Supplementary Figures

Supplementary Figures

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Nilotinib

PHA−665752

Erlotinib

lapatinib

PLX4720

Crizotinib

Sorafenib

AZD0530

Nutlin−3

AZD6244

TAE684

PD−0325901

17−AAG

PD−0332991

paclitaxel

0.0

Supplementary Figure 1: (A) Histogram of ABC estimates for all common drug dose-response curves between GDSC and CCLE. (B) Boxes represent the median and inter quartile range of ABC for drugcell line combinations screened in GDSC and CCLE.

A

B AZD6482:NCI−H1092

100

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GDSC 156 ● GDSC 1066

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AZD6482:BPH−1

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GDSC 156 GDSC 1066

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D GDSC 156 ● GDSC 1066 ●

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1

AZD6482:KURAMOCHI

Concentration (uM) 100

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GDSC 156 GDSC 1066

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0.1

E

0.1

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−1 −2

1

Concentration (uM)

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● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ●● ●●● ● ● ● ●●● ● ●● ● ● ●● ● ● ●●●● ● ● ● ●●●●●●● ● ● ●● ● ● ●● ●●●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ● ●● ● ● ● ●●● ● ● ●● ● ●● ●●● ● ● ●●● ● ●● ● ●●●●●● ●● ● ● ● ● ● ● ● ● ●● ●● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ●● ● ● ●● ● ● ●● ●● ●● ● ● ●●● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ●● ●● ● ● ● ● ● ● ●●● ●● ● ● ● ●● ● ● ●● ●● ● ● ● ●● ●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●●● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ●●●●● ● ● ●● ● ●● ●●● ●● ● ●● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ●● ● ● ●● ●● ● ● ● ● ● ● ● ●● ●● ● ●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●● ●● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ●●● ●●● ● ● ● ● ●● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ●●● ● ● ●●● ● ● ●●●●● ● ● ● ●● ● ●● ●● ●● ●● ● ●●● ● ● ●● ● ●●●●● ●●●●● ● ● ● ●● ● ● ● ●● ●● ●● ● ● ● ● ●● ●

0

1

2

Concentration (uM) PCC=0.39, p=0 #cell lines=617

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−Log10 IC50(MGH)

ABC= 0.46

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ABC= 0.47

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−3

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2

−Log10 IC50(WTSI)

Supplementary Figure 2: Examples of (A,B) consistent and (C,D) inconsistent replicated experiments screening AZD6482 in GDSC. The grey area represents the common concentration range between studies. (A) NCI-H1092; (B) BPH-1; (C) A498; and (D) KURAMOCHI cell line treated with AZD6482. (E) Consistency of sensitivity profiles across replicated experiments of AZD6482 performed in different sites (MGH and WTSI).

0.5

1.0

1.5 0.5

1.0

1.5

2.0

n

GDSC Camptothecin GDSC Vinblastine CCLE paclitaxel GDSC Gemcitabine GDSC AUY922 GDSC paclitaxel GDSC thapsigargin GDSC Rapamycin GDSC temsirolimus GDSC Obatoclax Mesylate GDSC Doxorubicin GDSC Mitomycin−C GDSC LAQ824 CCLE Panobinostat GDSC Elesclomol GDSC NVP−BEZ235 GDSC JW−7−52−1 GDSC CGP−60474 GDSC BI−2536 GDSC GW843682X GDSC shikonin GDSC 17−AAG CCLE 17−AAG GDSC JNK−9L GDSC MG−132 GDSC A−443654 GDSC CEP−701 GDSC Cytarabine GDSC AZD7762 GDSC TW 37 CCLE Irinotecan CCLE Topotecan GDSC Nutlin−3 GDSC AZD6244 GDSC JNJ−26854165 GDSC Cisplatin GDSC AG−014699 GDSC GW 441756 GDSC CCT018159 GDSC Pyrimethamine CCLE LBW242 CCLE Erlotinib GDSC lapatinib CCLE lapatinib GDSC Crizotinib CCLE Nutlin−3 GDSC Imatinib CCLE Nilotinib CCLE PHA−665752 GDSC PHA−665752 CCLE L−685458 GDSC Salubrinal GDSC BMS−708163 GDSC Erlotinib GDSC Roscovitine GDSC BIRB 0796 GDSC ATRA GDSC PF−4708671 GDSC CCT007093 GDSC EHT 1864 GDSC NU−7441 CCLE PLX4720 GDSC KU−55933 GDSC PLX4720 GDSC AMG−706 GDSC RO−3306 GDSC JNK Inhibitor VIII GDSC SL 0101−1 GDSC SB590885 GDSC AZD−2281 GDSC VX−702 GDSC GDC−0449 GDSC lenalidomide GDSC ABT−888 GDSC SB 216763 GDSC Cyclopamine GDSC FTI−277 GDSC KIN001−135 GDSC NSC−87877 GDSC OSI−906 GDSC Bexarotene GDSC LFM−A13 GDSC GNF−2 GDSC WZ−1−84 GDSC Pazopanib GDSC AZD0530 GDSC TGX221 GDSC ABT−263 CCLE AZD0530 CCLE Vandetanib GDSC Nilotinib CCLE Crizotinib GDSC PD173074 GDSC Bicalutamide CCLE Sorafenib GDSC Axitinib CCLE PD−0332991 GDSC Bosutinib GDSC 681640 GDSC Gefitinib CCLE TKI258 GDSC BX−795 GDSC ZM−447439 CCLE TAE684 GDSC AP−24534 CCLE AEW541 GDSC OSU−03012 GDSC GSK269962A GDSC QS11 GDSC pac−1 GDSC CMK GDSC CGP−082996 GDSC BMS−509744 GDSC Sunitinib GDSC PF−562271 GDSC Sorafenib GDSC CHIR−99021 GDSC Parthenolide GDSC GSK−1904529A GDSC AS601245 GDSC XMD8−85 GDSC AKT inhibitor VIII GDSC AZD6482 A GDSC AZD6482 B GDSC GSK−650394 GDSC BAY 61−3606 CCLE PD−0325901 GDSC RDEA119 GDSC CI−1040 CCLE AZD6244 GDSC Dasatinib GDSC A−770041 GDSC WH−4−023 GDSC BMS−754807 GDSC TAE684 GDSC BMS−536924 GDSC AZ628 GDSC PD−0325901 GDSC Vorinostat GDSC MS−275 GDSC Z−LLNle−CHO GDSC BIBW2992 GDSC S−Trityl−L−cysteine GDSC Midostaurin GDSC MK−2206 GDSC PD−0332991 GDSC Methotrexate CCLE RAF265 GDSC GDC−0941 GDSC AZD8055 GDSC etoposide GDSC VX−680 GDSC Tipifarnib

0.0

0.2

0.4

0.6

0.8

n

D

E FTI−277 pac−1 AP−24534 AKT inhibitor VIII AZD6482 A JNK−9L shikonin AUY922 Midostaurin PF−562271 BMS−754807 OSI−906 BMS−536924 Sunitinib Mitomycin−C LAQ824 thapsigargin Obatoclax Mesylate Bleomycin Doxorubicin embelin Vinorelbine etoposide Gemcitabine OSU−03012 QS11 BAY 61−3606 GSK−650394 Epothilone B Tipifarnib Bortezomib MG−132 CMK Z−LLNle−CHO VX−680 BI−2536 GW843682X paclitaxel S−Trityl−L−cysteine A−443654 JW−7−52−1 CGP−60474 Roscovitine GSK269962A Sorafenib Crizotinib Rapamycin TAE684 Parthenolide MS−275 Pyrimethamine Erlotinib lapatinib A−770041 AZD0530 WZ−1−84 Dasatinib WH−4−023 AZ628 Bexarotene TGX221 Cyclopamine Salubrinal DMOG BMS−509744 CGP−082996 XMD8−85 AS601245 CHIR−99021 Pazopanib GSK−1904529A LFM−A13 ABT−263 GNF−2 Imatinib PHA−665752 KIN001−135 BMS−708163 AZD6244 BIBW2992 AZD6482 B GDC−0941 MK−2206 CCT018159 temsirolimus AZD8055 NVP−BEZ235 Cytarabine PD−0332991 Docetaxel Elesclomol Vorinostat TW 37 Camptothecin CEP−701 AZD7762 Vinblastine BX−795 ZM−447439 Axitinib Bosutinib Nilotinib 17−AAG CI−1040 PD−0325901 RDEA119 AMG−706 JNK Inhibitor VIII GW 441756 BIRB 0796 RO−3306 Bryostatin 1 NSC−87877 Bicalutamide FH535 IPA−3 PLX4720 SL 0101−1 PD173074 ATRA Gefitinib CCT007093 EHT 1864 PF−4708671 AICAR Methotrexate AG−014699 Cisplatin JNJ−26854165 681640 KU−55933 NU−7441 SB 216763 Nutlin−3 SB590885 ABT−888 AZD−2281 lenalidomide GDC−0449 VX−702

0.0

0.4

0.8

1.2

n

0.0

BMS−708163 CCT007093 AZD6244 AZ628 CI−1040 PD−0325901 RDEA119 Bicalutamide NSC−87877 temsirolimus AZD8055 NVP−BEZ235 AZD6482 B GDC−0941 MK−2206 RO−3306 AG−014699 JNJ−26854165 Camptothecin Cisplatin BX−795 ZM−447439 Cytarabine PD−0332991 681640 Bosutinib BIRB 0796 AMG−706 JNK Inhibitor VIII 17−AAG GW 441756 CCT018159 Docetaxel Elesclomol KIN001−135 PHA−665752 Vinblastine AZD7762 CEP−701 TW 37 Vorinostat AICAR Methotrexate SB 216763 BIBW2992 Gefitinib ABT−263 Axitinib Nilotinib PLX4720 SL 0101−1 Nutlin−3 SB590885 ATRA lenalidomide VX−702 ABT−888 AZD−2281 PD173074 GDC−0449 KU−55933 NU−7441 EHT 1864 PF−4708671 Z−LLNle−CHO Bortezomib MG−132 Crizotinib GSK269962A Parthenolide Rapamycin Roscovitine MS−275 Pyrimethamine Salubrinal CGP−60474 BI−2536 GW843682X paclitaxel S−Trityl−L−cysteine A−443654 JW−7−52−1 Sunitinib VX−680 BMS−536924 TAE684 CMK Cyclopamine BMS−754807 OSI−906 GSK−1904529A LFM−A13 Bleomycin Gemcitabine Doxorubicin etoposide AUY922 Mitomycin−C BMS−509744 CGP−082996 XMD8−85 DMOG LAQ824 AKT inhibitor VIII AZD6482 A Epothilone B Vinorelbine Midostaurin PF−562271 GSK−650394 Tipifarnib OSU−03012 QS11 BAY 61−3606 Obatoclax Mesylate thapsigargin embelin FH535 IPA−3 pac−1 JNK−9L shikonin WZ−1−84 Erlotinib lapatinib Bryostatin 1 FTI−277 Dasatinib WH−4−023 A−770041 AZD0530 AP−24534 GNF−2 Imatinib Sorafenib TGX221 AS601245 CHIR−99021 Bexarotene Pazopanib

