Update of BATTLE-2 Trial

Waun Ki Hong

Vali Papadimitrakopoulou

UT MD Anderson Cancer Center

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 (2007- 9 )

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

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

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

7

Impact of BATTLE-1 Trial

Galvanize Whole Field of Precision Medicine:

NCI Match trial, Lung Squamous Master Trial

IMPACT Trial, ATTACK Trial, Winther Trial

BATTLE-2 Trial

EML4-ALK

Fusion or

EGFR Μut

exclusion

BATTLE-2 Trial (2012- )

Protocol enrollment

Biopsy performed

Stage 1: (n=200)

Adaptive Randomization

by KRAS mut status

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

First stage 200 pts completed (6/11-3/14)

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

Discovery Markers: Protein expression p-AKT (Ser473), PTEN, HIF-1a, LKB1

Mutation analysis (Sequenom)/NGS-Foundation Medicine

mRNA pathways activation signatures: Affymetrix®

RNA sequencing

Rationale + Novelty: Rationally designed targeted therapy combinations

More emphasis on KRAS targeting

Predictive biomarker discovery plan

Update of BATTLE-2

Mutational Landscape

Clinical Outcome

Characteristics of 200 patients in

BATTLE-2

(Chemorefractory metastatic NSCLC)

Smoking Status : 86 %

Prior chemotherapy > 2 : 75%

Prior Erotinib therapy : 37.5%

KRAS mutation : 27 %

ECOG PS ( 0- 2): 100%

Adenocarcinoma : 73%

Squamous Carcinoma: 17%

Others : 9 %

TP53

CDKN2A

PTEN

PIK3CA

KEAP1

MLL2

HLA-A

NFE2L2

NOTCH1

RB1

TCGA Lung Adenocarcinoma

(Nature, 2014)

TCGA Lung Squamous Cell Carcinoma (Nature, 2012)

Fre

qu

en

cy

Identification of Genomic Alterations in NSCLC

(Chemotherapy-naïve, resectable tumors)

Mutation Matrix in BATTLE-2 patients (N=187) Molecular Alterations in 119 Genes

ATM LRP1B SPOP TET2 PAX5 MLH1

EGFR KDM6A PTEN JAK1 FGF4 GSK3B

BRCA1 CTNNB1 SUFU ERBB2 MEN1 WT1

SMARCA4 BCOR PALB2 MSH6 FANCC SF3B1

TP53 STAT4 DNMT3A NCOR1 PIK3R1 SMAD4

ARID1A CSF1R MAP3K13 TBX3 ALK NOTCH3

KDM5C NFE2L2 VHL MLL ERBB3 ATR

CDH1 ASXL1 CRBN KEAP1 NF2 BACH1

RAD51 NOTCH1 EP300 PARP4 STAG2 NRAS

KRAS GRIN2A SMARCD1 PIK3C3 HRAS FANCA

BRCA2 PBRM1 APC RET INHBA MSH2

STK11 RB1 TIPARP EPHA3 RAD52 WISP3

BRAF SETD2 MYCN CDC73 TOP1 PPP2R1A

NOTCH4 BCL6 BCORL1 CTCF NOTCH2 CASP8

MET TRRAP RAD51C RAD50 CDK12 MAP2K1

CDKN2A ARID2 PIK3CA KLHL6 FGFR1 GATA1

KDR RUNX1 GATA3 BAP1 CEBPA PDGFRA

NF1 MLL2 ROS1 MAP2K4 KDM5A FLT3

SPEN FBXW7 IDH1 CHUK NUP93 PIK3CG

HGF BARD1 GNAS CREBBP MAP3K1

Papadimitrakopoulou

Copy Number Matrix in BATTLE-2 patients (N=187) Copy-number-alterations in 82 genes

