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

Mathew Garnett

NCT ConferenceHeidelberg, Sept 2013  

Precision Cancer MedicinePrecision Cancer Medicine

• Using targeted drugs to exploit specific vulnerabilities and dependencies within cancer cells.

• Genomic alterations can be used as biomarkers toGenomic alterations can be used as biomarkers to identify patients most likely to benefit from treatmenttreatment.

Combined BRAF and MEK inhibitors for the treatment of BRAF mutant 

lmelanoma

Flaherty et al, NEJM 2012

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 therapiesM l l bi k FDA d d Cli i l i di ti ( ) Th ti t tMolecular biomarker FDA-approved drug Clinical indication(s) Therapeutic targetBCR-ABL Imatinib, Dasatinib, Nilotinib CML, AML ABL1 KIT, PDGFR Imatinib Gastrointestinal stromal tumour KIT, PDGFRAEGFR Gefitinib, Erlotinib Non-small cell lung cancer, pancreatic EGFRERBB2/HER2 T t b L ti ib HER b t HER2ERBB2/HER2 Trastuzumab, Lapatinib HER+ breast cancer HER2BRAF Vemurafinib melanoma BRAFEML4-ALK Crizotinib Non-small cell lung cancer ALKER+ Tamoxifen ER+ breast cancer ER

Preclinical Biomarker DiscoveryPreclinical Biomarker Discovery

• To systematically explore pre‐clinically the diversityTo systematically explore pre clinically the diversity of cancer for biomarkers that predict drug sensitivitysensitivity.

• 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.

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

a b c [drug] [drug]

IC50

72 hour drug treatmentFluorescence based viability assay

concentration (uM)

Fluorescence based viability assay

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

Cancer cell linesCancer cell linesSingle drug screens Cancer cell linesPatient‐derived culturesCancer organoidsDrug resistant clones

Cancer cell linesPatient‐derived culturesCancer organoidsDrug resistant clones

Single drug screensCombinatorial screenssiRNA +/‐ drug

a b c Drug resistant clonesDrug resistant clones

[drug] [drug]

IC50

72 hour drug treatmentFluorescence based viability assay

concentration (uM)

Fluorescence based viability assayLink drug response with genomic features

Screening compounds are selected to t t thtarget 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 StressSenescence

Autophagy Senescence 

Apoptosis (Bcl2, IAP) 

Other Cell Death

The GDSC1000 Cell Line CollectionSoft tissueTestisBBoneHodgkin lymphomaBurkitt lymphomaOtherOther

Cell lines are grouped according to the TCGA classification system

Pharmacogenomic Characterisation of h GDSC1000the GDSC1000

Cancer Cell Lines as an Experimental Model

Experimental tractabilityBiological relevance

Systematic Analysis of Drug SensitivitySystematic Analysis of Drug SensitivityIC50 value heatmap

• 551 anti‐cancer drugs

• Mean of 432 cell lines screened per drug (range 7 – 672)

• 238,000 drug‐cell line combinationsg

• Correlated drug response with coding mutation, amplification and deletion in 71 frequently mutated cancer genes.

BRAFMutations Confer Sensitivity to BRAF and MEK inhibitor combo

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

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

Landscape of drug response in relation to cancer genes

drug Target(s)

9e−5

0

1e−45

1e−50

1e−55

PLX4720BRAF

SB590885BRAF

(BRAF)NilotinibABL

(BCR‐ABL)

g

gene

309e−4

0

1e−30

1e−35

1e−40

SCvlaue)

PLX4720BRAF

(BRAF)

g

9e−2

09e−

1e−20

1e−25

1e 30

GDSC

cance (p‐v

9e−1

0

1e−05

1e−10

1e−15

G0 0520% 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+0

0

1e+00

p = 0.0520% fdr = 1.18e 02

sensitivity resistance

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

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

FDA‐approved targeted therapiespp g pMolecular biomarker FDA-approved drug Clinical indication(s) Therapeutic targetBCR-ABL Imatinib, Dasatinib, Nilotinib CML, AML ABL1 KIT, PDGFR Imatinib Gastrointestinal stromal tumour KIT, PDGFRAEGFR 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 HER2BRAF Vemurafinib melanoma BRAFEML4-ALK Crizotinib Non-small cell lung cancer ALKER 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 targetBRAF e.g. PD0325907 melanoma, NSCLC MEKKRAS e.g. PD0325908 NSCLC MEKNRAS e.g. PD0325909 melanoma MEKFGFR2 e.g. PD173074 FGFR

✔✔

✔✔

gPIK3CA e.g. AZD6482 PI3KPIK3CA e.g. AKT inhibitor VIII AKTFLT3 e.g. sunitinib FLT3BRCA1/2 e.g. Olaparib Breast, ovarian PARP

✔✔✔

✔✔

g p ,

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

The Majority of Cancer Genes are Correlated With Drug Response

CDKN2A

200

150

orre

latio

ns

APC100

iti itsign

ifica

nt c

o

50

sensitivityresistance

Num

ber o

f s

0

median

N

1 11 21 31 41 51 61 71

0

Cancer Genes

The Activity of Most Drugs is Correlated with Cancer Genes

25

20

tions

15

cant

cor

rela

10SensitivityResistance

er o

f sig

nific

5

median

Num

be

1 51 101 151 201 251 301 351 401 451 501

0

1 51 101 151 201 251 301 351 401 451 501

Drugs

EWS-FLI1 Mutated Cells are Sensitive to PARP InhibitorsOlaparib(PARP1/2)

AG‐014699(PARP1/2)

C50

(uM

)

50(u

M)

IC ICn = 13 n = 467n = 14 n = 544

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

EWS FLI1EWS‐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 theA 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.