C

n

GDSC Pazopanib GDSC Sorafenib GDSC GSK−650394 CCLE Crizotinib GDSC AS601245 GDSC PF−562271 GDSC Vinorelbine GDSC LFM−A13 GDSC FTI−277 GDSC Bryostatin 1 GDSC JNK−9L GDSC QS11 GDSC FH535 GDSC thapsigargin GDSC Bleomycin GDSC Mitomycin−C GDSC Gemcitabine GDSC Doxorubicin GDSC etoposide GDSC AKT inhibitor VIII GDSC AZD6482 A GDSC CGP−082996 GDSC XMD8−85 GDSC DMOG GDSC LAQ824 GDSC Epothilone B GDSC Tipifarnib GDSC embelin GDSC BAY 61−3606 GDSC Obatoclax Mesylate GDSC AUY922 GDSC pac−1 GDSC Midostaurin GDSC OSU−03012 GDSC IPA−3 GDSC shikonin GDSC Cyclopamine CCLE PHA−665752 GDSC Crizotinib GDSC GSK269962A GDSC Parthenolide GDSC CMK GDSC VX−680 GDSC MS−275 GDSC Sunitinib GDSC CGP−60474 GDSC Roscovitine GDSC Rapamycin GDSC Salubrinal GDSC Pyrimethamine GDSC BI−2536 GDSC GW843682X GDSC paclitaxel GDSC S−Trityl−L−cysteine GDSC A−443654 GDSC JW−7−52−1 GDSC GSK−1904529A CCLE AEW541 GDSC BMS−754807 GDSC OSI−906 GDSC BMS−509744 GDSC BMS−536924 GDSC TAE684 GDSC GW 441756 GDSC BMS−708163 GDSC CCT007093 GDSC Bicalutamide GDSC lapatinib GDSC WZ−1−84 GDSC GNF−2 GDSC Imatinib CCLE AZD0530 GDSC AP−24534 GDSC Nilotinib CCLE Nilotinib GDSC Dasatinib GDSC WH−4−023 GDSC CHIR−99021 GDSC Erlotinib GDSC A−770041 GDSC AZD0530 GDSC Z−LLNle−CHO GDSC Bexarotene CCLE Sorafenib GDSC PD−0325901 GDSC RDEA119 CCLE AZD6244 CCLE PD−0325901 CCLE 17−AAG GDSC CEP−701 CCLE paclitaxel GDSC AZD6482 B GDSC GDC−0941 GDSC MK−2206 GDSC JNJ−26854165 GDSC KU−55933 GDSC NU−7441 GDSC AICAR GDSC Methotrexate CCLE RAF265 GDSC TW 37 GDSC BX−795 GDSC Vorinostat GDSC AZD8055 GDSC NVP−BEZ235 GDSC GDC−0449 GDSC VX−702 GDSC Axitinib GDSC Bosutinib GDSC 681640 GDSC ZM−447439 GDSC Camptothecin GDSC Cisplatin GDSC Docetaxel GDSC Elesclomol GDSC temsirolimus CCLE TKI258 GDSC Cytarabine GDSC PD−0332991 GDSC BIRB 0796 GDSC RO−3306 CCLE Nutlin−3 GDSC AMG−706 GDSC JNK Inhibitor VIII GDSC NSC−87877 GDSC SL 0101−1 GDSC PLX4720 GDSC SB590885 GDSC AG−014699 CCLE PLX4720 GDSC AZD−2281 GDSC Nutlin−3 GDSC BIBW2992 GDSC KIN001−135 GDSC PHA−665752 CCLE lapatinib GDSC ABT−263 CCLE Erlotinib GDSC AZ628 GDSC CI−1040 CCLE Vandetanib GDSC TGX221 GDSC Bortezomib GDSC MG−132 GDSC 17−AAG CCLE PD−0332991 GDSC CCT018159 CCLE TAE684 GDSC SB 216763 CCLE L−685458 GDSC Vinblastine CCLE Topotecan GDSC AZD7762 CCLE Irinotecan GDSC EHT 1864 GDSC PF−4708671 CCLE Panobinostat GDSC ATRA GDSC PD173074 GDSC ABT−888 GDSC Gefitinib GDSC AZD6244 GDSC lenalidomide CCLE LBW242

0.0

0.4

0.8

1.2

n

0.0

AUY922 LAQ824 JW−7−52−1 −60474 BI−2536 GW843682X Gemcitabine paclitaxel thapsigargin Camptothecin Vinblastine Elesclomol NVP−BEZ235 AZD6482 A AZD6482 B ATRA AG−014699 Gefitinib NU−7441 PF−4708671 CCT007093 EHT 1864 BIRB 0796 TGX221 BMS−708163 KIN001−135 NSC−87877 −082996 GNF−2 WZ−1−84 Erlotinib Roscovitine CHIR−99021 BMS−509744 Salubrinal OSI−906 Pazopanib Bexarotene LFM−A13 lapatinib Cyclopamine FTI−277 AZD6244 Nutlin−3 PLX4720 KU−55933 PD173074 AMG−706 RO−3306 JNK Inhibitor VIII SL 0101−1 SB590885 SB 216763 lenalidomide ABT−888 Bicalutamide GDC−0449 Nilotinib AZD−2281 VX−702 Pyrimethamine JNJ−26854165 Cisplatin GW 441756 CCT018159 Dasatinib A−770041 WH−4−023 OSU−03012 GSK269962A AZD0530 Sorafenib Crizotinib Imatinib PHA−665752 Parthenolide GSK−1904529A Sunitinib PF−562271 CMK XMD8−85 AKT inhibitor VIII AS601245 QS11 pac−1 GSK−650394 BAY 61−3606 Vorinostat ABT−263 Bosutinib 681640 ZM−447439 Axitinib BX−795 etoposide VX−680 Tipifarnib RDEA119 CI−1040 PD−0332991 MK−2206 GDC−0941 BMS−754807 TAE684 BMS−536924 AZ628 PD−0325901 Rapamycin temsirolimus Obatoclax Mesylate Doxorubicin Mitomycin−C shikonin 17−AAG MS−275 Z−LLNle−CHO BIBW2992 Midostaurin S−Trityl−L−cysteine AP−24534 CEP−701 Cytarabine AZD7762 Methotrexate AZD8055 TW 37 JNK−9L MG−132 A−443654

B

n

GDSC Pyrimethamine GDSC MS−275 GDSC Rapamycin CCLE PHA−665752 GDSC Cyclopamine CCLE Sorafenib GDSC Crizotinib CCLE Crizotinib GDSC TAE684 CCLE TAE684 GDSC lenalidomide CCLE LBW242 CCLE AEW541 GDSC OSI−906 GDSC BMS−754807 GDSC BMS−536924 CCLE RAF265 GDSC ABT−888 GDSC VX−702 GDSC GDC−0449 GDSC ABT−263 GDSC AICAR CCLE TKI258 GDSC Vorinostat CCLE Panobinostat CCLE Irinotecan CCLE Topotecan GDSC Methotrexate CCLE PD−0332991 GDSC Gefitinib GDSC PD173074 CCLE Vandetanib GDSC AZD8055 GDSC temsirolimus GDSC NVP−BEZ235 GDSC AZD7762 GDSC CEP−701 GDSC Camptothecin GDSC Vinblastine CCLE paclitaxel GDSC BX−795 GDSC ZM−447439 GDSC Cytarabine GDSC PD−0332991 GDSC SB 216763 GDSC AG−014699 GDSC AZD−2281 CCLE Nutlin−3 GDSC TW 37 GDSC Nutlin−3 GDSC JNJ−26854165 GDSC Bosutinib GDSC Axitinib GDSC Nilotinib CCLE Nilotinib CCLE AZD0530 GDSC Imatinib GDSC GNF−2 CCLE PLX4720 GDSC PLX4720 GDSC SB590885 GDSC AZ628 CCLE AZD6244 CCLE PD−0325901 GDSC CI−1040 GDSC RDEA119 GDSC PD−0325901 GDSC AZD6244 CCLE 17−AAG GDSC PHA−665752 GDSC AZD6482 GDSC MK−2206 GDSC GDC−0941 GDSC pac−1 GDSC IPA−3 GDSC shikonin GDSC AKT inhibitor VIII GDSC XMD8−85 GDSC CGP−082996 GDSC Salubrinal GDSC DMOG GDSC GSK269962A GDSC CMK GDSC Parthenolide CCLE L−685458 GDSC Roscovitine GDSC TGX221 GDSC NSC−87877 GDSC KIN001−135 GDSC ATRA GDSC KU−55933 GDSC NU−7441 GDSC SL 0101−1 GDSC 681640 GDSC AMG−706 GDSC BIRB 0796 GDSC CCT007093 GDSC EHT 1864 GDSC PF−4708671 GDSC Sorafenib GDSC AP−24534 GDSC Pazopanib GDSC FTI−277 GDSC Z−LLNle−CHO GDSC Bortezomib GDSC MG−132 GDSC WZ−1−84 GDSC Dasatinib GDSC WH−4−023 GDSC AZD0530 GDSC A−770041 GDSC S−Trityl−L−cysteine GDSC paclitaxel GDSC BI−2536 GDSC GW843682X GDSC CGP−60474 GDSC JW−7−52−1 GDSC A−443654 GDSC GSK−650394 GDSC VX−680 GDSC Sunitinib GDSC Gemcitabine GDSC Doxorubicin GDSC etoposide GDSC Mitomycin−C GDSC Bleomycin GDSC QS11 GDSC OSU−03012 GDSC thapsigargin GDSC BAY 61−3606 GDSC LAQ824 GDSC AS601245 GDSC Vinorelbine GDSC JNK−9L GDSC embelin GDSC FH535 GDSC AUY922 GDSC Obatoclax Mesylate GDSC Epothilone B GDSC Tipifarnib GDSC Midostaurin GDSC PF−562271 GDSC Bexarotene GDSC BMS−509744 GDSC Bicalutamide GDSC LFM−A13 GDSC Bryostatin 1 GDSC BIBW2992 GDSC lapatinib GDSC Erlotinib CCLE Erlotinib CCLE lapatinib GDSC CHIR−99021 GDSC GSK−1904529A GDSC Docetaxel GDSC 17−AAG GDSC JNK Inhibitor VIII GDSC RO−3306 GDSC Cisplatin GDSC Elesclomol GDSC BMS−708163 GDSC GW 441756 GDSC CCT018159

0.0

0.2

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0.6

0.8

A

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E

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Supplementary Figure 3: Dendrogram of the clustering of all drugs in GDSC based on their (A) distance based on median ABC values (B) IC50 -based distance (C) AUC-based distance. Dendogram of the clustering of all drugs in CCLE and GDSC based on their (D) distance based on mean ABC values (E) IC50 -based distance (F) AUC-based distance. Distance based on IC50 and AUC used 1 minus Pearson correlation coefficient. Overlapping drugs are shown with the same colour.

