Tumor and Microenvironment Evolution during Immunotherapy with Nivolumab

Abstract

The mechanisms by which immune checkpoint blockade modulates tumor evolution during therapy are unclear. We assessed genomic changes in tumors from 68 patients with advanced melanoma, who progressed on ipilimumab or were ipilimumab-naive, before and after nivolumab initiation (CA209-038 study). Tumors were analyzed by whole-exome, transcriptome, and/or T cell receptor (TCR) sequencing. In responding patients, mutation and neoantigen load were reduced from baseline, and analysis of intratumoral heterogeneity during therapy demonstrated differential clonal evolution within tumors and putative selection against neoantigenic mutations on-therapy. Transcriptome analyses before and during nivolumab therapy revealed increases in distinct immune cell subsets, activation of specific transcriptional networks, and upregulation of immune checkpoint genes that were more pronounced in patients with response. Temporal changes in intratumoral TCR repertoire revealed expansion of T cell clones in the setting of neoantigen loss. Comprehensive genomic profiling data in this study provide insight into nivolumab's mechanism of action.

Keywords: T cell receptor repertoire; clonal evolution/clonal selection; immunotherapy; ipilimumab; melanoma; neoantigen load; nivolumab; tumor immune evasion; tumor microenvironment; tumor mutation load/tumor mutation burden.

Copyright © 2017 Elsevier Inc. All rights reserved.

Figures

Figure 1. Genomic Features and Sculpting of the Tumor Mutational Landscape by Immunotherapy

(A) Baseline genomic characteristics of melanoma tumors from patients treated with immune checkpoint therapy. An OncoPrint image of WES data for the cohort sorted by response group (CR/PR, SD, PD). The OncoPrint displays genes recurrently mutated in melanoma and genes that have been recently associated with response to therapy. B) Left: Analysis of clonality in pre-therapy samples identifies a trend toward more subclonal mutations in Ipi-P patients (p = 0.08; Mann–Whitney test; see also Figure S1A). Right: OS in Ipi-N patients by mutation load (high mutation load defined as >100 mutations). (C) Waterfall plot of change in mutation (non-synonymous) and putative neoantigen load between pre-therapy biopsy and cycle 1, day 29 on-therapy biopsy by response status.

Figure 2. Changes in Tumor Clonal Composition after Treatment with Nivo Therapy

(A) Changes in CCF of mutations (synonymous and non-synonymous, clonal/subclonal) from pre- to on-therapy samples. Similar CCFs in both pre- and on-therapy samples (genomic persistence) in gray; increased CCF or novel in on-therapy samples (genomic expansion) in pink; decreased CCF/lost in on-therapy samples (genomic contraction) in blue. (B) Lost mutations indicating genomic contraction were ubiquitous in CR/PR samples, and significantly more frequent in patients with SD than PD. Persistent mutations were less common in samples without response and not significantly different between patients with SD and PD. Variant gains (genomic expansion) were significantly more frequent in patients with PD than SD. Data are presented as median and interquartile range (IQR). (C) Left: Waterfall plot of net change between fraction of mutations representing genomic contraction and genomic persistence. Right: OS and PFS by genomic contraction and genomic persistence (p = 0.003; log-rank test and p = 3.34e–4; log-rank test, respectively). (D) Changes of CCF in representative cases from patients with CR/PR (patient 53), SD (patient 10) and PD (patient 27). Tree diagrams illustrate the relationships between the clones. Colored lines and circles denote specific clones.

Figure 3. Pre-therapy Tumor Gene Expression Analysis

(A) Hierarchal clustering analysis of DEGs in tumors from pre-therapy biopsies. (B) Heatmap associations of gene expression signatures in the Ipi-P and Ipi-N cohorts. C) Analysis of DEGs in tumors with genomic contraction versus those with genomic persistence (n = 26). (D) Left: Clustering of the entire cohort of patients (n = 45) by DEGs identified in (C) clusters patients into two groups in entire cohort and into four groups in combined Hugo et. al. and Van Allen et. al. cohorts. Right: Long-term OS associates with clustered groups of patients from the entire cohort and from the combined Hugo et al. and Van Allen et al. cohorts.

Figure 4. Changes in Gene Expression Following Nivo Therapy

(A) Left: Analysis of ratio of DEGs and selected genes between pre- and on-therapy samples. Right: Examples of genes that change after initiation of Nivo. (B) Analysis of changes in gene expression (on-therapy compared with pre-therapy) that are altered in tumors that respond or do not respond to Nivo. (C) Graphical illustration of key pathways differentially expressed in (B). (D) Immune deconvolution of RNA-seq data comparing pre- and on-therapy samples. Data are presented as median and IQR.

Figure 5. T-Cell Infiltrate and Repertoire Association with Response to Nivo

Due to the reduced number of cases with paired TCR-seq data, patients with CR/PR and SD were grouped as having “benefit”, and patients with PD were considered to have “no benefit”. (A) Change in TIL abundance and activity as measured by multiple methods (DNA-based TCR-seq, IHC, and RNA-based cytolytic score). Data are presented as median and IQR. (B) Change in richness and evenness of intratumoral T-cell repertoires. *Two outliers were removed per Grubbs’ test, alpha = 0.1 (see Methods). Data are presented as median and IQR. (C) Median richness and evenness of CDR3s per VJ combinations pre-therapy and on-therapy (see also Figure S6E). (D) Kernel density plots of CDR3 evenness versus number of unique CDR3s for every observed VJ pair in selected patients. (E) Comparison of on-therapy TIL levels with changes in T-cell repertoire evenness (D90, defined as the minimum fraction of total unique CDR3 sequences that constitutes 90% of all sequencing reads). (F) Fraction of on-therapy TCR repertoire utilizing V-segments associated with CD8 or CD4 T cells. Data are presented as mean ± SEM.

Figure 6. T Cells Expand in Proportion to Depletion of Neoantigens

(A) Changes in T-cell population distribution (i.e., evenness) and changes in tumor mutation clonality by response. (B) Relationship between the number of predicted neoantigens lost and the number of T-cell clones expanded on-therapy. (C) Neoantigen ratios (mutations predicted to generate neoantigens per mutations not predicted to generate neoantigens) from mutations solely identified in pre-therapy samples compared with those identified solely in on-therapy samples. Data are presented as mean ± SEM. (D) Graphical model depicting changes during anti-PD-1 therapy. (a) Changes in mutations and neoantigens during therapy. (b) Changes in TCR repertoire depend on exposure to prior immunotherapy. (c) Changes in immune landscape and checkpoints during therapy.