Decade after BATTLE-1 Trial :

Opportunities and Challenges

Waun Ki Hong, M.D. Vali Papadimitrakopoulou, M.D.

MD Anderson Cancer Center

Disclosures

Scientific Advisory Board

Molecular Health

Imaging Endpoints

PharmAbcine

BATTLE-1

BATTLE-2

BATTLE Like Trials

BATTLE Adjuvant

Challenges and Opportunities

Order of my Presentation

Results of Empirical Targeted

Therapies in Lung Cancer

• INTACT-1 (gefitinib)

• INTACT-2 (gefitinib)

• ISEL (gefitinib)

• TALENT (erlotinib)

• TRIBUTE (erlotinib)

• BMS-099 (cetuximab)

• ESCAPE (Sorafenib)

• Farnesyltransferase inhibitors

• Bexarotene (SPIRIT I and II)

• Glutathione (NOV-002)

• ZEAL (vandetanib)

• ZEST (vandetanib)

• ZEPHYR (vandetanib)

• Affinitak (LY900003) antisense

• IGFR (Figitumumab)

10,000 + patients

Negative Studies Positive Studies (OS)

• BR.21 (erlotinib)

• ECOG 4599 (bevacizumab)

• INTEREST (gefitinib)

Clearly we do not know how to choose the right

therapy for patients with lung cancer

Empirical Targeted Therapy Strategy:

“Shooting in the Dark

“BATTLE” as a Novel Approach in 2004

Biomarker-Integrated Approaches of Targeted

Therapy for Lung Cancer Elimination

BATTLE Strategy For Lung Cancer

Tumor

BATTLE Concepts in 2004

Platform for integrated translational research

1.Personalized Targeted Therapy

2. Novel trial design

3. Biomarker discovery

Overall Hypotheses

1.Molecular analysis of fresh biopsies can accurately reflect aberrant signaling pathway

2. Matching targeted agents with altered signaling pathways will improve disease control in lung cancer patients

Erlotinib Sorafenib Vandetanib Erlotinib + Bexarotene

Randomization:

Equal Adaptive

BATTLE-1 Trial Schema

EGFR

KRAS/BRAF

VEGF

RXR/CyclinD1

Core Biopsy

Biomarker

Profile

Biopsy

Primary endpoint: 8-week disease control

Not feasible to do core biopsies for so many patients

Impossible to get biomarker data within 2 weeks

Impossible to get different drugs from four different

companies

Highly controversial adaptive randomization as new

concept

Will never complete trial

Skepticisms

11

0

50

100

150

200

250

300

350

1 4 7 10 13 16 19 22 25 28 31 34

Est. reg

Est. rand

Cum. reg

Cum. rand

Study Accrual and Randomization

Time (months)

Pa

tie

nts

En

rolle

d

341

255

12

BATTLE-1 Timeline

Mid 2004: Concept Developd

Mid 2005: Grant Submitted

April 2006: Grant Approved

Nov 2006: Trials Activated- 1st pt

Oct 2009: Trials completed accrual!

2004 2005 2006 2007 2008 2009 2007 2009

13

Assessment of BATTLE-1 Trial

• Successful completion of a prospective,

biopsy-driven, study in lung cancer • This is now an acceptable approach!

• Patients are guided toward more effective personalized treatments (Adaptive design)

• Traditional way to identify biomarkers • Retrospective analysis of patient archives sample

• The new way • Prospective biomarkers evaluation

• Unprecedented biospecimen resources for discovery

BATTLE Manuscript: Lead Article in

Inaugural Issue of Newest AACR Journal

Kim ES*, Herbst RS*, Wistuba II*,

Lee JJ*, et al…Lippman SM#, Hong WK#.

Cancer Discovery, 2011

*co-first authors

#co-senior authors

Champions for success of BATTLE-1

Trial

Ed Kim, MD

J. Jack Lee, PhD Ignacio Wistuba, MD

Roy Herbst, MD George

Blumenschein, MD Ann Tsao, MD

Scott Lippman, MD

The Battle trial : A bold step toward improving the

efficiency of biomarker-based drug development

Clinical Trials Game –Changer ?