Olaparib Selectively Induces Apoptosis p y p pin Ewing’s Cells

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Ewing’s 

PARP Inhibitors Induce DNA Damage in Ewing’ Cells

Nuclei H2AX8h

5uM AZD2281

Ctrl

5uM AZD2281

15

ES8

spon

ders

5

10

ncre

ase

in re

s

Olaparib

- 2 4 8 24

0

Time (hours)Fo

ld in

Time (hours)

H2AX – Marker of DSBsH2AX – Marker of DSBs

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

1.2emouse mesenchymal cells

0.8

1.0ab

ility

0 4

0.6

elat

ive

via

0.2

0.4R EWS-FLI1FUS-CHOPSKNMC

0.00

0.39

0.78

1.56

3.13

6.25

12.50

0.0

[Olaparib] (uM)[Olaparib] (uM)

PARP Inhibitors Trials in Ewing’s Patients

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

Modeling Drug ResistanceModeling Drug Resistance

1000 cell line drug sensitivity screen1000 cell line drug sensitivity screen

Intrinsic resistance Sensitive

Acquired resistance• drug combinations• RNAi +/‐ drug • Prolonged drug exp.

• Insertional Mutagenesis• Clinically observed resistance

Combinatorial Strategies to Overcome Drug Resistance

Transform of BRAF_PLX4720

10000

BRAF inhibitor PLX4720

1000M

10

100

IC50

uM

0 1

1

0.1WT

All tissuesV600

colorectalV600

melanoma

Combinatorial Drug Screening (Pilot Study)

12 combinations x 107 cell linesCombination Drug�1 Drug�2 Drug�1�target Drug�2�target

1 Camptothecin Olaparib topoisomerase�1 PARP1/22 Cisplatin Bortezomib DNA�crosslinker proteasome3 AZD7762 Olaparib CHK1/2 PARP1/2

/4 Vemurafenib Afatinib BRAF EGFR/ERBB25 GDC0941 Olaparib PI3K PARP1/26 Selumetinib Afatinib MEK1/2 EGFR/ERBB27 Obatoclax�Mesylate AZ628 BCL2‐family pan‐RAF8 Ob t l �M l t B t ib BCL 2�f il t8 Obatoclax�Mesylate Bortezomib BCL‐2�family proteasome9 5‐Fluorouracil Afatinib anti‐metabolite EGFR/ERBB210 Crizotinib Afatinib MET,�ALK EGFR/ERBB211 AZ628 Selumetinib pan‐RAF MEK12 Gemcitibine AZD7762 DNA�damage CHK1/212 Gemcitibine AZD7762 DNA�damage CHK1/2

Combinatorial Screening Approachvemurafenib� vemurafenib� m

�

Combinatorial Screening Approach

1.0

Normalized intensity (R).

vemurafenib� vemurafenib�

1.0

Combination Index.

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CI�value�=�R b�–�(Rd 1*Rd 2)�

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CI�value�=�Rcomb� �(Rdrug1 Rdrug2)�where�R�is�the�normalised�flu

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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

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.

SummarySummary•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 genescancer genes.

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

www.cancerRxgene.orgwww.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 of response.

• Partnered with

Acknowledgements

Sonja J Heidorn Elena J Edelman

EMBL-EBIFrancesco IorioJulio Saez-Rodriguez

g

N th l d CSonja J. HeidornIrina PshenichnayaChris D. GreenmanKing Wai LauHoward LightfootJ S

Elena J. EdelmanAnahita DasturPatricia GreningerXi LuoLi ChenR d J Mil

Netherlands Cancer InstituteTheo KnijnenburgLodewyk WesselsGurdon InstituteJon TraversJorge Soares

Graham R. BignellHelen DaviesSyd BarthorpeFiona Kogera

Randy J. MilanoAh T. TamJesse A. StevensonStephen R. LutzXeni Mitropoulos

Institute CurieDidier SurdezOlivier Delattre

Jon TraversSteve Jackson

Karl LawrenceAnne McLaren-DouglasTatiana MironenkoLaura Richardson

pHelen ThiJessica L. BoisvertJose BaselgaJeffrey A. EngelmanSreenath V Sharma

Olivier Delattre

Dana Farber Cancer InstituteQingsong LiuTinghu ZhangJ W ChLaura Richardson

Jennifer Fraser-FishWanjuan YangAdam Butler P. Andrew FutrealMichael R Stratton

Sreenath V. SharmaJeffrey SettlemanSridhar RamaswamyJeff EngelmanDaniel A. HaberC il H B

Jae Won ChangWenjun ZhouXianming DengHwan Geun ChoiWooyoung HurMichael R. Stratton

Ultan McDermottCyril H. Benes

y gNathanael S. Gray

Univ. of LausanneIvan Stamenkovic

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

cr (f>5%) = 0 73cr (f>5%) = 0.73 Tumour samples

GDSC cell lines

ines (n

=51)

Overlap

GDSC

 Cell l

TP53 included

Tumour samples (n=566)

TP53 excluded

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

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