Computers in biology and medicine · 2025
Tumor microenvironment-based classification for predicting gastric cancer prognosis.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Hong Yiyu, Chi Sang Ah, Lee Hye Seung, Hwang Inwoo, Kang So Young, Ahn Soomin, Kim Kyunga, An Ji Yeong, Choi Min Gew, Lee Jun Ho, Bae Jae Moon, Sohn Tae Sung, Kim Kyoung-Mee
Аннотация
BACKGROUND: The tumor microenvironment (TME), consisting of tumor-associated stroma and tumor-infiltrating lymphocytes (TIL), is crucial for prognostic information in gastric cancer (GC). Despite its potential, routine clinical adoption remains limited.
METHODS: In a study of 320 GC patients, virtual staining and image processing were applied to hematoxylin & eosin-stained slides. This method quantified the tumor-stroma ratio (TSR) and TIL, leading to a TME-based prediction model (TMEPATH) using a scoring system derived from univariate Cox regression. Subgroups were categorized to predict GC patient survival, with genomic analysis linking TME-based prognostic models to specific genetic alterations.
RESULTS: TSR was categorized into TSR_low (n = 113) and TSR_high (n = 207) using a 0.76 cut-off, selected to maximize the concordance index for overall survival prediction. Two TIL subtypes were defined based on a 0.03 cut-off. TMEPATH, a composite biomarker integrating the TSR- and TIL-based subtypes, stratified patients into low-risk (91 patients, 28.4 %), medium-risk (167 patients, 52.2 %), and high-risk (62 patients, 19.4 %) groups, correlating with survival outcomes (hazard ratio [HR] 1.281; 95 % CI 0.957-1.714 for medium vs. low-risk, and HR 1.768; 95 % CI 1.242-2.517 for high vs. low-risk; log-rank P = 0.0061). These findings were validated in a separate cohort (n = 186) with significant clinical relevance (HR 1.389; 95 % CI 0.855-2.257 for medium vs. low-risk, and HR 2.435; 95 % CI 1.380-4.298 for high vs. low-risk; log-rank P = 0.0064). TSR, TIL, and TMEPATH were associated with microsatellite instability, tumor mutation burden, and CDH1 mutations.
CONCLUSION: The classification of GC into three TME subtypes using TSR and TIL provides a reliable prognostic tool for survival prediction.
METHODS: In a study of 320 GC patients, virtual staining and image processing were applied to hematoxylin & eosin-stained slides. This method quantified the tumor-stroma ratio (TSR) and TIL, leading to a TME-based prediction model (TMEPATH) using a scoring system derived from univariate Cox regression. Subgroups were categorized to predict GC patient survival, with genomic analysis linking TME-based prognostic models to specific genetic alterations.
RESULTS: TSR was categorized into TSR_low (n = 113) and TSR_high (n = 207) using a 0.76 cut-off, selected to maximize the concordance index for overall survival prediction. Two TIL subtypes were defined based on a 0.03 cut-off. TMEPATH, a composite biomarker integrating the TSR- and TIL-based subtypes, stratified patients into low-risk (91 patients, 28.4 %), medium-risk (167 patients, 52.2 %), and high-risk (62 patients, 19.4 %) groups, correlating with survival outcomes (hazard ratio [HR] 1.281; 95 % CI 0.957-1.714 for medium vs. low-risk, and HR 1.768; 95 % CI 1.242-2.517 for high vs. low-risk; log-rank P = 0.0061). These findings were validated in a separate cohort (n = 186) with significant clinical relevance (HR 1.389; 95 % CI 0.855-2.257 for medium vs. low-risk, and HR 2.435; 95 % CI 1.380-4.298 for high vs. low-risk; log-rank P = 0.0064). TSR, TIL, and TMEPATH were associated with microsatellite instability, tumor mutation burden, and CDH1 mutations.
CONCLUSION: The classification of GC into three TME subtypes using TSR and TIL provides a reliable prognostic tool for survival prediction.