. 2023 Apr 10;41(4):791-806.e4.

doi: 10.1016/j.ccell.2023.03.010.

Prior anti-CTLA-4 therapy impacts molecular characteristics associated with anti-PD-1 response in advanced melanoma

Katie M Campbell¹, Meelad Amouzgar², Shannon M Pfeiffer³, Timothy R Howes³, Egmidio Medina⁴, Michael Travers³, Gabriela Steiner³, Jeffrey S Weber⁵, Jedd D Wolchok⁶, James Larkin⁷, F Stephen Hodi⁸, Silvia Boffo⁹, Lisa Salvador⁹, Daniel Tenney⁹, Tracy Tang⁹, Marshall A Thompson³, Christine N Spencer³, Daniel K Wells³, Antoni Ribas¹⁰

Affiliations

¹ Division of Hematology/Oncology, Department of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA. Electronic address: katiecampbell@mednet.ucla.edu.
² Department of Pathology, Stanford University, Stanford, CA 94305, USA; Parker Institute for Cancer Immunotherapy, San Francisco, CA 94129, USA.
³ Parker Institute for Cancer Immunotherapy, San Francisco, CA 94129, USA.
⁴ Division of Hematology/Oncology, Department of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.
⁵ Perlmutter Cancer Center, NYU School of Medicine, New York, NY 10016, USA.
⁶ Parker Institute for Cancer Immunotherapy, San Francisco, CA 94129, USA; Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Weill Cornell Medicine, New York, NY 10065, USA.
⁷ Department of Medical Oncology, The Royal Marsden NHS Foundation Trust, London SW3 6JJ, UK.
⁸ Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
⁹ Bristol Myers Squibb Corp., Princeton, NJ 08540, USA.
¹⁰ Division of Hematology/Oncology, Department of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Parker Institute for Cancer Immunotherapy, San Francisco, CA 94129, USA; Department of Molecular and Medical Pharmacology, University of California, Los Angeles, Los Angeles, CA 90095, USA; Division of Surgical Oncology, Department of Surgery, University of California, Los Angeles, Los Angeles, CA 90095, USA; Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA 90024, USA. Electronic address: aribas@mednet.ucla.edu.

PMID: 37037616
PMCID: PMC10187051
DOI: 10.1016/j.ccell.2023.03.010

Prior anti-CTLA-4 therapy impacts molecular characteristics associated with anti-PD-1 response in advanced melanoma

Katie M Campbell et al. Cancer Cell. 2023.

. 2023 Apr 10;41(4):791-806.e4.

doi: 10.1016/j.ccell.2023.03.010.

Authors

Affiliations

¹ Division of Hematology/Oncology, Department of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA. Electronic address: katiecampbell@mednet.ucla.edu.
² Department of Pathology, Stanford University, Stanford, CA 94305, USA; Parker Institute for Cancer Immunotherapy, San Francisco, CA 94129, USA.
³ Parker Institute for Cancer Immunotherapy, San Francisco, CA 94129, USA.
⁴ Division of Hematology/Oncology, Department of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.
⁵ Perlmutter Cancer Center, NYU School of Medicine, New York, NY 10016, USA.
⁶ Parker Institute for Cancer Immunotherapy, San Francisco, CA 94129, USA; Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Weill Cornell Medicine, New York, NY 10065, USA.
⁷ Department of Medical Oncology, The Royal Marsden NHS Foundation Trust, London SW3 6JJ, UK.
⁸ Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
⁹ Bristol Myers Squibb Corp., Princeton, NJ 08540, USA.
¹⁰ Division of Hematology/Oncology, Department of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Parker Institute for Cancer Immunotherapy, San Francisco, CA 94129, USA; Department of Molecular and Medical Pharmacology, University of California, Los Angeles, Los Angeles, CA 90095, USA; Division of Surgical Oncology, Department of Surgery, University of California, Los Angeles, Los Angeles, CA 90095, USA; Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA 90024, USA. Electronic address: aribas@mednet.ucla.edu.