CCND1 CDK6 FGF10 PIK3R1 KIT

EMSY JUN KRAS EPHA3 NFKBIA

FGF19 MCL1 MYCL1 SMARCA4 NKX2-1

FGF3 MET CCNE1 FGF14 BCL2L2

FGF4 RICTOR HGF SMAD4 IKBKE

CDKN2A ARFRP1 IRS2 JAK2 MDM4

CDKN2B TP53 PDGFRA FLT4 ERBB2

PTEN NRAS BRAF CASP8 AKT3

FGFR1 STK11 MYCN NF1 ATM

LRP1B MYST3 CCND2 ERBB3 RAD51C

PIK3CA ZNF703 FGF23 AXL SUFU

SOX2 CRKL FGF6 ERBB4 FGF12

CDK4 MYC ARID2 CDKN2C GNAS

EGFR REL RET KDM5C ARAF

MDM2 NF2 RB1 CCND3 AR

KDR AKT2 PRSS8 ZNF217 KDM6A

FANCD2 BCOR

Papadimitrakopoulou

Mutational Landscape in BATTLE-2 Patients

ADE

SCQ

Mutation prevalence of 187 samples

CNG/loss prevalence of 187 samples

TCGA (Chemo-naive) vs BATTLE-2 (Platinum-refractory)

: Mutational Evolution

• Evolution- enrichment in commonly mutated genes (“Trunk” Genetic

events) associated with refractory and metastatic NSCLC.

• Similar trend for copy number changes (NFKBIA, MYC, MDM4,

AKT2, CEBPA, PMS2)

* p<.05

LUAD

LUSC

KRAS

Group

Co-occurring Genetic Events are Dominant

Determinants of Gene Expression Cluster

Membership

TCGA (n=68)

Group

KRAS

TP53

LKB1

ATM

KEAP1

CDKN2A

KP KL KC

Skoulidis F et al., Cancer Discovery, 2015

Group

KRAS

BATTLE-2 (n=36)

Group

KRAS

TP53

LKB1

ATM

KEAP1

CDKN2A

Validation in Metastatic, Platinum-Refractory

KRAS- Mutant LUADs from the BATTLE-2 trial

Skoulidis F et al., Cancer Discovery, 2015

KP KL KC

What are the major features of the

different KRAS subgroups?

Are they biologically or therapeutically

relevant?

Higher somatic mutation burden in KP LUADs

(TCGA cohort)

A. B.

Reduced expression of PD-L1 in KRAS-mutant

LKB1-deficient LUADs (MDACC cohort)

Skoulidis F et al., 2015 ASCO Annual Meeting

A. B.

Reduced density of intra-tumoral

T lymphocytes in KRAS-mutant LKB1-deficient

LUACs (MDACC cohort)

Skoulidis F et al., 2015 ASCO Annual Meeting

Summary of Mutational Landscape

• Enrichment for known driver mutations in

chemotherapy-refractory NSCLC

• More TP53 mutations in BATTLE-2 patients

• Copy number changes are similar both group

• BATTLE-2 : Increased frequency of triple mutant

KRAS;LKB1;TP53 and KRAS;TP53

• Increased somatic mutation burden in KRAS/TP53

• Reduced expression of PD-L1 and T-cell infiltration

in KRAS/LKB1

• New biologically distinct KRAS co-mutational

subsets.

Update of BATTLE-2

Mutational Landscape

Clinical Outcome

PR SD PD not evaluable total

7 83 97 13 187

Yes No not evaluable total

90(48.1%) 97 13 187

Primary Endpoint

Response

8-wk DC

By arms

8 week disease

control E(1) E+M (2) M+A (3) S Total

8wk DC 7(35.0%) 18(50.0%) 37(52.9%) 28(45.9%) 90(48.1%)

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

p (Fisher’s exact

test)

.40 .20 .44

By arms

8 week response Arm1 Arm2 Arm3 Arm4 Total

PR 1(5.0%) 3(4.3%) 3(4.9%) 7(3.7%)

SD 6(30.0%) 18(50.0%) 34(48.6%) 25(41.0%) 83(44.4%)

