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

Abstract

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. 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. 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. The identified prognostic markers are fully explainable and easily implementable in routine practice, facilitating risk stratification upon the cessation of ICI therapy.