A New BATTLE in the Evolving War on Cancer

Time Has Come to Raise the Bar in Oncology

Trials

Set a new standard for biopsy mandated

personalized targeted therapy

Editorials

Results from BATTLE-1 Trial

Led to develop BATTLE-2 Trial

EML4-ALK

Fusion or

EGFR Μut

exclusion

BATTLE-2 Trial

Protocol enrollment

Biopsy performed

Stage 1: (n=200)

Adaptive Randomization

by KRAS Mut status

Primary endpoint: 8-week disease control (N = 400)

Sorafenib E+MK-2206

(AKTi) MK-2206+ AZD6244

(MEKi)

Stage 2: (n=200)

Refined Adaptive Randomization

“Best” discovery markers/signatures

Erlotinib

Statistical modeling and biomarker selection

Biomarkers:

• Protein expression (IHC): p-AKT

(Ser473), PTEN, HIF-1a, LKB1

• Mutation analysis (Sequenom):

PI3KCA, BRAF, AKT1, HRAS,

NRAS, MAP2K1 (MEK1), MET,

CTNNB1, STK11 (LKB1)

• mRNA pathways activation

signatures: Affymetrix®

• Protein profiling – RPPA (n=174)

• NGS-Foundation Medicine

• RNA sequencing

Papadimitrakopoulou and Herbst

0

10

20

30

40

50

60

70

MUTATION FREQUENCY BY NGS (targeted 181 genes FMI)

Total = 1664 mutations of known significance

FMI - NGS Summary

Completed FMI NGS 117

No material available for processing 41

158 Papadimitrakopoulou

MOST FREQUENT GENOMIC EVENTS BY Targeted NGS (181 genes FMI)

Papadimitrakopoulou

Yes No not evaluable total 90(48.1%) 97 13 187

Primary Endpoint

8 week disease control Erotinib E+AKT i AKT i+ MEK i Sorafenib Total 8wk DC 7(35.0%) 18(50.0%) 37(53%) 28(46%) 90(48.1%)

No 8wk DC 13(65.0%) 18(50.0%) 33(47.1%) 33(54.1%) 97(51.9%)

Papadimitrakopoulou

8-wk DC

By arms

PFS by KRAS Mutation

Trend for benefit with MEKi+AKTi for KRAS mut+

Erlotinib

Sorafenib

E+AKTi

AKTi + MEKi

.

AKTi+MEKi %DC=53.1

CDKN2A, FBXW7 mut+, KDM5c mut+, MLL2, Tp53 mut+, ARID1A, BRIP1, CDK8, CREBBP, EP300

KRASG12C, RUNX1 mut+, ALK mut, ARAF mut, ATRX, BTK, CDK4

Papadimitrakopoulou

10/11/2011 12/21/2011 Treatment: Arm 3 AKTi+MEKi KRAS mut in codon 12 (GGT to TGT) Gly to Cys (G12C).

KRASG12C, RUNX1 mut+, ALK mut, ARAF mut, ATRX, BTK, CDK4

• AKTi + MEKi : PR, PFS 241 days

KRAS G12C

ERB4 missense mutation activate PI3K/AKT

ARAF missense mutation (Potential oncogene. Sensitivity to Sorafenib

and the MEK inhibitor trametinib.)

RUNX1 deletion (runt related transcription factor 1, associated with AML,

deletion oncogenic)

Long Term Responder Profile

Multiple alterations along the MEK,

PI3K/AKT pathway may be predicting

Sensitivity.

