Renal cell carcinoma is the third type of urologic cancer and has a poor prognosis with 30% of metastatic patients at diagnosis. The antiangiogenics and targeted immunotherapies led to treatment remodeling emphasizing the role of the tumour microenvironment. However, long-term responses are rare with a high rate of resistance. New strategies are emerging to improve the efficacy and the emerging drugs are under evaluation in ongoing trials. With the different treatment options, there is an urgent need to identify biomarkers in order to predict the efficacy of drugs and to better stratify patients. Owing to the limitations of programmed death-ligand 1 (PD-L1), the most studied immunohistochemistry biomarkers, and of the tumor mutational burden, the identification of more reliable markers is an unmet need. New technologies could help in this purpose.

Zika virus (ZIKV) is a teratogenic mosquito-borne flavivirus that can be sexually transmitted from man to woman. The finding of high viral loads and prolonged viral shedding in semen suggests that ZIKV replicates within the human male genital tract, but its target organs are unknown. Using ex vivo infection of organotypic cultures, we demonstrated here that ZIKV replicates in human testicular tissue and infects a broad range of cell types, including germ cells, which we also identified as infected in semen from ZIKV-infected donors. ZIKV had no major deleterious effect on the morphology and hormonal production of the human testis explants. Infection induced a broad antiviral response but no IFN upregulation and minimal proinflammatory response in testis explants, with no cytopathic effect. Finally, we studied ZIKV infection in mouse testis and compared it to human infection. This study provides key insights into how ZIKV may persist in semen and alter semen parameters, as well as a valuable tool for testing antiviral agents.

Renal cell carcinoma encompass distinct diseases with different pathologic features and distinct molecular pathways. Immune checkpoint inhibitors targeting the programmed death receptor ligand 1 (PD-L1)/programmed death receptor 1 (PD-1) pathway alone or in combination have greatly changed clinical management of metastatic renal cell carcinoma, now competing with antiangiogenic drugs in monotherapy for first-line treatment. However, long-term response rates are low, and biomarkers are needed to predict treatment response. Quantification of PD-L1 expression by immunohistochemistry was developed as a promising biomarker in clinical trials, but with many limitations (different antibodies, tumour heterogeneity, specimens, and different thresholds of positivity). Other biomarkers, including tumour mutational burden and molecular signatures, are also developed and discussed in this review.

Objective: To propose and validate a nomogram to predict cancer‐specific survival ( CSS ) after radical nephroureterectomy ( RNU ) in patients with pT1–3/N0–x upper tract urothelial carcinoma ( UTUC ). Patients and Methods: The international and the French national collaborative groups on UTUC pooled data from 3387 patients treated with RNU . Only 2233 chemotherapy naïve pT1–3/N0–x patients were included in the present study. The population was randomly split into the development cohort (1563) and the external validation cohort (670). To build the nomogram, logistic regressions were used for univariable and multivariable analyses. Different models were generated. The most accurate model was assessed using Harrell's concordance index and decision curve analysis ( DCA ). Internal validation was then performed by bootstrapping. Finally, the nomogram was calibrated and externally validated in the external dataset. Results: Of the 1563 patients in the nomogram development cohort, 309 (19.7%) died during follow‐up from UTUC . The actuarial CSS probability at 5 years was 75.7% (95% confidence interval [ CI ] 73.2–78.6%). DCA revealed that the use of the best model was associated with benefit gains relative to prediction of CSS . The optimised nomogram included only six variables associated with CSS in multivariable analysis: age ( P < 0.001), pT stage ( P < 0.001), grade ( P < 0.02), location ( P < 0.001), architecture ( P < 0.001) and lymphovascular invasion ( P < 0.001). The accuracy of the nomogram was 0.81 (95% CI , 0.78–0.85). Limitations included the retrospective study design and the lack of a central pathological review. Conclusion: An accurate postoperative nomogram was developed to predict CSS after RNU only in locally and/or locally advanced UTUC without metastasis, where the decision for adjuvant treatment is controversial but crucial for the oncological outcome.