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Purpose of reviewDue to the limited number of cases, there are no guidelines for basal cell carcinoma (BCC) of the prostate. This review combines an unpublished case report of a 55-year-old patient with BCC with an assessment of the latest literature.Recent findingsBCC of the prostate has previously been described in only approximately 140 cases. We describe the diagnostic process, including the uropathological and DNA-sequencing results, which allowed us to start an experimental treatment with pemigatinib. BCC of the prostate is associated with an aggressive biological and clinical behavior, such as recurrence and metastasis. Several immunohistochemical stainings are available to differentiate BCC from adenocarcinoma of the prostate. Based on pathology and results from next-generation sequencing (NGS), patients can be offered targeted therapies.SummaryWith the aid of histological work-up and immunostaining, prostatic BCC can be accurately diagnosed. Our patient underwent radical prostatectomy and staged extended lymphadenectomy due to lymph node recurrence. The patient subsequently developed progressive disease and was treated with the FGFR-inhibitor pemigatinib. The patient's liver metastasis significantly responded. The present case confirms the possibility of aggressive behavior of prostatic BCC and highlights the importance of a thorough uropathological and molecular biological analysis with a precision medicine strategy.

Background Renal cell carcinoma (RCC) is a heterogeneous disease comprising histologically defined subtypes. For therapy selection, precise subtype identification and individualized prognosis are mandatory, but currently limited. Our aim was to refine subtyping and outcome prediction across main subtypes, assuming that a tumor is composed of molecular features present in distinct pathological subtypes. Methods Individual RCC samples were modeled as linear combination of the main subtypes (clear cell (ccRCC), papillary (pRCC), chromophobe (chRCC)) using computational gene expression deconvolution. The new molecular subtyping was compared with histological classification of RCC using the Cancer Genome Atlas (TCGA) cohort ( n = 864; ccRCC: 512; pRCC: 287; chRCC: 65) as well as 92 independent histopathologically well-characterized RCC. Predicted continuous subtypes were correlated to cancer-specific survival (CSS) in the TCGA cohort and validated in 242 independent RCC. Association with treatment-related progression-free survival (PFS) was studied in the JAVELIN Renal 101 ( n = 726) and IMmotion151 trials ( n = 823). CSS and PFS were analyzed using the Kaplan–Meier and Cox regression analysis. Results One hundred seventy-four signature genes enabled reference-free molecular classification of individual RCC. We unambiguously assign tumors to either ccRCC, pRCC, or chRCC and uncover molecularly heterogeneous tumors (e.g., with ccRCC and pRCC features), which are at risk of worse outcome. Assigned proportions of molecular subtype-features significantly correlated with CSS (ccRCC ( P = 4.1E − 10), pRCC ( P = 6.5E − 10), chRCC ( P = 8.6E − 06)) in TCGA. Translation into a numerical RCC-R(isk) score enabled prognosis in TCGA ( P = 9.5E − 11). Survival modeling based on the RCC-R score compared to pathological categories was significantly improved ( P = 3.6E − 11). The RCC-R score was validated in univariate ( P = 3.2E − 05; HR = 3.02, 95% CI: 1.8–5.08) and multivariate analyses including clinicopathological factors ( P = 0.018; HR = 2.14, 95% CI: 1.14–4.04). Heterogeneous PD-L1-positive RCC determined by molecular subtyping showed increased PFS with checkpoint inhibition versus sunitinib in the JAVELIN Renal 101 ( P = 3.3E − 04; HR = 0.52, 95% CI: 0.36 − 0.75) and IMmotion151 trials ( P = 0.047; HR = 0.69, 95% CI: 0.48 − 1). The prediction of PFS significantly benefits from classification into heterogeneous and unambiguous subtypes in both cohorts ( P = 0.013 and P = 0.032). Conclusion Switching from categorical to continuous subtype classification ...

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Purpose of review Artificial intelligence has made an entrance into mainstream applications of daily life but the clinical deployment of artificial intelligence-supported histological analysis is still at infancy. Recent years have seen a surge in technological advance regarding the use of artificial intelligence in pathology, in particular in the diagnosis of prostate cancer. Recent findings We review first impressions of how artificial intelligence impacts the clinical performance of pathologists in the analysis of prostate tissue. Several challenges in the deployment of artificial intelligence remain to be overcome. Finally, we discuss how artificial intelligence can help in generating new knowledge that is interpretable by humans. Summary It is evident that artificial intelligence has the potential to outperform most pathologists in detecting prostate cancer, and does not suffer from inherent interobserver variability. Nonetheless, large clinical validation studies that unequivocally prove the benefit of artificial intelligence support in pathology are necessary. Regardless, artificial intelligence may soon automate and standardize many facets of routine work, including qualitative (i.e. Gleason Grading) and quantitative measures (i.e. portion of Gleason Grades and tumor volume). For the near future, a model where pathologists are enhanced by second-review or real-time artificial intelligence systems appears to be the most promising approach.