David R. Gandara, MD University of California Davis

Comprehensive Cancer Center

Predictive Biomarkers for Immunotherapy: Non-Small Cell Lung Cancer (NSCLC) as a Model of Tumor Heterogeneity

DAVID GANDARA, MD

KEYNOTE LECTURE: “HOW I USE IMMUNOTHERAPY BIOMARKERS IN CLINIC”

RELEVANT FINANCIAL RELATIONSHIPS IN THE PAST TWELVE MONTHS BY PRESENTER OR

SPOUSE/PARTNER.

GRANT/RESEARCH SUPPORT: ASTRAZENECA/MEDI, GENENTECH CONSULTANT: ASTRAZENECA, CELGENE, GENENTECH, GUARDANT HEALTH, LILLY,

LIQUID GENOMICS

THE SPEAKER WILL DIRECTLY DISCLOSURE THE USE OF PRODUCTS FOR WHICH ARE NOT LABELED (E.G., OFF LABEL USE) OR IF THE PRODUCT IS STILL INVESTIGATIONAL.

14th Annual California Cancer Conference Consortium

August 10-12, 2018

Near-Future Approach (Patient-Based Therapy): Genomic profiling by high throughput next generation sequencing for decision-making in individual patients

Next Generation Sequencing (NGS): •Whole Genome or Exome capture Sequencing (DNA) •Whole or Targeted Transcriptome Sequencing (RNA) •Epigenetic profiling

1. Histomorphological Diagnosis:

Cancerous

Evolving Approach (Target-Based Therapy V2.0): Multiplexed molecular tests with increased sensitivity

& output for decision-making in individual patients

Current Approach (Target-Based Therapy V1.0): Single gene molecular testing for decision-making in

individual patients

2. Molecular Diagnosis:

Multiplex, Hot Spot Mutation Tests: •PCR-based SNapShot •PCR-based Mass Array SNP •Sequenom Initial High-Throughput Technologies: •SNP/CNV DNA microarray •RNA microarray

Single Biomarker Tests: •Sanger DNA Sequencing •RT-PCR •FISH •IHC

Representative technologies:

Extract tumor nucleic acids: Archival cancer

specimens

Archival FFPE tumor specimens

Macro- or Micro-dissection

of Tumors

DNA and RNA

Empiric Approach (Past) (Compound-Based Therapy): Clinical-histologic factors to select

drugs for individual patients

Evolution of Biomarker Testing in NSCLC: Past, Current & Future

from Li, Gandara, Mack, Kung: J Clin Oncol , 2013 Plasma ct DNA by NGS for Genomics & Immunophenotyping

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Mutations in Tumors Detected in Plasma

Monitoring Response to Treatment

Mutant Wild-type

* K-RAS 12th codon mutation

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T Kimura, W. S. Holland, T. Kawaguchi, S. Williamson, K. Chansky, J. Crowley, J. H. Doroshow, H.J. LENZ, D. R. Gandara, P. H. Gumerlock Ann NY Acad Sci: 55-60, 2004

Mutant DNA in Plasma of Cancer Patients: Potential for Monitoring Response to Therapy

Genomic Alteration (i.e. driver event) Available targeted agents with activity against

driver event in lung cancer*

EGFR mutations erlotinib, gefitinib, afatinib

ALK rearrangements crizotinib

HER2 mutations trastuzumab, afatinib

BRAF V600E mutations vemurafenib, dabrafenib + trametinib

MET amplification/mutation crizotinib

ROS1 rearrangements crizotinib

RET rearrangements cabozantinib

*Indicates recommended use in the NCCN Drugs and Biologics Compendium

“The NCCN NSCLC Guidelines Panel strongly endorses broader molecular profiling with the goal

of identifying rare driver mutations for which effective drugs may already be available, or to

appropriately counsel patients regarding the availability of clinical trials. Broad molecular

profiling is a key component of the improvement of care of patients with NSCLC).”

Why do Genomic Testing in Advanced NSCLC?

