4.6 Article

TGFβ signaling activation correlates with immune-inflamed tumor microenvironment across human cancers and predicts response to immunotherapy

Journal

CELL CYCLE
Volume 22, Issue 1, Pages 57-72

Publisher

TAYLOR & FRANCIS INC
DOI: 10.1080/15384101.2022.2109105

Keywords

Anti-TGF beta; immune checkpoint blockade; tumor microenvironment

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This study compared the activation of the TGF beta pathway in different tumor microenvironments using multi-omics data. The results showed increased activity of TGF beta signaling in inflamed tumors compared to non-inflamed tumors in non-cancer cell types within the tumor microenvironment. Significant correlations were found between TGF beta signaling and reliable biomarkers for immune checkpoint blockade therapy, as well as HPV status. These findings help explain the inconsistency between preclinical and clinical research and are crucial for optimizing clinical trial design and personalized prediction.
Considering the determining role of TGF beta signaling in the tumor microenvironment (TME) on immune evasion, the inhibition of signaling is expected to enhance the therapeutic efficacy of immunotherapies, especially immune checkpoint blockade (ICB), which is confirmed in preclinical data. However, successive failures in clinical translation occur at the initial stage. To provide a better understanding of TGF beta signaling within the TME and its relation to the individual immunological status, we performed a pan-cancer analysis comparing the activation of TGF beta pathway among different TMEs based on multi-omics data. Compared with non-inflamed tumors, increased TGF beta signaling activity appeared in four non-cancer cell types within TME in inflamed tumors. Significant correlations were revealed between TGF beta signaling and reliable biomarkers for ICB therapy, as well as between TGF beta signaling and HPV status. Our findings contribute to explain the inconsistency between preclinical and clinical research, and are crucial to optimizing upcoming clinical trial design and improving patient stratification for personalized prediction.

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