0.30

B

0.30

A

0.25 0.20 0.15

●

0.05

0.10

AUC published (CCLE)

0.20 0.15 0.10

0.00

●

0.00

0.05

AUC published (GDSC)

0.25

●

Cytotoxic

Targeted

Cytotoxic

Targeted

Supplementary Figure 4: Comparison of median absolute deviation (MAD) of published AUC values between cytotoxic and targeted drugs using all cell lines in (A) GDSC and (B) CCLE.

●

TA E

68 4

●

17 −

AA G

62 44 D AZ

P Er l Nilo HA C loPap Ntin −6 riz tinL at utib 6 5 AoZti iXb 4ini lin 72b −3 752 Dnib ra 05 0 fe 30 ni b

0.10

●

pa

cl ita

xe l

PD −0

32

59

01

0.25 0.20 0.15

●

●

●

−0 3

32

99

1

●

●

PD

0.05

MAD of AUC published (CCLE)

●

●

●

●

0.00

●

So

● ●

0.00

0.05

0.10

0.15

0.20

0.25

MAD of AUC published (GDSC)

Supplementary Figure 5: Comparison of median absolute deviation (MAD) of published AUC values between drugs using common cell lines in (A) GDSC and (B) CCLE.

17−AAG

0.8

0.8

0.8

0.6 0.4

0.6 0.4

0.4

0.2

0.2

0.0

0.0

0.0

0.4

0.6

0.8

1.0

0.0

0.2

0.4

0.6

0.8

1.0

0.6 0.4 0.2 0.0

0.0

0.2

AUC GDSC

TAE684

0.4

0.6

0.8

1.0

0.0

AZD0530

PD−0332991

0.8

0.8

0.8

0.2

0.4 0.2

0.0 0.2

0.4

0.6

0.8

1.0

0.6 0.4 0.2

0.0 0.0

AUC CCLE

0.8

AUC CCLE

1.0

0.4

0.2

AUC GDSC

0.4

0.6

0.8

1.0

0.2

0.4

0.6

0.8

1.0

0.0

Nutlin−3

lapatinib

0.8

0.8

0.6 0.4

0.2

0.2

0.2

0.0

0.0

0.0

0.2

0.4

0.6

0.8

1.0

0.0

0.2

AUC GDSC

0.4

0.6

0.8

1.0

Erlotinib

0.8

0.6 0.4 0.2

0.0

0.0 0.2

0.4

0.6

AUC GDSC

0.8

1.0

AUC CCLE

0.8 AUC CCLE

0.8

0.2

1.0

0.4

0.6

0.8

1.0

0.0

0.2

0.4

0.6

AUC GDSC

Sorafenib 1.0

0.4

0.4

AUC GDSC

1.0

0.6

0.8

0.6

0.0 0.2

AUC GDSC

PHA−665752

0.6

0.2

0.0

1.0

0.0

AUC CCLE

0.8 AUC CCLE

0.8 AUC CCLE

1.0

0.4

0.4

Nilotinib

1.0

0.0

0.2

AUC GDSC

1.0

0.4

1.0

0.4

AUC GDSC

0.6

0.8

0.6

1.0

0.6

1.0

0.0 0.0

AUC GDSC

PLX4720

0.8

0.2

0.0 0.0

0.6

Crizotinib

1.0

0.6

0.4

AUC GDSC

1.0

0.6

0.2

AUC GDSC

1.0

AUC CCLE

AUC CCLE

0.6

0.2

0.2

AUC CCLE

0.8

AUC CCLE

1.0

AUC GDSC

AUC CCLE

AZD6244

1.0

0.0

AUC CCLE

PD−0325901

1.0

AUC CCLE

AUC CCLE

paclitaxel 1.0

● Both sensitive ● ● Both resistant ● ● GDSC sensitive / CCLE resistant ● ● GDSC resistant / CCLE sensitive

0.6 0.4 0.2 0.0

0.0

0.2

0.4

0.6

AUC GDSC

0.8

1.0

0.0

0.2

0.4

0.6

0.8

1.0

AUC GDSC

Supplementary Figure 6: Comparison of AUC values between GDSC and CCLE, as recomputed within PharmacoGx. Cell lines with AUC > 0.2 (AUC > 0.4 for paclitaxel) were considered as sensitive. In case of perfect consistency, all points would lie on the grey diagonal. The drugs are ranked based on their category: broad effect (AZD6244, PD-0325901, 17-AAG and paclitaxel), narrow effect (nilotinib, lapatinib, nutlin-3, PLX4720, crizotinib, PD-0332991, AZD0530, and TAE684) and no/little effect (sorafenib, erlotinib and PHA–665752).

17−AAG

0.8

0.8

0.8

0.6 0.4

0.6 0.4

0.4

0.2

0.2

0.0

0.0

0.0

0.4

0.6

0.8

1.0

0.0

0.2

0.4

0.6

0.8

1.0

0.6 0.4 0.2 0.0

0.0

0.2

AUC GDSC

TAE684

0.4

0.6

0.8

1.0

0.0

AZD0530

PD−0332991

0.8

0.8

0.8

0.2

0.4 0.2

0.0 0.2

0.4

0.6

0.8

1.0

0.6 0.4 0.2

0.0 0.0

AUC CCLE

0.8

AUC CCLE

1.0

0.4

0.2

AUC GDSC

0.4

0.6

0.8

1.0

0.2

0.4

0.6

0.8

1.0

0.0

Nutlin−3

lapatinib

0.8

0.8

0.6 0.4

0.2

0.2

0.2

0.0

0.0

0.0

0.2

0.4

0.6

0.8

1.0

0.0

0.2

AUC GDSC

0.4

0.6

0.8

1.0

Erlotinib

0.8

0.6 0.4 0.2

0.0

0.0 0.2

0.4

0.6

AUC GDSC

0.8

1.0

AUC CCLE

0.8 AUC CCLE

0.8

0.2

1.0

0.4

0.6

0.8

1.0

0.0

0.2

0.4

0.6

AUC GDSC

Sorafenib 1.0

0.4

0.4

AUC GDSC

1.0

0.6

0.8

0.6

0.0 0.2

AUC GDSC

PHA−665752

0.6

0.2

0.0

1.0

0.0

AUC CCLE

0.8 AUC CCLE

0.8 AUC CCLE

1.0

0.4

0.4

Nilotinib

1.0

0.0

0.2

AUC GDSC

1.0

0.4

1.0

0.4

AUC GDSC

0.6

0.8

0.6

1.0

0.6

1.0

0.0 0.0

AUC GDSC

PLX4720

0.8

0.2

0.0 0.0

0.6

Crizotinib

1.0

0.6

0.4

AUC GDSC

1.0

0.6

0.2

AUC GDSC

1.0

AUC CCLE

AUC CCLE

0.6

0.2

0.2

AUC CCLE

0.8

AUC CCLE

1.0

AUC GDSC

AUC CCLE

AZD6244

1.0

0.0

AUC CCLE

PD−0325901

1.0

AUC CCLE

AUC CCLE

paclitaxel 1.0

● Both sensitive ● ● Both resistant ● ● GDSC sensitive / CCLE resistant ● ● GDSC resistant / CCLE sensitive

0.6 0.4 0.2 0.0

0.0

0.2

0.4

0.6

AUC GDSC

0.8

1.0

0.0

0.2

0.4

0.6

0.8

1.0

AUC GDSC

Supplementary Figure 7: Comparison of AUC* values between GDSC and CCLE, as recomputed within PharmacoGx using the concentration range common to GDSC and CCLE. Cell lines with AUC > 0.2 (AUC > 0.4 for paclitaxel) were considered as sensitive. In case of perfect consistency, all points would lie on the grey diagonal. The drugs are ranked based on their category: broad effect (AZD6244, PD-0325901, 17-AAG and paclitaxel), narrow effect (nilotinib, lapatinib, nutlin-3, PLX4720, crizotinib, PD-0332991, AZD0530, and TAE684) and no/little effect (sorafenib, erlotinib and PHA–665752).

paclitaxel

17−AAG

4 3 2

4 3 2

3

4

5

6

3

6

3 2

2

3

4

5

6

2

3

4

5

− log10(IC50) GDSC

− log10(IC50) GDSC

TAE684

AZD0530

PD−0332991

Crizotinib

4 3 2

3

4

5

6

3

PLX4720

4

5

3 2

3

4

5

3 2 4

5

5 4 3

6

3

4

5

− log10(IC50) GDSC

− log10(IC50) GDSC

PHA−665752

Erlotinib

Sorafenib

4 3 2

5 4 3 2

3

4

5

− log10(IC50) GDSC

6

6

5 4 3

6

2

3

4

5

6

− log10(IC50) GDSC

6 − log10(IC50 CCLE)

− log10(IC50 CCLE)

5

5

2 2

6

4

Nilotinib

2 3

3

− log10(IC50) GDSC

6

− log10(IC50) GDSC

6

2

lapatinib

4

2

3

6

6

5

6

4

− log10(IC50) GDSC

− log10(IC50 CCLE)

− log10(IC50 CCLE)

4

5

2 2

Nutlin−3

5

5

3

6

6

4

4

− log10(IC50) GDSC

6

3

5

2 2

6

6 − log10(IC50 CCLE)

3

− log10(IC50 CCLE)

− log10(IC50 CCLE)

4

6

5

− log10(IC50) GDSC

− log10(IC50 CCLE)

5

4

− log10(IC50) GDSC

2

− log10(IC50 CCLE)

4

6

2

3

5

− log10(IC50) GDSC

5

2

4

2 2

6

2

5

− log10(IC50 CCLE)

2

− log10(IC50 CCLE)

5

6 − log10(IC50 CCLE)

5

AZD6244

6 − log10(IC50 CCLE)

6 − log10(IC50 CCLE)

− log10(IC50 CCLE)

6

PD−0325901

5

● Both sensitive ● ● Both resistant ● ● GDSC sensitive / CCLE resistant ● ● GDSC resistant / CCLE sensitive

4 3 2

2

3

4

5

− log10(IC50) GDSC

6

2

3

4

5

6

− log10(IC50) GDSC

Supplementary Figure 8: Consistency of IC50 values between GDSC and CCLE, as published. Cell lines with IC50 < 1µM (IC50 < 10µM for paclitaxel) were considered as sensitive. In case of perfect consistency, all points would lie on the grey diagonal. The drugs are ranked based on their category: broad effect (AZD6244, PD-0325901, 17-AAG and paclitaxel), narrow effect (nilotinib, lapatinib, nutlin3, PLX4720, crizotinib, PD-0332991, AZD0530, and TAE684) and no/little effect (sorafenib, erlotinib and PHA–665752).

