Endogenous retroviral signatures predict immunotherapy response in clear cell renal cell carcinoma
Christof C. Smith, … , Sara R. Selitsky, Benjamin G. Vincent
Christof C. Smith, … , Sara R. Selitsky, Benjamin G. Vincent
Published August 23, 2018
Citation Information: J Clin Invest. 2018;128(11):4804-4820. https://doi.org/10.1172/JCI121476.
View: Text | PDF
Research Article Immunology Oncology

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

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.

Authors

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

×

Figure 1

Human endogenous retrovirus expression and association in TCGA pan-cancer dataset.

Options: View larger image (or click on image) Download as PowerPoint
Human endogenous retrovirus expression and association in TCGA pan-cance...
(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.