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Background: Clear cell renal cell carcinoma (ccRCC) is the dominant subtype of renal cancer. With currently available therapies, cure of advanced and metastatic ccRCC is achieved only in rare cases. Here, we developed a workflow integrating different -omics technologies to identify ccRCC-specific HLA-presented peptides as potential drug targets for ccRCC immunotherapy., Methods: We analyzed HLA-presented peptides by MS-based ligandomics of 55 ccRCC tumors (cohort 1), paired non-tumor renal tissues, and 158 benign tissues from other organs. Pathways enriched in ccRCC compared to its cell type of origin were identified by transcriptome and gene set enrichment analyses in 51 tumor tissues of the same cohort. To retrieve a list of candidate targets with involvement in ccRCC pathogenesis, ccRCC-specific pathway genes were intersected with the source genes of tumor-exclusive peptides. The candidates were validated in an independent cohort from The Cancer Genome Atlas (TCGA KIRC, n = 452). DNA methylation (TCGA KIRC, n = 273), somatic mutations (TCGA KIRC, n = 392), and gene ontology (GO) and correlations with tumor metabolites (cohort 1, n = 30) and immune-oncological markers (cohort 1, n = 37) were analyzed to characterize regulatory and functional involvements. CD8 + T cell priming assays were used to identify immunogenic peptides. The candidate gene EGLN3 was functionally investigated in cell culture., Results: A total of 34,226 HLA class I- and 19,325 class II-presented peptides were identified in ccRCC tissue, of which 443 class I and 203 class II peptides were ccRCC-specific and presented in ≥ 3 tumors. One hundred eighty-five of the 499 corresponding source genes were involved in pathways activated by ccRCC tumors. After validation in the independent cohort from TCGA, 113 final candidate genes remained. Candidates were involved in extracellular matrix organization, hypoxic signaling, immune processes, and others. Nine of the 12 peptides assessed by immunogenicity analysis were able to activate naïve CD8 + T cells, including peptides derived from EGLN3. Functional analysis of EGLN3 revealed possible tumor-promoting functions., Conclusions: Integration of HLA ligandomics, transcriptomics, genetic, and epigenetic data leads to the identification of novel functionally relevant therapeutic targets for ccRCC immunotherapy. Validation of the identified targets is recommended to expand the treatment landscape of ccRCC.

Background: Renal cell carcinoma (RCC) consists of prognostic distinct subtypes derived from different cells of origin (eg, clear cell RCC [ccRCC], papillary RCC [papRCC], and chromophobe RCC [chRCC]). ccRCC is characterized by lipid accumulation and metabolic alterations, whereas data on metabolic alterations in non-ccRCC are limited., Objective: We assessed metabolic alterations and the lipid composition of RCC subtypes and ccRCC-derived metastases. Moreover, we elucidated the potential of metabolites/lipids for subtype classification and identification of therapeutic targets., Design, Setting, and Participants: Metabolomic/lipidomic profiles were quantified in ccRCC (n=58), chRCC (n=19), papRCC (n=14), corresponding nontumor tissues, and metastases (n=9) through a targeted metabolomic approach. Transcriptome profiling was performed in corresponding samples and compared with expression data of The Cancer Genome Atlas cohorts (patients with ccRCC, n=452; patients with papRCC, n=260; and patients with chRCC, n=59)., Outcome Measurements and Statistical Analysis: In addition to cluster analyses, metabolomic/transcriptomic data were analyzed to evaluate metabolic differences of ccRCC and chRCC using Welch's t test or paired t test as appropriate. Where indicated, p values were adjusted for multiple testing using Bonferroni or Benjamini-Hochberg correction., Results and Limitations: Based on their metabolic profiles, RCC subtypes clustered into two groups separating ccRCC and papRCC from chRCC, which mainly reflected the different cells of origin. ccRCC-derived metastases clustered with primary ccRCCs. In addition to differences in certain lipids (lysophosphatidylcholines and sphingomyelins), the coregulation network of lipids differed between ccRCC and chRCC. Consideration of metabolic gene expression indicated, for example, alterations of the polyamine pathway at metabolite and transcript levels. In vitro treatment of RCC cells with the ornithine-decarboxylase inhibitor difluoromethylornithine resulted in reduced cell viability and mitochondrial activity. Further evaluation of clinical utility was limited by the retrospective study design and cohort size., Conclusions: In summary, we provide novel insight into the metabolic profiles of ccRCC and non-ccRCC, thereby confirming the different ontogeny of RCC subtypes. Quantification of differentially regulated metabolites/lipids improves classification of RCC with an impact on the identification of novel therapeutic targets., Patient Summary: Several subtypes of renal cell carcinoma (RCC) with different metastatic potentials and prognoses exist. In the present study, we provide novel insight into the metabolism of these different subtypes, which improves classification of subtypes and helps identify novel targets for RCC therapy., (Copyright © 2018 European Association of Urology. Published by Elsevier B.V. All rights reserved.)

