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. 2016 Aug;6(8):827-37.
doi: 10.1158/2159-8290.CD-15-1545. Epub 2016 Jun 14.

Analysis of Immune Signatures in Longitudinal Tumor Samples Yields Insight into Biomarkers of Response and Mechanisms of Resistance to Immune Checkpoint Blockade

Pei-Ling Chen  1 , Whijae Roh  2 , Alexandre Reuben  3 , Zachary A Cooper  4 , Christine N Spencer  2 , Peter A Prieto  3 , John P Miller  2 , Roland L Bassett  5 , Vancheswaran Gopalakrishnan  3 , Khalida Wani  6 , Mariana Petaccia De Macedo  6 , Jacob L Austin-Breneman  3 , Hong Jiang  3 , Qing Chang  2 , Sangeetha M Reddy  7 , Wei-Shen Chen  1 , Michael T Tetzlaff  8 , Russell J Broaddus  8 , Michael A Davies  9 , Jeffrey E Gershenwald  3 , Lauren Haydu  3 , Alexander J Lazar  10 , Sapna P Patel  9 , Patrick Hwu  9 , Wen-Jen Hwu  9 , Adi Diab  9 , Isabella C Glitza  9 , Scott E Woodman  9 , Luis M Vence  11 , Ignacio I Wistuba  6 , Rodabe N Amaria  9 , Lawrence N Kwong  6 , Victor Prieto  8 , R Eric Davis  12 , Wencai Ma  12 , Willem W Overwijk  9 , Arlene H Sharpe  13 , Jianhua Hu  5 , P Andrew Futreal  2 , Jorge Blando  6 , Padmanee Sharma  14 , James P Allison  11 , Lynda Chin  2 , Jennifer A Wargo  15
Affiliations

Analysis of Immune Signatures in Longitudinal Tumor Samples Yields Insight into Biomarkers of Response and Mechanisms of Resistance to Immune Checkpoint Blockade

Pei-Ling Chen et al. Cancer Discov. 2016 Aug.

Abstract

Immune checkpoint blockade represents a major breakthrough in cancer therapy; however, responses are not universal. Genomic and immune features in pretreatment tumor biopsies have been reported to correlate with response in patients with melanoma and other cancers, but robust biomarkers have not been identified. We studied a cohort of patients with metastatic melanoma initially treated with cytotoxic T-lymphocyte-associated antigen-4 (CTLA4) blockade (n = 53) followed by programmed death-1 (PD-1) blockade at progression (n = 46), and analyzed immune signatures in longitudinal tissue samples collected at multiple time points during therapy. In this study, we demonstrate that adaptive immune signatures in tumor biopsy samples obtained early during the course of treatment are highly predictive of response to immune checkpoint blockade and also demonstrate differential effects on the tumor microenvironment induced by CTLA4 and PD-1 blockade. Importantly, potential mechanisms of therapeutic resistance to immune checkpoint blockade were also identified.

Significance: These studies demonstrate that adaptive immune signatures in early on-treatment tumor biopsies are predictive of response to checkpoint blockade and yield insight into mechanisms of therapeutic resistance. These concepts have far-reaching implications in this age of precision medicine and should be explored in immune checkpoint blockade treatment across cancer types. Cancer Discov; 6(8); 827-37. ©2016 AACR.See related commentary by Teng et al., p. 818This article is highlighted in the In This Issue feature, p. 803.

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Conflict of interest statement

No other potential conflicts of interest were disclosed.