Because we now have effective targeted therapies for 8

different genomically defined subsets of NSCLC

TRK rearrangements entrectinib

•Biomarkers indicative of

hypermutation & neoantigens

may predict response to

immuno-oncology therapies

Examples:

‒TMB, MSI-high, neoantigens

Tumor antigens

•Biomarkers that identify tumor

immune system evasion

beyond PD-1/CTLA-4 to inform

new immuno-oncology targets

and rational combinations

Examples:

‒Tregs, MDSCs, IDO, LAG-3

Tumor immune

suppression/evasion

•Biomarkers (intra- or peri-

tumoral) indicative of an

inflamed phenotype may predict

response to immuno-oncology

therapies

Examples:

‒PD-L1, inflammatory signatures

Tumor

microenvironment

(inflammation)

•Biomarkers that characterize the

host environment, beyond tumor

microenvironment, may predict

response to immuno-oncology

therapies

Examples:

‒Microbiome, germline genetics

Host environment

Tumor

antigens

Tumor immune

suppression

Inflamed

tumor

Adapted from Blank CU, et al. Science 2016;352:658–660

Tumor & Immune Microenvironment Factors as potential

Predictive Biomarkers for benefit from Immunotherapy

6

Overall Survival in 2nd line+ Trials of Nivolumab vs Docetaxel:

CheckMate 017 (Squamous) versus 057 (Non-Squamous)

Squamous (CM 017)

Non-Squamous (CM 057)

Brahmer et al: NEJM 2015 & Borghei et al: NEJM 2015 Survival benefit of nivolumab was independent of PDL1 expression levels

in Squamous lung cancer but not Non-Squamous

Analytical Validation of PD-L1 Assay Systems in the Blueprint Project

Adapted from Hirsch et al. J Thorac Oncol. 2017 Feb;12(2):208-222

0

10

20

30

40

50

60

70

80

90

100

% T

um

or S

tain

ing

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39

Cases

SP263SP14228-822C3

Comparison of PD-L1 assays

(Dako 22C3 vs Ventana SP142) in OAK Trial Specimens

Gadgeel, Gandara et al. ESMO 2017 Abstract 1296O.

OS in PD-L1-High Subgroups OS in PD-L1-Negative Subgroups

Measurement of PD-L1 by Plasma ctRNA assay & association of Efficacy from Checkpoint Immunotherapy

Courtesy of K. Danenberg

CellMax Circulating Tumor Cell (CTC) Platform: Protein Expression Analysis of PD-L1 Expression for Immunotherapy Selection & Monitoring

• 51 NSCLC patients

• CTCs detectable in 86% (44/51) of samples (median of 4 CTCs) including 87% of non-metastatic patients (28/32)

• 55% had PD-L1 positive CTCs (consistent with % observed in tissue)

• Comparison to IHC (35 samples)

• 63% of samples were + by IHC vs 66% by CTCs

• 9/25 (25%) were IHC >50%

H. Hsieh: AACR 2018

• Somatic mutations in cancers are multifactorial (multiple

etiologies)

• These mutations produce neoantigens that induce anti-

tumor immune responses

• TMB is an emerging predictive biomarker for checkpoint

immunotherapy (measurable in tissue or blood) • TMB can be evaluated using whole-exome sequencing (WES)

or comprehensive genomic profiling (CGP; e.g.,

FoundationOne, FACT or Guardant)

• Previous studies show that TMB either by WES or CGP

correlates with efficacy of checkpoint immunotherapy in

multiple cancer types1-3

• Predicted neoantigen load (NAL) is a component of TMB

that has been most closely linked to immune response4,5

• TMB identifies a distinct patient population not captured

by PD-L1 IHC or other immune biomarkers5,6

Tumor Mutational Burden (TMB) as a Candidate Predictive Biomarker for Cancer Immunotherapy

IHC, immunohistochemistry; PD-L1, programmed death-ligand 1; TMB, tumor mutational burden.

1. Yarchoan M, et al. N Engl J Med. 2017; 2. Chalmers ZR, et al. Genome Med. 2017; 3. Goodman AM, et al. Mol Cancer Ther. 2017;

4. Efremova M, et al. Front Immunol. 2017; 5. Topalian SL, et al. Nat Rev Cancer. 2016; 6. Kowanetz M, et al. WCLC 2017.

NA

L

TMB

r = 0.89

PD-L1 expression

TMB

From Gandara, LeGrand et al:

ASCO 2018

Adapted from The Cancer Genome Atlas Project: Kandoth et al Nature 2013.