PMID: 37037616
PMCID: PMC10187051
DOI: 10.1016/j.ccell.2023.03.010

Abstract

Immune checkpoint inhibitors (ICIs), including CTLA-4- and PD-1-blocking antibodies, can have profound effects on tumor immune cell infiltration that have not been consistent in biopsy series reported to date. Here, we analyze seven molecular datasets of samples from patients with advanced melanoma (N = 514) treated with ICI agents to investigate clinical, genomic, and transcriptomic features of anti-PD-1 response in cutaneous melanoma. We find that prior anti-CTLA-4 therapy is associated with differences in genomic, individual gene, and gene signatures in anti-PD-1 responders. Anti-CTLA-4-experienced melanoma tumors that respond to PD-1 blockade exhibit increased tumor mutational burden, inflammatory signatures, and altered cell cycle processes compared with anti-CTLA-4-naive tumors or anti-CTLA-4-experienced, anti-PD-1-nonresponsive melanoma tumors. We report a harmonized, aggregate resource and suggest that prior CTLA-4 blockade therapy is associated with marked differences in the tumor microenvironment that impact the predictive features of PD-1 blockade therapy response.

Keywords: immune checkpoint blockade; immunotherapy; melanoma; meta-analysis.

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Conflict of interest statement

Declaration of interests K.M.C. is a shareholder in Geneoscopy LLC and received consulting fees from PACT Pharma, Tango Therapeutics, Geneoscopy LLC, Flagship Labs 81 LLC, and the Rare Cancer Research Foundation. M.A. received consulting fees from PICI. J.S.W. received consulting fees from Merck, Genentech, AstraZeneca, GlaxoSmithKline, Novartis, Nektar, Celldex, Incyte, Biond, ImCheck, Sellas, Evaxion, and EMD Serono; is or has been a member of the scientific advisory board for Bristol-Myers Squibb (BMS), CytoMx, Incyte, ImCheck, Biond, Sellas, Instil Bio, OncoC4 and Neximmune; and holds equity in Biond, Evaxion, Instil Bio, and Neximmune. J.S.W. has been named on patents filed by Moffitt Cancer Center (ipilimumab biomarker and TIL growth method) and Biosedix (PD-1). NYU has received research support from BMS, Merck, GlaxoSmithKline, Moderna, Pfizer, Novartis, and AstraZeneca. J.D.W. received consulting fees from Apricity, CellCarta, Ascentage Pharma, AstraZeneca, Astellas, Bicara Therapeutics, Boehringer Ingelheim, BMS, Dragonfly, Georgiamune, Imvaq, Larkspur, Maverick Therapeutics, Psioxus, Recepta, Tizona, and Sellas; has received research/grant support from BMS and Sephora; and has equity in Apricity, Arsenal IO, Ascentage, Beigene, Imvaq, Linnaeus, Georgiamune, Maverick, Tizona Pharmaceuticals, and Trieza. J.L. has worked in a consulting/advisory role for iOnctura, Apple Tree, Merck, BMS, Eisai, Debipharm, and Incyte; received honoraria from AstraZeneca, BMS, Eisai, EUSA Pharma, GlaxoSmithKline, Incyte, Ipsen, Merck, touchEXPERTS, Royal College of Physicians, Cambridge Healthcare Research, Royal College of General Practitioners, VJOncology, Agence Unik, Merck Sharp & Dohme, Novartis, Aptitude, Pierre Fabre, Pfizer, Roche, Seagen, Inselgruppe, eCancer, Ultimovacs, Calithera, and Goldman Sachs; and received research/grant support from Achilles Therapeutics, BMS, Immunocore, Aveo, Pharmacyclics, MSD, Nektar Therapeutics, Covance, Novartis, Pfizer, and Roche. F.S.H. received research support from the NCI of the NIH, BMS, Novartis, and Genentech; royalties or licenses from BMS and Novartis; consulting fees from BMS, EMD Serono, Surface, Sanofi, Genentech, Gossamer, Trillium, Immunocore, Merck, Novartis, Compass Therapeutics, Pieris, Bioentre, Iovance, Catalym, and Amgen; patents for methods for treating MHC class I polypeptide-related sequence A disorders (20100111973, pending, with royalties paid), tumor antigens and uses thereof (7250291, issued), angiopoiten-2 biomarkers predictive of anti-immune checkpoint response (20170248603, pending), compositions and methods for the identification, assessment, prevention, and treatment of melanoma using PD-L1 isoforms (20160340407, pending), therapeutic peptides (20160046716, 20140004112, 20170022275, and 20170008962, all pending, and 9402905, issued), methods of using pembrolizumab and trebananib (pending), vaccine compositions and methods for restoring NKG2D pathway function against cancers (10279021, issued), antibodies that bind to MHC class I polypeptide-related sequence A (10106611, issued), and anti-galectin antibody biomarkers predictive of anti-immune checkpoint and anti-angiogenesis responses (20170343552, pending); data safety monitoring board and advisory board participation for Aduro and Checkpoint Therapeutics; scientific advisory board leadership for Bicara and Apricity; and stock options in Checkpoint Therapeutics, Pionyr, Apricity, and Bicara. S.B., L.S., D.T., and T.T. are employees of BMS. C.N.S. received consulting fees from the Rare Cancer Research Foundation. D.K.W. is an employee, founder, and equity holder at Santa Ana Bio; holds equity in Immunai, and has received consulting fees from Rubius Therapeutics, DeepMind, Illumina, and Guardant Health. A.R. has received honoraria from consulting with Amgen, BMS, Chugai, Genentech, Merck, Novartis, Roche, and Sanofi; is or has been a member of the scientific advisory board and holds stock in Advaxis, Arcus Biosciences, Bioncotech Therapeutics, Compugen, CytomX, Five Prim, FLX-Bio, ImaginAb, Isoplexis, Kite-Gilead, Lutris Pharma, Merus, PACT Pharma, Rgenix, and Tango Therapeutics; and has received research funding from Agilent Technologies and BMS through Stand Up to Cancer.

