Proteomics of Melanoma Response to Immunotherapy Reveals Mitochondrial Dependence

Abstract

Immunotherapy has revolutionized cancer treatment, yet most patients do not respond. Here, we investigated mechanisms of response by profiling the proteome of clinical samples from advanced stage melanoma patients undergoing either tumor infiltrating lymphocyte (TIL)-based or anti- programmed death 1 (PD1) immunotherapy. Using high-resolution mass spectrometry, we quantified over 10,300 proteins in total and ∼4,500 proteins across most samples in each dataset. Statistical analyses revealed higher oxidative phosphorylation and lipid metabolism in responders than in non-responders in both treatments. To elucidate the effects of the metabolic state on the immune response, we examined melanoma cells upon metabolic perturbations or CRISPR-Cas9 knockouts. These experiments indicated lipid metabolism as a regulatory mechanism that increases melanoma immunogenicity by elevating antigen presentation, thereby increasing sensitivity to T cell mediated killing both in vitro and in vivo. Altogether, our proteomic analyses revealed association between the melanoma metabolic state and the response to immunotherapy, which can be the basis for future improvement of therapeutic response.

Keywords: anti-PD-1; cancer metabolism; immune checkpoint inhibitors; immunotherapy; lipid metabolism; mass spectrometry; melanoma; mitochondrial metabolism; proteomics; tumor-infiltrating lymphocytes.

Figure 1.. Proteomics of Melanoma Response to Immunotherapy

(A) The study cohort includes 42 and 74 patients undergoing TIL-based or anti-PD1 immunotherapy, respectively. Clinical parameters are indicated in the heatmap. See also Figures S1A–S1C and Table S1A. (B) Kaplan-Meier plots show highly significant differences between responders and non-responders to TIL or anti-PD1 treatments in overall survival (OS). See also Table S1A. (C) The proteomics workflow involved protein extraction from FFPE tissues and mixing with a super-SILAC standard. The proteins were then trypsin-digested, followed by peptide fractionation. We used liquid chromatography coupled with the Q Exactive Plus or Q Exactive HF MS followed by computational analysis in MaxQuant and Perseus. (D) Total number of proteins quantified in each group of samples. (E) A Venn diagram showing the overlap of quantified proteins in each group. Abbreviations are as follows: CR, complete response; PR, partial response; PD, progressive disease; SILAC, stable isotope labeling with amino acids in cell culture; NR, non-responders (including PD); R, responders (including CR and PR); IB, infusion bag.

Figure 2.. Functional Differences between Responders and Non-responders to Immunotherapy

(A) Functional categories higher in responders (left) and non-responders (right), as illustrated by using Proteomaps (Liebermeister et al., 2014). Each polygon corresponds to a single KEGG pathway, and the size correlates with the ratio between the groups. The maps show high similarity between the KEGG pathways of DEPs upon TIL and anti-PD1 treatments. See also Figure S2A and Tables S2A and S2B. (B) Two-dimensional annotation enrichment analysis shows similar enrichments in the two immunotherapy regimens (FDR q value < 0.02). In both treatments, responders are enriched with mitochondrial metabolism-related pathways and antigen presentation related categories, whereas non-responders are enriched with mRNA processing pathways and cell cycle proteins. See also Table S2C. (C) WGCNA of 116 melanoma samples shows module eigengenes (MEs) highly correlated with long PFS and CR or PR classification (upper heatmap). Enrichment analysis for the different MEs is presented in the lower heatmap (FDR q value < 0.05). See also Table S2D.

Figure 3.. Protein Signatures of Response to Immunotherapy

(A) Heatmap of the TIL signature shows eight proteins that discriminate between responders and non-responders. TUBB2B and ATRIP are more highly expressed in non-responders than in responders, and CROCC, HTATIP2, SUPV3L1, ACAT1, HADHA, and ACOT1 or ACOT2 are higher in responders than in non-responders. See also Figures S3A and S3B. (B) Heatmap of anti-PD1 signature shows 15 proteins that discriminate between responders and non-responders, all of them higher in the responders. See also Figure S3C and S3D. (C) A volcano plot shows the results of a Student’s t test comparing responders and non-responders to anti-PD1 (FDR q value < 0.1, S0 = 0.1). Signature proteins are in orange. Additional selected proteins are in blue. See also Table S4A. (D) A volcano plot that shows the results of a Student’s t test between responders and non-responders in the integrated cohort of TIL- and anti-PD1-treated samples (FDR q value < 0.1, S0 = 0.1). Blue proteins are the TIL signature and orange proteins are the anti-PD1 signature. See also Table S4B.