Magnitude of Genomic Derangement (“Mutational Load”) in Various Cancers & Subtypes

Phase III CheckMate 026 Study Design:

Nivolumab vs Chemotherapy in First-line NSCLC

Carbone et al: NEJM 2017

Primary Endpoint: PFS in patients with >5% PD-L1

15

CheckMate 026 TMB Analysis (WES): Nivolumab vs Chemotherapy in 1st-line therapy —PFS by TMB Subgroup

Nivolumab

Chemotherapy

47 30 26 21 16 12 4 1

60 42 22 15 9 7 4 1

111 54 30 15 9 7 2 1 1

94 65 37 23 15 12 5 0 0

Nivolumab N = 47 N = 60

9.7 (5.1, NR)

5.8 (4.2, 8.5)

Chemotherapy

Median PFS, mo

(95% CI)

High TMB

PF

S, %

3 6 9 12 15 18 21

No. at Risk Months

100

90

80

70

60

50

40

30

20

10

0

0

Nivolumab

Chemotherapy

0 3 6 9 12

Months

15 18 21 24

Nivolumab

Chemotherapy

100

90

80

70

60

50

40

30

20

10

0

N = 111 N = 94

4.1 (2.8, 5.4)

6.9 (5.5, 8.6)

HR = 1.82 (95% CI: 1.30, 2.55)

Nivolumab Chemotherapy

(95% CI)

Median PFS, mo

Low/Medium TMB

HR = 0.62 (95% CI: 0.38, 1.00)

Carbone DP et al. N Engl J Med. 2017;376(25):2415-2426.

No Association between TMB & PD-L1 Expressiona

aAll patients had ≥1% PD-L1 tumor expression

CheckMate 026 TMB Analysis:

Nivolumab vs Platinum Chemotherapy in 1st line NSCLC

Total Exome Mutations vs Genes in FoundationOne Panela

aBased on in silico analysis filtering on 315 genes in FoundationOne comprehensive genomic profile (Foundation Medicine, Inc, Cambridge, MA, USA)1

Peters S et al.: AACR 2017; Carbone DP et al. N Engl J Med. 2017;376:2415-2426.

Months

100

75

50

25

0

6 18 9 3 0 12 15 21

Months

100

75

50

25

0

6 18 9 3

PFS

(%

)

0 12 15 24 21

High TMB, PD-L1 ≥50%

High TMB, PD-L1 1%–49%

Low/medium TMB, PD-L1 1%–49%

Low/medium TMB, PD-L1 ≥50%

Low/medium TMB, PD-L1 ≥50%

High TMB, PD-L1 1%–49%

Low/medium TMB, PD-L1 1%–49%

High TMB, PD-L1 ≥50%

CheckMate 026: PFS by TMB Subgroup and PD-L1 Status

Chemotherapy Arm Nivolumab Arm

Peters S et al.: AACR 2017; Carbone DP et al. N Engl J Med. 2017;376:2415-2426.

Gandara DR, et al. High-TMB Analysis. http://bit.ly/2xibbxU 18

Tumor Mutational Burden (TMB) as a Predictive Biomarker for Atezolizumab Efficacy

Retrospective Assessment of Tissue TMB Using the FoundationOne Assay Across 7 Atezolizumab Studies and Multiple Tumor Types (N=987)

1L, first-line; 2L, second-line; 3L, third line; BEP, biomarker-evaluable population; ITT, intention-to-treat; mUC, metastatic urothelial cancer; NSCLC, non-small cell lung cancer; UC, urothelial cancer. a Includes glioblastoma; squamous cell carcinoma of the head and neck; melanoma; squamous cell carcinoma of skin; renal cell carcinoma; soft tissue sarcoma; colorectal, endometrial, esophageal, gastric,

ovarian, pancreatic, prostate, breast and small cell lung cancer.

