Prediction of immunotherapy response using live tumor fragments from routine clinical biopsies

Functional ex vivo assays using live tumor tissues have demonstrated strong predictive accuracy for response to immune checkpoint inhibitors (ICIs) but are not scalable, requiring manual processing of large resections collected at academic centers. Here, an ex vivo live tumor fragment platform was developed using standard-of-care biopsies from 228 patients with suspected malignancy across prospective, multicenter observational studies. Hierarchical clustering of changes in cytokine production between an IgG baseline phase and an ICI treatment phase identified two clusters: heatmap responder and nonresponder. A binary classifier (elive index) using eight cytokines achieved an area under the curve (AUC) of 0.99 for heatmap cluster prediction. elive index aligned with clinical benefit in 93% (26/28) of patients (p = 3.2 × 10^-5) and identified 83% (10/12) of objective responders. elive responders were found among biomarker-negative patients, highlighting the platform as a scalable approach that may complement existing companion diagnostics. This study was registered at ClinicalTrials.gov (NCT05478538, NCT05520099, NCT06349642, and NCT07327489).