Conserved Interferon-γ Signaling Drives Clinical Response to Immune Checkpoint Blockade Therapy in Melanoma

Abstract

We analyze the transcriptome of baseline and on-therapy tumor biopsies from 101 patients with advanced melanoma treated with nivolumab (anti-PD-1) alone or combined with ipilimumab (anti-CTLA-4). We find that T cell infiltration and interferon-γ (IFN-γ) signaling signatures correspond most highly with clinical response to therapy, with a reciprocal decrease in cell-cycle and WNT signaling pathways in responding biopsies. We model the interaction in 58 human cell lines, where IFN-γ in vitro exposure leads to a conserved transcriptome response unless cells have IFN-γ receptor alterations. This conserved IFN-γ transcriptome response in melanoma cells serves to amplify the antitumor immune response. Therefore, the magnitude of the antitumor T cell response and the corresponding downstream IFN-γ signaling are the main drivers of clinical response or resistance to immune checkpoint blockade therapy.

Keywords: RNA-seq; anti-CTLA-4; anti-PD-1; biopsies; clinical trial; immune checkpoint blockade; immune exclusion; interferon-γ; resistance; response; transcriptomics.

Figure 1.

Outline of sample collection, patient treatments and response to therapy. A) Consort diagram describing data generation and selection of final transcriptome cohort. B) Alluvial plot showing the number of patients of each treatment subtype that resulted in each response. The numbers in the treatment subtype box represent the number of patients of that treatment type that had that response (Red = PD, Green = SD, Blue = CRPR). See also Figures S1 and S2.

Figure 2.

Immune cell infiltration in patient samples from clinical study CheckMate 038. A) Box plot the MCP-Counter T cell score according to response to therapy combining all treatment groups using an optimal pooled t-test since data is paired and unpaired. B) Principal component analysis showing all RNAseq samples, paired and unpaired, plotted on the first two components. The size of the data point is proportional to the T cell score. Open circles correspond to pre-treatment samples, while closed circles correspond to on-treatment samples. The data points are colored by response (Red = PD, Green = SD, and Blue = CRPR). The vector for the T cell score is plotted as well. Outlier samples are labeled by case. The twelve paired outlier samples were either due to quality control issues that had not been detected, or may be due to an actual different biology. For example, cases 48, 20038, and 30022 all had interferon-gamma signaling either going down or not changing on treatment, while cases 30004 and 20001 have G2M cell cycle genes that either increase or did not respond. C) Box plots showing the average expression of the genes correlated with the T cells broken down by response and pre- and on-treatment using an optimal pooled t-test since data is paired and unpaired. D) Box plots of MCP-Counter immune cell deconvolution types according to response to therapy. Mixed t-test for paired and unpaired samples using an optimal pooled t-test since data is paired and unpaired (*p value < 0.05, **p value < 0.01, ***p value < 0.001, ****p value < 0.0001). See also Figures S3, S4 and S5.

Figure 3.

Interferon-gamma-induced changes in gene expression in 58 human cell lines. A) Heatmap of changes in key interferon-gamma response genes after 6 hours of treatment at 5 ng/ml expressed as fold changes pre-treatment to post-treatment. Genes are organized by class: anti-proliferative (Black), antigen presentation (White), chemoattractants (Cyan), cytotoxic effectors (Orange), feedback/signaling (Purple). B) The pre- and post-treatment gene expression levels of IRF1, the transcription factor executing the down-stream interferon gamma program, shown as arrows (Red = up, Blue = down). C) The pre- and post-treatment gene expression levels of CD274, the gene that codes for PD-L1, shown as arrows (Red = up, Blue = down). See also Figures S6, S7, S8, S9 and S10.

Figure 4.

Interferon-gamma-induced changes in gene expression in specimens in clinical study CheckMate 038. Heatmap of key interferon-gamma response genes in biopsies obtained at baseline and on-treatment with immune blockade therapy, separated by response to therapy. Samples are ordered by T cell score and annotated by ipilimumab naïve status, monotherapy versus combination therapy and melanoma subtype. Genes are organized by class: anti-proliferative (Black), antigen presentation (White), chemoattractants (Cyan), cytotoxic effectors (Orange), feedback/signaling (Purple). See also Figures S11 and S12.

Figure 5.

Global changes in gene expression in clinical study CheckMate 038 consistent with interferon-gamma induced changes in biopsies from patients with response but not without response to therapy. A and B) Gene Set Enrichment Analysis (GSEA) using Hallmark Gene Sets for significantly up- or down-regulated genes for CRPR post-treatment relative to pre-treatment for clinical data set CheckMate 038 (A) and significantly up- or down-regulated genes for interferon gamma 6-hour treatment for the unmodified human cell lines (B). At most 2,000 of most significant genes by q-value submitted to GSEA for each class (maximum set by GSEA). See also Figures S13, S14, S15, S16, S17 and S18.

Figure 6.

Immune exclusion signatures and WNT signaling in biopsies of patients receiving ICB therapy. A) Box plot of the 9-gene Wnt Score (the geometric average of APC, APC2, CTNNB1, MYC, SOX11, SOX2, TCF12, TCF7, VEGFA), by RNAseq according to response to therapy in patient biopsies using an optimal pooled t-test since data is paired and unpaired (*p value < 0.05, **p value < 0.01, ***p value < 0.001). B) Plot of Wnt score versus T cell score with arrows connecting pre-treatment and on-treatment and one figure for each response PD (Red), SD (Green), and CRPR (Blue). C to E) Pre- and post-treatment gene expression levels of the Wnt Score (C), MYC (D), and WNT5A (E), the Wnt ligand initiating the down-stream Wnt signaling programming (Red = up, Blue = down). F to J) Box plots of immune exclusion genes/signatures by RNAseq according to response to therapy: Jerby-Arnon, et al. associated immune exclusion gene sets 1 up in immune excluded tissues (F) and 2, down in immune excluded samples (G), CDK4 (H), MYC (I) and PAK4 (J) using an optimal pooled t-test since data is paired and unpaired (*p value < 0.05, **p value < 0.01, ***p value < 0.001). K) Heatmap showing the level of correlation between the immune exclusion genes and signatures. See also Figures S19, S20 and S21.