Figures

**Figure 1.. Project Overview**
**(A)** Overview of the datasets and pipeline for harmonized data processing and analysis. **(B)** Alluvial plot depicts the demographics of patients with melanoma tumor samples in the final dataset (x-axis; cohort, subtype, prior ICI therapy, treatment regimen, and RECIST response). Each individual (alluvium) is colored by whether the corresponding sample was included in analysis of cutaneous melanoma tumors treated with anti-PD-1. See Table 1 and Figure S1.

**Figure 2.. Genomic correlates of anti-PD-1 response**
Comparison of TMB (Mut/Mb), quantified by the number of nonsilent mutations and normalized by tumor purity and sequencing coverage, across (A) subtypes or (B) clinical groups, defined by anti-PD-1 response and prior anti-CTLA-4 treatment. Groups are compared using a Wilcoxon test (**, p<0.01; ns, p>0.05). In (A), each subtype is compared to the cutaneous group. (C) Logistic regression comparing genes and mutation types between anti-PD-1 responsive (CR/PR) and nonresponsive (PD) tumors. Significance and log-odds ratio (x-axis) are indicated. (D) Lolliplot of *PIK3C2G* mutations in all baseline tumors, stratified by CR/PR (top) and PD (bottom). (E) Corresponding cohorts of *PIK3C2G*-mutant tumors. See Figure S2.

**Figure 3.. Prior CTLA-4 blockade differentially stratifies the expression landscape of melanoma tumors responding or not to anti-PD-1**
(A-C) Differential expression analysis comparing baseline anti-CTLA-4-experienced to anti-CTLA-4-naive, cutaneous melanoma tumors (N=131): **(A)** all-samples, (anti-CTLA-4-naive=92, anti-CTLA-4-experienced=39), **(B)** CR/PR-only (anti-CTLA-4-naive=48, anti-CTLA-4experienced=17), and **(C)** PD-only (anti-CTLA-4-naive=44, anti-CTLA-4-experienced=22). **(D)** UpSet plot of differential genes comparing anti-CTLA-4-experienced to anti-CTLA-4-naive patients. **(E)** Scatter plot of each gene comparing the log fold-change difference between anti-CTLA-4-experienced and anti-CTLA-4-naive patients. Color indicates significance of a gene in both, neither, CR/PR-only, or PD-only subsets. **(F)** Barplots showing the number of statistically significant pathways using differential genes. **(G)** Normalized Enrichment Score (NES) results from ranked-based GSEA of anti-CTLA-4-experienced versus anti-CTLA-4-naive tumors in each patient subset. Filled circles indicate statistical significance, hollow circles indicate no statistical significance. **(H)** GSEA rank plots showing differential or shared enrichment directions between anti-CTLA-4-experienced vs anti-CTLA-4-naive patients, colored as in (G). See Figure S3 and Table S1–2.