IMPACT-1 : Non-Randomized Targeted Therapy Trial

IMPACT-2 : Randomized Trial based on Molecular Profiling

ATTACC: Assessment of Targeted Therapy Against Colon Cancer

MeLTT: Melanoma Targeted Therapy Trial

BEAT-IT: Breast Cancer evaluation and targeted investigational

therapy

BATTLE-Like Trials at MDACC

Randomized Trial Evaluating Targeted Agent based on

Molecular Profiling (IMPACT-2)

Tumor biopsy for molecular profiling, 100% (n≈1,362)

Targetable molecular aberrations (≥1 aberration)

Yes, 50% (n≈613) No, 50% (n≈613)

FDA-approved drugs within labeled indication

Yes, 30% (n≈184) No, 70% (n≈429)

Excluded from

randomization

Is there commercially

available targeted agent

or clinical trial?

Yes, 70% (n≈300)

Randomize

Targeted

Therapy*

Treatment not

selected based

on profiling Tsimberidou

CpG Island Methylation Demethylator Azacitadine + XELOX Mitotic inhib Nab-paclitaxel

BRAF Mutation BRAF+EGFR+irino Vemurafenib +cetux+ irino

MD

An

de

rso

n A

TTA

CC

Pro

gram

: B

iom

arke

r Sc

ree

nin

g fo

r 5

-FU

R

efra

cto

ry M

etas

tati

c C

RC

PTEN Loss or PIK3CAmut Akt inhibitor MK-2206

KRAS or NRAS mutant CDK4+MEK Novartis ERK inhibition Biomed Valley

HER2 overexpression HER2 inhibition Trastuzumab +/- EGFR

Enrichment Therapeutic Agent(s) Mechanism

PTEN Loss/Any KRAS PI3K-beta inhibitor SAR26031

cMET overexpression MET inhibition INC280

EGFR ectodomain mutation Alternate EGFR Panitumumab

KRAS and PIK3CA mutation Dual MEK, PI3K BYL719 and MEK162

MSI High CTLA4 and PD1 Nivolumumab, Ipilumumb

Triple KRAS/BRAF/NRAS WT EGFR+HER2 Cetuximab + trastuzumab N=550

Umbrella Basket Test impact of different drugs on

different mutations in a single type

of cancer

•BATTLE

•I-SPY2

•SWOG Squamous Lung Master

Test the effect of a drug(s) on a

single mutation(s) in a variety of

cancer types

•Imatinib Basket

•BRAF+

•NCI MATCH

Ongoing BATTLE-Like Biomarker

Integrated Trials

S1400: MASTER LUNG-1: Squamous

Lung Cancer- 2nd Line Therapy CT*

CT=chemotherapy (docetaxel or gemcitabine), E=erlotinib

FGFRi CT*

Endpoint

PFS/OS

FGFR ampl,

Mut, Fusion

CDK 4/6i CT*

Endpoint

PFS/OS

CCND1, CCND2,

CCND3, cdk4 ampl

PI3Ki CT*

Endpoint

PFS/OS

PiK3CA Mut

Biomarker

Profiling (NGS/CLIA)

HGFi+E E*

Endpoint

PFS/OS

C-MET Expr

Biomarker

Non-Match

Multiple Phase II- III Arms with “rolling” Opening & Closure

PI: V. Papadimitrakopoulou (MDACC,SWOG)

Steering Committee Chair: R. Herbst (YALE, SWOG)

Lung Committee Chair: D. Gandara

Translational Chair: F. Hirsch

Statistical Chair: M. Redman

Preneoplasia

Resectable Disease

Metastatic

Disease

Local-Regional Disease BATTLE

Adjuvant

Trial

BATTLE Strategy in Lung Cancer

• Identify molecular driver

• Match with right agent

EGFR Mutation in Normal Bronchial Epithelium:

Localized Field Effect

Tang and Wistuba et al

13/63 (21%)

Adjacent

2/32 (6%)

Distant

Mutant Tumors: 9/21

(43%)

Wild-Type Tumors: 0/16

(0%)

13/46 (28%)

Inside

EGFR Mapping Strategy

Lung Tumor

FISH

Sequencing

Immunohistochemistry

Microdissection

59 year-old Caucasian male – Current Smoker

Adenocarcinoma

Lung Adenocarcinoma in Smokers

Adenocarcinoma

Global Gene

Expression

Analysis TIME-dependent

Analysis

• Baseline

• 12 months

• 24 months

• 36 months

SITE-dependent

Analysis

• Adjacent

• Non-adjacent

• Contralateral

Kadara and Wistuba et al; Kim and Hong et al

A Strategy to Detect Molecular Drivers:

Molecular Profiling of Bronchial Brushes and

Biopsies

Adjacent

Non-adjacent Contralateral

Resected

Tumor

Samples: Brushes/Tissues (6

sites)

Clinical Endpoint: Recurrences

Biologic Endpoint : Detection of molecular drivers

Paired t-test analysis of

expression in adjacent

compared to non-adjacent

airways

PI3K Gene Expression in Adjacent

Fields are Associated with Recurrence

Top activated network and potential

target for adjuvant therapy: PI3K

PI3K

Kadara and Wistuba et al

Recurrence/SPT

Clustering of PATIENTS by adjacent vs

non-adjacent gene expression

differences

Hypothetical Proposal of BATTLE Adjuvant

Trial

Resected Tumor and Adjacent Field

All patients with NSCLC Stages I–III undergoing resection

Enrollment into Umbrella Protocol

EGFR

Inhibitor

vs

Adjuvant CT

EGFR

CT + Ras

Inhibitor

vs

CT

RAS

ALK Inhibitor

Vs

Adjuvant CT

EML-ALK

CT +

Celecoxib

Vs

CT

COX-2

NF-B

CT+ PI3K

Inhibitors

vs

CT

PI3K

Molecular Profiling and Randomization

Adjuvant Lung Cancer Enrichment Marker Identification and Sequencing Trial (ALCHEMIST)

Presented By David Gerber at 2014 ASCO Annual Meeting

• Linking markers to targeted therapy

Prognostic markers

Predictive markers

- Sensitivity

- Resistance

• Genomic Heterogeneity

• Functional genomics:

- Drivers and passengers

• Cost and Insurance reimbursement

• Need to train more translational investigators

Challenges

1. Matched targeted therapy is better than

standard non-targeted therapy?

2. Combined targeted therapy?

3. Combined targeted and cytotoxic therapy?

4. Combined targeted and immunotherapy?

Opportunities

Concepts and Completion of BATTLE-1 trial

Progress of BATTLE-2 trial

Ongoing BATTLE-Like trials in MDACC

Ongoing BATTLE-Like trials ( Umbrella ,Basket )

BATTLE Adjuvant Trial

Challenges and Opportunities for personalized targeted

therapy

Summary of my Talk

George Blumenschein, MD

Kathryn Gold, MD

Roy S. Herbst, MD, PhD

Edward S. Kim, MD

Scott Kopetz, M D

J. Jack Lee, PhD

Scott M. Lippman, MD

Vali Papadimitrkopoulou, MD

Lia Tsimberidou, MD

Anne Tsao, MD

Ignacio I. Wistuba, MD

Acknowledgements

Lung Cancer

Moonshot Program at MDACC

Lung Cancer

Moonshot

Program

BATTLE-XRT

BATTLE- Prevention

Bioinformatics Genomic

Profiling

DOD Lung Cancer Research Program

(1997-2012)