paclitaxel

17−AAG

4 3 2

4 3 2

3

4

5

6

3

6

3 2

2

3

4

5

6

2

3

4

5

− log10(IC50) GDSC

− log10(IC50) GDSC

TAE684

AZD0530

PD−0332991

Crizotinib

4 3 2

3

4

5

6

3

PLX4720

4

5

3 2

3

4

5

3 2 4

5

5 4 3

6

3

4

5

− log10(IC50) GDSC

− log10(IC50) GDSC

PHA−665752

Erlotinib

Sorafenib

4 3 2

5 4 3 2

3

4

5

− log10(IC50) GDSC

6

6

5 4 3

6

2

3

4

5

6

− log10(IC50) GDSC

6 − log10(IC50 CCLE)

− log10(IC50 CCLE)

5

5

2 2

6

4

Nilotinib

2 3

3

− log10(IC50) GDSC

6

− log10(IC50) GDSC

6

2

lapatinib

4

2

3

6

6

5

6

4

− log10(IC50) GDSC

− log10(IC50 CCLE)

− log10(IC50 CCLE)

4

5

2 2

Nutlin−3

5

5

3

6

6

4

4

− log10(IC50) GDSC

6

3

5

2 2

6

6 − log10(IC50 CCLE)

3

− log10(IC50 CCLE)

− log10(IC50 CCLE)

4

6

5

− log10(IC50) GDSC

− log10(IC50 CCLE)

5

4

− log10(IC50) GDSC

2

− log10(IC50 CCLE)

4

6

2

3

5

− log10(IC50) GDSC

5

2

4

2 2

6

2

5

− log10(IC50 CCLE)

2

− log10(IC50 CCLE)

5

6 − log10(IC50 CCLE)

5

AZD6244

6 − log10(IC50 CCLE)

6 − log10(IC50 CCLE)

− log10(IC50 CCLE)

6

PD−0325901

5

● Both sensitive ● ● Both resistant ● ● GDSC sensitive / CCLE resistant ● ● GDSC resistant / CCLE sensitive

4 3 2

2

3

4

5

− log10(IC50) GDSC

6

2

3

4

5

6

− log10(IC50) GDSC

Supplementary Figure 9: Consistency of IC50 values between GDSC and CCLE, as recomputed within PharmacoGx. Cell lines with Cell lines with IC50 < 1µM (IC50 < 10µM for paclitaxel) were considered as sensitive. In case of perfect consistency, all points would lie on the grey diagonal. The drugs are ranked based on their category: broad effect (AZD6244, PD-0325901, 17-AAG and paclitaxel), narrow effect (nilotinib, lapatinib, nutlin-3, PLX4720, crizotinib, PD-0332991, AZD0530, and TAE684) and no/little effect (sorafenib, erlotinib and PHA–665752).

** * * ** *

* *

** * * ** ** 0.2

** 0.8

1.0

IC50 published IC50 recomputed

* * *

* *

*

* *

PA C 0.0 LI TA X 1 E PD 7− L −0 AA 32 G 5 A 90 ZD 1 62 TA 44 E A 68 PD ZD 4 −0 053 0 3 C R 329 IZ O 91 TI PL NIB X N 47 U T 20 LA LIN PA −3 N TIN IL PH OT IB A IN −6 IB ER 65 L 75 SO OT 2 R INI A FE B N IB

0.2

** ** 0.2

* * *

* 0.6

DXY.SENS

PA C 0.0 LI TA X 1 E PD 7− L A −0 A 32 G 5 A 90 ZD 1 62 TA 44 E A 68 PD ZD 4 −0 053 0 3 C R 329 IZ O 91 TI N PL IB X N 47 U T 20 LA LIN PA −3 N TIN IL PH OT IB A IN −6 IB ER 65 L 75 SO OT 2 R INI A FE B N IB

0.2

*

0.4

1.0

PA C 0.0 LI TA X 1 E PD 7− L −0 AA 32 G 5 A 90 ZD 1 62 TA 44 E A 68 PD ZD 4 −0 053 C 332 0 R IZ 99 O 1 T PL INIB X N 47 U T 20 LA LIN PA −3 N TIN IL PH OT IB A IN −6 IB ER 65 L 75 SO OT 2 R INI A FE B N IB 0.6

PCC.FULL 0.4

0.6

SCC.FULL 0.4

0.4

0.6

DXY.FULL

* * * *

Cramer's V for binary data

* TA X 1 E PD 7− L −0 AA 32 G 59 A ZD 01 62 TA 44 E A 68 Z PD D 4 −0 053 C 332 0 R IZ 99 O 1 T PL INIB X N 47 U TL 20 LA IN PA −3 N TIN IL PH OT IB A IN −6 IB ER 65 L 75 SO OT 2 R INI A FE B N IB

0.0

0.8

Pearson corr for sensitive data

*

LI

0.6

SCC.SENS 0.4

1.0

** * *

PA C

*

1.0

*

0.8

0.2

IC50 published IC50 recomputed

* *

0.6

Matthew corr for binary data

*

INFORM

IC50 published IC50 recomputed

** * *

0.4

0.0

0.8

*

TA X 1 E PD 7− L −0 AA 32 G 5 A 90 ZD 1 62 TA 44 E A 68 PD ZD 4 −0 053 0 3 C R 329 IZ O 91 TI N PL IB X N 47 U T 20 LA LIN PA −3 N TIN IL PH OT IB A IN −6 IB ER 65 L 75 SO OT 2 R INI A FE B N IB

LI

*

PA C

0.6

PCC.SENS 0.4

*

1.0

0.2

** * * *

0.2

0.8

0.0

* * *

0.6

TA

X 1 E PD 7− L −0 AA 32 G 5 A 90 ZD 1 62 TA 44 E A 68 PD ZD 4 −0 053 C 332 0 R IZ 99 O 1 T PL INIB X N 47 U T 20 LA LIN PA −3 N TIN IL PH OT IB A IN −6 IB ER 65 L 75 SO OT 2 R INI A FE B N IB

LI

PA C

* ** ** *

CRAMERV

1.0

**

0.4

0.8

*

0.8

0.8

0.8

*

0.0 LI TA X 1 E PD 7− L −0 AA 32 G 5 A 90 ZD 1 62 TA 44 E A 68 PD ZD 4 −0 053 0 3 C R 329 IZ O 91 TI PL NIB X N 47 U T 20 LA LIN PA −3 N TIN IL PH OT IB A IN −6 IB ER 65 L 75 SO OT 2 R INI A FE B N IB

0.6

1.0

1.0

1.0

Pearson corr for full data

PA C

MCC

*

0.2

0.4

** * ** *

0.0 LI TA X 1 E PD 7− L A −0 A 32 G 5 A 90 ZD 1 62 TA 44 E A 68 PD ZD 4 −0 053 0 3 C R 329 IZ O 91 TI N PL IB X N 47 U T 20 LA LIN PA −3 N TIN IL PH OT IB A IN −6 IB ER 65 L 75 SO OT 2 R INI A FE B N IB

0.2

IC50 published IC50 recomputed

PA C

0.0 LI TA X 1 E PD 7− L −0 AA 32 G 5 A 90 ZD 1 62 TA 44 E A 68 PD ZD 4 −0 053 C 332 0 R IZ 99 O 1 T PL INIB X N 47 U T 20 LA LIN PA −3 N TIN IL PH OT IB A IN −6 IB ER 65 L 75 SO OT 2 R INI A FE B N IB

PA C

A

IC50 published IC50 recomputed

Spearman corr for full data IC50 published IC50 recomputed

Somer's Dxy for full data

* *

* *

IC50 published IC50 recomputed

Spearman corr for sensitive data

*

**

*

*

** **

* *

B

IC50 published IC50 recomputed

Somer's Dxy for sensitive data

** *

* **

C

IC50 published IC50 recomputed

Informedness for binary data

*

Supplementary Figure 10: Consistency of IC50 values between GDSC and CCLE, as published and recomputed within PharmacoGx.(A) Consistency assessed using the full set of cancer cell lines screened in both studies. (B) Consistency assessed using only sensitive cell lines (IC50 < 1µM and IC50 < 10µM for targeted and cytotoxic drugs, respectively). (C) Consistently assessed by discretizing the drug sensitivity data using the aforementioned cutoffs for IC50 . PCC: Pearson correlation coefficient; SCC: Spearman rank-based correlation coefficient; DXY: Somers’ Dxy rank correlation; MCC: Matthews correlation coefficient; CRAMERV: Cramer’s V statistic; INFORM: Informedness. The symbol ’*’ indicates whether the consistency is statistically significant (p< 0.05).

Distribution of CCLE Affymetrix data

Distribution of CCLE RNA−seq data

0.5

0.5

0.4

0.4

0.4

0.3

Density

0.5

Density

Density

Distribution of GDSC Affymetrix data

0.3

0.3

0.2

0.2

0.2

0.1

0.1

0.1

0.0

0.0 2

4

6

8

10

12

Affymetrix HG−U219 expression values

14

0.0 4

6

8

10

12

14

Affymetrix HG−U133PLUS2 expression values

0

5

10

15

Illumina RNA−seq expression values

Supplementary Figure 11: Distribution of gene expression values and corresponding cutoffs for the microarray Affymetrix HG-U219 platform in GDSC (cutoff = 4), the microarray Affymetrix HG-U133PLUS2 platform in CCLE (cutoff = 5) and the new Illumina RNA-seq data in CCLE (cutoff = 1). To distinguish between lowly vs highly expressed genes, we fitted a mixture of two gaussians to each gene expression distribution and estimated the cutoffs as the 90% left interval of the distribution of the highly expressed genes.