The efflux transporter breast cancer resistance protein BCRP/ABCG2 is well-known for its contribution to multi-drug resistance in cancer. Its relevance in cancer biology independent from drug efflux remains largely elusive. Our study aimed at elucidating the biological relevance and regulatory mechanisms of BCRP/ABCG2 in clear cell renal cell carcinoma (ccRCC) and disease progression. Two independent ccRCC-cohorts [Cohort 1 (KIRC/TCGA): n = 453, Cohort 2: n = 64] were investigated to elucidate BCRP/ABCG2 mRNA and protein expression and their association with survival. The impact of BCRP/ABCG2 on response to sunitinib treatment was investigated in two independent sunitinib-treated ccRCC-cohorts based on mRNA levels. Moreover, underlying regulatory mechanisms for interindividual variability of BCRP/ABCG2 expression were systematically assessed. Owing to redundant functional properties, mRNA and protein expression of the multidrug resistance protein MDR1/ABCB1 were additionally evaluated in these cohorts. In independent ccRCC-cohorts, low BCRP/ABCG2 and MDR1/ABCB1 mRNA and protein expression were associated with severity (e.g., tumor stage) of ccRCC and poor cancer-specific survival. BCRP/ABCG2 and MDR1/ABCB1 mRNA expression were linked to decreased progression-free survival after sunitinib treatment. Germline and somatic variants influenced interindividual variability of BCRP/ABCG2 expression only moderately. miR-212-3p and miR-132-3p were identified to regulate BCRP/ABCG2 posttranscriptionally by interaction with the ABCG2 3'UTR as confirmed through reporter gene assays in RCC cell lines. In summary, BCRP/ABCG2 expression in ccRCC underlies considerable interindividual variability with impact on patient survival and response to sunitinib treatment. While germline or somatic genetic variants and DNA methylation cannot explain aberrant BCRP/ABCG2 expression, miR-212-3p and miR-132-3p were identified to contribute to posttranscriptional regulation of BCRP/ABCG2., (© 2018 UICC.)

Background: Stratification of cancer patients to identify those with worse prognosis is increasingly important. Through in silico analyses, we recently developed a gene expression-based prognostic score (S3-score) for clear cell renal cell carcinoma (ccRCC), using the cell type-specific expression of 97 genes within the human nephron. Herein, we verified the score using whole-transcriptome data of independent cohorts and extend its application for patients with metastatic disease receiving tyrosine kinase inhibitor treatment. Finally, we sought to improve the signature for clinical application using qRT-PCR., Methods: A 97 gene-based S3-score (S3 97 ) was evaluated in a set of 52 primary non-metastatic and metastatic ccRCC patients as well as in 53 primary metastatic tumors of sunitinib-treated patients. Gene expression data of The Cancer Genome Atlas (n = 463) was used for platform transfer and development of a simplified qRT-PCR-based 15-gene S3-score (S3 15 ). This S3 15 -score was validated in 108 metastatic and non-metastatic ccRCC patients and ccRCC-derived metastases including in part several regions from one metastasis. Univariate and multivariate Cox regression stratified by T, N, M, and G were performed with cancer-specific and progression-free survival as primary endpoints., Results: The S3 97 -score was significantly associated with cancer-specific survival (CSS) in 52 ccRCC patients (HR 2.9, 95% Cl 1.0-8.0, P Log-rank  = 3.3 × 10 -2 ) as well as progression-free survival in sunitinib-treated patients (2.1, 1.1-4.2, P Log-rank  = 2.2 × 10 -2 ). The qRT-PCR based S3 15 -score performed similarly to the S3 97 -score, and was significantly associated with CSS in our extended cohort of 108 patients (5.0, 2.1-11.7, P Log-rank  = 5.1 × 10 -5 ) including metastatic (9.3, 1.8-50.0, P Log-rank  = 2.3 × 10 -3 ) and non-metastatic patients (4.4, 1.2-16.3, P Log-rank  = 1.6 × 10 -2 ), even in multivariate Cox regression, including clinicopathological parameters (7.3, 2.5-21.5, P Wald  = 3.3 × 10 -4 ). Matched primary tumors and metastases revealed similar S3 15 -scores, thus allowing prediction of outcome from metastatic tissue. The molecular-based qRT-PCR S3 15 -score significantly improved prediction of CSS by the established clinicopathological-based SSIGN score (P = 1.6 × 10 -3 )., Conclusion: The S3-score offers a new clinical avenue for ccRCC risk stratification in the non-metastatic, metastatic, and sunitinib-treated setting.