VITAL

Vanguard Investigations of

Therapeutic Approaches to

Lung Cancer

BATTLE

Biomarker–based Approaches of Targeted

Therapy for Lung Cancer Elimination

IMPACT

Imaging and Molecular Markers for Patients with

Lung Cancer: Approaches with Molecular

Targets, Complementary, Innovative and

Therapeutic Modalities

PROSPECT

Profiling of Resistance Patterns &

Oncogenic Signaling Pathways in

Evaluation of Cancers of the

Thorax and Therapeutic Targets

BESCT

Biology, Education, Screening,

Chemoprevention & Treatment

Basic, Clinical,

and

Translational Research …

Towards a

Personalized Medicine

Approach in

Lung Cancer

BATTLE-1

BATTLE-2

BATTLE Like Trials

BATTLE Adjuvant

Challenges and Opportunities

Order of my presentation

BATTLE-1

BATTLE-2

BATTLE Like Trials

BATTLE Adjuvant

Challenges and Opportunities

Order of my Presentation

Order of my Presentation

BATTLE-1

BATTLE-2

BATTLE-Like Trials

BATTLE Adjuvant

Challenges and Opportunities

BATTLE-1

BATTLE-2

BATTLE Like Trials

BATTLE Adjuvant

Challenges and Opportunities

Order of my Presentation

BATTLE

Therapeutic

Approach

BATTLE

Adjuvant

Approach

BATTLE

Prevention

Approach

Pre-Cancer

Resectable Disease

Advanced

Disease

Local-Regional Disease

Reverse Migration BATTLE Strategy

• First prospectively conducted biopsy-driven,

biomarker-integrated study in lung cancer in

history

• Epitomizes as Team Science Research

• BATTLE concept paves the way for novel adjuvant

and screening / early detection / prevention

strategies

• Galvanize biopsy driven biomarker integrated

trials worldwide

Significance of BATTLE-1 Trial

52

0

50

100

150

200

250

300

350

1 4 7 10 13 16 19 22 25 28 31 34

Est. reg

Est. rand

Cum. reg

Cum. rand

Study Accrual and Randomization

Time (months)

Pa

tie

nts

En

rolle

d

341

255

Randomized Arm 8-wk disease-control rate, %

(Evaluable, n=167)

All

Eval.

KRAS

wild

KRAS

mutated

Erlotinib 20 25 0

Erlotinib+AKT inhibitor 51 53 40

AKT+MEK inhibitors 53 50 61

Sorafenib 43 44 43

BATTLE-2: Stage 1. Results

Papadimitrakopoulou et al, ASCO 2014, Abstr. 8042

Bayesian Adaptive Randomization:

The Basics

We learn as we go!

Success is dependent on good biomarkers guiding

assignments to good treatment options

Prior

Probability:

• Estimated efficacy

of markers to predict

benefit from RX

• Used for

randomizing patient

to a treatment

Disease

Control + +

Posterior

Probability

Updating Prior

Prob by observed

outcomes: How

did markers do at

predicting benefit?

Pt equally

randomized

to treatment

Pt adaptively

randomized

to treatment

Posterior

Probability

Updating Prior

Prob by observed

outcomes: How

did markers do at

predicting benefit?

• More patients are assigned to more effective

therapies

• Based on accumulating patient data

BATTLE

Therapeutic

Approach

BATTLE

Adjuvant

Approach

BATTLE

Prevention

Approach

Pre-Cancer

Resectable Disease

Advanced

Disease

Local-Regional Disease

Reverse Migration BATTLE Strategy

MSKCC’s “Genotyping” and “BASKET” Trials

SWOG MASTER Protocol

(Biomarker-Driven Squamous Cell Lung Cancer)

NCI’s MATCH

(Molecular Analysis for Therapy Choice)

NCI’s M-PACT Clinical Trial

I-SPY2 Trial

(Investigation of Serial Studies to Predict Your Therapeutic

Response with Imaging and Molecular Analysis)

Biomarker- Integrated BATTLE-like

Trials in USA

BATTLE Discovery Platform

Personalized

Medicine

Tumor

Host

Tumor genetic profiling •Mutation profiling (Sequenom, Inc)

•Copy number changes (FISH, etc)

Gene expression profiling •Affymetrix array

•RT-PCR

Blood-based markers •Profiling of cytokines and

angiogenic factors (multiplex

bead assays)

