug combinations
  • RNAi +/‐ drug
  • Prolonged drug exp.
  • Insertional Mutagenesis
  • Clinically observed resistance
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SLIDE 26

Combinatorial Strategies to Overcome Drug Resistance

Transform of BRAF_PLX4720

10000

BRAF inhibitor PLX4720

1000 M 10 100 IC50 uM 0 1 1 0.1 WT All tissues V600 colorectal V600 melanoma

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Combinatorial Drug Screening (Pilot Study)

12 combinations x 107 cell lines

Combination Drug 1 Drug 2 Drug 1 target Drug 2 target 1 Camptothecin Olaparib topoisomerase 1 PARP1/2 2 Cisplatin Bortezomib DNA crosslinker proteasome 3 AZD7762 Olaparib CHK1/2 PARP1/2 / 4 Vemurafenib Afatinib BRAF EGFR/ERBB2 5 GDC0941 Olaparib PI3K PARP1/2 6 Selumetinib Afatinib MEK1/2 EGFR/ERBB2 7 Obatoclax Mesylate AZ628 BCL2‐family pan‐RAF 8 Ob t l

  • M

l t B t ib BCL 2 f il t 8 Obatoclax Mesylate Bortezomib BCL‐2 family proteasome 9 5‐Fluorouracil Afatinib anti‐metabolite EGFR/ERBB2 10 Crizotinib Afatinib MET, ALK EGFR/ERBB2 11 AZ628 Selumetinib pan‐RAF MEK 12 Gemcitibine AZD7762 DNA damage CHK1/2 12 Gemcitibine AZD7762 DNA damage CHK1/2

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Combinatorial Screening Approach

vemurafenib vemurafenib m

Combinatorial Screening Approach

1 .

N

  • r

m a l i z e d i n t e n s i t y ( R ) .

vemurafenib vemurafenib

1 .

C

  • m

b i n a t i

  • n

I n d e x .

agonism fa nib

4 . 6 . 8

a nib

. . 5

anta gy Af

. . 2 . 4

Afa

− 1 . − . 5

synerg S‐ score (3rd lowest CI‐value)

CI value = R

b

– (Rd

1*Rd 2)

f b

  • CI

value = Rcomb (Rdrug1 Rdrug2)

where R is the normalised flu

  • r esence

intensity compared to untreated wells.

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SLIDE 29

BRAF‐mutated colon are sensitive to a EGFR/BRAF inhibitor combination

Sensitivity to BRAF and EGFR inhibitor combination:

core S‐ sc Cell line (n = 108)

sensitivity resistance

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BRAF‐mutated colon are sensitive to a EGFR/BRAF inhibitor combination

colon

Sensitivity to BRAF and EGFR inhibitor combination:

skin

BRAF mut. BRAF wt. Other

core S‐ sc Cell line (n = 108)

sensitivity resistance **Highly significant enrichment for sensitivity in BRAF‐muted colon cancer cell lines.

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Summary Summary

  • Pharmacogenomic profiling in cancer cell lines identifies

clinically relevant interactions between cancer genes and drug response (single or drug combinations).

  • Novel interactions are found – many are poorly understood.
  • The activity of most anti‐cancer drugs is influenced by

cancer genes cancer genes.

  • We are now systematically validating putative biomarkers in

We are now systematically validating putative biomarkers in more complex biological systems.

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www.cancerRxgene.org www.cancerRxgene.org

bli d b f ll li d i i i d i k

  • Largest public database for cell line drug sensitivity and genomic markers
  • f response.
  • Partnered with
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SLIDE 33

Acknowledgements

Sonja J Heidorn Elena J Edelman EMBL-EBI Francesco Iorio Julio Saez-Rodriguez

g

N th l d C Sonja J. Heidorn Irina Pshenichnaya Chris D. Greenman King Wai Lau Howard Lightfoot J S Elena J. Edelman Anahita Dastur Patricia Greninger Xi Luo Li Chen R d J Mil Netherlands Cancer Institute Theo Knijnenburg Lodewyk Wessels Gurdon Institute Jon Travers Jorge Soares Graham R. Bignell Helen Davies Syd Barthorpe Fiona Kogera Randy J. Milano Ah T. Tam Jesse A. Stevenson Stephen R. Lutz Xeni Mitropoulos Institute Curie Didier Surdez Olivier Delattre Jon Travers Steve Jackson Karl Lawrence Anne McLaren- Douglas Tatiana Mironenko Laura Richardson p Helen Thi Jessica L. Boisvert Jose Baselga Jeffrey A. Engelman Sreenath V Sharma Olivier Delattre Dana Farber Cancer Institute Qingsong Liu Tinghu Zhang J W Ch Laura Richardson Jennifer Fraser-Fish Wanjuan Yang Adam Butler

  • P. Andrew Futreal

Michael R Stratton Sreenath V. Sharma Jeffrey Settleman Sridhar Ramaswamy Jeff Engelman Daniel A. Haber C il H B Jae Won Chang Wenjun Zhou Xianming Deng Hwan Geun Choi Wooyoung Hur Michael R. Stratton Ultan McDermott Cyril H. Benes y g Nathanael S. Gray

  • Univ. of Lausanne

Ivan Stamenkovic

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The Mutational Landscape of Cell Lines R bl Cli i l S l Resembles Clinical Samples

cr (f>5%) = 0 73 cr (f>5%) = 0.73 Tumour samples GDSC cell lines ines (n=51) Overlap GDSC Cell l TP53 included Tumour samples (n=566) TP53 excluded

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The Mutational Landscape of Cell Lines Resembles Cli i l S l A M lti l Ti T Clinical Samples Across Multiple Tissue Types

Primary tumour data from >7000 exomes or genomes from patient tumours Primary tumour data from 7000 exomes or genomes from patient tumours