Multimodal Approach Predicts Relapse upon Cessation of Immune Checkpoint Inhibitors in Advanced Melanoma

PURPOSE: Treatment with immune checkpoint inhibitors (ICI) in advanced melanoma can result in durable responses, yet an algorithm to decide which patients can safely discontinue ICI is still lacking. EXPERIMENTAL DESIGN: We used a multimodal approach combining clinical data, artificial intelligence-based analysis of hematoxylin and eosin-stained whole-slide images of melanoma before ICI start, and gene expression signatures to identify biomarkers for relapse after discontinuing ICI in the absence of treatment progression. RESULTS: Univariable Cox regression analysis identified the best overall response, mRNA expression of six genes, tumor cell density, and the lymphocyte-to-plasma cell ratio as factors predictive of relapse upon the cessation of the ICI. Multivariable Cox regression analysis showed that both TGFBR1 expression and the integral digital pathology parameter-based prognostic system were independently associated with relapse after ICI discontinuation. Training a multivariate adaptive regression spline model achieved the highest overall predictive accuracy of 84.6% for relapse after ICI discontinuation. CONCLUSIONS: The identified prognostic markers are fully explainable and easily implementable in routine practice, facilitating risk stratification upon the cessation of ICI therapy.