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. 2016 Nov 29;113(48):E7759-E7768.
doi: 10.1073/pnas.1609376113. Epub 2016 Nov 11.

Density of immunogenic antigens does not explain the presence or absence of the T-cell-inflamed tumor microenvironment in melanoma

Stefani Spranger  1 , Jason J Luke  2 , Riyue Bao  3 , Yuanyuan Zha  4 , Kyle M Hernandez  3 , Yan Li  3 , Alexander P Gajewski  3 , Jorge Andrade  3 , Thomas F Gajewski  5   2   4
Affiliations

Density of immunogenic antigens does not explain the presence or absence of the T-cell-inflamed tumor microenvironment in melanoma

Stefani Spranger et al. Proc Natl Acad Sci U S A. .

Abstract

Melanoma metastases can be categorized by gene expression for the presence of a T-cell-inflamed tumor microenvironment, which correlates with clinical efficacy of immunotherapies. T cells frequently recognize mutational antigens corresponding to nonsynonymous somatic mutations (NSSMs), and in some cases shared differentiation or cancer-testis antigens. Therapies are being pursued to trigger immune infiltration into non-T-cell-inflamed tumors in the hope of rendering them immunotherapy responsive. However, whether those tumors express antigens capable of T-cell recognition has not been explored. To address this question, 266 melanomas from The Cancer Genome Atlas (TCGA) were categorized by the presence or absence of a T-cell-inflamed gene signature. These two subsets were interrogated for cancer-testis, differentiation, and somatic mutational antigens. No statistically significant differences were observed, including density of NSSMs. Focusing on hypothetical HLA-A2+ binding scores, 707 peptides were synthesized, corresponding to all identified candidate neoepitopes. No differences were observed in measured HLA-A2 binding between inflamed and noninflamed cohorts. Twenty peptides were randomly selected from each cohort to evaluate priming and recognition by human CD8+ T cells in vitro with 25% of peptides confirmed to be immunogenic in both. A similar gene expression profile applied to all solid tumors of TCGA revealed no association between T-cell signature and NSSMs. Our results indicate that lack of spontaneous immune infiltration in solid tumors is unlikely due to lack of antigens. Strategies that improve T-cell infiltration into tumors may therefore be able to facilitate clinical response to immunotherapy once antigens become recognized.

