NorCal SOT Fall Symposium: New Frontiers in Oncology Drug Development September 27, 2012 Jocelyn...

NorCal SOT Fall Symposium: New Frontiers in Oncology Drug DevelopmentSeptember 27, 2012

Jocelyn HolashNovartis Institutes for Biomedical Research

Preclinical strategies to help better identify responder populations in the clinic

| NorCal SOT Fall Symposium | Holash | September 20122

Limitations of existing preclinical model systems

• The number of models used in preclinical evaluation typically under-represents the number of distinct tumor types

• Models often inadequately characterized at the molecular level (making alignment to human disease impossible)

• In vitro selection pressures alleviate dependence on key oncogenic pathways

• In vitro models do not replicate stromal-tumor cell interactions

• Use of immuno-compromised animals eliminates the ability to study modulators of antitumor immunity, and suffer from cross-species imperfections in ligand-receptor interactions etc

• Low throughput of in vivo models limits the number of tumor types that can be tested

Building a robust pre-clinical translational infrastructure

Human Cancer Therapeutic-ProfilingModel Systems• Build databases from

public sources

• Fill gaps with external and internal efforts:- Sequencing project

• Cancer Cell Line Encyclopedia

• Murine tumor allograft models

• Primary tumor models

• CLiP (cell line profiling)

• Drug Combinations Zalicus screen

• Drug resistance

• shRNA pooled screens

Improving indication selection and patient stratification – what can we do pre-clinically ?

Building a robust pre-clinical translational infrastructure

3 | NorCal SOT Fall Symposium | Holash | September 2012

Human Cancer

3 key human cancer databases integrate internal and external (GEO, TCGA, COSMIC) data meticulous sample curation consistent QC and data normalization accessible for analysis and visualization

OncExpress OncCNAOnc*Base• N= ~50,000 samples

• Expression data for tumors, models and normal tissue

• Analytical tools: Prevalence analysis, Outlier analysis, Correlation analysis

• N= ~580,000

• Allows retrieval of mutation data in human cancer

• Analytical tools: Find gene mutation frequency, recurrent gene mutations, lineage patterns, structure

• N = ~6,000

• Copy number data base derived from SNP array analysis

• Analytical tools: Find recurrent chromosome alterations, delineate regions of conserved copy number change, correlate with expression

Human cancer databases provide the foundation

4| NorCal SOT Fall Symposium | Holash | September 2012


Cancer Cell Line Encyclopedia: 1000 cell lines annotated by molecular profiles and response Goal: to identify predictive biomarkers for patient selection

Expression profiling Affy U133 Plus2 array

Copy number/LOH analysis, genotyping: Affy SNP6.0 array

Mutation analysis:• Oncomap Ver 3.0, 353 mutations (Sequenom

iPlex) • Hybrid (exon) capture ~1600 genes (Illumina)

Compound Sensitivity2000 cpd x 500 lines x 8 pt IC50

shRNA Sensitivity145 cell lines x ~2000 genes


Cell lines model the genetic diversity of tumors


CCLE portalhttp://www.broadinstitute.org/ccle

| NorCal SOT Fall Symposium | Holash | September 2012810 15 20 25

0

500

1000

1500

2000Vehicle0.6 mg/kg bid6 mg/kg bid60 mg/kg bid300 mg/kg bid

Time Post-Implant (Days)

Tu

mo

r V

olu

me

(mm

3)

mea

n

SE

M

Progress towards targeting the RAS/RAF/MAPK pathway

Kinase Assay IC50 (nM)

BRAFV600E 0.4

BRAF 0.5

CRAF 0.3

Cell-Based – A375

pERK 3

Proliferation 4

LGX818 Potency

A375 (BRAFV600E) xenograft

pERK Ki67

Veh

icle

10 m

g/k

g

Growth Factors

RTKs

Grb2/SOS

RAS

RAF

MEK

ERK


The Cancer Cell Line Encyclopedia: CLiP

Cpds (2

00

0)

Therapeutic Profiling

Sensitive Insensitive Cell Lines (>500)

LGX818

CliP: Large-scale compound profiling using CCLE cell lines


LGX818 Cellular GI50(uM) across 501 cell lines

1-

0.1-

0.01-

LGX818 is a potent and highly selective RAF kinase inhibitor

LGX818 is selective for cells expressing BRAFV600E/D/KIC

50 (

uM

)