Germline genetic profiling •SNP arrays

Proteins •Immunohistochemistry

•Proteomic arrays

H1607

H2227

H378

HC

C970

H1963

HC

C33

H1184

H2141

H289

H128

H2107

H2195

H889

H82

H187

H2171

H524

HC

C95

RM

-H358

RM

-HC

C461

HC

C366

H1299

H2882

HC

C1359

RM

-H157

RM

-HC

C44

HC

C1195

RM

-H23

RM

-CA

LU

-6R

M-H

1355

RM

-A549

RM

-H1792

EM

-HC

C827

H2087

RM

-H2887

CA

LU

-1R

M-H

CC

1171

RM

-H2009

RM

-H441

HC

C2450

EM

-H1975

HC

C78

EM

-H820

H2347

HC

C193

CA

LU

-3R

M-H

CC

515

EM

-HC

C2279

EM

-HC

C4006

EM

-H3255

EM

-HC

C2935

H1993

EM

-H1650

H1666

H1819

H1395

H3122

H1648

H322

H2126

H1437

RM

-H2122

NEUROD1: neurogenic differentiation 1ISL1: ISL1 transcription factor, LIM/homeodomain, (islet-1)ASCL1: achaete-scute complex homolog 1 (Drosophila)ASCL1: achaete-scute complex homolog 1 (Drosophila)SOX11: SRY (sex determining region Y)-box 11RUNX1T1: runt-related transcription factor 1; translocated to, 1 (cyclin D-related)INSM1: insulinoma-associated 1STMN2: stathmin-like 2RTN1: reticulon 1RTN1: reticulon 1SOX11: SRY (sex determining region Y)-box 11TUBB2B: tubulin, beta 2BFXYD6: FXYD domain containing ion transport regulator 6ST18: suppression of tumorigenicity 18 (breast carcinoma) (zinc finger protein)NCAM1: neural cell adhesion molecule 1KIF5C: kinesin family member 5CTMSL8: thymosin-like 8LCN2: lipocalin 2 (oncogene 24p3)G0S2: G0/G1switch 2PLAT: plasminogen activator, tissueCEACAM6: carcinoembryonic antigen-related cell adhesion molecule 6 (non-specific cross reacting antigen)CD44: CD44 molecule (Indian blood group)CD44: CD44 molecule (Indian blood group)CD44: CD44 molecule (Indian blood group)IGFBP7: insulin-like growth factor binding protein 7TGFBI: transforming growth factor, beta-induced, 68kDaIGFBP3: insulin-like growth factor binding protein 3ABCC3: ATP-binding cassette, sub-family C (CFTR/MRP), member 3AMIGO2: adhesion molecule with Ig-like domain 2CCND1: cyclin D1S100A6: S100 calcium binding protein A6S100A10: S100 calcium binding protein A10S100A11: S100 calcium binding protein A11LOC729659 /// LOC730278 /// LOC730558: similar to Putative S100 calcium-binding protein A11 pseudogeneBHLHB2: basic helix-loop-helix domain containing, class B, 2VAMP8: vesicle-associated membrane protein 8 (endobrevin)LRP10: low density lipoprotein receptor-related protein 10LGALS3: lectin, galactoside-binding, soluble, 3AHR: aryl hydrocarbon receptorITGB5: Integrin, beta 5KRT7: keratin 7KRT19: keratin 19TACSTD2: tumor-associated calcium signal transducer 2GPRC5A: G protein-coupled receptor, family C, group 5, member ACDNA clone IMAGE:6025865ITGA3: integrin, alpha 3 (antigen CD49C, alpha 3 subunit of VLA-3 receptor)ANXA3: annexin A3CAV2: caveolin 2FER1L3: fer-1-like 3, myoferlin (C. elegans)FER1L3: fer-1-like 3, myoferlin (C. elegans)CYR61: cysteine-rich, angiogenic inducer, 61IFI16: interferon, gamma-inducible protein 16IFITM1: interferon induced transmembrane protein 1 (9-27)IFITM2: interferon induced transmembrane protein 2 (1-8D)IFITM3: interferon induced transmembrane protein 3 (1-8U)NT5E: 5'-nucleotidase, ecto (CD73)TM4SF1: transmembrane 4 L six family member 1TM4SF1: transmembrane 4 L six family member 1PLAU: plasminogen activator, urokinasePLAU: plasminogen activator, urokinaseAREG /// LOC727738: amphiregulin (schwannoma-derived growth factor) /// similar to Amphiregulin precursor (AR) (Colorectum cell-derived growth factor) (CRDGF)EREG: epiregulinTGM2: transglutaminase 2 (C polypeptide, protein-glutamine-gamma-glutamyltransferase)GNG11: guanine nucleotide binding protein (G protein), gamma 11TFPI2: tissue factor pathway inhibitor 2IER3: immediate early response 3SRGN: serglycinCXCL2: chemokine (C-X-C motif) ligand 2TFPI: tissue factor pathway inhibitor (lipoprotein-associated coagulation inhibitor)GJA1: gap junction protein, alpha 1, 43kDaCFH /// CFHR1: complement factor H /// complement factor H-related 1IL1R1: interleukin 1 receptor, type IPTGS2: prostaglandin-endoperoxide synthase 2 (prostaglandin G/H synthase and cyclooxygenase)SLC16A3: solute carrier family 16, member 3 (monocarboxylic acid transporter 4)LGALS1: lectin, galactoside-binding, soluble, 1 (galectin 1)CAV1: caveolin 1, caveolae protein, 22kDaCAV1: caveolin 1, caveolae protein, 22kDaC8orf4: chromosome 8 open reading frame 4NQO1: NAD(P)H dehydrogenase, quinone 1AKR1C3: aldo-keto reductase family 1, member C3 (3-alpha hydroxysteroid dehydrogenase, type II)AKR1C2: aldo-keto reductase family 1, member C2 (dihydrodiol dehydrogenase 2; bile acid binding protein; 3-alpha hydroxysteroid dehydrogenase, type III)S100P: S100 calcium binding protein PS100A14: S100 calcium binding protein A14

