Decade after BATTLE-1 Trial : Opportunities and...
Transcript of Decade after BATTLE-1 Trial : Opportunities and...
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