Melanoma and CRCV600E/D/K lines are themost sensitive

BRAF mutation status

V600E

V600D/K

Non -V600

Wildtype

Sensitivity is greatest within melanoma and CRC lineages

11 | NorCal SOT Fall Symposium | Holash | September 2012


A genotype centric view of the “cube” (CLE)Pharmacologic sensitivity for 1300 compounds in >500 cell lines

BRAF V600E mutated cell line

Cell Lines (>500)

Cp

ds (1

30

0)

Sensitive Insensitive


BRAF mutant cancer cell lines are more sensitive to RAF or MEK inhibitors

Compound Rank

Class Mechanism of Action Rank

RA

F in

hib

ME

K in

h

BR

AF

mut

Sel

ectiv

ityB

RA

F m

ut S

elec

tivity


A Computational Framework for IdentifyingPredictive Biomarkers

Copy number

Expression

Explicit feature selection (Fisher or Wilcoxon +

local FDR)

Categorical machine

learning and cross-validation (predict class)

Mutation

Lineage

Sensitive

Intermediate

Insensitive

Cell line rank

Co

mp

ou

nd

re

spo

nse

Assign Response class

Output of predictive “features”

Effe

ct v

s co

ntro

l

Concentration (log)

IC50

Amax

AUC


Genetic features identified as predictors of sensitivity

LGX818 “feature matrix” 50K features#1 predictive feature: BRAF mutation

Compound Target(s) Feature Rank

LGX818 BRAF BRAF mut 1*

RAF265 BRAF BRAF mut 1*

lapatinibEGFR, ERBB2 ERBB2 amp 1

BYL719 PI3Ka PIK3CA mut 1

PD0325901 MEK BRAF mut 10*

AZD6244 MEK BRAF mut 1*

PF2341066 MET Met amp 3

*additional MAPK pathway features in top 50 (DUSP, SPRY, ETV, NRAS for MEK inhibitors)

Cell Lines

Fea

ture

s


BRAF

MEK1ERK2

Finding synthetic lethality through shRNA screeningPooled shRNA screens across many cell lines

Growth: Inhibition Induction

PooledshRNA POOLS

T24Count

Cell Line

Countby NextGenSequencing

Day 0

Day 7

Day 14


Com

bina

tions

Mut

atio

ns

• Mid-scale hypothesis directed screens enabled through a global effort e.g. PI3K-RAS pathway combination screens across melanoma and CRC cell lines

0

100

200

300

400

500

600

0 5 10 15 20

Tumor

Volum

e (mm

3+/

-SEM

)

Days Post-dose

Vehicle

RAF265 25 mg/kg q4dx7

BKM120 20 mg/kg qdx19

RAF265 25 mg/kg + BKM120 20 mg/kg

Vehicle

RAF265 25mg/kg q4d

BKM120 20 mg/kg qd

RAF265 + BKM120

* p < 0.05 vs Vehicle

Days

Mea

n tum

or v

olum

e (m

m3) �S

EM

RAF265 + BKM120 HT29 (B-RafV600E; PI3KaP449T)

Colon Xenograft Model

• Large-scale systematic discovery of combination activity on-going in collaboration with Zalicus.

Towards the systematic study of combination effects


Rationale/Goal

• Human tumors propagated in culture undergo artificial selection

e.g. p16 deletion increases loss of Hedgehog signaling loss of Wnt signaling

• Primary propagation in nude mice may lead to more predictive models or provide models that otherwise are very limited

e.g. Pancreatic xenograft modelsvs. pancreatic cancer cell lines

Building predictive preclinical modelsPrimary human tumor xenograft models

Primary Tumor

Implant


Novartis Primary Human tumor model bank

Primary Tumor

Implant

Tumor type Received Established

Colon 125 63

Lung 196 55

Breast 362 47

Pancreas 159 52

Ovary 231 39

Sarcoma 199 47

Kidney 328 25

Melanoma 61 27

Uterus 25 10

Esophagus 21 10

Brain 49 8

Lymphoma 73 4

Stomach 43 4

Liver 56 2

Intestine 8 3

Others 179 15

Total 2115 410

Molecular Annotations

Progress(total = 410)