PD 13(65.0%) 18(50.0%) 33(47.1%) 33(54.1%) 97(51.9%)

Non Evaluable 2 6 5 0 13

Median PFS: 2 months

OS by treatment

PFS by treatment

P=0.4636 P=0.1652

PFS by KRAS mutation status

P=0.8919

P=0.3288 P=0.0739

P=0.3139 P=0.6041

Arm 1 Arm 2

Arm 3 Arm 4

KRAS Mut

Genotype Arm 3 PFS <3 mos

PFS >3 mos

p

KRAS wt 16 7

mut+ 8 12 0.069

KRAS wt 13 2

KRAS mut+ and MEK/PI3K mut+*

7 7 0.05

• MEKi (selumetinib) and sorafenib:

benefit for KRAS mut+

• Trend for benefit with MEKi+AKTi for

KRAS mut+, enriched by co-mutations

*co-mutations examined: PIK3CA, CDKN2A, PTEN, FBXW7,

BRAF, ERBB3, MEK1, MEK2, ARAF

P=0.0422 P=0.1309

PFS by KRAS mut PFS by KRAS wild

EGFR exon 19

del/TP53mut+

Erlotinib DCR 35%

CDKN2A, TP53 , BRCA2,

amp CCNE1 , AKT 2,

PIK3CA, SOX2 EGFR ,

CEBPA, MAP3K13, BCL6 ,

SETD2 mut

Erlotinib +MK-2206 DCR

50%

PTEN , TP53 , KRAS

ampl, CCND2, FGF23

ampl,ARID1A,

ERBB3, KEAP1,,

NOTCH2

CDKN2A, FBXW7 , KDM5c , MLL2,

TP53, ARID1A, BRIP1, CDK8,

CREBBP, EP300

KRASG12C, RUNX1 mut, ERBB4

mut, ALK mut, ARAF mut, ATRX,

BTK, CDK4

MK-2206+AZD6244

DCR:53%

EGFR T790M,/L858R, TIPARP, TP53, CL2 ,

MCL , MYC Ampl, NFKBIA, NKX2-1, AR,

AXL, CARD11, FGF6, MED12,TSC2

TP53mut, CCNE1, AKT2 ,AXL, PIK3CA, SOX2,

FGF12, NOTCH2, KLHL6, MAPK3K13, BCL6

Ampl,, ATM,AR, SF1R, EPHA3, FANCM, FAT3,

HGF, SETD2,SMO.

KRAS, STK11 mut, MYC,

GNA13,ampl, ATM,

ATRX, KDR, NOTCH3,

PIK3R2.

Sorafenib

DCR:46%

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

• Multiple alterations within MAPK pathway may drive growth and determine sensitivity .

• Response in the setting of KRASG12C/ ARAF, CDK4 mutations

• Responses to MEK inhibitors may be driven by a critical number of alterations within the MAPK pathway.

Model of Multiplicity of Mutations within MAPK pathway

Activation of PI3K/AKT Pathway

• Multiple alterations leading to PI3K/AKT pathway activation including FBXW7, KDM5C associated with response to pan-AKT inhibitor.

• FBXW7: is the substrate recognition component of a SCF-type E3 ubiquitin ligase. Targets Notch1, c-Jun, cyclin E and mTOR for degradation. Loss of FBXW7 may be a biomarker for human cancers susceptible to treatment with mTOR/AKT inhibitors.

Summary of Clinical Outcome

• 8-Week DCR : 48 %

• PFS by KRAS mut vs wild : No difference across the board

• Potential harmful effect of Erlotinib in KRAS mut

• Trend improvement of PFS in KRAS mut : MEK &AKT i and

Sorafenib

• Potential benefit of MEKi+AKTi for KRAS mut with MEK /PI3K

Co-mutation

• Targeting MAPK pathway by MEK i and PI3K/AKT pathway

pan-AKT inhibitor as combination needs further validation