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Analysis of Human T Cell Activity in an Allogeneic Co-Culture Setting of Pre-Treated Tumor Cells
Published on: March 7, 2025
Multicellular immune ecotypes within solid tumors predict real-world therapeutic benefits with immune checkpoint
Xuefeng Wang1, Tingyi Li2, Islam Eljilany3
1Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. Xuefeng.Wang@moffitt.org.
Abstract:
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 E1609 trial as well as in external harmonized melanoma datasets. Together, these findings establish an ecotype-based framework and provide real-world evidence for their translational utility as clinically actionable biomarkers with prognostic and predictive value to guide ICI therapy.
Insights
Carcinoma ecotypes, which reflect cancer-immune interactions, can predict patient response to immune checkpoint inhibitors (ICIs). This study validates ecotypes as prognostic biomarkers across cancers, particularly melanoma, offering real-world evidence for guiding ICI therapy.
Area of Science:
- Oncology
- Immunology
- Computational Biology
Background:
- Immune checkpoint inhibitors (ICIs) have revolutionized cancer therapy.
- Predicting patient response to ICIs remains a significant clinical challenge.
- Carcinoma ecotypes, representing cancer-immune interactions, show potential as biomarkers but require real-world validation.
Purpose of the Study:
- To define and assess the prognostic and predictive value of carcinoma ecotypes in cancer patients treated with ICIs.
- To develop and validate a melanoma-specific ICI predictive model.
- To establish an ecotype-based framework for clinical decision-making in ICI therapy.
Main Methods:
- Analysis of the ORIEN Avatar ICI cohort (1610 patients) and a broader dataset (14,997 individuals) with gene expression data.
- EcoTyper-based immunophenotyping to define ecotypes and assess their association with survival outcomes.
- Development and validation of a melanoma-specific ICI predictive model using clinical trial and external datasets.
Main Results:
- Distinct cell states and ecotypes were consistently associated with survival outcomes across various cancer types.
- The developed melanoma-specific ICI predictive model demonstrated significant validation.
- Real-world evidence supports the translational utility of ecotypes as prognostic and predictive biomarkers.
Conclusions:
- Carcinoma ecotypes provide a robust framework for understanding cancer-immune interactions.
- Ecotype-based immunophenotyping offers clinically actionable insights for guiding ICI therapy.
- This study establishes the real-world prognostic and predictive value of ecotypes in oncology.
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