AI-detected tumor-infiltrating lymphocytes for predicting outcomes in anti-PD1 based treated melanoma.

ImportanceEasy and accessible biomarkers to predict response to immune checkpoint inhibition (ICI)-treated melanoma are limited. ObjectiveTo evaluate artificial intelligence (AI) detected tumor-infiltrating lymphocytes (TILs) on pretreatment melanoma metastases as a biomarker for response and survival in ICI-treated patients. DesignMulticenter cohort study including patients with advanced melanoma treated with first-line anti-PD1 {+/-} anti-CTLA4 between 2016 and 2023. Median follow-up was 36.3 months. Setting11 melanoma treatment centers in the Netherlands. Participants1,202 patients with advanced cutaneous melanoma. ExposureAll patients received first-line anti-PD1 {+/-} anti-CTLA4. Main Outcome(s) and Measure(s)The percentage of TILs inside manually annotated tumor area in H&E stained pretreatment metastases was determined using the Hover-NeXt model trained and evaluated on an independent melanoma dataset containing 166,718 pathologist-verified manually annotated cells. The primary outcome was objective response rate (ORR); secondary outcomes were progression-free survival (PFS) and overall survival (OS). Correlation with manual TILs, scored according to the guidelines stated by the immune-oncology working group, was evaluated with Spearman correlation coefficients. Logistic regression and Cox proportional regression were conducted, adjusted for age, sex, disease stage, ICI type, BRAF status, brain metastases, LDH level, and performance status. ResultsMetastatic melanoma specimens were available for 1,202 patients, of whom 423 received combination therapy. Median TIL percentage was 9.9% (range 0.3% - 69.4%). A 10% increase in TILs was associated with increased ORR (adjusted OR 1.40 [95% 1.23-1.59]), PFS (adjusted HR 0.85 [95% CI 0.79 - 0.92]) and OS (adjusted HR 0.83 [95% CI 0.76 - 0.91]. Results were consistent for both patients treated with anti-PD1 monotherapy and combination treatment with anti-PD1 plus anti-CTLA4. When comparing manual TIL scoring with AI-detected TILs, associations with response and survival were consistently stronger for AI-detected TILs. Conclusions and RelevanceIn patients with advanced melanoma, higher levels of AI-detected TILs on pre-treatment H&E slides were independently associated with improved ICI response and survival. Given the accessibility of TIL scoring on routine histology, TILs may serve as a predictive biomarker for ICI outcomes. To facilitate broader validation, the Hover-NeXt architecture and model weights are publicly available. Key pointsQuestion: What is the predictive value of artificial intelligence-detected tumor-infiltrating lymphocytes (TILs) for clinical outcomes in patients with advanced melanoma receiving first-line immune checkpoint inhibition? Findings: In this multicenter cohort of 1202 patients, TILs in pretreatment metastases were quantified using a melanoma-specific publicly available AI model trained on an independent dataset. A 10% increase was associated with response (aOR 1.40 [95% CI 1.23-1.59]), progression-free survival (aHR 0.85 [95% CI 0.79-0.92]), and overall survival (aHR 0.83 [95% CI 0.76-0.91]). Associations were independent of clinical predictors. Meaning: AI-detected TILs in pretreatment melanoma metastases independently correlate with response and survival.