Atezolizumab-Treated Patients

Tumor Type Study Name Phase Study Details ITT Population BEP

(n)

High TMB

≥ 16 mut/Mb

(n)

NSCLC

POPLAR II Randomized in 2L/3L NSCLC Atezolizumab (N = 144)

vs Docetaxel (N = 143) 14 5

OAK (ITT850) III Randomized in 2L+ NSCLC Atezolizumab (N = 425)

vs Docetaxel (N = 425) 180 40

BIRCH and FIR II Single-arm in 1L+ and 2L+ NSCLC Atezolizumab (N = 805) 148 38

mUC

IMvigor210 II Single-arm in 1L cisplatin-ineligible

or 2L+ locally advanced or mUC Atezolizumab (N = 429) 141 26

IMvigor211 III Randomized in 2L+ locally advanced

mUC

Atezolizumab (N = 467) vs

Vinflunine, Paclitaxel or Docetaxel (N = 464) 259 44

Pan-tumor PCD4989g Ia/Ib Multi-cohort in solid tumors (including

melanoma) or hematologic malignancies Atezolizumab (N = 660) 245 22

Pooled total 987 175

• FoundationOne: FDA-approved hybrid-capture NGS method targeting 315 genes; ~1.1 Mb of coding genome

• Identifies all four classes of genomic alterations: Base substitutions, Indels,CNA, Rearrangements

• Accuracy and precision comparable to WES (Chalmers: Genome Med 2017; Mariathasan et al Nature 2018)

Correlation Between FoundationOne TMB, Predicted NAL and ORR IMvigor210 (1L/2L mUC)1

• TMB measured by FoundationOne assay is positively correlated with WES-based NAL ([N = 218]

Pearson r = 0.85)

• TMB by FoundationOne assay is associated with atezolizumab ORR (two tailed t-test, P = 6.9 x 10-7)

• Predicted NAL is associated with atezolizumab ORR (two tailed t-test, P = 2.7 × 10−9)

Neoantigens

CR n=19

PR n=34

SD n=44

PD n=119

CR n=21

PR n=40

SD n=44

PD n=128

FoundationOne TMB WES TMB WES

Gandara/Legrand: ASCO 2018 & Mariathasan et al. Nature. 2018.

Pe

ars

on

co

rre

latio

n c

oe

ffic

ients

High TMB (≥ 16 mut/Mb in tissue) is associated with enriched ORR, DOR & PFS from Atezo across Multiple Tumor Types (NSCLC, Bladder, etc) & Lines of Therapy

Randomized Trials (POPLAR, OAK, IMvigor211)

Gandara/Legrand et al: ASCO 2018

Abstract 12000

Gandara DR, et al. High-TMB Analysis. http://bit.ly/2xibbxU

• TMB is a continuous variable

• A ≥ 16-mut/Mb TMB cutoff balances a high ORR and reasonable prevalence across numerous tumor types

21

Selection of a High-TMB Cutoff for FoundationOne Assay

a TMB cutoffs shown are measured in mut/Mb.

Date of analysis: November 1, 2017.

Numerical ORR increase at all TMB cutoffs examineda

0.2 0.4 0.6 0.8 1.0 0

0.2

0.4

0.6

0.8

1.0

0

ROC analysis

Se

nsitiv

ity

1-Specificity

http://bit.ly/2xibbxU Gandara DR, et al. High-TMB Analysis. 22

TMB at ≥ 16 mut/Mb Identifies a Patient Population Distinct from PD-L1 IHC

SP142 PD-L1 assay: IC, tumor-infiltrating immune cell; TC, tumor cell; IC0/1 or TC0/1, ≤1% PD-L1 expressing IC or TC; IC2/3 or TC2/3, ≥ 5% PD-L1 expressing IC or TC.

a Cisplatin-ineligible patients with 1L mUC.

1L, 2L mUC (IMvigor210, IMvigor211)

n = 86 n = 26 n = 44

TMB-H IC2/3

2nd Line NSCLC (OAK Trial)

n = 45 n = 16 n = 24

TMB-H IC2/3 or TC2/3

PD-L1 Status TMBa ORR (n/n), %

2L NSCLC 1L,a 2L mUC

IC0/1 or IC/TC0/1 TMB-L

< 16 mut/Mb 9%

(9/95)

12% (29/244)

IC2/3 or IC/TC2/3 TMB-L

< 16 mut/Mb 20% (9/45)

27% (23/86)

IC0/1 or IC/TC0/1 TMB-H

≥ 16 mut/Mb 8%

(2/24)

25% (11/44)

IC2/3 or IC/TC2/3 TMB-H

≥ 16 mut/Mb 38% (6/16)

50% (13/26)

