Integrative Tumor and Immune Cell Multi-omic Analyses Predict Response to Immune Checkpoint Blockade in Melanoma

Abstract

In this study, we incorporate analyses of genome-wide sequence and structural alterations with pre- and on-therapy transcriptomic and T cell repertoire features in immunotherapy-naive melanoma patients treated with immune checkpoint blockade. Although tumor mutation burden is associated with improved treatment response, the mutation frequency in expressed genes is superior in predicting outcome. Increased T cell density in baseline tumors and dynamic changes in regression or expansion of the T cell repertoire during therapy distinguish responders from non-responders. Transcriptome analyses reveal an increased abundance of B cell subsets in tumors from responders and patterns of molecular response related to expressed mutation elimination or retention that reflect clinical outcome. High-dimensional genomic, transcriptomic, and immune repertoire data were integrated into a multi-modal predictor of response. These findings identify genomic and transcriptomic characteristics of tumors and immune cells that predict response to immune checkpoint blockade and highlight the importance of pre-existing T and B cell immunity in therapeutic outcomes.

Trial registration: ClinicalTrials.gov NCT01621490.

Keywords: T cell repertoire; cancer genomics; immune checkpoint blockade; integrative predictive model; melanoma; multi-omics.

Conflict of interest statement

V.A. and J.T. receive research funding from Bristol-Myers Squibb. J.T. serves as a consultant/advisory board member to Bristol-Myers Squibb, Merck, Astra Zeneca, and Compugen. J.R.W. is a consultant for Personal Genome Diagnostics; is the founder and owner of Resphera Biosciences; and holds patents, royalties, or other intellectual property from Personal Genomic Diagnostics. A.B. receives honoraria from Proscia and Corista; is a consultant of Bristol-Myers Squibb, Genentech, and Bayer; and receives research funding from Genentech. C.G. has patents, royalties, or other intellectual property from Karyopharm and Arcus. A.R. has received honoraria from consulting with Amgen, Bristol-Myers Squibb, 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 Prime, FLX-Bio, ImaginAb, Isoplexis, Kite-Gilead, Lutris Pharma, Merus, PACT Pharma, Rgenix, and Tango Therapeutics; and has received research funding from Agilent and from Bristol-Myers Squibb through Stand Up to Cancer (SU2C). P.R.-M. and M.W.-R. are employees of Bristol-Myers Squibb. D.M.P. and S.L.T. report stock and other ownership interests in Aduro Biotech, DNAtrix, Dracen Pharmaceuticals, Dragonfly Therapeutics, Ervaxx, Five Prime Therapeutics, Potenza Therapeutics, RAPT, Tizona Therapeutics, Trieza Therapeutics, and WindMIL; a consulting or advisory role in Amgen, DNAtrix, Dragonfly Therapeutics, Dynavax, Ervaxx, Five Prime Therapeutics, Immunocore, Immunomic Therapeutics, Janssen Pharmaceuticals, MedImmune/AstraZeneca, Merck, RAPT, and WindMIL; research grants from Bristol-Myers Squibb and Compugen; patents, royalties, and/or other intellectual property through their institution with Aduro Biotech, Arbor Pharmaceuticals, Bristol-Myers Squibb, Immunomic Therapeutics, NexImmune, and WindMIL; and travel, accommodations, and expenses from Bristol-Myers Squibb and Five Prime Therapeutics. V.E.V. is a founder of Delfi Diagnostics and Personal Genome Diagnostics, serves on the Board of Directors and as a consultant for both organizations, and owns Delfi Diagnostics and Personal Genome Diagnostics stock, which are subject to certain restrictions under university policy. Additionally, Johns Hopkins University owns equity in Delfi Diagnostics and Personal Genome Diagnostics. V.E.V. is an advisor to Bristol-Myers Squibb, Genentech, Merck, and Takeda Pharmaceuticals. Within the last 5 years, V.E.V. has been an advisor to Daiichi Sankyo, Janssen Diagnostics, and Ignyta. These arrangements have been reviewed and approved by the Johns Hopkins University in accordance with its conflict of interest policies.

© 2020 The Authors.

Figures

Graphical abstract

Figure 1

Tumor Genomic Features Associated with Clinical Benefit Tumors of responding patients had a higher total and clonal TMB compared to non-responders (FDR-adjusted p = 0.0048 and p = 0.0037, respectively). Overall, a higher number of single-base substitutions and indels were found in tumors of responders, which was largely driven by their higher TMB. A UV-related mutational signature was found to be enriched in tumors of responders for all patients and patients in the ipilimumab/nivolumab group (FDR-adjusted p = 0.03 and p = 0.0096, respectively). Following an exome-wide unbiased approach, we investigated potential differential abundance of sequence alterations in tumors of responding patients. LRP1B and EYS mutations appeared to accumulate in tumors of responding patients (FDR p = 0.058 for both genes and TMB-adjusted p = 0.036 and p = 0.025 for LRP1B and EYS, respectively), most likely due to the expected larger number of passenger mutations in larger DNA regions. There was a non-significant trend in enrichment of ERBB4 mutations in tumors of responders, likely reflecting TMB-high tumors (TMB-adjusted p = 0.133). There were no differences in the abundance of BRAF and NF1 mutations between tumors of responders and non-responders. The AAMDC, CLNS1A, INTS4, KCTD14, NDUFC2, NDUFC2-KCTD14, RSF1, and THRSP loci on chromosome 11q14.1 were found to be co-amplified in five tumors of non-responders (FDR-adjusted p = 0.094). Genome-wide copy number analyses revealed a trend toward increased tumor aneuploidy in tumors of non-responding patients (denoted by fraction of the genome with complete allelic imbalance; FDR-adjusted p = 0.19). AI, allelic imbalance; BOR, best overall response; CNV, copy number variation; Conseq, mutation consequence; CR, complete response; OS, overall survival; PD, disease progression; PR, partial response; SBS, single-base substitution; SD, stable disease. Dots represent hotspot mutations, and X denotes monoallelic loss of the wild-type allele.

