in silico prescription of anticancer drugs reveals targeting opportunities

In Silico Prescription of Anticancer Drugs Reveals Targeting Opportunities

Nuria Lopez-Bigas @nlbigas ICREA Research Professor at University Pompeu Fabra

Barcelona http://bg.upf.edu

Drugs targeting Cancer Drivers

BRAF (V600E) Vemurafenib

Drugs targeting Cancer Drivers

BRAF (V600E) Vemurafenib

How many patients could benefit from current and future targeted drugs against cancer drivers?

6792 tumor samples covering 28 different cancer types

Others from literature

Landscape of therapeutic opportunities of drugs targeting cancer drivers in a pan-cancer cohort

Tumor type Tumor Type description Projects Samples

ALL Acute lymphocytic leukemia 3 122

AML Acute myeloid leukemia 1 196

BLCA Bladder carcinoma 1 98

BRCA Breast carcinoma 6 1148

CLL Chronic lymphocytic leukemia 2 290

CM Cutaneous melanoma 2 369

COREAD Colorectal adenocarcinoma 2 229

DLBC Diffuse large B cell lymphoma 1 23

ESCA Esophageal carcinoma 1 146

GBM Glioblastoma multiforme 2 379

HC Hepatocarcinoma 2 90

HNSC Head and neck squamous cell carcinoma 2 375

LGG Lower grade glioma 1 169

LUAD Lung adenocarcinoma 2 391

Tumor type Tumor Type description Projects Samples

LUSC Lung squamous cell carcinoma 1 174

MB Medulloblastoma 2 210

MM Multiple myeloma 1 69

NB Neuroblastoma 1 210

NSCLC Non small cell lung carcinoma 1 31

OV Serous ovarian adenocarcinoma 1 316

PA Pylocytic astrocytoma 2 101

PAAD Pancreas adenocarcinoma 3 214

PRAD Prostate adenocarcinoma 1 243

RCCC Renal clear cell carcinoma 1 417

SCLC Small cell lung carcinoma 2 69

STAD Stomach adenocarcinoma 2 161

THCA Thyroid carcinoma 1 322

UCEC Uterine corpus endometrioid carcinoma 1 230

Total 28 6792

6792 tumor samples covering 28 different cancer types


Assign targeted drugs to patients

Find drugs targeting those drivers

targeted drugDriver

Identify Driver Events

Driver

...



targeted drugDriver


Driver

...

Therapeutic Landscape of Cancer

Drivers



targeted drugDriver


Driver

...

Normal Cell Tumor Cell

Sequencing

Somatic mutations

Mrs. McDaniel

Sequencing tumor genomes

Normal Cell Tumor Cell

Sequencing

Somatic mutations

Mrs. McDaniel

Sequencing tumor genomes

Which mutations are drivers?

Driver alterations: Needle in a haystack

Tumor genomes often have thousands of mutations

Yates and Campbell et al, Nat Rev Genet 2012

Cancer is an evolutionary process

Find signals of positive selection across tumour re-sequenced genomes

How to Identify Cancer Drivers?

Recurrence

Identify genes mutated more frequently than background mutation rate

MuSiC-SMG / MutSigCV

Mutation

Signals of positive selection

Recurrence


MuSiC-SMG / MutSigCV

Mutation


Challenge: Background mutation rate varies across patients and genomic regions

Replication time

Stamatoyannoppoulos et al., Nature Genetics 2009 Schuster-Böckler and Lehner, Nature 2011

Chromatin organization


Functional impact bias (FMbias)

Mutation

OncodriveFM

Gonzalez-Perez and Lopez-Bigas. NAR 2012

Functional Impact


• Based on consequences of mutations (eg. synonymous is

lowest and STOPgain, frameshift indel highest)

• And SIFT, PPH2 and MA for missense

How to measure functional impact of mutations?


Mutation

OncodriveFM

Gonzalez-Perez and Lopez-Bigas. NAR 2012

Functional Impact



Mutation

One example: TCGA Glioblastoma FMbiasqvalue

TP53PTENEGRFNF1RB1FKBP9ERBB2PIK3R1PIK3CAPIK3C2GIDH1ZNF708FGFR3CDKN2AALDH1A3PDGFRAFGFR1MAPK9DCNPIK3C2ACHEK2PSMD13GSTM5

8.5E-118.5E-118.5E-118.5E-112.5E-98.5E-111.2E-81.2E-82.3E-40.0028.5E-117.4E-103.2E-92.5E-85.2E-51.5E-62.0E-62.2E-51.5E-66.2E-5111

not mutatedMA score

5-2 0 0.05 10

FM bias qvalue

OncodriveFM

Functional Impact


Mutation clustering

Mutation

OncodriveCLUST

Tamborero et al., Bioinformatics 2013

OncodriveFM

OncodriveCLUST

MuSiC-SMG

F

C

RUsing complementary signals help obtaining a more comprehensive list of cancer drivers

