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
A5
49
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LNC
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Pre
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st
SD PD CR
Pre
*
Pre
*
Po
st*
Po
st
135 bp
106 bp
Mutations in Tumors Detected in Plasma
Monitoring Response to Treatment
Mutant Wild-type
* K-RAS 12th codon mutation
Pla
sma
Pt 1
Pla
sma*
Tum
or*
Pt 20
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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