Associations between spatial distribution of immune cell subsets and clinical outcomes in patients with advanced melanoma treated with immune checkpoint inhibitors: results from the PUMA challenge.
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Schuiveling Mark, Liu Hong, Eek Daniel, Hanusová Martina, van Duin Isabella A J, Ter Maat Laurens S, van der Weerd Janneke C, van den Berkmortel Franchette, Blank Christian U, Breimer Gerben E, Burgers Femke H, Boers-Sonderen Marye, van den Eertwegh Alfons J M, de Groot Jan Willem B, Haanen John B A G, Hospers Geke A P, Kapiteijn Ellen, Piersma Djura, Simkens Lieke, Westgeest Hans M, Schrader Anne M R, van Diest Paul J, Lv Jiaqi, Zhu Yijie, Tenorio Carmen Guadalupe Colin, Chohan Brinder Singh, Eastwood Mark, Raza Shan E Ahmed, Torbati Nima, Meshcheryakova Anastasia, Mechtcheriakova Diana, Mahbod Amirreza, Adams Daniel, Galdran Adrian, Pluim Josien P W, Blokx Willeke A M, Suijkerbuijk Karijn P M, Veta Mitko
Аннотация
Patients with advanced melanoma are treated with immune checkpoint inhibitors (ICIs), yet <50% of patients achieve a durable response while all patients are exposed to the risk of severe side effects. Tumor-infiltrating lymphocytes (TILs) in pathology images are associated with ICI outcomes, but manual assessment is subjective. In addition, the predictive value of other immune cell subsets, including plasma cells, neutrophils, histiocytes, and melanophages, remains unclear. We organized the Panoptic segmentation of nUclei and tissue in advanced MelanomA (PUMA) challenge to evaluate whether the spatial localization of TILs and other immune cell subsets on melanoma H&E slides collected before start of treatment was associated with treatment outcomes. Algorithm performance was evaluated on a hidden test set, after which top-ranked algorithms were applied to pre-treatment metastatic whole-slide images from a large, multicenter cohort of patients with advanced melanoma treated with first-line ICIs (n = 1102). Automatically quantified tissue features and immune cell subsets were then associated with clinical outcomes. Top-performing algorithms improved detection of immune cell subsets through pathology foundation model features, multi-stage tissue-context learning, and class-specific architectures, although accuracy for rare classes remained limited. Across challenge participants, TIL density showed the most consistent association with treatment response and survival. Associations for stromal TILs were weaker, while plasma cells, histiocytes, melanophages, neutrophils, necrosis and blood vessels did not show independent associations with outcomes. Overall, the PUMA challenge advanced immune cell detection in melanoma histopathology and showed that intratumoral lymphocytes are most consistently associated with treatment response and survival.