Background: Immune checkpoint inhibitors (ICIs) have become common lines of therapy for genitourinary cancers (GUcs). Given their widespread use, understanding the risk factors, comparative profiles, and timing of immune-related adverse events (irAEs) is essential. Methods: We created an IRB-approved retrospective registry of all patients who received at least one dose of an ICI for any indication between 1 February 2011 and 7 April 2022 at a comprehensive cancer center and its outreach clinics. Dichotomous outcomes were modeled using multivariable logistic regression. Survival outcomes were compared using multivariable Cox regression. Results: Among 3101 patients, 196 had renal cell carcinoma (RCC) and 170 had urothelial tumors. RCC patients were more likely to experience irAEs (OR 1.78; 95% CI 1.32–2.39), whereas urothelial carcinoma patients were not (OR 1.22; 95% CI 0.88–1.67). RCC patients were more prone to dermatitis, thyroiditis, acute kidney injury, and myocarditis, compared to other tumors, while urothelial carcinoma patients were not. The impact of irAEs on survival was not significantly different for GUcs compared to other tumors. Conclusions: RCC primaries have a significantly different irAE profile than most tumors, as opposed to urothelial primaries. Further, RCC was more likely to experience any irAEs. Heterogeneity of survival benefits by irAEs was not seen.

Background/Objectives: Cutaneous squamous cell carcinoma (cSCC) harbors one of the most mutated genomes. There are limited data on the genomic profile and its predictive potential for response to immunotherapy with PD-1 inhibitors in cSCC. Methods: This study retrospectively reviewed cSCC patients treated with PD-1 inhibitor monotherapy at a single institution. Clinical characteristics, treatment outcomes, PD-L1 expression, tumor mutation burden (TMB), and genomic profile in tumor and blood were analyzed. Logistic regression and a support vector classifier were used to validate identified biomarkers of significance. Results: Twenty-five patients were evaluable for response and had genomics tested in tumor and/or blood. Of the total, 80% of patients achieved an objective response: 40% complete response (CR), 32% partial response (PR) for more than 6 months, and 8% stable disease (SD) for more than 1 year; 20% of patients progressed on treatment. With a median follow-up of 21 months, progression-free survival (PFS) was 28 months in responders vs. 3 months in non-responders (p = 0.00001). Median PD-L1 was 25% in responders vs. 10% in non-responders (p = 0.39). There was no difference in median TMB between responders and non-responders. Eight gene mutations were significantly more frequent in non-responders than in responders: CDK12 (p = 0.005), CTCF (p = 0.033), CTNNB1 (p = 0.033), IGF1R (p = 0.038), IKBKE (p = 0.016), MLH1 (0.033), QKI (p = 0.016), and TIPARP (p = 0.033). A support vector model of these genes classified responders and non-responders with an accuracy of 0.88 in the training data and 1.0 in the testing data. Conclusions: PD-1 inhibitor monotherapy produces an impressive response. Eight gene mutations were significantly more frequent in non-responders. PD-L1 and TMB were inconclusive in predicting treatment response to anti-PD-L1.