Tamborero et al., Scientific Reports 2013

OncodriveFMF

OncodriveCLUSTC

Rec

urre

nce


FM b

ias

Identify genes with a bias towards high functional mutations (FM bias)

Identify genes with a significant regional clustering of mutations

CLU

ST b

ias

Functional Impact (FI) Score

MutSig-CVR

Mutation

Mutation

Mutation

Tumor somatic mutations of 6792 tumor samples from 49 projects from 28 different cancer types


459 Cancer Driver Genes

Complementary signals of positive selection

156 Bladder Cancer, 75 Glioblastoma, 184 Breast Cancer, ...

Tumor type Tumor Type description Projects Samples Mutational driver genes

ALL Acute lymphocytic leukemia 3 122 12

AML Acute myeloid leukemia 1 196 32

BLCA Bladder carcinoma 1 98 156

BRCA Breast carcinoma 6 1148 184

CLL Chronic lymphocytic leukemia 2 290 38

CM Cutaneous melanoma 2 369 250

COREAD Colorectal adenocarcinoma 2 229 95

DLBC Diffuse large B cell lymphoma 1 23 10

ESCA Esophageal carcinoma 1 146 98

GBM Glioblastoma multiforme 2 379 75

HC Hepatocarcinoma 2 90 30

HNSC Head and neck squamous cell carcinoma 2 375 167

LGG Lower grade glioma 1 169 50

LUAD Lung adenocarcinoma 2 391 181

Tumor type Tumor Type description Projects Samples Mutational driver genes

LUSC Lung squamous cell carcinoma 1 174 147

MB Medulloblastoma 2 210 24

MM Multiple myeloma 1 69 18

NB Neuroblastoma 1 210 27

NSCLC Non small cell lung carcinoma 1 31 11

OV Serous ovarian adenocarcinoma 1 316 83

PA Pylocytic astrocytoma 2 101 2

PAAD Pancreas adenocarcinoma 3 214 21

PRAD Prostate adenocarcinoma 1 243 88

RCCC Renal clear cell carcinoma 1 417 105

SCLC Small cell lung carcinoma 2 69 61

STAD Stomach adenocarcinoma 2 161 175

THCA Thyroid carcinoma 1 322 32

UCEC Uterine corpus endometrioid carcinoma 1 230 149

Total 48 6792 459

Mutational cancer drivers in 28 cancer types

175

165 121

pooled analysisper-project analysis

459 drivers : per-project and pooled analysis

0.4

0.3

0.2

0.1

TP53

PIK3CA

PTENAPC

CDKN2CHRASSF3B1

BladderBreastColonGlioblastomaHead and neck

KidneyLeukemia (AML)Lung adenocarcinomaLung squamousOvarianEndometrial


Mut

atio

n fre

quen

cy

Most driver genes are lowly frequently mutated

0.4

0.3

0.2

0.1

TP53

PIK3CA

PTENAPC

CDKN2CHRASSF3B1

8 / 3205 (0.002)

BladderBreastColonGlioblastomaHead and neck

KidneyLeukemia (AML)Lung adenocarcinomaLung squamousOvarianEndometrial


Mut

atio

n fre

quen

cy

Most driver genes are lowly frequently mutated

459 Cancer Drivers

Act = Gain/Switch of Function LoF = Loss of Function

Classification of drivers in Act and LoF

Schroeder et al., Bioinformatics 2014



Mutation

Clustered mutations

MutationTruncating mutation

Truncating mutations

Act genes

LoF genes

CNAs increased

CNAs decreased

OncodriveROLERandom Forest Classifier




Mutation

Clustered mutations



Act genes

LoF genes

CNAs increased

CNAs decreased


AccuracyMCC

0.930.85




207 LoF 169 Act 83 Unclassified

Classification of 459 drivers

Mutation

Clustered mutations



Act genes

LoF genes

CNAs increased

CNAs decreased


AccuracyMCC

0.930.85


Few drivers dominate the clonal landscapem

edia

n pr

opor

tion

of c

lona

l cel

l fre

quen

cy

73 “Major” drivers

http://www.intogen.org

Including actionable CNA and Gene Fusion drivers6792 tumors from 28 cancer types Somatic mutations


