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Background: Cutaneous immune-related adverse events (cirAEs) are the most common toxicities to occur in the setting of immune checkpoint inhibitor (ICI) therapy. Identifying patients who are at increased risk of developing cirAEs may improve quality of life and outcomes., Objectives: To investigate the influence of cancer type and histology on the development of cirAEs in the setting of ICI therapy and survival outcomes., Methods: This retrospective cohort study included patients recruited between 1 December 2011 and 30 October 2020. They received ICI from 2011 to 2020 with follow-up of outcomes through October 2021. We identified 3668 recipients of ICI therapy who were seen at Massachusetts General Brigham and Dana-Farber. Of these, 669 developed cirAEs. Records that were incomplete or categories of insufficient sample size were excluded from the study cohort. Multivariate Cox proportional hazards models were used to investigate the impact of cancer organ system and histology on cirAE development, after adjusting for demographics, Charlson Comorbidity Index, ICI type, cancer stage at ICI initiation, and year of ICI initiation. Time-varying Cox proportional hazards modelling was used to examine the impact of cirAE development on mortality., Results: Compared with other nonepithelial cancers (neuroendocrine, leukaemia, lymphoma, myeloma, sarcoma and central nervous system malignancies), cutaneous squamous cell carcinoma [cSCC; hazard ratio (HR) 3.57, P < 0.001], melanoma (HR 2.09, P < 0.001), head and neck adenocarcinoma (HR 2.13, P = 0.009), genitourinary transitional cell carcinoma (HR 2.15, P < 0.001) and genitourinary adenocarcinoma (HR 1.53, P = 0.037) were at significantly higher risk of cirAEs in multivariate analyses. The increased risk of cirAEs translated into an adjusted survival benefit for melanoma (HR 0.37, P < 0.001) and cSCC (HR 0.51, P = 0.011)., Conclusions: The highest rate of cirAEs and subsequent survival benefits were observed in cutaneous malignancies treated with ICI therapies. This study improves our understanding of patients who are at highest risk of developing cirAEs and would, therefore, benefit from appropriate counselling and closer monitoring by their oncologists and dermatologists throughout their ICI therapy. Limitations include its retrospective nature and cohort from one geography., Competing Interests: Conflicts of interest S.G.K. is an advisory board member/consultant for AbbVie, Celldex Therapeutics, Galderma, Incyte Corporation, Johnson & Johnson, Kiniksa Pharmaceuticals, Novartis Pharmaceuticals Corporation, Pfizer, Regeneron Pharmaceuticals and Sanofi, and has served as an investigator for Galderma, Kiniksa Pharmaceuticals, Pfizer Inc. and Sanofi. N.R.L. is a consultant and has received honoraria from Bayer, Sanofi, Seattle Genetics, Silverback and Synox Therapeutics outside the submitted work. Y.R.S. is an advisory board member/consultant and has received honoraria from Castle Biosciences, Galderma, Incyte Corporation and Sanofi outside the submitted work., (© The Author(s) 2024. Published by Oxford University Press on behalf of British Association of Dermatologists. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Previous studies have shown that combining immune checkpoint inhibitors (ICIs) with talimogene laherparepvec (TVEC) may improve antitumor responses. However, the risk of developing cutaneous immune-related adverse events (cirAEs) in patients treated with ICI and TVEC has not been studied. To evaluate the differences in cirAE development between patients treated with ICI alone and both ICI and TVEC (ICI + TVEC). Patients with cutaneous malignancy receiving ICI with or without TVEC therapy at the Massachusetts General Brigham healthcare system were included. CirAE development, time from ICI initiation to cirAE, cirAE grade, cirAE morphology, and survival were analyzed. Pearson's χ2 test or Fisher's exact test for categorical variables and t test or Kruskal-Wallis test for continuous variables were used. To account for immortal time bias, we performed adjusted time-varying Cox proportional hazards modeling. The rate of cirAE development was 32.3% and 38.7% for ICI only and ICI + TVEC, respectively. After adjusting for covariates, ICI + TVEC was associated with a 2-fold increased risk of cirAE development (hazard ratio: 2.03, P =.006) compared to patients receiving ICI therapy alone. The retrospective nature and limited sample size from a tertiary-level academic center. These findings underscore potential opportunities for dermatologists and oncologists in counseling and monitoring patients. [ABSTRACT FROM AUTHOR]

Prognostic analysis for early-stage (stage I/II) melanomas is of paramount importance for customized surveillance and treatment plans. Since immune checkpoint inhibitors have recently been approved for stage IIB and IIC melanomas, prognostic tools to identify patients at high risk of recurrence have become even more critical. This study aims to assess the effectiveness of machine-learning algorithms in predicting melanoma recurrence using clinical and histopathologic features from Electronic Health Records (EHRs). We collected 1720 early-stage melanomas: 1172 from the Mass General Brigham healthcare system (MGB) and 548 from the Dana-Farber Cancer Institute (DFCI). We extracted 36 clinicopathologic features and used them to predict the recurrence risk with supervised machine-learning algorithms. Models were evaluated internally and externally: (1) five-fold cross-validation of the MGB cohort; (2) the MGB cohort for training and the DFCI cohort for testing independently. In the internal and external validations, respectively, we achieved a recurrence classification performance of AUC: 0.845 and 0.812, and a time-to-event prediction performance of time-dependent AUC: 0.853 and 0.820. Breslow tumor thickness and mitotic rate were identified as the most predictive features. Our results suggest that machine-learning algorithms can extract predictive signals from clinicopathologic features for early-stage melanoma recurrence prediction, which will enable the identification of patients that may benefit from adjuvant immunotherapy. [ABSTRACT FROM AUTHOR]