SNP 6.0 286

Affy U133 288

RNA-Seq 55

Whole Exome 12

2K Exome 185


In vitro to in vivo translation: LGX818 is efficacious only in human tumor xenografts expressing BRAFV600E

22 24 26 28 30 32 34 360

300

600

900

1200

Vehicle bid

20mg/kg bid


Tum

or V

olum

e (m

m3)

mea

n

SE

M HMEX1655 (BRAFwt, KITN822K)

HMEX1906 (BRAFV600E)Malme-3M (BRAFV600E)

15 18 21 24 270

500

1000

1500

2000

Vehicle bid

0.6mg/kg bid6mg/kg bid

60mg/kg bid300mg/kg bid


Tum

or V

olum

e (m

m3)

mea

n

SE

M SW620 (BRAFwt, KRASG13D)

55 60 65 700

100

200

300

400

500

Vehicle qd0.5mg/kg qd3mg/kg qd20mg/kg qd


Tum

or V

olum

e (m

m3)

mea

n

SE

M

**

21

Taking Advantage of Primary Tumors: Modeling Resistance to RAF inhibitors in BRAF(V600E) Human Melanoma Xenografts

Time

Con

cent

ratio

n

• Treatment with vemurafinib models human clinical response: initial tumor regression followed by the emergence of resistant tumors

0

500

1,000

1,500

2,000

2,500

20 40 60 80 100 120 140

Tum

or

volu

me

(mm

3 ) resistant

HMEX1906 Human melanoma xenograft


Serial biopsy of resistant tumors for PD analysis


Gene signature predicted from cell line sensitivity screening accurately predicted 3/3 responses on primary tumor models

A mini-randomized trial of therapeutics in pancreatic cancer

stasis

Tum

or G

row

th

Tested 7 NVS compounds in 9 pancreas primary tumors.

One therapeutic candidate was the most active.

Tumor Growth Curve (HPAX2406)

27 34 41 480

250

500

750

1000

Days post implantation

Tum

or V

olum

e (m

m3 )

mea

n

SE

M


59 66 73 80 870

250

500

750

1000

Days post implantation

Tum

or V

olum

e (m

m3 )

mea

n

SE

M


38 45 52 59 66 730

250

500

750

1000

Days Post Implantation

Tum

or V

olum

e (m

m3 )

mea

n

(SE

M)


57 64 71 78 850

250

500

750

1000


Tum

or V

olum

e (m

m3 )

mea

n

(SE

M)


31 38 45 52 590

250

500

750

1000


Tum

or V

olum

e (m

m3 )

mea

n

(SE

M)


21 28 35 42 490

500

1000

1500


Tum

or V

olum

e (m

m3 )

mea

n

(SE

M)

HPAX2402 HPAX1317 HPAX1959




Primary Mouse Tumor Allograft Models

Primary Tissue

MonitorTumor growth

Passage (P1)

Fix Tissue Flash Freeze

HistologyIHC

Fix / freeze

SNP arraysExp Prof.

Exome seq.

Efficacy studies

Freeze (master stock)

Passage (P2)

Passage (P3)

Passage (P4)(working stock)

Implant

Rationale

• Use of immunocompetent mice supports species-matched host- tumor interactions

• Tumor immunology• Tumor stroma interactions• Developmental signaling pathways

• GEMM-derived tumors may provide broader coverage of disease progression

• Include tissues not available from patients

• Forward genetics approach


Model SystemsMolecularProfiles

Cancer

Human Cancer

CompoundProfiles

Towards curative therapy for cancer

• Define to completion the genetic basis of cancer

• Create a large collection of genetically annotated human cancer cell lines and preclinical animal models representative of human cancers for therapeutic profiling

• Define resistance mechanisms early (prior to clinical entry) and develop either second-generation inhibitors or combination strategies

• Define highly active combinations that can lead to curative therapies


NIBR

Emeryville, Basel

Shanghai

Cambridge

GNF San Diego

Acknowledgements

• External Collaborators and partners• Patients and their families

Date post:	28-Dec-2015
Category:	Documents
Upload:	arline-sims
View:	216 times
Download:	4 times

NorCal SOT Fall Symposium: New Frontiers in Oncology Drug Development September 27, 2012 Jocelyn...

Documents