Copy Number Alterations

Expression

4068 tumors from 16 cancer types

Fusion Genes


Copy Number Alterations

Expression

4068 tumors from 16 cancer types

Fusion Genes

437

9

72

190

1

mutational drivers459

29 CNA drivers

driver fusion genes

10

Num

ber o

f driv

er e

vent

s

90% of tumors show at least one driver alteration

0

4068

417

921

801

624

417

282

16912883

147

>10109876543210

0.47

0.67

0.90

Num

ber o

f sam

ples

Mean driver events per sample = 5.34 Median driver events per sample = 3

95 189 278 172 223 221 168 297 169 105 311 750 317 415 179 179

158 99 82 147 149 247 52 163 174 171 85 185 33 102 32 76

7.9 3.6 3.8 4.7 5.3 10.1 2.7 4.5 5.4 5.8 2.0 2.5 1.2 1.9 1.7 1.9

7 3 4 5 4 8 3 4 5 3 2 2 1 2 1 2

1.00 0.99 0.99 0.99 0.98 0.97 0.96 0.96 0.95 0.94 0.94 0.87 0.79 0.80 0.77 0.67

Number of samples

Number of driver genes

Mean driver events per sample

Median driver events per sample

Proportion of samples with at least 1 driver event

BLC

A

CO

READ

GBM

LUSC

UC

EC

CM

LGG

HN

SC

LUAD

STAD

OV

BRC

A

THC

A

RCC

C

AML

PRAD

Important differences per tumor typePr

opor

tion

of s

ampl

es

Num

ber o

f driv

er e

vent

s

Cancer Drivers Database


Driver

...



targeted drugDriver

PTEN*

Temsirolimus

Vemurafenib(V600E) (R130G)

PI3K

AKT

mTOR

BRAF*

(R175H)

TP53*

rAd-p53

Strategies to target cancer drivers

Indirect Gene TherapyDirect

Drugs targeting cancer drivers, their clinical guidelines, dependencies and

rules for repurposing

Drivers Actionability Database

Extensive search for drugs targeting cancer driversFDA approvedIn clinical trialsPre-clinical / Ligand

0 475

Drugs targeting cancer drivers

non-targeteddrivers

379

targeted drivers

96

0 475


5

FDA approved drugs57 molecules51 targeted drivers

Drugs in Clinical Trials47 molecules

66 targeted drivers

Pre-clinical ligands20470 molecules

77 targeted drivers

6 71

3095

3915

111

7

19

4

non-targeteddrivers

37951 26 19

0 475


non-targeteddrivers

379

5

Direct targeting

Indirect targeting

Gene Therapy2

74

7

13

51 26 19

0 475


123

Potentially Druggable

Potentially Biopharmable

35 26

targeted drivers

non-targeteddrivers

195

non-LoF drivers LoF drivers

51 26 19

Cancer Drivers Actionability Database

• Drug-target interactions

• Rules for prescribing FDA approved drugs according to clinical guidelines

• Rules for repurposing FDA approved drugs

• Rules for prescription of experimental drugs• Tumor type repurposing • Disease repurposing • Alteration repurposing • Indirect target repurposing • Off-target repurposing

• Drugs in clinical trials • Pre-clinical molecules



targeted drugDriver

Cancer Drivers Database


Driver

...




targeted drugDriver

Mutations CNAs Gene Fusions

In silico drug prescription


Cancer Drivers database

Driver alterations in the tumor

Prescribed drugs targeting drivers

In silico drug prescription to 4068 patients

5.9% of patients are assigned FDA approved drugs following clinical guidelines

FDA approved drug following clinical

guidelines241 - 5.9%

Tumor Type Repurposing

Disease Repurposing

Alteration Repurposing

Indirect Target Repurposing

Off-target strong Repurposing

Off-target mild Repurposing

Up to 40% are assigned FDA approved drugs considering repurposing opportunities

1635 - 40.2%


guidelinesFDA approved drug repurposing

241 - 5.9%

240

692

1153

32

74

Tier 1Tier 2

Tier 3

274

700

835

0

4068

Up to 40% are assigned FDA approved drugs considering repurposing opportunities

Tier 1

Tier 2

Tier 3

1635 - 40.2%



241 - 5.9%

5.9%241

697

587

11040%

1635 - 40.2%



Drug in clinical trials2726 - 67%

241 - 5.9%

0

4068

67% of patients could benefit from drugs currently in clinical trials

73%

40%

241

587

110

1346

5.9%

1635 - 40.2%




241 - 5.9%

67% of patients could benefit from drugs currently in clinical trials

Direct Target

Gene Therapy

Indirect Target

1672

1254

1284

1635 - 40.2%




Pre-clinical ligands2055 - 50.5%

241 - 5.9%

49.3% of patients have mutated Act drivers bound with high affinity by a pre-clinical ligand