TUMOR SUBTYPES S S S S S S S S S S S S S S S S N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N

-3.0 -2.9 -2.8 -2.6 -2.5 -2.4 -2.3 -2.1 -2.0 -1.9 -1.8 -1.7 -1.5 -1.4 -1.3 -1.2 -1.0 -0.9 -0.8 -0.7 -0.6 -0.4 -0.3 -0.2 -0.1 0.1 0.2 0.3 0.4 0.6 0.7 0.8 0.9 1.0 1.2 1.3 1.4 1.5 1.7 1.8 1.9 2.0 2.1 2.3 2.4 2.5 2.6 2.8 2.9 3.0

"TUMOR SUBTYPE" is SCLC: 17/17 (all: 17/62, PValue: 0.0000)

ES Kim et al. AACR Plenary Session 2010

BATTLE Team Members in MDACC

Results of Empirical Targeted

Therapies in

Lung Cancer

• INTACT-1 (gefitinib)

• INTACT-2 (gefitinib)

• ISEL (gefitinib)

• TALENT (erlotinib)

• TRIBUTE (erlotinib)

• BMS-099 (cetuximab)

• ESCAPE (Sorafenib)

• Farnesyltransferase inhibitors

• Bexarotene (SPIRIT I and II)

• Glutathione (NOV-002)

• ZEAL (vandetanib)

• ZEST (vandetanib)

• ZEPHYR (vandetanib)

• Affinitak (LY900003) antisense

• IGFR (Figitumumab)

10,000 + patients

Negative Studies Positive Studies (OS)

• BR.21 (erlotinib)

• ECOG 4599 (bevacizumab)

• INTEREST (gefitinib)

• FLEX (cetuximab)

Clearly we do not know how to choose the right

therapy for patients with lung cancer

60

BATTLE Results: Disease Control in % (n)

EGFR KRAS VEGF RXR/

CycD1 None Total

Erlotinib 35% (17) 14% (7) 40% (25) 0% (1) 38% (8) 34% (58)

Vandetanib 41% (27) 0% (3) 38% (16) NA (0) 0% (6) 33% (52)

Erlotinib +

Bexarotene 55% (20) 33% (3) 0% (3) 100% (1) 56% (9) 50% (36)

Sorafenib 39% (23) 79% (14) 64% (39) 25% (4) 61% (18) 58% (98)

Total 43% (87) 48% (27) 49% (83) 33% (6) 46% (41) 46% (244)

