Discovery of Drug Sensitizing Genotypes in · Combination Drug˜1Drug˜2Drug˜1˜target...
Transcript of Discovery of Drug Sensitizing Genotypes in · Combination Drug˜1Drug˜2Drug˜1˜target...
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
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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.
agon
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04
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CI�value�=�R b�–�(Rd 1*Rd 2)�
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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