0

4068

241

697

587

110

1346

1635 - 40.2%




Pre-clinical ligands2055 - 50.5%

241 - 5.9%

Therapeutic landscape of cancer drivers

0

4068

241

697

587

110

1346

Potentially biopharmable


Unknown druggability

No driverdetected

Important differences per tumor type

0

0.75

0.5

0.25

1

FDA drug following clinical guidelinesFDA drug repurposing Tier 1FDA drug repurposing Tier 2FDA drug repurposing Tier 3Drug in clinical trialsPre-clinical LigandPotentially druggablePotentially biopharmableUnknown druggabilityNo driver detectedPr

opor

tion

of p

atie

nts

BLC

A

CO

READ

GBM

LUSC

UC

EC

CM

LGG

HN

SC

LUAD

STAD

OV

BRC

A

THC

A

RCC

C

AML

PRAD

FDA approved drugs

377 231 254

336 336

291 134 226

148 116 46

144 8 136

138 138

60 60

46 24 23

44 36 8

43 43

33 16 20 8

34 34

32 32

27 27

25 25

23 23

22 22

21 21

19 19

15 15

2 8 85 25 107 22 4 5 2 9 14 40 5 14 35

2 7 6 2 112 3 12 4 1 3 182 2

9 3 148 16 3 17 39 22 3 19 7 2 3

2 20 19 6 27 8 7 14 23 17 5

8 9 18 109

4 114 9 7 4

24 36

3 9 3 3 2 14 6 6

2 4 21 2 3 11 1

2 2 4 10 25

13 10 3 7 6

4 3 2 3 13 7 2

2 2 2 1 3 3 2 17

2 23 2

25

7 16

10 9 3

2 2 11 5 1

3 2 5 3 3 1 2

15

PTEN Temsirolimus

BRAF Dasatinib,Vemurafenib,Nilotinib,Pazopanib,Dabrafenib,...

EGFR Dasatinib, Erlotinib hydrochloride ,Cetuximab,Lapatinib,...

NF1 Sorafenib, Everolimus

ERBB2 Dasatinib,Pertuzumab, Trastuzumab, Afatinib dimaleate,,..

APC Erythromycin

IGF1R Crizotinib, Nordihydroguaiaretic acid

MET Axitinib, Crizotinib, Cabozatinib S-malate

FGFR2 Sunitinib, Regorafenib, Axitinib, Pazopanib

MTOR Everolimus, Tacrolimus, Sirolimus, Temsirolimus, Pimecrolimus,...

FGFR3 Sinitinib, Pazopanib, hydrochloride,AxitnibMAP3K4 Bosutinib, Dasatinib

FLT3 Axitinib, Sunitinib malate, Crizotinib, Sorafenib tosylate, ...

HDAC9 Romidepsin, Vorinostat

FGFR1 Sunitinib, Pazopanib hydrochloride, Regorafenib, Axitinib

KDR Pazopanib hydrochloride, Ramucirumab, Axitinib, Vandetanib,...