Figures

Figure 1
Figure 1. Immune profiling in early on-treatment biopsies is predictive of response to CTLA-4 blockade in a unique cohort of patients treated with sequential CTLA-4 and PD-1 blockade
(a) Patients with metastatic melanoma were initially treated with CTLA-4 blockade (n=53) and non-responders to CTLA-4 blockade were then treated with PD-1 blockade (n=46; Expanded Access Program for MK-3475 at the MD Anderson Cancer Center). Of these 46 patients, 13 responded to PD-1 blockade, while 33 progressed. Tumor biopsy samples were collected at multiple time points during their treatment when feasible, including pre-treatment, on-treatment and progression anti-CTLA-4 biopsies, and pre-treatment, on-treatment (dose 2–3), and progression anti-PD-1 biopsies, for downstream immune profiling by immunohistochemistry and gene expression studies. The median elapsed time between tumor biopsies and treatment are shown for each time point. The profile and kinetics of immune cell infiltrates in the tumor microenvironment were compared between responders and non-responders to CTLA-4 blockade. Tumor samples available for immune profiling by IHC included pre-treatment anti-CTLA-4 (n=36; 5 responders and 31 non-responders), on-treatment anti-CTLA-4 (n=5; 2 responders and 3 non-responders) and progression anti-CTLA-4 biopsies (n=22). (b) CD8 and (c) CD4 density, and (d) PD-L1 H-score in responders versus non-responders on CTLA-4 blockade are shown. Representative images at pre-treatment (e), early on-treatment (f) time points are shown in responders versus non-responders to CTLA-4 blockade (20× magnification). Error bars represent standard error mean. *= p≤0.05, n.s.= not significant. Scale bars=200 µm.
Figure 2
Figure 2. Immune profiling in early on-treatment biopsies is highly predictive of response to PD-1 blockade
Longitudinal tumor biopsies were performed (at pre-treatment, early on-treatment, and late on-treatment / progression time points) in patients undergoing treatment with PD-1 blockade (n=47). The profile and kinetics of immune cell infiltrates in the tumor microenvironment were compared between responders and non-responders to PD-1 blockade. Tumor samples available for immune profiling by IHC included pre-treatment anti-PD-1 (n=24; 7 responders and 17 non-responders), on-treatment anti-PD-1 (dose 2–3) (n=11; 5 responders and 6 non-responders), and progression anti-PD-1 (n=12) biopsies (Table S1c). CD8 (a), CD4 (b), CD3 (c), PD-1 (d), PD-L1 (H-Score) (e), and LAG-3 (f) density are shown in responders versus non-responders. Representative images at pre-treatment (g) and early on-treatment (h) time points are shown in responders versus non-responders to PD-1 blockade (20× magnification). Error bars represent standard error mean. *= p≤0.05, **= p≤0.01, ***= p≤0.001, n.s.= not significant. Scale bars=200 µm.
Figure 3
Figure 3. Gene expression profiling in longitudinal tumor biopsies is predictive of response in a unique cohort of patients treated with sequential CTLA-4 and PD-1 blockade
Gene expression profiling was performed via NanoString in longitudinal tumor biopsies from patients treated with sequential CTLA-4 and PD-1 blockade (n=54), including pre-treatment anti-CTLA-4 (n=16; 5 responders and 11 non-responders), on-treatment anti-CTLA-4 (n=5; 3 responders and 2 non-responders) and progression anti-CTLA-4 biopsies (n=15), pre-treatment anti-PD-1 (n=16; 7 responders and 9 non-responders), on-treatment anti-PD-1 (dose 2–3) (n=10; 5 responders and 5 non-responders), and progression anti-PD-1 (n=7) biopsies (Supplementary Table S1d, S6a and S9b–c). Volcano plots illustrate the log2 fold change (FC) in gene expression (responders vs. non-responders) on the x-axis and unadjusted p-values from Student’s t-tests between responders and non-responders on the y-axis. Differentially expressed genes (FDR-adjusted p<0.05 and FC >2 or <-1/2) between responders and non-responders were highlighted in green at time of pre-treatment (a) and on-treatment (b) CTLA-4 blockade, pre-treatment, and (c) and on-treatment (d) PD-1 blockade. Interaction of time covariate (pre-treatment, on-treatment) and response covariate (responders, non-responders) was illustrated in volcano plots. Genes with significant interaction were highlighted in green (FDR-adjusted p<0.05 and interaction >1.5 or <-1.5) for CTLA-4 blockade (e) and PD-1 blockade (f). Venn diagram illustrates shared and unique genes up- and down-modulated in CTLA-4 (red) and PD-1 (blue) blockade over treatment time course (g).
Figure 4
Figure 4. Nanostring paired analysis
For analysis of paired samples, raw NanoString counts were compared between samples after anti-PD-1 therapy to those in the corresponding pre-treatment sample. Shown are the 37 Up-DEGs identified by paired analysis. FDR = False-discovery rate, R = Responder, NR = Non-responder.
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