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. 2018 Nov 1;128(11):4804-4820.
doi: 10.1172/JCI121476. Epub 2018 Oct 2.

Endogenous retroviral signatures predict immunotherapy response in clear cell renal cell carcinoma

Christof C Smith  1   2 , Kathryn E Beckermann  3 , Dante S Bortone  2   4 , Aguirre A De Cubas  3 , Lisa M Bixby  2 , Samuel J Lee  1   2 , Anshuman Panda  5 , Shridar Ganesan  5 , Gyan Bhanot  5 , Eric M Wallen  2   6 , Matthew I Milowsky  2   7 , William Y Kim  2   6   7   8 , W Kimryn Rathmell  3 , Ronald Swanstrom  2   9 , Joel S Parker  2   4   8 , Jonathan S Serody  1   2   7 , Sara R Selitsky  2   4 , Benjamin G Vincent  1   2   7   10
Affiliations

Endogenous retroviral signatures predict immunotherapy response in clear cell renal cell carcinoma

Christof C Smith et al. J Clin Invest. .

Abstract

Human endogenous retroviruses (hERVs) are remnants of exogenous retroviruses that have integrated into the genome throughout evolution. We developed a computational workflow, hervQuant, which identified more than 3,000 transcriptionally active hERVs within The Cancer Genome Atlas (TCGA) pan-cancer RNA-Seq database. hERV expression was associated with clinical prognosis in several tumor types, most significantly clear cell renal cell carcinoma (ccRCC). We explored two mechanisms by which hERV expression may influence the tumor immune microenvironment in ccRCC: (i) RIG-I-like signaling and (ii) retroviral antigen activation of adaptive immunity. We demonstrated the ability of hERV signatures associated with these immune mechanisms to predict patient survival in ccRCC, independent of clinical staging and molecular subtyping. We identified potential tumor-specific hERV epitopes with evidence of translational activity through the use of a ccRCC ribosome profiling (Ribo-Seq) dataset, validated their ability to bind HLA in vitro, and identified the presence of MHC tetramer-positive T cells against predicted epitopes. hERV sequences identified through this screening approach were significantly more highly expressed in ccRCC tumors responsive to treatment with programmed death receptor 1 (PD-1) inhibition. hervQuant provides insights into the role of hERVs within the tumor immune microenvironment, as well as evidence that hERV expression could serve as a biomarker for patient prognosis and response to immunotherapy.

Keywords: Antigen; Bioinformatics; Cancer immunotherapy; Immunology; Oncology.

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Conflict of interest statement

Conflict of interest: WYK is the inventor on the BASE47 gene classifier (US patent application WO2015073949A1).