A Spearman corr for full data

Somer's Dxy for full data

IC50.PUBLISHED

IC50.RECOMPUTED

IC50.RECOMPUTED

IC50.RECOMPUTED

GE.CCLE.ARRAY.RNASEQ

AUC.RECOMPUTED

AUC.PUBLISHED

GE.ARRAYS

GE.ARRAY.RNASEQ

GE.CCLE.ARRAY.RNASEQ

AUC.RECOMPUTED

AUC.PUBLISHED

GE.ARRAYS

GE.ARRAY.RNASEQ

AUC.STAR

IC50.PUBLISHED

IC50.PUBLISHED

AUC.STAR

IC50.PUBLISHED

AUC.STAR

AUC.RECOMPUTED

AUC.STAR

IC50.PUBLISHED

AUC.RECOMPUTED

AUC.STAR

CNV

AUC.RECOMPUTED

AUC.STAR

CNV

AUC.PUBLISHED

IC50.PUBLISHED

CNV

AUC.PUBLISHED

CNV

CNV

AUC.PUBLISHED

GE.CCLE.ARRAY.RNASEQ

p < 0.05 0.05 = 0.10

GE.ARRAY.RNASEQ

AUC.RECOMPUTED

GE.ARRAY.RNASEQ

GE.ARRAYS

CNV

p < 0.05 0.05 = 0.10

AUC.PUBLISHED

GE.ARRAY.RNASEQ

GE.ARRAYS

GE.ARRAYS

p < 0.05 0.05 = 0.10

GE.ARRAY.RNASEQ

Pearson corr for full data GE.ARRAYS

B

IC50.PUBLISHED

IC50.RECOMPUTED

IC50.RECOMPUTED

IC50.RECOMPUTED

GE.CCLE.ARRAY.RNASEQ

AUC.RECOMPUTED

AUC.PUBLISHED

GE.ARRAY.RNASEQ

GE.ARRAYS

GE.CCLE.ARRAY.RNASEQ

AUC.RECOMPUTED

AUC.PUBLISHED

GE.ARRAY.RNASEQ

GE.ARRAYS

IC50.PUBLISHED

IC50.PUBLISHED

AUC.STAR

AUC.STAR

IC50.PUBLISHED

IC50.PUBLISHED

AUC.RECOMPUTED

AUC.STAR

AUC.STAR

AUC.RECOMPUTED

AUC.STAR

CNV

AUC.RECOMPUTED

IC50.PUBLISHED

CNV

AUC.PUBLISHED

AUC.STAR

CNV

AUC.PUBLISHED

CNV

CNV

AUC.PUBLISHED

GE.CCLE.ARRAY.RNASEQ

p < 0.05 0.05 = 0.10

GE.ARRAY.RNASEQ

AUC.RECOMPUTED

GE.ARRAY.RNASEQ

Somer's Dxy for high expression/sensitive data GE.ARRAYS

CNV

p < 0.05 0.05 = 0.10

AUC.PUBLISHED

GE.ARRAY.RNASEQ

Spearman corr for high expression/sensitive data GE.ARRAYS

GE.ARRAY.RNASEQ

p < 0.05 0.05 = 0.10

GE.ARRAYS

Pearson corr for high expression/sensitive data GE.ARRAYS

C Matthew corr for binary data

Cramer's V for binary data

GE.ARRAY.RNASEQ

IC50.PUBLISHED

AUC.STAR

AUC.RECOMPUTED

AUC.STAR

IC50.PUBLISHED

AUC.PUBLISHED

AUC.RECOMPUTED

CNV

MUTATION

IC50.RECOMPUTED

GE.ARRAY.RNASEQ

IC50.RECOMPUTED

GE.ARRAYS

IC50.RECOMPUTED

GE.CCLE.ARRAY.RNASEQ

IC50.PUBLISHED

IC50.PUBLISHED

IC50.PUBLISHED

AUC.STAR

AUC.STAR

IC50.PUBLISHED

AUC.PUBLISHED

AUC.RECOMPUTED

AUC.STAR

AUC.RECOMPUTED

AUC.RECOMPUTED

AUC.STAR

CNV

AUC.RECOMPUTED

MUTATION

MUTATION AUC.PUBLISHED

GE.ARRAY.RNASEQ

MUTATION AUC.PUBLISHED

GE.ARRAYS

MUTATION

AUC.PUBLISHED

CNV

AUC.PUBLISHED

GE.CCLE.ARRAY.RNASEQ

p < 0.05 0.05 = 0.10

GE.ARRAY.RNASEQ

CNV

CNV

GE.ARRAYS

MUTATION

CNV

Informedness for binary data

p < 0.05 0.05 = 0.10

GE.ARRAY.RNASEQ

GE.ARRAY.RNASEQ

GE.ARRAYS

GE.ARRAYS

p < 0.05 0.05 = 0.10

GE.CCLE.ARRAY.RNASEQ

GE.ARRAYS

Supplementary Figure 12: Statistical test for difference in consistency for molecular and drug sensitivity data (A) for the full drug sensitivity data; (B) for the highly expressed genes and cell lines sensitive to the drugs (AUC > 0.2 / IC50 < 1µM and AUC > 0.4 / IC50 < 10µM for targeted and cytotoxic drugs, respectively); (C) for the binary gene expression and drug sensitivity calls. Each cell in the matrix represents the p-value (coded by colour) for a given pairwise comparison of consistency estimates. For instance, consistency of gene expression data is statistically significantly higher than consistency of drug sensitivity data. GE.CCLE.ARRAY.RNASEQ: Consistency between gene expression data generated using Affymetrix HG-U133PLUS2 microarray and Illumina RNA-seq platforms within CCLE; GE.ARRAYS: Consistency between gene expression data generated using Affymetrix HG-U133A and HG-U133PLUS2 microarray platforms in GDSC and CCLE, respectively; GE.ARRAY.RNASEQ: Consistency between gene expression data generated using Affymetrix HGU133A microarray and Illumina RNA-seq platforms in GDSC and CCLE, respectively; CNV: Consistency of copy number variation data in CCLE and GDSC, respectively; MUTATION: Consistency of mutation profiles in CCLE and GDSC, respectively; AUC.PUBLISHED: Consistency of AUC values as published in GDSC and CCLE; AUC.PUBLISHED: Consistency of AUC values as published in GDSC and CCLE; AUC.RECOMPUTED: Consistency of AUC values in GDSC and CCLE as recomputed using PharmacoGx; AUC.STAR: Consistency of AUC values in GDSC and CCLE as recomputed from the common concentration range using PharmacoGx ; IC50.PUBLISHED: Consistency of IC50 values as published in GDSC and CCLE; IC50.RECOMPUTED: Consistency of IC50 values in GDSC and CCLE as recomputed using PharmacoGx.

PD−0325901

AZD6244

0.6

1.0

2

17−AAG

1.0

paclitaxel

0.4

0.6 ●

0.2

0.4

0.6

●

●● ● ● ● ● ●●● ●●● ● ●

● ●

●

0.0

GDSC

−0.5

● ● ● ● ● ●● ● ● ● ● ●

−0.6

−0.2 0.0

Expression: 0 CNV: 0 Mutation: 0

0.2

0.4

0.4

−0.4

●

0.6

−0.4

−0.2

0.0

Expression: 0 CNV: 0 Mutation: 0

0.2

0.4

●

−0.6 −0.4 −0.2

0.0 CCLE

PLX4720

Nutlin−3

lapatinib

Nilotinib

●

1.0

1.0

1.0

CCLE

● ●● ● ● ●● ●● ●● ●● ● ● ● ●● ●● ● ● ● ●● ● ●● ● ● ●●●●●●● ● ● ● ● ● ● ● ●●● ● ●● ● ● ● ●●●● ● ● ● ●● ● ● ●● ● ● ● ●● ●●● ●● ● ●●● ● ● ●● ●●● ●● ● ●● ●● ● ●● ● ● ●● ● ●● ● ●●●● ● ●● ●●● ● ●● ● ● ● ●● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ●●●● ●● ●● ●

−0.5

−0.5

● ● ● ●●

●

0.5 ●

●

0.0

● ● ●● ● ● ●● ● ● ● ● ● ● ● ●●●● ● ●●●●●●● ● ● ●● ● ● ●● ● ● ●●● ●

● ●

0.4

● ● ●● ●●● ● ● ●● ● ● ●●●● ● ● ● ● ●●●● ● ●● ● ●● ● ●●● ● ● ● ●● ●● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ●● ●● ● ● ● ●●●●●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ●● ● ●● ●● ●● ● ●● ●●● ● ●●●●● ● ● ● ● ● ●● ● ●●●●●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ●●● ● ● ● ● ● ●●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ●●● ●●● ● ●● ●● ● ● ● ● ● ●● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●● ● ● ● ● ● ● ● ●

GDSC

0.5 GDSC

●

0.0

0.5 GDSC

● ● ●● ●● ●

Expression: 0 CNV: 0 Mutation: 0

0.6

●

●

−0.5

●

●● ● ●

● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ●

0.0

0.5

● ● ●● ●●

● ●● ●● ● ● ● ● ● ●● ● ● ● ● ●●● ● ●

0.2

CCLE

● ● ● ● ●● ●● ● ● ● ● ● ● ●●● ● ● ● ●● ● ● ●● ●● ● ● ●● ● ● ●● ● ● ●

1.0

0.2 ●

●

CCLE

0.0

GDSC

−1.0

Crizotinib

●

●

0.5

PD−0332991

●

−0.5

0.0

0.0

● ●

●

−0.6

−0.6

−0.2 0.0

1.5

−0.6

−0.5

CCLE

0.2

0.4 ●

● ●

Expression: 0 CNV: 0 Mutation: 0

−1.0

●

● ● ● ● ●●● ● ● ●●● ● ● ● ● ● ● ● ● ●●●●● ● ● ●●●● ● ● ● ●● ● ●● ● ● ●● ● ● ●● ●● ● ●●● ● ●● ● ●● ● ● ●● ●● ● ● ●● ●● ●● ●● ●

●

●

2

●

0.2

●

1

GDSC

● ●

●

0 CCLE

−0.6 −0.4 −0.2

●

−1

1.0

●

−2

−0.2

● ● ● ●●

GDSC

● ● ● ● ●

−0.2 0.0

0.4

●

0.6

Expression: 0 CNV: 0 Mutation: 1

●

0.6

AZD0530

● ● ● ● ● ● ●● ● ● ●● ●● ●●● ● ●● ●● ● ●● ● ● ● ●● ●● ● ●● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ●● ●● ●● ● ● ● ● ● ● ● ● ● ●● ● ●●

0

GDSC

−1 0.2

TAE684

0.2 GDSC

−0.2 CCLE

●

0.5

1 −0.6

Expression: 89 CNV: 0 Mutation: 2

●

0.4

1.0

CCLE

● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ●●● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ●● ●● ● ● ● ●● ● ●

●

0.0

0.5

Expression: 7 CNV: 0 Mutation: 0

●

GDSC

0.0

● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●

−2

GDSC −0.5

0.6

−1.0

Expression: 0 CNV: 0 Mutation: 0

●●

−0.6

−1.0

●

−0.2 0.0

● ●●●● ● ● ● ● ● ● ● ● ● ●● ●● ●● ● ●● ● ● ● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ●●● ● ● ● ●● ● ● ●●●● ●● ● ●●● ●● ● ● ●●● ● ● ●● ●

−0.2

0.0 −0.5

GDSC

0.2

0.5

0.4

●

●● ●● ● ● ●● ● ●● ● ●● ● ● ●● ●●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ●● ● ●● ●● ●● ● ●● ● ● ● ● ● ● ●● ●● ● ●

●

● ●

●

● ●

−0.5

0.0

0.5

1.0

1.5

−1.0

−0.5

0.0

0.5

CCLE

CCLE

PHA−665752

Erlotinib

●

1.0

−1.0

−0.5

0.0

1.0 −1

0 CCLE

1

2

3

−0.5

0.0

0.5

1.0

CCLE

●

● ●●

−0.5

● ● ● ● ●●● ●● ● ●

Both significant GDSC significant CCLE significant

●

●

−1.0

●

0.0

GDSC

0.5

● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ●● ●● ● ●●● ● ●● ● ●● ● ●● ● ●● ● ● ●●●● ●● ● ●● ● ●● ●● ● ●●● ● ● ●●● ●● ● ● ●● ●●● ● ●● ●

0.0 CCLE

Expression: 0 CNV: 0 Mutation: 0

0.5

1.0

−0.5

0.5 −2

−1.0

●

0.0

GDSC

−0.5 −3

Expression: 0 CNV: 0 Mutation: 0

●

−1.0

−2 −3

●

1.0

Expression: 248 CNV: 238 Mutation: 0

Sorafenib

3 0

● ●● ●

0.5

●

● ● ●

● ●

2 1

● ● ● ● ● ●● ●● ● ●● ● ● ● ● ●

Expression: 8 CNV: 0 Mutation: 0

CCLE

●

−1

GDSC

Expression: 2 CNV: 0 Mutation: 1

●

−1.0

Expression: 7 CNV: 0 Mutation: 1

−1.0

−1.5 −1.5

−1.0

●

●

●

−0.5

0.0

Expression: 0 CNV: 0 Mutation: 0

0.5

CCLE

Supplementary Figure 13: Scatterplot representing the effect size of the significant gene-drug associations (FDR < 5%) identified using continuous AUC and the common cell lines screened both in GDSC and CCLE. Gene-drug associations are identified using molecular profiles including gene expression, mutation and copy number variation data and continuous published AUC as input and output of a linear model, respectively. In case of perfect consistency, all points would lie on the grey diagonal.