Figure 2

Antigen Presentation Genomic Diversity and Expression Is Associated with Response to Immune Checkpoint Blockade (A) There were no differences in the number of germline HLA class I alleles or the degree in homozygosity found between responders and non-responders. HLA class I germline zygosity and somatic HLA class I LOH were combined to calculate the unique number of HLA class I alleles in tumor cells. The β2-microglobulin locus frequently underwent monoallelic loss, but there was no evidence pointing to an enrichment in concurrent inactivating mutations in tumors from non-responder patients. HLA class I and II as well as β2-microglobulin expression was significantly higher in tumors from responding patients. (B) HLA class II genotypes for DPA1, DPB1, DQA1, DQB1, and DRB1 were derived from whole-exome sequence data. Patients with maximal heterozygosity for HLA class II (HLA class II alleles = 10) had a significantly longer progression-free survival (log rank p = 0.043). (C and D) Patients heterozygous at the HLA-PD locus and more specifically at the HLA-DPB1 locus had a significantly longer progression-free survival (log rank p = 0.007 and p = 0.005, respectively).

Figure 3

T Cell Receptor Repertoire Features and Dynamics during Immune Checkpoint Blockade Differentiate Tumors of Responders from Non-responders (A) Differential abundance analysis employing the number of TCR clones revealed an enrichment of unique TCR rearrangements in tumors of responding patients (FDR-adjusted p = 0.0018). (B) Dynamic shifts in the TCR repertoire composition on therapy were reflective of clinical outcome, such that tumors of responders harbored a higher fraction of expanding and regressing clones. A representative example of TCR repertoire reshaping is shown in (B) for patient 20002, who achieved a complete response on ipilimumab and nivolumab. (C) Overall, the fraction of expanding and regressing TCR clones was significantly higher in responders compared to non-responders (FDR p = 0.02 for both fractions of responding and regressing clones). (D) Representative example of TCR repertoire dynamics for a patient experiencing disease progression on the ipilimumab/nivolumab arm; a less dynamic repertoire is observed, denoted by a significantly lower number of clonotypic expansions and regressions after therapy initiation.

Figure 4

Transcriptome Deconvolution Reveals B Cell Enrichment in Tumors of Responding Patients (A) Fusion analysis utilizing transcriptome data revealed an enrichment in immunoglobulin rearrangements in tumors of responding patients, which was reflective of a higher pre-existing intratumoral B cell infiltration (Mann Whitney p = 7e−04). (B) Through gene expression signature analysis and deconvolution of RNA sequence data, we identified an enrichment in tumor associated B cells in baseline tumors of responding patients (Mann Whitney p = 9.3e−05). (C and D) This observation was driven by enrichment in the naive B cell and plasma cell populations (Mann Whitney p = 0.013 and p = 0.03, respectively). (E) Through unsupervised clustering of relative abundance of 22 immune-cell-type populations, patients with clinical response to therapy clustered together and showed higher relative abundance in B cell subsets as well as CD8+ T cells. Z scores were computed across samples for each immune cell type separately using the relative abundance measurements obtained from CIBERSORT.

Figure 5

B and T Cell Interactions Shape Clinical Responses to Immune Checkpoint Blockade Independent of Tumor Mutation Burden Immunoglobulin rearrangements highly correlated with TCR rearrangements in tumors of responders (Spearman rho = 0.68; p = 1e−06), delineating a group of patients that derived benefit from immune checkpoint blockade (shown as blue circles and triangles and clustered in the right upper corner of the plot). This correlation between pre-existing B and T cell rearrangements with each other and with response to therapy was not affected by TMB. Circles indicate treatment with ipilimumab and nivolumab, although triangles denote treatment with nivolumab. Responders are shown in blue and non-responders in magenta. The size of each point (solid circle or triangle) is proportional to the TMB of the corresponding baseline tumor.

Figure 6

Multi-parameter Integrative Modeling Accurately Predicts Therapeutic Outcome (A) Non-parametric correlations among genomic, transcriptomic, and T cell repertoire features were assessed by the Spearman’s rho statistic, and p values were corrected for multiple comparisons. The color of each dot refers to the Spearman rho coefficient value (darkest blue being 1 and darkest red being −1), and the size of each dot is proportional to the strength of the correlation. ∗∗∗FDR-adjusted p