biostudies-literatureWang XNIDCR NIH HHSNCI NIH HHSU.S. Department of Health & Human Services | NIH | National Institute of Dental and Craniofacial Research (NIDCR)9968https://www.ebi.ac.uk/biostudies/studies/S-EPMC12615696biostudies-literatureUnknown16(1)Immune checkpoint inhibitors (ICIs) have transformed cancer treatment, yet predicting patient response remains a major challenge. Carcinoma ecotypes, which capture the cancer-immune interactions, show promise as prognostic biomarkers but remain untested in real-world settings. We compile and analyze the ORIEN Avatar ICI cohort of 1610 patients with matched gene expression data from a broader dataset of 14,997 individuals. Using EcoTyper-based immunophenotyping, we define ecotypes and assess their prognostic value across cancers, with a focused analysis in melanoma. Distinct cell states and ecotypes are consistently associated with survival outcomes across cancer types. We further develop a melanoma-specific ICI predictive model and validate it using data from the phase III ECOG-ACRIN E1609Nature communicationsMulticellular immune ecotypes within solid tumors predict real-world therapeutic benefits with immune checkpoint inhibitors.PMC12615696UG1 CA233324U10 CA180794UG1 CA233191UG1 CA233180U10 CA180820UG1 CA233184U10 CA180821U10 CA180888UG1 CA233331UG1 CA233178P30 CA076292R01DE030493R01 DE030493McCarter MEljilany IRatan AAgius PSukrithan VMarin-Acevedo JAArnold SChurchman MRadmacher MNaqash ARSalhia BLi TSoupir AGanesan SRodriguez PCHwu PColman HDalton WSWang XWeiner GJPuzanov ITarhini AAfalseMulticellular immune ecotypes within solid tumors predict real-world therapeutic benefits with immune checkpoint inhibitors.Immune checkpoint inhibitors (ICIs) have transformed cancer treatment, yet predicting patient response remains a major challenge. Carcinoma ecotypes, which capture the cancer-immune interactions, show promise as prognostic biomarkers but remain untested in real-world settings. We compile and analyze the ORIEN Avatar ICI cohort of 1610 patients with matched gene expression data from a broader dataset of 14,997 individuals. Using EcoTyper-based immunophenotyping, we define ecotypes and assess their prognostic value across cancers, with a focused analysis in melanoma. Distinct cell states and ecotypes are consistently associated with survival outcomes across cancer types. We further develop a melanoma-specific ICI predictive model and validate it using data from the phase III ECOG-ACRIN E16092025-01-01T00:00:00Z2025 Nov2026-06-05T12:09:32.816Z2026-05-16T03:13:07.697ZS-EPMC126156964123332910.1038/s41467-025-65016-3