Tre

atm

en

ts

Marker Groups

1. Marginal benefit from adjuvant chemotherapy

2. Toxicity and cost of adjuvant chemotherapy

3. Conflicting data from empirically selected targeted

agent trials

4. Persistent Molecular defects in Bronchial Airway

after surgery

Rationale for Adjuvant BATTLE Trial

Project 1: BATTLE-1

Trial

WK Hong, ES Kim, R Herbst,

G Blumenschein, A Tsao

Project 2: Molecular

Mechanisms of Response

or Resistance

B Johnson, H Lee,

J Heymach, R Lotan

Project 3: Identify

Biomarkers as Novel

Predictors and

Potential Targets

L Mao

Personalized

Targeted Therapy

and Prevention

Biostatistics Core JJ Lee

Pathology Core I Wistuba

Project 4: Explore New

Combinations with mTOR

(Preclinical Clinical)

F Khuri, S Sun

BATTLE Program Project (2004 - 2011 ) PI: Waun Ki Hong

Characteristics of Randomized Patients (N=255)

Age (Range) 62 yrs (26-84)

Male / Female 54% / 46%

Caucasian 82%

Never / Current Smokers 22% / 9%

ECOG PS 0 / 1 9% / 77%

Adenocarcinoma / Squamous 63% / 18%

Prior Brain Metastases 33%

Prior EGFR-TKI 45%

Prior Docetaxel / Pemetrexed 40% / 40%

Prior Systemic Therapy 2 (1-6)

64

8-week Disease Control Predicts

Overall Survival

• Median Overall Survival: 9 months

• 1-year survival: 38%

11.3 months

7.5 months

P = 0.002

0 5 10 15 20 25 30

0.0

0.2

0.4

0.6

0

.8

1.0

Time (months)

Overa

ll S

urv

ival

DC at 8wk (N = 104)

Non-DC at 8 wk (N = 112)

65

Individual Biomarkers for Response

and Resistance to Targeted Treatment:

Drug Treatment Biomarker P–value DC

Erlotinib EGFR mutation 0.04 Improved

Vandetanib High VEGFR-2 expression 0.05 Improved

Erlotinib +

Bexarotene High Cyclin D1 expression 0.001 Improved

EGFR FISH Amp 0.006 Improved

Sorafenib EGFR mutation 0.012 Worse

EGFR high polysomy 0.048 Worse

BATTLE Team Members in MDACC

Molecular Signaling Landscape in 2004

EGFR

Ras/Raf

RXR/Cyclin D1

VEGF/VEGFR

BATTLE Hypothesis in 2004:

Can we develop personalized therapy for lung

cancer patients?

• Erlotinib

• Vandetanib

• Erlotinib +

Bexarotene

• Sorafenib

4 promising therapies 4 promising targets

ES Kim et al. Cancer Discovery 2011

BATTLE Implications

• A step towards personalizing medicine

• New research paradigm

• Real-time tumor profiling of “current” disease

status

• Novel adaptive clinical trial design

• Discovery of new biomarkers

• BATTLE paves the way forward for our

next study

Traditional

Characterization of NSCLC

by Histology

Characterizing Lung Cancer: Paradigm Shift

Adenocarcinoma

Squamous

Large Cell

Wistuba, 2011

Molecular

characterization

Unknown

FGFR 2 Amp

EGFRvIII

PI3KCA

EGFR TK

DDR2

Adenocarcinoma

Squamous Cell Ca

Pao and Hutchinson, Nat Med 2012

NCI RO1 BATTLE-2 Trial

Co-PIs

Roy Herbst

Vali Papadimitrakopoulou

Most Frequent Genomic Events by Targeted NGS (181 genes FMI)

Papadimitrakopoulou

IMPACT 2. Study Design (II)

I

Z

E

Targeted

therapy*

Treatment

not selected

based on

molecular

analysis

1

1

Crossover If:

• Progressive disease

• Toxicity

* Expansion phase of phase I studies or phase II studies