−0.2

0.0

0.2

0.4

0.6

−1.0

−0.5

0.0

Expression: 8 CNV: 0 Mutation: 1

0.5

1.0

1.0 0.5 0.0 −0.5

●

−0.6 −0.4 −0.2

0.0

0.4 0.2

●

Expression: 225 CNV: 36 Mutation: 0

0.2

Erlotinib

Sorafenib

0.4

0.6

0.0

0.4 0.2 ●

0.0

GDSC

● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●

−0.5

−1.5

● ● ● ● ● ● ●● ●● ● ●● ● ● ● ●●● ●● ●●● ● ● ●● ● ●● ● ● ● ● ●● ●●● ● ●● ● ● ●●● ●● ●● ● ●● ●● ● ●● ● ● ●● ● ● ● ● ●● ● ● ●● ●●● ● ●● ● ● ● ● ● ● ● ● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●●● ● ● ● ● ● ●● ●● ●● ● ● ●● ● ● ●●● ●● ● ● ●● ● ● ● ●● ●● ● ● ●● ●● ● ● ● ● ● ● ● ●● ●● ● ●● ● ● ● ● ●● ● ● ●● ● ● ● ●●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●● ●●● ●●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ● ● ● ●● ●●●● ● ● ● ●●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●●● ● ● ● ●● ● ● ● ● ● ●●●● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ●● ● ● ● ● ● ●●● ● ●● ● ● ● ●●●● ● ● ●● ●● ● ● ●●●●● ●●● ● ● ● ●●● ● ● ● ●●● ● ●● ●● ● ● ●

PHA−665752

●

●

Expression: 8 CNV: 2 Mutation: 0

●

0.0

0.2

0.4

0.6

Nilotinib ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ●● ● ● ●●●● ● ● ●● ●● ●● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ●● ●● ●● ● ●● ●● ● ● ●● ●●●● ● ● ●●● ●● ● ●● ● ● ● ● ● ● ●● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ●●● ●●● ● ●● ● ● ● ● ●● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●●●●●●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ●● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ●●● ● ●●● ● ● ● ● ●● ● ● ● ●● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ● ● ● ● ● ● ● ● ● ●●●● ●● ● ● ● ● ● ●● ● ● ● ● ●●● ● ● ● ● ● ●●● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ●● ● ● ● ●● ●● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ●● ● ● ● ● ● ●●● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ●● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●● ● ● ● ●● ●● ●● ● ● ●● ● ● ●● ● ●● ●●● ● ●● ● ● ●●● ●● ●● ● ● ● ● ● ●●● ● ● ● ● ● ● ●

●

●

−0.6 −0.4 −0.2

0.0

●

●

●

●

Expression: 279 CNV: 157 Mutation: 0

0.2

0.4

0.6

CCLE

●

●●

1.0

● ● ● ●

1.0

●

●

0.0 CCLE

0.2

0.4

−1.0

Expression: 0 CNV: 0 Mutation: 0

●

−1.0

−0.5

0.0 CCLE

0.0

GDSC

Expression: 135 CNV: 0 Mutation: 0

0.5

● ● ● ●●● ● ● ● ● ● ●●

1.0

● ● ●● ● ●● ●● ● ● ●●● ● ● ● ● ●● ●●● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ●● ●● ● ● ●● ● ● ●

−0.5

0.0

GDSC

●● ● ● ● ●● ●● ●●● ●●● ● ● ● ●● ●●● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ●● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ●● ●

−0.5

0.0 −0.2 −0.4

●

0.5

●

0.5

0.2

●

●

−1.0

−0.5

Both significant GDSC significant CCLE significant

●

−1.0

0.4

●

−0.6 −0.4 −0.2

●

● ●

CCLE

−0.2

1.0

● ●● ●●● ●● ● ●●●● ● ●● ● ● ●●● ●● ● ● ● ●● ● ● ●● ●●●● ● ●●●● ● ● ● ● ●● ● ● ● ● ● ●● ● ●● ● ● ● ● ●● ● ● ● ● ●

CCLE

CCLE

●●● ●

●

● ● ● ● ●●● ● ●● ●●● ● ● ●●●●● ● ●● ● ●● ●● ● ●● ● ● ● ● ●●●● ● ● ● ●● ● ● ● ●● ● ● ●● ● ●●● ● ● ●● ● ● ● ● ●● ●● ●● ● ● ● ● ● ●● ● ● ● ●●● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ●● ● ●● ●● ●● ● ● ● ●● ● ● ● ● ●● ● ● ● ●● ● ● ●● ●● ●● ● ● ●● ●●●● ● ● ● ● ● ●● ● ● ● ●● ● ●● ● ●●

0.2 0.5

0.0

GDSC

0.0

Expression: 0 CNV: 0 Mutation: 0

●

−0.6 −0.4 −0.2

0.5 GDSC

●● ● ●● ●● ● ● ●●● ● ● ●● ●● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

0.0

0.5 GDSC −0.5

−0.5

CCLE

●

1.0

●

−1.0

1.5

0.5

●

0.4

−1.0

1.0

1.5 1.0

Expression: 48 CNV: 3 Mutation: 1

−0.6 −0.4 −0.2

●

0.6

−0.4

Expression: 8 CNV: 0 Mutation: 0

lapatinib

●

GDSC

0.5 0.5

●

●

0.4

0.0

GDSC

−0.5

●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●●●● ● ●●● ● ● ● ● ●● ●

Nutlin−3

1.0

0.0

Crizotinib

PLX4720

0.5

−0.5

PD−0332991

CCLE

−0.5

−1.0

CCLE

CCLE

●

1.0

● ● ● ●● ● ●●●● ● ●● ● ●● ●● ● ● ● ● ●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●

−1.0

0.2

● ●

−0.4

0.0

GDSC

−0.5 1.0

0.4 −0.2

0.0

0.5

CCLE

CCLE

● ●● ● ● ● ● ●● ●● ● ●● ●● ● ●● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ●● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ●● ●

−1.5

GDSC

● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●●● ●● ●● ●● ●● ●●● ●● ●● ●● ● ● ● ●

●

●

−0.4

−0.2

0.0

Expression: 138 CNV: 0 Mutation: 1

●

0.2 ●

Expression: 16 CNV: 0 Mutation: 0

● ●

−0.4

−0.5

● ●● ● ● ●● ● ● ● ●● ● ● ● ● ●● ●●● ● ● ●● ● ● ● ● ●● ●● ●● ● ●● ●● ●● ●●● ● ●● ● ●●● ●● ●●●● ●● ●● ● ● ●●● ● ●● ● ● ● ●● ●● ● ● ● ● ●● ● ●● ● ●●● ●● ●●● ● ●● ● ●● ●● ● ● ● ● ●

● ●

●

−1.0

●

●

●●

●

0.6

●

0.0

● ● ● ● ● ●● ● ● ● ● ●● ● ●●● ● ● ● ●● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ●●●● ●● ● ●● ● ●● ● ● ● ●● ● ● ●●●● ● ● ● ●● ● ● ● ●● ● ●● ● ● ● ●● ●● ● ● ● ●● ●● ● ● ● ●● ● ● ● ● ● ●● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ●

GDSC

0.2 0.0

GDSC

−0.2

● ●●● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ●●● ● ● ●● ● ● ● ●●● ● ●● ●●●● ● ● ●● ● ● ● ● ●● ●●● ● ● ● ●● ● ●● ● ●● ● ● ●● ● ●● ● ●● ●● ● ● ● ● ● ●●● ● ● ●●● ● ●● ●● ●● ● ●● ●● ●● ● ●●● ● ● ● ● ● ● ● ● ● ●● ● ●

0.4

AZD0530 ●

●

●

0.2

−0.4

0.4

TAE684 ● ● ●● ● ●

0.0 CCLE

−1.0

−0.6 −0.4 −0.2

● ● ●● ●● ● ● ● ● ●● ● ●● ●●● ● ●●●● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ●● ● ● ●● ●● ●

●

0.6

0.3

Expression: 837 CNV: 0 Mutation: 3

●

GDSC

0.2

●

−0.6 −0.4 −0.2

0.1

Expression: 283 CNV: 0 Mutation: 0

●

CCLE

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0.1 GDSC −0.1 −0.3

0.2

Expression: 1 CNV: 0 Mutation: 0

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GDSC

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−0.6 −0.4 −0.2

● ● ● ● ●● ● ● ●● ● ● ●●● ●● ● ●● ● ● ● ●●● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ●●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ●● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ●● ● ● ● ● ●● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●●● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ● ●●●● ● ●● ● ●● ●● ● ● ● ●

●

● ● ● ● ●● ●● ●● ● ● ●● ● ● ● ●● ● ●●● ● ● ● ●● ● ● ●● ●●● ● ●● ●● ● ● ● ● ●●●●● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ●●● ● ●● ● ● ●● ●●● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●●● ●● ●● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ●● ● ●● ●● ● ● ● ● ● ●● ● ● ● ●● ● ●● ● ● ● ●● ●●● ● ●● ●●● ●● ● ●● ●● ●● ●● ● ●● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ●

AZD6244

●

0.4

0.3 0.2

● ●

PD−0325901

1.0

17−AAG 0.6

paclitaxel

0.0

Expression: 4 CNV: 0 Mutation: 0

0.5

1.0

CCLE

Supplementary Figure 14: Scatterplot representing the effect size of the significant gene-drug associations (FDR < 5%) identified using continuous AUC and all cell lines screened in each study. Gene-drug associations are identified using molecular profiles including gene expression, mutation and copy number variation data and continuous published AUC as input and output of a linear model, respectively. In case of perfect consistency, all points would lie on the grey diagonal.