Keywords: T-cell inflammation; checkpoint blockade; immunotherapy; neoantigens; tumor microenvironment.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
Two-dimensional principle component analysis of 266 metastatic melanoma patient samples. Each circle represents a melanoma sample. The blue, gray, and red colors represent samples with low (noninflamed), intermediate, and high (inflamed) levels of T-cell gene expression, respectively. The variance of the three subsets is described mostly by principle component 1 (PC1; 45.8%) and much less by principle component 2 (PC2; 4.29%).
Fig. 2.
Fig. 2.
Expression levels of tumor-associated differentiation and cancer–testis antigen genes in inflamed and noninflamed melanomas. (A) Log2-transformed expression levels of the melanoma differentiation antigens DCT, gp100, Melan-A, Tyr, and TyrP are shown for each tumor within the inflamed and noninflamed cohorts (P values in order: 0.53, 0.43, 0.25, 0.28, and 0.29). (B) Log2-transformed expression levels for the cancer–testis antigens GAGE, MAGE-A3, NY-ESO, SAGE, and XAGE are shown for individual tumors within each cohort (P values in order: 0.75, 0.2, 0.92, 0.0011, and 0.59).
Fig. 3.
Fig. 3.
Neoantigen expression in inflamed and noninflamed patient cohorts. (A) The overall number of NSSMs is presented per tumor, among the inflamed and noninflamed cohorts (P = 0.73, 453.0 = 9 ± 55.9 inflamed, 481.5 ± 58.3 noninflamed, mean ± SEM). (B) Predicted neoepitopes displayed according to HLA-A2 binding scores. Along the x axis are the absolute binding scores (BSs), and along the y axis are the differential binding scores (dBSs) of mutated compared with wild-type peptides (P = 0.36, 65 ± 13 inflamed, 48 ± 15 noninflamed, mean ± SEM). Color code indicates binding strength: gray, BS ≤ 17; blue, BS 17–25 and dBS ≤ 5; orange, BS 17–25 and dBS > 5; peach, BS ≥ 25 and dBS ≤ 5; and red, BS ≥ 25 and dBS > 5. (C) The number of identified candidate HLA-A*0201 binding peptides are plotted for the inflamed and noninflamed cohorts. (D) The subset of candidate peptides with high predicted binding is plotted for the inflamed and noninflamed cohorts (P = 0.44, 5.6 ± 1.2 inflamed, 6.9 ± 1.2 noninflamed, mean ± SEM).
Fig. S1.
Fig. S1.
Correlation analysis of gene expression in T-cell–inflamed and non–T-cell–inflamed melanomas. (A) Log2-transformed expression levels CCL4 correlated with CD8b expression level. (B and C) Log2-transformed expression levels of CD274 (PD-L1) and IDO1 compared with CD8b expression. (D) Correlation between CTNNB1 score and CD8b expression. All plots show T-cell–inflamed patients as red, intermediate patients as gray, and non–T-cell–inflamed patients as blue. Correlation analysis was done using a Pearson correlation.
Fig. S2.
Fig. S2.
Neoantigen expression in HLA-A*0201 inflamed and noninflamed patient cohorts. (A) The overall distribution of binding scores across both tumor cohorts is plotted (P = 0.45, 24.7 ± 3.9 inflamed, 24.1 ± 4.3 noninflamed, mean ± SD). (B) The fraction of peptides containing the so-called tetrapeptide sequences are displayed according to the inflamed and noninflamed cohorts (neopeptides P = 0.99, all peptides P = 0.22, two-way ANOVA).
Fig. 4.
Fig. 4.
Correlation analysis of major-histocompatibility gene expression in T-cell–inflamed and non–T-cell–inflamed melanomas. (A) Expression levels HLA-A correlated with IFN-γ expression level. (B) Expression of Pan-MHCI (HLA-A, HLA-B, HLA-C, HLA-E, HLA-F, HLA-G, HLA-H, HLA-J, and HLA-L), MHCII (HLA-DMA, HLA-DMB, HLA-DOA, HLA-DOB, HLA-DPA1, HLA-DPB1, HLA-DPB2, HLA-DQA1, HLA-DQA2, HLA-DQB1, HLA-DQB2, HLA-DRA, HLA-DRB1, HLA-DRB5, and HLA-DRB6), TAP1, TAP2, and B2M genes, all part of the T-cell signature, in correlation with IFN-γ in melanoma tumors from TCGA. Correlation analysis was performed using a Spearman correlation.
Fig. 5.
Fig. 5.
Immunogenicity of neoantigens obtained from the inflamed and noninflamed patient cohorts. (A) A total of 707 peptides were synthesized and tested for HLA-A2 binding using a high-throughput T2 binding assay. The percent increase of MFI of HLA-A2 expression is shown, among neoantigen peptides identified in the inflamed and noninflamed cohorts (P = 0.04, 43.7 ± 1.8 inflamed, 48.4 ± 2.3 noninflamed, mean ± SEM). As control we assessed the binding of MAGE-A3 (#) and melan-A (§). (B) Plotted are the HLA-A2 binding scores of 20 randomly selected peptides. (C) Selected neoantigen peptides were used to induce de novo T-cell priming using peripheral blood CD8+ T cells (P = 0.02, 58.2 ± 3.3 inflamed, 68.5 ± 3.1 noninflamed, mean ± SEM). The resulting expanded T cells were restimulated with specific peptide and analyzed using an IFN-γ ELISPOT assay (P = 0.44, 24.9 ± 12 inflamed, 43.0 ± 20 noninflamed, mean ± SEM).
Fig. 6.
Fig. 6.
Correlation analysis of DC gene expression in T-cell–inflamed and non–T-cell–inflamed melanomas. Expression levels of BATF3, IRF8, THBD (CD141), and CD1c compared with CD8b expression. Correlation analysis was performed using a Spearman correlation.
Fig. 7.
Fig. 7.
Neoantigen expression in CTNNB1/PTEN altered inflamed and noninflamed patient cohorts. (A) The overall number of NSSMs is presented per HLA-A*0201 tumor, among the inflamed and noninflamed cohorts separated into CTNNB1/PTEN altered and unknown alterations (no alterations). (B and C) The number of identified candidate (B) and predicted high-binding (C) HLA-A*0201 binding peptides are plotted for CTNNB1/PTEN altered and nonaltered, inflamed and noninflamed cohorts. One-way ANOVA was used to determine significance; shown are boxplots with a 0.05 confidence interval.
Fig. 8.
Fig. 8.
The spectrum of the inflamed tumor microenvironment phenotype across human cancer types. (A) Distribution of inflamed, noninflamed, and intermediate tumors. Each dot represents a single sample, with the black horizontal lines indicating the median value of weighted gene expression score (shown on the vertical axis) in the respective cancer types. The numbers on the horizontal axis represent the total number of tumor samples in each cancer type. The color of dots represents the inflamed (red), intermediate (gray), and noninflamed (blue) tumor cohorts. Cancer types are ordered by their percentage of inflamed tumors, with the extreme noninflamed cancer (Left) (paraganglioma) and the extreme inflamed cancer (Right) (renal clear cell carcinoma). (B) Somatic mutation density observed in exomes from the same tumors in A. The vertical axis (log10 scaled) shows the total number of NSSM and indels, with the cancer types ordered the same as in A. Within each cancer, samples are ordered by the gene expression score, with the lowest to the Left (blue, noninflamed) and the highest to the Right (red, inflamed). Note that exome sequencing data were not available for mesothelioma, so that tumor type does not appear in B.
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