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BackgroundIn metastatic clear cell renal cell carcinoma (ccRCC), different combination therapies, each including anti-PD-1 immune checkpoint blockade (ICB), are applied as first-line treatment. Robust predictive biomarkers for rational upfront therapy decisions are lacking, although they are urgently needed. Recently, we showed that CTLA4 promoter methylation predicts response to ICB in melanoma. Here, we aimed to investigate CTLA4 methylation in ccRCC and its utility to serve as a predictive biomarker for anti-PD-1 based ICB in metastatic ccRCC.MethodsCTLA4 methylation was analyzed with regard to transcriptional gene activity (mRNA expression), intratumoral immune cell composition, and clinical course in two ccRCC cohorts obtained from The Cancer Genome Atlas (TCGA cohort, n=533) and the University Hospital Bonn (UHB Non-ICB Cohort, n=116). In addition, CTLA4 methylation as well as CD8+ T cell infiltrates and PD-L1 expression were evaluated in pre-treatment samples from a multicenter cohort (RCC-ICB Cohort, n=71). Patients included in the RCC-ICB Cohort were treated with either first line anti-PD-1 based combination therapy (n=25) or monotherapy post–tyrosine kinase inhibition in second line or later. Analyses were performed with regard to treatment response according to RECIST, progression-free survival (PFS), event-free survival (EFS), and overall survival (OS) following treatment initiation.ResultsCTLA4 promoter hypomethylation was significantly correlated with CTLA4 mRNA expression, lymphocyte infiltration, and poor OS in both primary ccRCC cohorts (TCGA: HR 0.30 (95% CI 0.18 to 0.49), p<0.001; UHB Non-ICB: HR 0.35 (95% CI 0.16 to 0.75), p=0.007). In contrast, CTLA4 promoter hypomethylation predicted response and, accordingly, favorable outcomes (PFS and OS) in patients with ICB-treated ccRCC, overcompensating the negative prognostic value of CTLA4 hypomethylation at initial diagnosis. Moreover, in multivariable Cox regression, CTLA4 promoter hypomethylation remained an independent predictor of improved outcome in ICB-treated ccRCC after co-adjustment of the International Metastatic Renal Cell Carcinoma Database Consortium score (HR 3.00 (95% CI 1.47 to 6.28), p=0.003).ConclusionsOur study suggests CTLA4 methylation as a powerful predictive biomarker for immunotherapy response in metastatic RCC.

The aim of this study was to discriminate malignant and benign clinical T1 renal masses on routinely acquired computed tomography (CT) images using radiomics and machine learning techniques.Adult patients undergoing surgical resection and histopathological analysis of clinical T1 renal masses were included. Preoperative CT studies in venous phase from multiple referring centers were included, without restriction to specific CT scanners, slice thickness, or degrees of artifacts. Renal masses were segmented and 120 standardized radiomic features extracted. Machine learning algorithms were used to predict malignancy of renal masses using radiomics features and cross-validation. Diagnostic accuracy of machine learning models and assessment by independent blinded radiologists were compared based on the gold standard of histopathologic diagnosis.A total of 94 patients met inclusion criteria (benign renal masses: n = 18; malignant: n = 76). CT studies from 18 different scanners were assessed with median slice thickness of 2.5 mm and artifacts in 15 cases (15.9%).Area under the receiver-operating-characteristics curve (AUC) of random forest (random forest [RF], AUC = 0.83) was significantly higher compared to the radiologists (AUC = 0.68, P = .047). Sensitivity was significantly higher for RF versus radiologists (0.88 vs 0.80, P = .045), whereas specificity was numerically higher for RF (0.67 vs 0.50, P = .083).Although limited by an overall small sample size and few benign renal tumors, a radiomic features and machine learning approach suggests a high diagnostic accuracy for discrimination of malignant and benign clinical T1 renal masses on venous phase CT. The presented algorithm robustly outperforms human readers in a real-life scenario with nonstandardized imaging studies from various referring centers. Abbreviations: AML = angiomyolipoma, AUC = area under the ROC curve, CD = cluster of differentiation, CK = cytokeratin, CT = computed tomography, HE = hematoxylin-eosin, HMB = human melanoma black, ICC = interobserver correlation coefficient, IQR = interquartile range, KNN = k-nearest neighbor, NN = neural network, POM = probability of malignancy, RCC = renal cell carcinoma, RF = random forest, RFE = recursive feature elimination, ROC = receiver operating characteristics, ROI = region of interest, SVM = c, US = United States, XG boost = extreme gradient boosting.

Female patients who underwent radical cystectomy for bladder cancer demonstrated worse disease-free, cancer specific and overall survival than their male counterparts. The multifactorial etiology might include epidemiological differences, gender specific health care discrepancies and hormonal influences.