PD−0325901

AZD6244

2

4

2

3

17−AAG

1.0

paclitaxel

2

●

1 GDSC

●●

−2

● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ●● ●●● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ●

●

●

● ● ●

● ● ●

−2 −0.5

0.0

0.5

1.0

−2

−1

0

1

●

●

2

−4

−2

0

Expression: 9 CNV: 0 Mutation: 0

2

●

−3

Expression: 0 CNV: 0 Mutation: 0

−4

● ●

4

−3

−2

−1

0 CCLE

TAE684

AZD0530

PD−0332991

Crizotinib

3

0.5 0.0

0.0

GDSC

GDSC

0.5

4 0

GDSC

2

0.5

2

1.0

CCLE 1.0

CCLE

0.0

Expression: 0 CNV: 0 Mutation: 0

1

CCLE 1.0

−1.0

Expression: 0 CNV: 0 Mutation: 0

−2

−1.0

●

GDSC

●

−1

−0.5

●

●● ● ●● ●● ● ●●

−1

●

● ●●● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ●●● ●● ●● ●● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ●

0

● ●

0

GDSC

0.0

GDSC

●

0

●

GDSC

1

0.5

●

−0.5

0.0

0.5

1.0

−4

−2

0

2

●

4

−1.0

−0.5

0.0

Expression: 0 CNV: 0 Mutation: 0

0.5

●

−1.0

Expression: 0 CNV: 0 Mutation: 0

−1.0

●

−4

−1.0

1.0

−1.0

−0.5

0.0

CCLE

CCLE

PLX4720

Nutlin−3

lapatinib

Nilotinib

0.5

1.0

0.5

0.5

0.5

4

6

Expression: 0 CNV: 0 Mutation: 0

1.0

CCLE 1.0

CCLE 1.0

−1.0

Expression: 0 CNV: 0 Mutation: 0

−0.5

−0.5

−2

−0.5

●

●

−2

0

2

4

6

−0.5

0.0

0.5

1.0

−0.5

0.0

0.5

1.0

0.5

1.0

−1.0

−0.5

0.0

Expression: 0 CNV: 0 Mutation: 0

0.5

1.0

CCLE

Both significant GDSC significant CCLE significant

0.0

GDSC

0.5

0.0 CCLE

−1.0

−0.5

0.0 CCLE

Expression: 0 CNV: 0 Mutation: 0

0.5

1.0

●

−1.0

●

−1.0

−1.0

−0.5

1.0

●

−0.5

0.0 −0.5

0.0

GDSC

1.0

Sorafenib

0.5

Erlotinib

1.0

PHA−665752

0.5

CCLE

Expression: 0 CNV: 0 Mutation: 0

0.0

GDSC −1.0

Expression: 0 CNV: 0 Mutation: 0

−1.0

●

CCLE

●

−1.0

−0.5

0.0

GDSC −1.0

Expression: 0 CNV: 0 Mutation: 0

−1.0

−1.0

●

CCLE

−0.5

GDSC

−4

Expression: 9 CNV: 0 Mutation: 1

−0.5

−0.5

0 −6

●

●

−6

0.0

GDSC

● ●● ● ●● ● ● ● ●●●● ● ● ● ● ●● ● ●● ●●● ●● ● ● ● ●● ● ●● ● ●●● ●● ●● ●

−4

−2

GDSC

2

●

●

● ●● ● ● ● ● ● ●● ● ● ●● ● ● ●● ● ● ●● ● ● ● ●● ● ●● ●● ● ●● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ●● ● ● ● ● ●● ●● ● ● ●● ● ● ● ● ●● ● ● ●

−1.0

−0.5

0.0

Expression: 0 CNV: 0 Mutation: 0

0.5

1.0

CCLE

Supplementary Figure 15: Scatterplot representing the effect size of the significant gene-drug associations (FDR < 5%) identified using discretized AUC and the common cell lines screened both in GDSC and CCLE. Gene-drug associations are identified using molecular profiles including gene expression, mutation and copy number variation data and discretized published AUC (AUC > 0.4 for paclitaxel, AUC > 0.2 for the other drugs) as input and output of a linear model, respectively. Note that the small number of cell lines classified as "sensitive" did not allow for finding enough significant gene-drug associations for the majority of the drugs. This is due to the lack of convergence of the logistic regression model when 3 or less cell lines are in one category.

17−AAG

PD−0325901

AZD6244

0.5

−3

−2

−1

0

1

CCLE

TAE684

AZD0530

−2

−1

0

2 0

GDSC

−2

1 0

GDSC

−2 3

● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ●

●

Expression: 141 CNV: 0 Mutation: 1

●

● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●●● ● ● ●● ●●● ● ●●●●●● ●● ● ● ● ●● ●

1

Expression: 25 CNV: 0 Mutation: 0

●

2

●

−4

−2

0

2

CCLE

CCLE

PD−0332991

Crizotinib

●

4

●

2

2

−1

2

2

CCLE

●

3

1 0

GDSC 0.0

Expression: 122 CNV: 0 Mutation: 2

●

−3

Expression: 0 CNV: 0 Mutation: 0

●

●

−0.5

−2

−0.5

−1

0.0

GDSC

● ●● ● ● ●● ● ●● ●● ● ● ●● ● ●● ● ● ● ●● ●●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ●● ●● ● ● ● ●●● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ●● ●● ● ●●● ● ●● ● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ●● ● ● ●

● ●● ● ●● ● ●● ● ● ● ●● ● ● ●●● ● ●● ● ●● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ●● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ●● ● ● ●●● ● ● ●●●●● ● ● ● ●● ● ●● ●● ● ● ● ●● ●● ● ● ● ● ● ● ●● ●● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ● ●●●● ● ●● ● ● ● ●● ●● ● ● ● ● ●● ● ● ●●●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ●●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●●●●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●

−4

0.5

2

2

4

3

paclitaxel

●

0

1

−2

−1

1

Expression: 0 CNV: 0 Mutation: 0

0

1

2

0

GDSC

−1 Expression: 0 CNV: 0 Mutation: 1

●

−3

−2

−1

0

1

CCLE

CCLE

PLX4720

Nutlin−3

lapatinib

2

3

●

●

−2

−1

0

Expression: 0 CNV: 0 Mutation: 0

1

2

CCLE

Nilotinib

4

● ●

●

●

0

2

−4

−4

−2

4

−4

−2

0

Expression: 4 CNV: 3 Mutation: 1

2

4

−2

−1

2

Expression: 11 CNV: 0 Mutation: 0

●

●

●

0

1

●

● ● ●

CCLE

CCLE

PHA−665752

Erlotinib

Sorafenib

2

●● ● ● ● ● ●● ● ● ● ●●●●●● ●● ●● ●● ●● ● ●● ● ● ● ●● ●● ● ● ● ● ●● ●●●● ●● ● ● ● ●●● ● ● ● ●

●

−4

−2

0

Expression: 29 CNV: 0 Mutation: 0

2

4

CCLE

●

●

●

●

−0.5

0.0 CCLE

0.5

1.0

● ●

−3

−1.0 −1.0

Expression: 0 CNV: 0 Mutation: 0

−2

●

●

−3

−2

−1

Both significant GDSC significant CCLE significant

0.0

● ● ●

●● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

−0.5

●

GDSC

●

●

0 CCLE

1

Expression: 8 CNV: 0 Mutation: 0

2

3

●

−1.0

●

0

GDSC

●

−1

0.0 −0.5

GDSC

1

0.5

● ● ●● ● ● ●● ● ● ●● ● ● ●● ●● ●● ●● ●●●● ● ● ● ● ● ● ●● ●●●● ●●● ● ● ● ●● ● ●● ● ●● ● ●●● ● ●● ● ● ●●●●● ●● ● ● ●●●●● ●● ● ● ● ● ● ● ●●● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ●

0.5

2

3

1.0

CCLE 1.0

−4

●

−2

● ●

Expression: 18 CNV: 1 Mutation: 1

●

● ●● ● ● ●● ● ● ● ● ● ● ●● ●● ● ●● ●● ●●●● ● ● ● ● ● ● ● ●● ●● ● ●● ●● ● ●● ●●● ●● ●●● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ●● ●●●●● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ●● ●● ●●● ● ● ● ● ●● ●● ● ● ● ●● ● ●● ● ●● ● ● ●● ● ●●●● ●● ● ●● ●● ● ● ●● ● ●● ● ● ● ●

●

0

0

●

● ● ●● ● ●● ● ● ●● ● ●● ● ●● ● ● ●● ●● ● ● ●● ● ●● ●● ● ● ● ●● ● ● ● ●●● ● ●● ● ● ● ●● ●●● ● ● ● ●● ●●●● ● ●●● ●●●● ● ● ● ●● ● ●● ● ● ● ●● ●● ● ● ● ●● ● ● ● ● ● ● ●●● ● ●●●● ● ● ● ●● ● ●● ● ● ●● ● ● ● ● ● ● ●●●● ●● ●●● ● ● ●● ● ● ● ● ● ● ●● ●● ●● ●

−2

●

● ●

−1

●

● ●● ●

GDSC

●● ●●● ●●●● ● ● ●● ● ●

● ●

● ● ●● ● ●● ● ●● ● ●● ● ● ●● ●● ●● ●● ●● ●● ● ● ● ●●●●● ●● ● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ● ● ●●● ● ● ●● ● ● ● ● ● ●● ●● ● ●● ● ● ●● ● ●● ● ● ●●● ● ●● ● ●● ●●● ● ●● ●● ● ● ●● ● ● ●●● ● ●● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ●● ● ● ● ● ●● ● ●● ● ●● ● ● ● ●●●●● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ●●●●● ● ●●● ●● ● ●●● ●●● ●●● ●● ● ●●●●●● ● ● ● ● ●●● ●● ● ●● ●●● ● ● ● ●

●

GDSC

1

2

● ●●●● ● ● ● ●●●● ● ●●● ●● ●●● ●●●● ●● ● ● ●● ●● ● ●● ● ●

0

GDSC

● ●● ●●●● ● ●● ●●

−2

●

● ● ● ● ●● ● ● ● ●● ● ● ●●●●● ● ●●● ● ●● ● ● ●● ● ● ● ●● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ●● ● ●● ●●

−2

● ● ●●● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●●● ● ● ● ● ●● ●● ● ● ●●●●● ● ● ● ● ● ●● ● ● ● ● ●●●●

0

GDSC

2

2

●

−4

4

4

CCLE

●

−2

−2

●

2

●

●

−3

Expression: 0 CNV: 0 Mutation: 0

●

●

●

●

● ● ●● ● ● ● ●● ● ●

●

1 −1

●● ● ● ●● ●●●● ● ● ●●● ●● ●● ●● ● ● ● ●● ●●● ●● ● ●●

●

−1

● ●

−1

●● ● ● ●● ● ● ●● ●● ●● ●● ●●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●●● ● ● ● ●●●● ●● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ●● ●

0

●

−2

−1

●

−2

● ●● ●

●

●●●● ● ●● ● ●

●

−2

●

GDSC

GDSC

● ●● ● ●● ●● ●

0

GDSC

1

● ● ●● ● ● ● ● ●● ● ●●● ● ● ● ●●● ●● ● ●●●●● ● ●● ●

●

0

1

2

● ● ●

−1.0

−0.5

0.0

Expression: 0 CNV: 0 Mutation: 0

0.5

1.0

CCLE

Supplementary Figure 16: Scatterplot representing the effect size of the significant gene-drug associations (FDR < 5%) identified using discretized AUC and all cell lines screened in each study. Gene-drug associations are identified using molecular profiles including gene expression, mutation and copy number variation data and discretized published AUC (AUC > 0.4 for paclitaxel, AUC > 0.2 for the other drugs) as input and output of a linear model, respectively. Note that the small number of cell lines classified as "sensitive" did not allow for finding enough significant gene-drug associations for PHA-665752 and sorafenib. This is due to the lack of convergence of the logistic regression model when 3 or less cell lines are in one category.

