Discover oncology · 2025
Pan cohort immune biomarker of CD8 lymphocyte activation enabling HGSOC outcome prediction and treatment response.
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Liu Xinkui, Zhang Zhen, Wang Bin, Qin Liping, Fan Nannan, Wang Ruohan, Yu Xiaoyan, Han Qiaoqiao, Lu Zihan, Kikete Siambi, Shi Feifei, Chu Chu, Zhang Yunhong, Niu Liangzhong, Wei Ran, Wu Jiarui, Li Xia
Аннотация
BACKGROUND: High-grade serous ovarian cancer (HGSOC) exhibits poor prognosis due to late diagnosis, chemoresistance, and limited responses to immune checkpoint inhibitors. Although tumor-infiltrating CD8+ T cells correlate with improved survival, current prognostic models remain inadequate. Thus, robust biomarkers linked to CD8+ T cell activation are urgently needed to guide clinical management.
METHODS: Transcriptomic and clinical profiles from 874 late-stage HGSOC patients were analyzed via single-sample gene set enrichment analysis for immune infiltration and weighted gene co-expression network analysis to identify CD8+ T cell-associated genes. An integrative machine learning approach was employed to develop a CD8⁺ T cell-associated immune prognostic signature (CIPS), which was then validated across multiple independent cohorts and benchmarked against 56 published models. CIPS was further characterized using single-cell RNA-seq analysis.
RESULTS: The resulting 10-gene signature independently predicted overall survival in all cohorts and consistently surpassed most clinicopathological variables and comparator models. Low-risk patients exhibited significantly enhanced CD8+ T cell and cytotoxic gene scores, correlating with better responses to chemotherapy and immunotherapy. CIPS inversely correlated with tumor-mutation burden, BRCA1/2 mutations and homologous-recombination deficiency. Single-cell analysis localized signature genes to T lymphocyte and myeloid compartments and linked elevated CIPS activity to augmented intercellular communication in platinum-resistant tumors.
CONCLUSION: CIPS captures a CD8+ T cell activation program that powerfully stratifies late-stage HGSOC, forecasts therapeutic benefit and offers a practicable biomarker for personalized immuno-oncology strategies.
METHODS: Transcriptomic and clinical profiles from 874 late-stage HGSOC patients were analyzed via single-sample gene set enrichment analysis for immune infiltration and weighted gene co-expression network analysis to identify CD8+ T cell-associated genes. An integrative machine learning approach was employed to develop a CD8⁺ T cell-associated immune prognostic signature (CIPS), which was then validated across multiple independent cohorts and benchmarked against 56 published models. CIPS was further characterized using single-cell RNA-seq analysis.
RESULTS: The resulting 10-gene signature independently predicted overall survival in all cohorts and consistently surpassed most clinicopathological variables and comparator models. Low-risk patients exhibited significantly enhanced CD8+ T cell and cytotoxic gene scores, correlating with better responses to chemotherapy and immunotherapy. CIPS inversely correlated with tumor-mutation burden, BRCA1/2 mutations and homologous-recombination deficiency. Single-cell analysis localized signature genes to T lymphocyte and myeloid compartments and linked elevated CIPS activity to augmented intercellular communication in platinum-resistant tumors.
CONCLUSION: CIPS captures a CD8+ T cell activation program that powerfully stratifies late-stage HGSOC, forecasts therapeutic benefit and offers a practicable biomarker for personalized immuno-oncology strategies.