ERBB3 Gefitinib, Dasatinib, Vandetanib, Bosutinib

MAP2K1 Sunitinib, Bosutinib, Trametinib dimethyl sulfoxide

TAOK2 Sorafenib, Ruxolitinib, Crizotinib

AURKA Doxorubicin, Axitinib, Crizotinib

Total samples ta

rgeted

Samples targeted mutations

Samples targeted CNAs

Samples targeted fusio

ns

Target

Therapeutic agents

Mutation frequency

0 0.1 1

BLC

A C

ORE

AD

GBM

LU

SC

UC

EC

CM

LG

G

HN

SC

LUAD

ST

AD

OV

BRC

A TH

CA

RCC

C

AML

PRAD

Drugs in clinical trials

1250 1230 198

566 68 540

525 525

377 231 254

291 134 226

212 212

188 188

144 8 136

134 134

129 129

80 5 75

69 69

60 60

46 24 23

44 36 8

43 43

42 38 8

39 16 20 8

34 34

32 32

TP53 rad-p53 Gene mono therapy

CDKN2A LEE011, Palbociclib

PIK3CA Idelalisib, PKI-587, Stautosporine, GSK2636771

PTEN MK2206,GSK2636771

EGFR Dacomitinib, Cediranib, Stautosporine, Alisertib, Vatalanib

KRAS MEK162, VS-6063

IDH1 AG-120

ERBB2 Stautosporine, Tanespimycin

NRAS MEK162, Selumetinib

CCNE1 Stautosporine, Alvocidib

CDK4 LEE011,Stautosporine,Palbociclib,Alvociclib

MDM2 DS-3032

IGF1R Stautosporine, Linsitinib

MET Cediranib,Stautosporine,Midostaurin

FGFR2 Cediranib,Stautosporine,Dovitinib

MTOR PKI-587,Rapamycin

BRCA1 Olaparib, Veliparib

FGFR3 Cediranib,Dovitinib,Stautosporine,Alisertib

HRAS Lonafernib,Selumetinib

FLT3 Dovitinib,Stautosporine,Midostaurin,Enzastaurin

45 91 72 118 53 28 82 166 71 38 242 204 3 15 22

36 169 62 81 18 115 39 1 32 13

33 24 25 107 6 9 58 20 2 240 1

2 8 85 25 107 22 4 5 2 9 14 40 5 14 35

9 3 148 16 3 17 39 22 3 19 7 2 3

86 2 2 43 3 47 11 2 5 3 8

3 14 2 2 11 130 2 2 2 18 2

8 9 18 109

2 17 6 65 2 2 1 24 15

16 17 66 30

47 14 8 11

7 25 10 14 13

24 36

3 9 3 3 2 14 6 6

2 4 21 2 3 11 1

2 2 4 10 25

4 2 2 6 8 3 5 12

13 10 3 7 6

4 8 14 3 2 6

4 5 2 10 11 2

2 2 2 1 3 3 2 17

Target

Therapeutic agents

Total samples ta

rgeted

Samples targeted mutations

Samples targeted CNAs

Samples targeted fusio

ns

Mutation frequency

0 0.1 1

BLC

A C

ORE

AD

GBM

LU

SC

UC

EC

CM

LG

G

HN

SC

LUAD

ST

AD

OV

BRC

A TH

CA

RCC

C

AML

PRAD

0.16

1081

1389

962

445

135

0.39

0.73

0.05

39% of patients are prescribed targeted therapies for more than one driver considering FDA approved drugs and drugs in clinical trials

Num

ber o

f tar

gete

d dr

iver

s

012345>5

4068

Important differences per tumor type

1

0.75

0.5

0.25

0

Num

ber o

f tar

gete

d dr

iver

s

012345>5

Prop

ortio

n of

pat

ient

sBL

CA

CO

READ

GBM

LUSC

UC

EC

CM

LGG

HN

SC

LUAD

STAD

OV

BRC

A

THC

A

RCC

C

AML

PRAD

0 475


12351 26 19 35 26

targeted drivers

non-targeteddrivers

195




0 475


12351 26 19 35 26

targeted drivers

non-targeteddrivers

195


80 good driver candidates to target

e.g. CTNNB1: Beta-catenin (28.3% mutated in Uterine cancer) RHOA: Ras Homolog Family Member A SPOP: Speckle-Type POZ Protein PTPRU: Protein Tyrosine Phosphatase, Receptor Type, U PLCB1: Phospholipase C, Beta 1



Summary

Design panels for early detection and diagnosis

Interpret the genome of newly sequenced tumors

Cancer Genome Interpreter

Prioritize targets for novel drug development

0.00

0.25

0.50

0.75

1.00

APC

TP53

KRAS

PIK3

CA

FBXW

7

SMAD

4

NR

AS

0.600.77

0.87 0.88 0.88 0.89 0.90

Panel Design

Target Prioritization

Rubio-Perez et al., In Silico Prescription of Anticancer Drugs to Cohorts of 28 Tumor Types Reveals Targeting Opportunities. Cancer Cell (2015)

Acknowledgements

@bbglab@nlbigas

http://bg.upf.edu/blog

Albert AntolinJordi Mestres

Christian Perez-LlamasJordi Deu-Pons

Michael Schroeder

Carlota RubioNuria Lopez-Bigas

David Tamborero

Abel Gonzalez-Perez

Loris Mularoni

Drugs!

Iden%fica%on*of*pa%ent*cancer*driver*genes**

Iden%fica%on*of*response*to*therapy*biomarkers**

Iden%fica%on*of*ac%onable*muta%ons**

Iden%fica%on*of*clinical*trials*suitable*for*the*pa%ent**

Pa%ent*results*

http://bg.upf.edu/blog

in silico prescription of anticancer drugs reveals targeting opportunities

Health & Medicine

patientsfind drugs

tumor samples

neck squamous cell carcinoma

sclc small cell lung

different cancer typesothers

future targeted drugs

thca thyroid carcinoma

esca esophageal carcinoma