S-Space College of Medicine/School of Medicine (의과대학/대학원) Dept. of Medicine (의학과) Journal Papers (저널논문_의학과)
Pan-cancer immunogenomic perspective on the tumor microenvironment based on PD-L1 and CD8 T-cell infiltration
- Issue Date
- American Association for Cancer Research
- Clinical Cancer Research, Vol.22 No.9, pp.2261-2270
- Purpose: There is currently no reliable biomarker to predict who wou ld benefit from anti-PD-1/PD-L1 inhibitors. We comprehensively analyzed the immunogenomic properties in The Cancer Genome Atlas (TCGA) according to the classification of tumor into four groups based on PD-L1 status and tumor-infiltrating lymphocyte recruitment (TIL), a combination that has been suggested tobe a theoretically reliable biomarker of anti-PD-1/PD-L1 inhibitors. Experimental Design: The RNA expression levels of PD-L1 and CD8A in the samples in the pan-cancer database of TCGA (N = 9,677) were analyzed. Based on their median values, the samples were classified into four tumor microenvironment immune types (TMIT). The mutational profiles, PD-L1 amplification, and viral association of the samples were compared according to the four TMITs. Results: The proportions of TMIT I, defined by high PD-L1 and CD8A expression, were high in lung adenocarcinoma (67.1%) and kidney clear cell carcinoma (64.8%) among solid cancers. The number of somatic mutations and the proportion of microsatellite instable-high tumor in TMIT I were significantly higher than those in other TMITs, respectively (P < 0.001). PD-L1 amplification and oncogenic virus infection were significantly associated with TMIT I, respectively (P < 0.001). A multivariate analysis confirmed that the number of somatic mutations, PD-L1 amplification, and Epstein-Barr virus/human papillomavirus infection were independently associated with TMIT I. Conclusions: TMIT I is associated with a high mutational burden, PD-L1 amplification, and oncogenic viral infection. This integrative analysis highlights the importance of the assessment of both PD-L1 expression and TIL recruitment to predict responders to immune checkpoint inhibitors. (C) 2016 AACR.
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