Metabolite profiling of tissue samples is a promising approach for the characterization of cancer pathways and tumor classification based on metabolic features. Here, we present an analytical method for nontargeted metabolomics of kidney tissue. Capitalizing on different chemical properties of metabolites allowed us to extract a broad range of molecules covering small polar molecules and less polar lipid classes that were analyzed by LC-QTOF-MS after HILIC and RP chromatographic separation, respectively. More than 1000 features could be reproducibly extracted and analyzed (CV < 30%) in porcine and human kidney tissue, which were used as surrogate matrices for method development. To further assess assay performance, cross-validation of the nontargeted metabolomics platform to a targeted metabolomics approach was carried out. Strikingly, from 102 metabolites that could be detected on both platforms, the majority (>90%) revealed Spearman's correlation coefficients ≥0.3, indicating that quantitative results from the nontargeted assay are largely comparable to data derived from classical targeted assays. Finally, as proof of concept, the method was applied to human kidney tissue where a clear differentiation between kidney cancer and nontumorous material could be demonstrated on the basis of unsupervised statistical analysis.

Current therapies for metastatic clear cell renal cell carcinoma (ccRCC) show limited efficacy. Drug efficacy, typically investigated in preclinical cell line models during drug development, is influenced by pharmacogenes involved in targeting and disposition of drugs. Here we show through genome-wide DNA methylation profiling, that methylation patterns are concordant between primary ccRCC and macro-metastases irrespective of metastatic sites (rs ≥ 0.92). However, 195,038 (41%) of all investigated CpG sites, including sites within pharmacogenes, were differentially methylated (adjusted P < 0.05) in five established RCC cell lines compared to primary tumors, resulting in altered transcriptional expression. Exemplarily, gene-specific analyses of DNA methylation, mRNA and protein expression demonstrate lack of expression of the clinically important drug transporter OCT2 (encoded by SLC22A2) in cell lines due to hypermethylation compared to tumors or metastases. Our findings provide evidence that RCC cell lines are of limited benefit for prediction of drug effects due to epigenetic alterations. Similar epigenetic landscape of ccRCC-metastases and tumors opens new avenue for future therapeutic strategies.

Unlabelled: There is evidence that molecular features support subclassification of tumours, thereby improving prediction of patient outcome. Currently, two gene expression signatures (ccA/ccB and ClearCode34) have been established to classify clear cell renal cell carcinoma (ccRCC). Because RCC arises from nephron cell types, we aimed to explore its heterogeneity on a molecular level by comparing gene expression between tumour tissue and nephron regions. Based on genes that differ in expression between nephron regions, expression data of 479 ccRCCs and 212 papillary and 66 chromophobe RCCs from The Cancer Genome Atlas were correlated to those of nephron cell types. Cancer-specific survival (CSS) of ccRCC patients was significantly associated with gene expression similarity to the proximal tubules. Subsequently, a ccRCC risk score (S3-score) was established. Survival analyses indicated that the S3-score was significantly associated with CSS considering all cases of ccRCC, as well as metastatic and nonmetastatic ccRCC. Results could be validated in an independent cohort. The S3-score significantly improved the predictive ability of the ccA/ccB and ClearCode34 signatures, and the clinicopathologic-based stage, size, grade, and necrosis score (p [chi-square] = 1.56E-04). Intratumour heterogeneity of the S3-score was observed in 6 of 10 ccRCCs. In summary, the nephron-based S3-score enables prognostic risk stratification for ccRCC. Further studies are needed to evaluate its clinical utility., Patient Summary: We developed a novel risk score for clear cell renal cell carcinoma to identify patients at risk of worse outcome that may improve patient care in the future., (Copyright © 2015 European Association of Urology. Published by Elsevier B.V. All rights reserved.)