• Response Rate was higher in patients whose cancers

had both high TMB and high PD-L1 expression

Recent 1st Line Clinical Trial Results of Checkpoint Immunotherapy in Advanced NSCLC

Study Drug PDL1 Selection

Line of Therapy

Control Primary Endpoint

HR-Primary Endpoint

Press Release-Presentation

MYSTIC Durva or Durva-Tremi

>25% 1st Plat Chemo

PFS & OS NR Negative

KN189 (Non-SQ)

Pembro- Chemo

≥1% 1st Plat Chemo

PFS 0.52 Positive

KN042

Pembro vs Chemo

≥1% 1st Plat Chemot

OS 0.81 for OS 0.69 for 50%

Positive

KN047 (SQ) Pembro-Chemo None 1st Plat-Nab Paclitax

PFS & OS 0.64 for OS

Positive

Impower 150 (Non-SQ)

Atezo +Bev/ Pac/Carbo

None 1st Bev/Pac Carbo

PFS OS

0.71 Positive

Impower 131 (SQ)

Atezo + Nab/Carbo

None 1st Pac/ Carbo

PFS OS

0.71 (PFS) Positive

CM227 Nivo or Nivo-Ipi

Checkmate 227 Study Design (Part 1)

aNSQ: pemetrexed + cisplatin or carboplatin, q3w for ≤4 cycles, with optional pemetrexed maintenance following chemotherapy or nivolumab + pemetrexed maintenance following nivolumab + chemotherapy; SQ: gemcitabine + cisplatin, or gemcitabine + carboplatin; q3w for ≤4 cycles.

Nivolumab+ Ipilimumab n = 583

Chemotherapya n = 583

≥1% PD-L1 Expression

N = 1189

In patients with TMB

CheckMate 227: Progression-free survival by tumor mutation burden and PD-L1 expression

Exploratory analysis. Chemo, chemotherapy; mut, mutations; ipi, ipilimumab; nivo, nivolumab; TMB, tumor mutation burden. a95% CI: nivo + chemo (4.3–9.1 mo), nivo + ipi (2.7–NR mo), chemo (4.0–6.8 mo); b95% CI: nivo + chemo (4.2–6.9 mo), nivo + ipi (1.6–5.4 mo), chemo (3.9–6.2 mo).

Borghaei H, et al. ASCO 2018. Abstract 9001.

Nivo + chemo (n = 54)

Nivo + ipi (n = 52)

Chemo (n = 59)

Median PFS,b mo 4.7 3.1 4.7

HR (vs chemo) (95% CI)

0.87 (0.57–1.33)

1.17 (0.76–1.81)

TMB

Emerging Options for 1st-line Therapy of Advanced Non-Squamous Lung Cancer (Non-Oncogene Driver)

Emerging Options for 1st-Line Immunotherapy of Advanced NSCLC

Parameters Drug Regimen (Monotherapy/Combo)

Biomarker Selection (PD-L1/TMB) Histology

Strength of the Trial

Adapted from Gandara: Best of ASCO 2018

PD-L1 ≥50% Pembrolizumab

(KN024/KN042)

PD-L1

Emerging Options for 1st-line Therapy of Advanced Squamous Lung Cancer

Adapted from Gandara: Best of ASCO 2018

PD-L1 ≥50% Pembrolizumab (KN024/KN042)

PD-L1< 1% Pembrolizumab-

Paclitaxel/Carboplatin (KN407)

PD-L1 1-49% Pembrolizumab-

Paclitaxel/Carboplatin (KN407)

PD-L1

Tumor mutational burden in blood (bTMB) is associated with Atezolizumab efficacy in 2nd-Line+ NSCLC (POPLAR & OAK Trials)

Gandara DR, et al.: Nature Med 2018

OAK Study

B-F1RST: Blood Tumor Mutational Burden (bTMB) Selection of Atezolizumab Immunotherapy

Velcheti V, et al. ASCO 2018. Abstract 12001.

Prospectively evaluate TMB in blood by

FoundationOne NGS (bTMB)

➢ Primary Endpoints: ORR & PFS in

bTMB high (≥16) vs low

TMB from Plasma ctDNA (Guardant) associates with ORR & PFS from Pembrolizumab in Gastric Cancer

Kim, Kang et al: Nature Med 2018