BackgroundTumor-infiltrating immune cells have been linked to prognosis and response to immunotherapy; however, the levels of distinct immune cell subsets and the signals that draw them into a tumor, such as the expression of antigen presenting machinery genes, remain poorly characterized. Here, we employ a gene expression-based computational method to profile the infiltration levels of 24 immune cell populations in 19 cancer types.ResultsWe compare cancer types using an immune infiltration score and a T cell infiltration score and find that clear cell renal cell carcinoma (ccRCC) is among the highest for both scores. Using immune infiltration profiles as well as transcriptomic and proteomic datasets, we characterize three groups of ccRCC tumors: T cell enriched, heterogeneously infiltrated, and non-infiltrated. We observe that the immunogenicity of ccRCC tumors cannot be explained by mutation load or neo-antigen load, but is highly correlated with MHC class I antigen presenting machinery expression (APM). We explore the prognostic value of distinct T cell subsets and show in two cohorts that Th17 cells and CD8+ T/Treg ratio are associated with improved survival, whereas Th2 cells and Tregs are associated with negative outcomes. Investigation of the association of immune infiltration patterns with the subclonal architecture of tumors shows that both APM and T cell levels are negatively associated with subclone number.ConclusionsOur analysis sheds light on the immune infiltration patterns of 19 human cancers and unravels mRNA signatures with prognostic utility and immunotherapeutic biomarker potential in ccRCC.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-1092-z) contains supplementary material, which is available to authorized users.

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Background Gene expression signatures have proven to be useful tools in many cancers to identify distinct subtypes of disease based on molecular features that drive pathogenesis, and to aid in predicting clinical outcomes. However, there are no current signatures for kidney cancer that are applicable in a clinical setting. Objective To generate a signature biomarker for the clear cell renal cell carcinoma (ccRCC) good risk (ccA) and poor risk (ccB) subtype classification that could be readily applied to clinical samples to develop an integrated model for biologically defined risk stratification. Design, setting, and participants A set of 72 ccRCC sample standards was used to develop a 34-gene classifier (ClearCode34) for assigning ccRCC tumors to subtypes. The classifier was applied to RNA-sequencing data from 380 nonmetastatic ccRCC samples from the Cancer Genome Atlas (TCGA), and to 157 formalin-fixed clinical samples collected at the University of North Carolina. Outcome measurements and statistical analysis Kaplan-Meier analyses were performed on the individual cohorts to calculate recurrence-free survival (RFS), cancer-specific survival (CSS), and overall survival (OS). Training and test sets were randomly selected from the combined cohorts to assemble a risk prediction model for disease recurrence. Results and limitations The subtypes were significantly associated with RFS (p < 0.01), CSS (p < 0.01), and OS (p < 0.01). Hazard ratios for subtype classification were similar to those of stage and grade in association with recurrence risk, and remained significant in multivariate analyses. An integrated molecular/clinical model for RFS to assign patients to risk groups was able to accurately predict CSS above established, clinical risk-prediction algorithms. Conclusions The ClearCode34-based model provides prognostic stratification that improves upon established algorithms to assess risk for recurrence and death for nonmetastatic ccRCC patients. Patient summary We developed a 34-gene subtype predictor to classify clear cell renal cell carcinoma tumors according to ccA or ccB subtypes and built a subtype-inclusive model to analyze patient survival outcomes.

Renal cell carcinoma stands out as one of the most immune-infiltrated tumors in pan-cancer comparisons. Features of the tumor microenvironment heavily affect disease biology and may affect responses to systemic therapy. With evolving frontline options in the metastatic setting, several immune checkpoint blockade regimens have emerged as efficacious, and there is growing interest in characterizing features of tumor biology that can reproducibly prognosticate patients and/or predict the likelihood of their deriving therapeutic benefit. Herein, we review pertinent characteristics of the tumor microenvironment with dedicated attention to candidate prognostic and predictive signatures as well as possible targets for future drug development.Research.

Background Despite a similar histologic appearance, upper tract urothelial carcinoma (UTUC) and urothelial carcinoma of the bladder (UCB) tumors have distinct epidemiologic and clinicopathologic differences. Objective To investigate whether the differences between UTUC and UCB result from intrinsic biological diversity. Design, setting, and participants Tumor and germline DNA from patients with UTUC (n = 83) and UCB (n = 102) were analyzed using a custom next-generation sequencing assay to identify somatic mutations and copy-number alterations in 300 cancer-associated genes. Outcome measurements and statistical analysis We described co-mutation patterns and copy-number alterations in UTUC. We also compared mutation frequencies in high-grade UTUC (n = 59) and high-grade UCB (n = 102). Results and limitations Comparison of high-grade UTUC and UCB revealed significant differences in the prevalence of somatic alterations. Alterations more common in high-grade UTUC included fibroblast growth factor receptor 3 (FGFR3; 35.6% vs 21.6%; p = 0.065), Harvey rat sarcoma viral oncogene homolog (HRAS; 13.6% vs 1.0%; p = 0.001), and cyclin-dependent kinase inhibitor 2B (p15, inhibits CDK4) (CDKN2B; 15.3% vs 3.9%; p = 0.016). Genes less frequently mutated in high-grade UTUC included tumor protein p53 (TP53; 25.4% vs 57.8%; p < 0.001), retinoblastoma 1 (RB1; 0.0% vs 18.6%; p < 0.001), and AT rich interactive domain 1A (SWI-like) (ARID1A; 13.6% vs 27.5%; p = 0.050). Because our assay was restricted to genomic alterations in a targeted panel, rare mutations and epigenetic changes were not analyzed. Conclusions High-grade UTUC tumors display a spectrum of genetic alterations similar to high-grade UCB. However, there were significant differences in the prevalence of several recurrently mutated genes including HRAS, TP53, and RB1. As relevant targeted inhibitors are being developed and tested, these results may have important implications for the site-specific management of patients with urothelial carcinoma. Patient summary Comparison of next-generation sequencing of upper tract urothelial carcinoma (UTUC) with urothelial bladder cancer identified that similar mutations were present in both cancer types but at different frequencies, indicating a potential need for unique management strategies. UTUC tumors were found to have a high rate of mutations that could be targeted with novel therapies.