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Background: Comprehensive genomic profiling (CGP) has gained an important role in patients with advanced prostate cancer following the introduction of PARP inhibitors in daily clinical practice. Here, we report an overview of CGP results, specifically of BRCA1 and BRCA2 HRD-repair system genes, from patients with prostate cancer analyzed in our institution, and we compare our results with those available from more recent scientific literature. Methods: The study cohort consisted of 70 patients. Somatic DNA was extracted from Formalin-Fixed Paraffin-Embedded (FFPE) tissue using a MagCore Genomic DNA FFPE One-Step Kit for MagCore System. The DNA was quantified by EasyPGX® Real-Time qPCR and EasyPGX® Analysis Software (version 4.0.13). Tissue somatic DNA libraries were prepared with Myriapod® NGS BRCA1-2 panel-NG035 and sequenced in a Mi-Seq® System. The sequence alignment in hg19 and the variant calling were performed using Myriapod® NGS Data Analysis Software version 5.0.8 NG900-SW 5.0.8 with a software detection limit (LoD) of 95%. Variants with a coverage of 500 and VAF% ≥ 5 were evaluated. Results: Tumor tissue NGS was unsuccessful in 46/70 patients (66%). Mutations of the BRCA2 gene were detected in 4 of the samples: (1) BRCA2 ex10 c.1244A>G p.His415Arg VAF = 51.03%; (2) BRCA2 ex11 c.5946delT p.Ser1982fs VAF = 72.1%; (3) BRCA2 ex11 c.3302A>G p.His1101Arg VAF = 52.9%; and (4) BRCA2 ex11 c.3195_3198delTAAT p.Asn1066fs VAF = 51.1%. Conclusions: The results from our internal overview seem to support the data and to confirm the performance of the technical issues reported in the literature. Considering the advanced age of our patients, with 84% of men over the age of 65, the application of alternative and less invasive procedures such as liquid biopsy, could be a more suitable solution for some cases.

We analyzed the clinical and pathological features of renal cell carcinoma (RCC) patients treated with cabozantinib stratified by body mass index (BMI). We retrospectively collected data from 16 worldwide centers involved in the treatment of RCC. Overall survival (OS) and progression-free survival (PFS) were analyzed using Kaplan–Meier curves. Cox proportional models were used at univariate and multivariate analyses. We collected data from 224 patients with advanced RCC receiving cabozantinib as second- (113, 5%) or third-line (111, 5%) therapy. The median PFS was significantly higher in patients with BMI ≥ 25 (9.9 vs. 7.6 months, p < 0.001). The median OS was higher in the BMI ≥ 25 subgroup (30.7 vs. 11.0 months, p = 0.003). As third-line therapy, both median PFS (9.2 months vs. 3.9 months, p = 0.029) and OS (39.4 months vs. 11.5 months, p = 0.039) were longer in patients with BMI ≥ 25. BMI was a significant predictor for both PFS and OS at multivariate analysis. We showed that a BMI ≥ 25 correlates with longer survival in patients receiving cabozantinib. BMI can be easily assessed and should be included in current prognostic criteria for advanced RCC.

The increasing availability of molecular data provided by next-generation sequencing (NGS) techniques is allowing improvement in the possibilities of diagnosis and prognosis in renal cancer. Reliable and accurate predictors based on selected gene panels are urgently needed for better stratification of renal cell carcinoma (RCC) patients in order to define a personalized treatment plan. Artificial intelligence (AI) algorithms are currently in development for this purpose. Here, we reviewed studies that developed predictors based on AI algorithms for diagnosis and prognosis in renal cancer and we compared them with non-AI-based predictors. Comparing study results, it emerges that the AI prediction performance is good and slightly better than non-AI-based ones. However, there have been only minor improvements in AI predictors in terms of accuracy and the area under the receiver operating curve (AUC) over the last decade and the number of genes used had little influence on these indices. Furthermore, we highlight that different studies having the same goal obtain similar performance despite the fact they use different discriminating genes. This is surprising because genes related to the diagnosis or prognosis are expected to be tumor-specific and independent of selection methods and algorithms. The performance of these predictors will be better with the improvement in the learning methods, as the number of cases increases and by using different types of input data (e.g., non-coding RNAs, proteomic and metabolic). This will allow for more precise identification, classification and staging of cancerous lesions which will be less affected by interpathologist variability.

Ciliopathies are a group of multi-organ diseases caused by the disruption of the primary cilium. This event leads to a variety of kidney disorders, including nephronophthisis, renal cystic dysplasia, and renal cell carcinoma (RCC). Primary cilium contributes to the regulation of the cell cycle and protein homeostasis, that is, the balance between protein synthesis and degradation by acting on the ubiquitin-proteasome system, autophagy, and mTOR signaling. Many proteins are involved in renal ciliopathies. In particular, fibrocystin (PKHD1) is involved in autosomal recessive polycystic kidney disease (ARPKD), while polycystin-1 (PKD1) and polycystin-2 (PKD2) are implicated in autosomal dominant polycystic kidney disease (ADPKD). Moreover, primary cilia are associated with essential signaling pathways, such as Hedgehog, Wnt, and Platelet-Derived Growth Factor (PDGF). In this review, we focused on the ciliopathies associated with kidney diseases, exploring genes and signaling pathways associated with primary cilium and the potential role of cilia as therapeutic targets in renal disorders.