Figure 4.. Integrated Analysis of Response to Immunotherapy

(A) Enrichment analysis of selected processes higher in the responders to immunotherapy from the joint TIL-PD1 dataset (FDR q value < 0.2). Radial plot indicates the enrichment factor. The entire list of enriched processes is in Table S4C. (B) Protein-protein interaction network of the significantly upregulated proteins in the responders to immunotherapy in the combined dataset. The node size correlates with the ratio between responders and non-responders. Enriched KEGG pathways are colored as indicated. See also Table S4B. (C) Schematic representation of the metabolic pathways of proteins significantly higher in the responder group. (D) Dot plot shows the changes in the TIL signature proteins in the anti-PD1 cohort data. (E) Dot plot of the anti-PD1 signature proteins on the TIL cohort data. (F) Kaplan-Meier analyses of both signatures on each proteomics cohort separately, the integrated proteomics cohort of 109 samples (excluding SD), and the TCGA mRNA melanoma dataset (Cancer Genome Atlas Network, 2015). Heatmap indicates the –log q value, the significance (q value < 0.05), and the Kaplan-Meier directionality with respect to expression (below or above median). Abbreviations are as follows: OS, overall survival; PFS, progression free survival.

Figure 5.. Tissue-Level Validation of the Metabolic Proteins and T Cell Infiltration

(A) IHC of selected metabolic proteins from the TIL signature: ACAT1, ACOT1, and HADHA. MITF is used as a melanoma marker. SDHA is used as a mitochondrial marker. FABP7 is a melanoma antigen that shows an opposite trend in the TIL proteomics data. Scale bar, 100 μm. (B) Boxplots show the quantification of the IHC results. (C) Correlation heatmap of IHC-stained proteins. *p < 0.05; **p < 0.1. (D) Boxplots for the quantification of CD3 and CD8 cells. See also Figure S5.

Figure 6.. Metabolic Control of Antigen Presentation

(A) Proteomic profiles of all “antigen processing and presentation” (GOBP category) proteins that were significantly different upon DCA treatment in at least one cell line (FDR q value < 0.05, S0 = 0.1). Values are log2 LFQ intensity ratio of DCA-treated to vehicle. Color bar indicates the number of significant systems per protein. See also Table S5. (B and C) Flow-cytometry analysis of the change in the HLA-ABC and HLA-DR signal (B) or percentage of stained cells (C) upon treatment with 30 mM DCA in four melanoma cell lines. Values are ratio of DCA-treated to vehicle control. Data are represented as mean ± SEM. *p < 0.05; **p < 0.1. (D) RT-qPCR analysis of the RNA expression changes of HLA-A, HLA-B, and HLA-C upon treatment with 30 mM DCA. Values are ratio of DCA-treated to vehicle control. Data are represented as mean ± SEM. *p < 0.05; **p < 0.1. (E) One-dimension annotation enrichment analysis shows antigen presentation or type I IFN signaling enrichment along with mitochondrial respiration enrichment in the CRISPR control cells (FDR q value < 0.02). See also Table S6A and S6B. (F and G) Flow-cytometry measurement of the changes in HLA-ABC and HLA-DR signal (F) or percentage of stained Mel526 cells (G). Data are represented as mean ± SEM. *p < 0.05; **p < 0.1. (H) Proteomic profiles of all antigen processing and presentation (GOBP category) proteins that were significantly different in at least three gene knockouts in Mel526. (FDR q value < 0.05, S0 = 0.1). Values are log2 LFQ intensity ratio of CRISPR control to CRISPR KO.

Figure 7.. CRISPR KO Effects on Tumor Immunogenicity and T Cell Activity

(A) CRISPR-Cas9 knockout of ACAT1, HADHA, and CPT1A in WM266–4 (right) and Mel526 (left) cells reduces specific killing by T cells. KO cells were co-cultured with matched T cells and cell death was quantified based on LDH secretion. Data are represented as mean ± SEM. *p < 0.05; **p < 0.1. (B–N) KO of Acat1 was performed in YUMMER1.7 (clone D4J) mouse cells that were then injected into mice (control n = 8; Acat1 KO n = 10). See also Figure S8H. Acat1 KO cells presented enhanced tumor growth compared with control. Data are represented as mean ± SEM (B). (C–F) Control and KO cells were analyzed by flow cytometry to determine MHC class I MFI (C); percentage of tumor-cell-presenting MHC class I (D); Pdl1 MFI (E); and percentage of tumor-cell-presenting Pdl1 (F). (G) CRISPR Control and KO cells were either treated with IFNG (10 ng) or not and examined for IFN-induced mRNA expression of the indicated genes. *p < 0.05; **p < 0.1. (H–N) Flow-cytometry-based profiling of immune cell population. Shown is the percentage of CD45⁺CD11b⁻CD8⁺TNFA⁺IFNG⁺-expressing cells (H); percentage of CD45⁺CD11b⁻CD4⁺TNFA⁺-expressing cells (I); percentage of CD45⁺CD11b⁻CD8⁺TNFA⁺IFNG⁺-expressing cells (J); percentage of CD45⁺CD11b⁻CD8⁺expressing cells (K); percentage of CD45⁺CD11b⁻CD4⁺-expressing cells (L); percentage of CD45⁺CD11b⁺F4/80^lowLY6C^high-expressing cells (M); and percentage of CD45⁺CD11b⁺F4/80^highLY6C^low-expressing cells (N). All values are relative to untreated CRISPR control cells. Gene expression was normalized relative to actin. Data are represented as mean ± SEM.