50 Continuous Common Continuous All Binary Common Binary All

40

Jaccard Index

30

20

10

Sorafenib

Erlotinib

PHA−665752

Nilotinib

lapatinib

Nutlin−3

PLX4720

Crizotinib

PD−0332991

AZD0530

TAE684

AZD6244

PD−0325901

17−AAG

paclitaxel

0

Supplementary Figure 17: Barplot representing the overlap, as estimated by the Jaccard index, between the gene-drug associations found in GDSC and CCLE. ’Continuous Common’ refers to the associations identified using continuous published AUC values on the common cell lines in GDSC and CCLE; ’Continuous All’ refers to the associations identified using continuous published AUC values on the entire panel of cell lines screened in each study; ’Binary Common’ refers to the associations identified using the discretized (binary) published AUC values on the common cell lines in GDSC and CCLE; ’Binary All’ refers to the associations identified using the discretized (binary) published AUC values on the entire panels of cell lines screened in each study

80

60

40

40

20

0

0

1% 0.1%

* * 50% 20% 10% 5%

60

**

**

**

*

**

80

20

0

1% 0.1%

AZD0530

50% 20% 10% 5%

PD−0332991

80

80

20

* *

*

20

*

0

*

1% 0.1%

50% 20% 10% 5%

PLX4720

*

*

*

** *

40

20

0

* * *

*

*

*

1% 0.1%

80

60

1% 0.1%

*

*

60

40

20

0

0

1% 0.1%

Validation rate

80

Validation rate

80

20

*

* *

*

*

* *

*

*

1% 0.1%

*

*

* 50% 20% 10% 5%

1% 0.1%

FDR

GDSC CCLE FDR: False Discovery Rate * The validation ratio is significant

60

40

20

*

* *

40

20

*

*

60

Sorafenib

80

40

*

* *

*

*

*

0

Erlotinib 100

60

*

*

FDR

100

FDR

*

*

50% 20% 10% 5%

100

50% 20% 10% 5%

*

1% 0.1%

Nilotinib

80

FDR

PHA−665752

*

FDR

0

50% 20% 10% 5%

FDR

*

50% 20% 10% 5%

100

20

0

50% 20% 10% 5%

1% 0.1%

100

40

*

*

* *

40

lapatinib

Validation rate

*

60

*

*

0

50% 20% 10% 5%

*

80

*

FDR

100

Validation rate

** *

**

40

*

Nutlin−3 * **

*

60

1% 0.1%

60

20

FDR

100

*

*

* *

0

FDR

80

40

20

0

50% 20% 10% 5%

20

*

*

*

60

Validation rate

*

* 40

Validation rate

80

Validation rate

80

*

1% 0.1%

Crizotinib 100

60

*

FDR

100

40

*

*

*

*

*

FDR

100

60

*

40

0

50% 20% 10% 5%

*

60

20

1% 0.1%

AZD6244 *

100

*

100

Validation rate

Validation rate

80

40

*

*

FDR

TAE684

Validation rate

*

*

*

FDR

Validation rate

*

60

20

50% 20% 10% 5%

*

*

*

100

Validation rate

80

PD−0325901 * **

Validation rate

17−AAG 100

Validation rate

Validation rate

paclitaxel 100

*

*

*

*

0

50% 20% 10% 5%

FDR

1% 0.1%

50% 20% 10% 5%

1% 0.1%

FDR

Supplementary Figure 18: Proportion of validated biomarkers with decreasing FDR using common cell lines screened both in GDSC and CCLE. Gene-drug associations are identified using molecular profiles including gene expression, mutation and copy number variation data and continuous published AUC as input and output of a linear mode, respectively. The symbol ’*’ represents the significance of the proportion of validated gene-drug associations, computed as the frequency of 1000 random subsets of markers of the same size having equal or greater validation rate compared to the observed rate.

17−AAG

PD−0325901

100

100

80

80

60

40

* 40

20

20

0

0

50% 20% 10% 5%

*

60

1% 0.1%

*

*

*

* *

*

40

**

0

* *

*

*

1% 0.1%

50% 20% 10% 5%

PLX4720

*

*

**

**

*

*

*

*

0

*

40

1% 0.1%

* *

1% 0.1%

60

40

*

*

0

*

*

*

20

1% 0.1%

1% 0.1%

* *

80

** ** *

60

40

*

*

*

**

20

0

1% 0.1%

50% 20% 10% 5%

1% 0.1%

FDR

Sorafenib 100

*

*

*

*

*

*

*

*

60

40

*

FDR

*

* *

*

80

60

*

*

40

20

0

FDR

*

50% 20% 10% 5%

Validation rate

80

Validation rate

80

*

*

Erlotinib 100

50% 20% 10% 5%

Nilotinib

*

40

*

*

100

0

PHA−665752

50% 20% 10% 5%

*

FDR

100

1% 0.1%

*

60

*

*

FDR

*

80

20

50% 20% 10% 5%

FDR

**

*

**

0

lapatinib

*

*

*

*

*

*

*

100

0

*

*

*

*

*

40

FDR

*

*

*

60

60

20

50% 20% 10% 5%

*

80

20

50% 20% 10% 5%

*

Nutlin−3

40

*

40

1% 0.1%

100

Validation rate

Validation rate

*

80

80

60

FDR

100

Crizotinib

80

*

1% 0.1%

FDR

100

0

FDR

50% 20% 10% 5%

100

20

Validation rate

50% 20% 10% 5%

Validation rate

*

0

60

0

PD−0332991

* **

*

20

*

**

*

* *

*

FDR

*

*

*

60

* 40

*

*

60

1% 0.1%

Validation rate

80

* *

80

20

50% 20% 10% 5%

Validation rate

*

*

Validation rate

Validation rate

**

*

AZD0530 ** **

*

*

40

20

1% 0.1%

100

80

*

*

*

*

*

0

TAE684

60

*

40

FDR

100

20

*

*

*

*

60

20

50% 20% 10% 5%

FDR

20

**

*

Validation rate

Validation rate

Validation rate

* 80

AZD6244 100

* Validation rate

paclitaxel * * *

Validation rate

*

100

**

*

* *

* **

GDSC CCLE FDR: False Discovery Rate * The validation ratio is significant

*

0

50% 20% 10% 5%

FDR

1% 0.1%

50% 20% 10% 5%

1% 0.1%

FDR

Supplementary Figure 19: Proportion of validated biomarkers with decreasing FDR using all cell lines in each study. Gene-drug associations are identified using molecular profiles including gene expression, mutation and copy number variation data and continuous published AUC as input and output of a linear mode, respectively. The symbol ’*’ represents the significance of the proportion of validated gene-drug associations, computed as the frequency of 1000 random subsets of markers of the same size having equal or greater validation rate compared to the observed rate.

CCLE/GDSC drugs

B

CCLE/GDSC drugs 2.0

A

●

●

wilcoxon pvalue=2e−04

1.0

1.0

distance(IC50)

1.2

1.5

● ● ● ● ●

0.8

distance(AUC)

1.4

wilcoxon●●●pvalue=0.13

0.4

0.5 ● ● ●

Similar

C

Different

0.0

0.6

● ●

Similar

Different

CCLE/GDSC drugs ●

wilcoxon pvalue=6.3e−05 ● ●

0.4 0.2

ABC

0.6

0.8

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

0.0

●

Similar

Different

Supplementary Figure 20: Comparison of the distance between similar drugs versus different drugs using (A) distance based on 1-pearson correlation of published AUC; (B) distance based on 1-pearson correlation of published IC50 ; and (C) Distance based on median ABC. The red point shows the distance between AZD6482 replicates in GDSC.

Supplementary Tables

paclitaxel 17-AAG PD-0325901 AZD6244 TAE684 AZD0530 PD-0332991 Crizotinib PLX4720 Nutlin-3 lapatinib Nilotinib PHA-665752 Erlotinib Sorafenib

# GDSC 0 39 455 0 0 2 19 41 20 30 78 1256 39 134 31

# CCLE 0 435 811 418 221 851 3 12 149 9 910 865 7 0 1

% GDSC

% CCLE

% Both

8 30 0 0 0 86 72 11 65 8 42 85 100 97

88 53 100 100 100 14 21 78 20 91 29 15 0 3

4 17 0 0 0 0 7 11 15 1 30 0 0 0

Supplementary Table 1: Table reporting the total number of gene-drug associations identified using continuous published AUC and only the cell lines in common between GDSC and CCLE. The proportion of associations that are dataset-specific or reproducible across GDSC and CCLE are provided in the last three columns. The column ’% Both’ reports the overlap of gene-drug associations between the two studies, as computed using the Jaccard index.

paclitaxel 17-AAG PD-0325901 AZD6244 TAE684 AZD0530 PD-0332991 Crizotinib PLX4720 Nutlin-3 lapatinib Nilotinib PHA-665752 Erlotinib Sorafenib

# GDSC 1 2950 2847 603 97 31 2635 235 142 2609 291 1328 164 174 30

# CCLE 2119 978 738 1301 318 314 0 159 279 9 2482 1975 0 757 245

% GDSC 0 68 58 27 22 9 100 57 29 99 9 35 100 15 10

% CCLE 100 23 15 58 71 88 0 39 57 0 80 53 0 67 86

% Both 0 9 27 15 7 3 0 4 14 0 10 12 0 17 4

Supplementary Table 2: Table reporting the total number of gene-drug associations identified using continuous published AUC and all cell lines in GDSC and CCLE. The proportion of associations that are dataset-specific or reproducible across GDSC and CCLE are provided in the last three columns. The column ’% Both’ reports the overlap of gene-drug associations between the two studies, as computed using the Jaccard index