Figures

Figure 1
Figure 1. Human endogenous retrovirus expression and association in TCGA pan-cancer dataset.
(A) Schematic of the hervQuant workflow. (B) hERV expression displayed by heatmaps in the outermost layer, ranked by mean expression across the pan-cancer dataset. Tumor groups shown in the middle ring, with colors representing clusters determined from a cut-tree (height = 140) of hierarchical clustering of Euclidean distance of mean hERV expression between each cancer type. Innermost lines represent hERV expression pairwise Euclidean distance ≤40 between tumor types. Opacity and width of inner lines increase with greater similarity. (C) Volcano plot of association (GLM) between read-normalized hERV expression and the mean of the methylation β coefficient, with GLM coefficient along the x axis and –log10 FDR-corrected P value along the y axis. (D and E) Association (GLM) between read-normalized hERV expression and (D) IGS expression and (E) survival among TCGA pan-cancer dataset. FDR- (D) or Bonferroni-corrected (E) P represented by intensity of color and direction of coefficient represented by color (red, positive; blue, negative). Color bar displays hERV superfamily and canonical clade classifications. (D) Rows and columns are ordered by number of significantly positive associations. (E) Survival analysis filtered by hERVs and tumor types with at least 1 significant comparison. See Supplemental Table 2 for number of samples per TCGA cancer cohort.
Figure 2
Figure 2. Mechanism of hERV-mediated RIG-I–like pathway signaling in ccRCC.
(A) Heatmap of association (GLM) between hERV expression and RIG-I–like pathway–associated genes. FDR-corrected –log10(P value) represented by intensity of color, and direction of coefficient represented by color (red: positive, blue: negative). Group 1 (blue) and 2 (orange) hERVs are represented by color along the left-side color bar. (B) PC1 versus PC2 from PCA of association matrix in A between hERV expression and RIG-I–like pathway–associated genes from for group 1 and 2 hERVs. Percentage of variance for principal component 1 (PC1) and PC2 is shown in parentheses along each axis. (C) Volcano plot of CoxPH analysis of UQN hERV expression as a predictor of survival, with Bonferroni-corrected –log10(P value) displayed as a function of hazard ratio for each hERV. Dashed horizontal line represents FDR-corrected P = 0.05. (B and C) Groups 1 and 2, and other hERVs defined from A (group 1: blue; group 2: orange; neither: gray). (D) Heatmap of association (GLM) between expression of IGSs with group 1 and 2 hERV signatures (average expression), split by either significant or nonsignificant association with patient prognosis. FDR-corrected P values represented by intensity of color, and direction of coefficient represented by color (red, positive; blue, negative).
Figure 3
Figure 3. hERVs associated with expression of BCR clonotypes are negatively prognostic in ccRCC.
(A) Heatmap of association (GLM) between hERV expression and expression of B cell clonotypes, displaying all TCRs and BCRs that demonstrate association (left, FDR-corrected P ≤ 0.05) and a magnified view of the top 4 B cell clones with highest numbers of significantly associated hERVs (right, underscored by black box to the bottom left). FDR-corrected P values represented by intensity of color and direction of coefficient represented by color (red: positive, blue: negative). (B) Multiple sequence alignment of areas of DNA identity in ≥25% of hERVs (all hERVs significantly associated with the top 4 B cell clones) and ≥24 base pairs in length (minimum BCR epitope length). Base pair sequences displayed by color (A: blue; T: red; C: green; G: yellow; gap: gray) and sequence below. y axis order is conserved in all plots. (A and B) Color bars at left show superfamily and canonical clade classification. (C) Hazard ratios among all hERVs significantly associated to the top 4 B cell clones (left) or non-BCR-associated hERVs (right) within TCGA KIRC, with Welch’s t test P value displayed. Data represent median (middle line), with boxes encompassing the 25th to 75th percentile, whiskers encompassing 1.5× the interquartile range from the box, and outliers shown by dots. (D) Waterfall plot displaying the log2 fold change in mean expression of hERVs associated with the top 4 B cell clones in the tumor compared with matched normal tissue. FDR-adjusted P value significance (P ≤ 0.05) from DESeq2 analysis displayed in red (positive fold difference), blue (negative fold difference), and gray (nonsignificant).
Figure 4
Figure 4. Immune-related hERV signatures are prognostic for patient overall survival.
(A) Schematic summary of hERV interactions with the immune system in the context of an anti-tumor immune response. (B) Venn diagram showing the number hERVs significantly associated (GLM, FDR-corrected P < 0.05) with genes corresponding to the upregulation (blue) or downregulation (orange) of the RIG-I–like pathway or positively associated (GLM, FDR corrected P < 0.05) with expression of B cell clones (green). (C) Kaplan-Meier survival curves for TCGA KIRC patients split by the upper (blue) and lower (red) 50th percentile of expression for each of the 3 hERV group signatures represented in A. (D) Change in multivariable CoxPH log-likelihood ratios in TCGA KIRC using clinical stage and/or M1–M4 molecular subtyping and the 3 classes of hERV groups represented in B as predictors for survival. Stacked bars show the change in likelihood ratio for each feature when removed from the full model, as well as the χ2 test P value for each hERV group signature when removed from the full model (*P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001). (E) Univariable CoxPH coefficients for hERV signatures as a predictor for overall survival among each cancer type. FDR-corrected P value represented by red asterisks (*P ≤ 0.05).
Figure 5
Figure 5. hERVs demonstrate evidence of targetable epitope expression in ccRCC.
(A) Association (GLM) of the 10 most positively (left) and negatively (right) differentially expressed hERVs (TCGA KIRC tumor relative to matched normal tissue) with IGS expression. FDR-corrected P values represented by intensity of color and direction of coefficient represented by color (red: positive, blue: negative). (B) Read coverage from ccRCC Ribo-Seq data for hERV 4700, demonstrating read coverage of coding regions for gag (red), pol (blue), and env (green) genes. (C) Percent identity between all reading frames of translated amino acid sequences from the reference gag (red), pol (blue), and env (green) sequences for hERV 4700 with known hERV proteins in the NIH retroviral protein BLAST database. (D) Exchange efficiency for HLA-A*02:01 monomer UV exchange of predicted hERV 4700 epitopes. (E) Left: RT-qPCR (responders: n = 7; nonresponders: n = 6) log2 expression of hERV 4700 gag, pol, and, env sequences. Right: hervQuant-derived (responders: n = 10; nonresponders: n = 10) hERV 4700 expression in nivolumab-treated (aPD1-treated) ccRCC tumor biopsies. Statistical analysis performed using Mann-Whitney U test (*P ≤ 0.05, **P ≤ 0.01, NS: P > 0.05). Data presented as values (dots) and median (middle line), with boxes encompassing the 25th to 75th percentile and whiskers encompassing minimum to maximum values.
Figure 6
Figure 6. hERV 4700 epitope–derived HLA-A*02:01 tetramers identify the presence of gag- and pol-specific T cells in ccRCC.
(A) Flow cytometric representative gating strategy for identification of CD8+ epitope-specific T cells in ccRCC tumor. (B) Epitope gating for 5 pools of 6 tetramers (top), as well as staining of individual tetramers from pool 4 (bottom) in ccRCC. (C) Percent tetramer-specific CD8+ T cells for epitopes identified in B (tetramer 2: NSWQEMVPV; tetramer 3: MVGPWPRPV) in ccRCC tumors (n = 4) and healthy donor PBMC samples (n = 4). Dots represent values for each sample, with bars representing the mean across each group. Negative controls for gating definitions include tetramer fluorescence-minus-one (FMO) (A) and nonspecific HLA-A*02:01-negative tetramer (B and C). Data presented in Figure 6 represent results from 4 independent experiments.
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