Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade

Renal cell carcinoma (RCC) denotes cancer originated from the renal epithelium and accounts for >90% of cancers in the kidney. The disease encompasses >10 histological and molecular subtypes, of which clear cell RCC (ccRCC) is most common and accounts for most cancer-related deaths. Although somatic VHL mutations have been described for some time, more-recent cancer genomic studies have identified mutations in epigenetic regulatory genes and demonstrated marked intra-tumour heterogeneity, which could have prognostic, predictive and therapeutic relevance. Localized RCC can be successfully managed with surgery, whereas metastatic RCC is refractory to conventional chemotherapy. However, over the past decade, marked advances in the treatment of metastatic RCC have been made, with targeted agents including sorafenib, sunitinib, bevacizumab, pazopanib and axitinib, which inhibit vascular endothelial growth factor (VEGF) and its receptor (VEGFR), and everolimus and temsirolimus, which inhibit mechanistic target of rapamycin complex 1 (mTORC1), being approved. Since 2015, agents with additional targets aside from VEGFR have been approved, such as cabozantinib and lenvatinib; immunotherapies, such as nivolumab, have also been added to the armamentarium for metastatic RCC. Here, we provide an overview of the biology of RCC, with a focus on ccRCC, as well as updates to complement the current clinical guidelines and an outline of potential future directions for RCC research and therapy.

Renal cell carcinoma.

Understanding the Intersections between Metabolism and Cancer Biology

Glutathione (GSH) is the most abundant antioxidant found in living organisms and has multiple functions, most of which maintain cellular redox homeostasis. GSH preserves sufficient levels of cysteine and detoxifies xenobiotics while also conferring therapeutic resistance to cancer cells. However, GSH metabolism plays both beneficial and pathogenic roles in a variety of malignancies. It is crucial to the removal and detoxification of carcinogens, and alterations in this pathway can have a profound effect on cell survival. Excess GSH promotes tumor progression, where elevated levels correlate with increased metastasis. In this review, we discuss recent studies that focus on deciphering the role of GSH in tumor initiation and progression as well as mechanisms underlying how GSH imparts treatment resistance to growing cancers. Targeting GSH synthesis/utilization therefore represents a potential means of rendering tumor cells more susceptible to different treatment options such as chemotherapy and radiotherapy.

/pdf/glutathione-metabolism-in-cancer-progression-and-treatment-ulzmh36qgc.pdf

Glutathione metabolism in cancer progression and treatment resistance.

Tumor-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. We 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. Our 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.

/pdf/tumor-immune-microenvironment-characterization-in-clear-cell-71xz7gkpok.pdf

Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures

An Integrated Metabolic Atlas of Clear Cell Renal Cell Carcinoma

Methods for identifying and evaluating biochemical entities useful as biomarkers for kidney cancer, target identification/validation, and monitoring of drug efficacy are provided. Also provided are suites of small molecule entities as biomarkers for kidney cancer.

Biomarkers for kidney cancer and methods using the same

Background Hepatocellular carcinoma (HCC) is a global health burden with poor prognosis. Anoikis, a novel programmed cell death, has a close interaction with metastasis and progression of cancer. In this study, we aimed to construct a novel bioinformatics model for evaluating the prognosis of HCC based on anoikis-related gene signatures as well as exploring the potential mechanisms. Materials and methods We downloaded the RNA expression profiles and clinical data of liver hepatocellular carcinoma from TCGA database, ICGC database and GEO database. DEG analysis was performed using TCGA and verified in the GEO database. The anoikis-related risk score was developed via univariate Cox regression, LASSO Cox regression and multivariate Cox regression, which was then used to categorize patients into high- and low-risk groups. Then GO and KEGG enrichment analyses were performed to investigate the function between the two groups. CIBERSORT was used for determining the fractions of 22 immune cell types, while the ssGSEA analyses was used to estimate the differential immune cell infiltrations and related pathways. The “pRRophetic” R package was applied to predict the sensitivity of administering chemotherapeutic and targeted drugs. Results A total of 49 anoikis-related DEGs in HCC were detected and 3 genes (EZH2, KIF18A and NQO1) were selected out to build a prognostic model. Furthermore, GO and KEGG functional enrichment analyses indicated that the difference in overall survival between risk groups was closely related to cell cycle pathway. Notably, further analyses found the frequency of tumor mutations, immune infiltration level and expression of immune checkpoints were significantly different between the two risk groups, and the results of the immunotherapy cohort showed that patients in the high-risk group have a better immune response. Additionally, the high-risk group was found to have higher sensitivity to 5-fluorouracil, doxorubicin and gemcitabine. Conclusion The novel signature of 3 anoikis-related genes (EZH2, KIF18A and NQO1) can predict the prognosis of patients with HCC, and provide a revealing insight into personalized treatments in HCC.

An anoikis-related gene signature predicts prognosis and reveals immune infiltration in hepatocellular carcinoma

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An Integrated Metabolic Atlas of Clear Cell Renal Cell Carcinoma

Biomarkers for kidney cancer and methods using the same

An anoikis-related gene signature predicts prognosis and reveals immune infiltration in hepatocellular carcinoma