**Figure 4.. Strength of inflammatory gene expression patterns in anti-PD-1 responding biopsies is associated with anti-CTLA-4 experience**
**(A)** PC1 and PC2 from PCA of the top 15% most variable genes across 131 baseline, cutaneous melanoma samples, following batch effect correction by cohort. Colors indicate four clinical groups stratified across response and prior treatment. Ellipses indicate the distribution of clinical groups. **(B-D)** Differential expression analysis comparing tumors between CR/PR and PD patients across patient subsets: *(B)* all-samples (CR/PR=65, PD=66), *(C)* anti-CTLA-4-experienced-only (CR/PR=17, PD=22), and *(D)* anti-CTLA-4-naive-only (CR/PR=48, PD=44). **(E)** UpSet plot of differential genes comparing CR/PR to PD patients across each patient subset. **(F)** NES results from ranked-based GSEA of CR/PR versus PD patients in each patient subset. Hollow circles indicate no statistical significance. **(G)** Scatterplot of adjusted p-values with NES direction for GSEA results comparing CR/PR versus PD patients in the anti-CTLA-4-experienced and anti-CTLA-4-naive subsets. Pathways in the colored boxes indicate statistical significance in only the anti-CTLA-4-experienced or anti-CTLA-4-naive subset. **(H)** Heatmaps showing scaled mean expression of cytokine (top) and immunoglobulin-related genes (bottom). See Figure S3 and Tables S3–4.

**Figure 5.. Anti-CTLA-4 experience is associated with globally increased adaptive immune infiltration signatures**
**(A)** Heatmap of immune cell-type scores computed using published gene signatures. Colors represent the averaged, raw score from patients in each clinical group. Size of each point represents the averaged, relative score within each clinical group (see STAR Methods). **(B)** Heatmap of the mean score difference of each cell-type between CR/PR and PD tumors from 3 analyses either observing all samples together or stratified by prior treatment. Black tile outlines indicate statistical significance by Wilcoxon signed-rank test with BH-adjusted p-values <=0.1. **(C)** Scatter plot comparing the averaged cell-type signature score of CR/PR and PD patients, colored by prior treatment. **(D)** Boxplots of TIARA-PD-1 score compared between response and prior treatment clinical groups (see STAR Methods). Statistical significance determined by Wilcoxon signed-rank test. **(E)** Differential expression analysis as a function of TIARA-PD-1. The x-axis is the expression difference associated with a unit change in TIARA-PD-1. **(F)** NES of ranked-based GSEA for statistically significant pathways curated from HALLMARK and NABA (Table S5). **(G)** Barplots of the number of genes significantly different as a function of TIARA-PD-1 within patient subsets. **(H)** Rank-based GSEA results for significant pathways curated from HALLMARK and NABA across each clinical group. Filled circles indicate statistical significance, hollow circles indicate no statistical significance. See Figure S4–6 and Tables S4–7.

**Figure 6.. Statistical models are improved by including prior anti-CTLA-4 experience to predict response to anti-PD-1 in cutaneous melanoma**
**(A)** Feature selection using cross-validated lasso-regularized logistic regression for 90 paired WES and RNAseq baseline cutaneous melanoma tumors treated with anti-PD-1, with or without prior anti-CTLA-4, comparing CR/PR to PD. **(B)** Heatmap of selected features for 3 fitted models. Color indicates magnitude and direction of the standardized coefficient for that feature. Gray indicates that the feature was not selected. **(C)** Predicted probabilities from leave-one-out cross-validation (LOOCV) logistic regression comparing CR/PR vs PD using all selected features from *(B)* in each respective model. The y-axis indicates probability of response. The top and bottom facets indicate the clinically annotated true response to anti-PD-1. LOOCV accuracy is at the bottom. See Figure S7.

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Prior anti-CTLA-4 therapy impacts molecular characteristics associated with anti-PD-1 response in advanced melanoma

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Prior anti-CTLA-4 therapy impacts molecular characteristics associated with anti-PD-1 response in advanced melanoma

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Conflict of interest statement

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