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Multicellular immune ecotypes within solid tumors predict real-world therapeutic benefits with immune checkpoint inhibitors
Preprint   Open access

Multicellular immune ecotypes within solid tumors predict real-world therapeutic benefits with immune checkpoint inhibitors

Xuefeng Wang, Tingyi Li, Islam Eljilany, Vineeth Sukrithan, Aakrosh Ratan, Martin McCarter, John Carpten, Howard Colman, Alexandra P Ikeguchi, Igor Puzanov, …
medRxiv : the preprint server for health sciences
Cold Spring Harbor Laboratory
07/21/2024
DOI: 10.1101/2024.07.19.24310726
PMCID: PMC11275692
PMID: 39072034
url
https://doi.org/10.1101/2024.07.19.24310726View
Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

Cancer initiation, progression, and immune evasion depend on the tumor microenvironment (TME). Thus, understanding the TME immune architecture is essential for understanding tumor metastasis and therapy response. This study aimed to create an immune cell states (CSs) atlas using bulk RNA-seq data enriched by eco-type analyses to resolve the complex immune architectures in the TME.BackgroundCancer initiation, progression, and immune evasion depend on the tumor microenvironment (TME). Thus, understanding the TME immune architecture is essential for understanding tumor metastasis and therapy response. This study aimed to create an immune cell states (CSs) atlas using bulk RNA-seq data enriched by eco-type analyses to resolve the complex immune architectures in the TME.We employed EcoTyper, a machine-learning (ML) framework, to study the real-world prognostic significance of immune CSs and multicellular ecosystems, utilizing molecular data from 1,610 patients with multiple malignancies who underwent immune checkpoint inhibitor (ICI) therapy within the ORIEN Avatar cohort, a well-annotated real-world dataset.MethodsWe employed EcoTyper, a machine-learning (ML) framework, to study the real-world prognostic significance of immune CSs and multicellular ecosystems, utilizing molecular data from 1,610 patients with multiple malignancies who underwent immune checkpoint inhibitor (ICI) therapy within the ORIEN Avatar cohort, a well-annotated real-world dataset.Our analysis revealed consistent ICI-specific prognostic TME carcinoma ecotypes (CEs) (including CE1, CE9, CE10) across our pan-cancer dataset, where CE1 being more lymphocyte-deficient and CE10 being more proinflammatory. Also, the analysis of specific immune CSs across different cancers showed consistent CD8+ and CD4+ T cell CS distribution patterns. Furthermore, survival analysis of the ORIEN ICI cohort demonstrated that ecotype CE9 is associated with the most favorable survival outcomes, while CE2 is linked to the least favorable outcomes. Notably, the melanoma-specific prognostic EcoTyper model confirmed that lower predicted risk scores are associated with improved survival and better response to immunotherapy. Finally, de novo discovery of ecotypes in the ORIEN ICI dataset identified Ecotype E3 as significantly associated with poorer survival outcomes.ResultsOur analysis revealed consistent ICI-specific prognostic TME carcinoma ecotypes (CEs) (including CE1, CE9, CE10) across our pan-cancer dataset, where CE1 being more lymphocyte-deficient and CE10 being more proinflammatory. Also, the analysis of specific immune CSs across different cancers showed consistent CD8+ and CD4+ T cell CS distribution patterns. Furthermore, survival analysis of the ORIEN ICI cohort demonstrated that ecotype CE9 is associated with the most favorable survival outcomes, while CE2 is linked to the least favorable outcomes. Notably, the melanoma-specific prognostic EcoTyper model confirmed that lower predicted risk scores are associated with improved survival and better response to immunotherapy. Finally, de novo discovery of ecotypes in the ORIEN ICI dataset identified Ecotype E3 as significantly associated with poorer survival outcomes.Our findings offer important insights into refining the patient selection process for immunotherapy in real-world practice and guiding the creation of novel therapeutic strategies to target specific ecotypes within the TME. Trial Registration: NCT04526730.ConclusionOur findings offer important insights into refining the patient selection process for immunotherapy in real-world practice and guiding the creation of novel therapeutic strategies to target specific ecotypes within the TME. Trial Registration: NCT04526730.

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