Objective:The previously integrated tumor-inflammation-nutrition(HI-GC)score has demonstrated dynamic monitoring value for recurrence and clinical decision-making in patients with postsurgical gastric cancer(GC).Howev...Objective:The previously integrated tumor-inflammation-nutrition(HI-GC)score has demonstrated dynamic monitoring value for recurrence and clinical decision-making in patients with postsurgical gastric cancer(GC).However,its failure to incorporate clinical-pathological factors limits its capacity for baseline risk assessment.This study aimed to develop a model that accurately identifies patients for adjuvant chemotherapy and dynamically evaluates recurrence risk.Methods:This retrospective,multicenter,longitudinal cohort study,spanning nine hospitals,included 7,085patients with GC post-radical gastrectomy.A baseline prognostic model was constructed using 117 machinelearning algorithms.The dynamic survival decision tree model(dy SDT)was employed to combine the baseline model with the HI-GC score.Results:A Cox regression model incorporating six factors was used to create a nomogram[Harrell's C-index:training cohort:0.765;95%confidence interval(95%CI):0.747,0.783;validation set:0.810;95%CI:0.747,0.783],including p T stage,positive lymph node ratio,p N stage,tumor size,age,and adjuvant chemotherapy.The best-performing machine learning model exhibited similar predictive accuracy to the nomogram(C-index:0.770).For the short-term dy SDT at 1 month,the mortality hazard ratios(HRs)for groups IIa,IIb,andⅢwere 2.61(95%CI:2.24,3.04),5.02(95%CI:4.15,6.06),and 8.88(95%CI:7.57,10.42),respectively,compared to group I.Stratified analysis revealed a significant interaction between adjuvant chemotherapy and overall survival in each subgroup(P<0.001).The long-term dy SDT at 1 year showed HRs of 3.25(95%CI:2.12,4.97)for group II,6.73(95%CI:4.29,10.56)for groupⅢa,and 17.88(95%CI:10.71,29.84)for groupⅢb.Conclusions:The dy SDT effectively stratifies mortality risk and provides valuable assistance in clinical decision-making after gastrectomy.展开更多
Objective: DNA damage response(DDR) deficiency has emerged as a prominent determinant of tumor immunogenicity. This study aimed to construct a DDR-related immune activation(DRIA) signature and evaluate the predictive ...Objective: DNA damage response(DDR) deficiency has emerged as a prominent determinant of tumor immunogenicity. This study aimed to construct a DDR-related immune activation(DRIA) signature and evaluate the predictive accuracy of the DRIA signature for response to immune checkpoint inhibitor(ICI) therapy in gastrointestinal(GI) cancer.Methods: A DRIA signature was established based on two previously reported DNA damage immune response assays. Clinical and gene expression data from two published GI cancer cohorts were used to assess and validate the association between the DRIA score and response to ICI therapy. The predictive accuracy of the DRIA score was validated based on one ICI-treated melanoma and three pan-cancer published cohorts.Results: The DRIA signature includes three genes(CXCL10, IDO1, and IFI44L). In the discovery cancer cohort, DRIA-high patients with gastric cancer achieved a higher response rate to ICI therapy than DRIA-low patients(81.8% vs. 8.8%;P < 0.001), and the predictive accuracy of the DRIA score [area under the receiver operating characteristic curve(AUC) = 0.845] was superior to the predictive accuracy of PD-L1 expression, tumor mutational burden, microsatellite instability, and Epstein–Barr virus status. The validation cohort demonstrated that the DRIA score identified responders with microsatellite-stable colorectal and pancreatic adenocarcinoma who received dual PD-1 and CTLA-4 blockade with radiation therapy. Furthermore, the predictive performance of the DRIA score was shown to be robust through an extended validation in melanoma, urothelial cancer, and pan-cancer.Conclusions: The DRIA signature has superior and robust predictive accuracy for the efficacy of ICI therapy in GI cancer and pancancer, indicating that the DRIA signature may serve as a powerful biomarker for guiding ICI therapy decisions.展开更多
基金supported by the Noncommunicable Chronic Diseases-National Science and Technology Major Project(No.2023ZD0501400)the National Key R&D Program of China(No.2022YFC2505100)the National Natural Science Foundation of China(No.82202837,82421002 and 82350122)。
文摘Objective:The previously integrated tumor-inflammation-nutrition(HI-GC)score has demonstrated dynamic monitoring value for recurrence and clinical decision-making in patients with postsurgical gastric cancer(GC).However,its failure to incorporate clinical-pathological factors limits its capacity for baseline risk assessment.This study aimed to develop a model that accurately identifies patients for adjuvant chemotherapy and dynamically evaluates recurrence risk.Methods:This retrospective,multicenter,longitudinal cohort study,spanning nine hospitals,included 7,085patients with GC post-radical gastrectomy.A baseline prognostic model was constructed using 117 machinelearning algorithms.The dynamic survival decision tree model(dy SDT)was employed to combine the baseline model with the HI-GC score.Results:A Cox regression model incorporating six factors was used to create a nomogram[Harrell's C-index:training cohort:0.765;95%confidence interval(95%CI):0.747,0.783;validation set:0.810;95%CI:0.747,0.783],including p T stage,positive lymph node ratio,p N stage,tumor size,age,and adjuvant chemotherapy.The best-performing machine learning model exhibited similar predictive accuracy to the nomogram(C-index:0.770).For the short-term dy SDT at 1 month,the mortality hazard ratios(HRs)for groups IIa,IIb,andⅢwere 2.61(95%CI:2.24,3.04),5.02(95%CI:4.15,6.06),and 8.88(95%CI:7.57,10.42),respectively,compared to group I.Stratified analysis revealed a significant interaction between adjuvant chemotherapy and overall survival in each subgroup(P<0.001).The long-term dy SDT at 1 year showed HRs of 3.25(95%CI:2.12,4.97)for group II,6.73(95%CI:4.29,10.56)for groupⅢa,and 17.88(95%CI:10.71,29.84)for groupⅢb.Conclusions:The dy SDT effectively stratifies mortality risk and provides valuable assistance in clinical decision-making after gastrectomy.
基金supported by the National Natural Science Foundation of China (Grant Nos. 81972761 and 82202837)the National Key R&D Program of China (Grant Nos. 2016YFC1303200 and 2022YFC2505100)。
文摘Objective: DNA damage response(DDR) deficiency has emerged as a prominent determinant of tumor immunogenicity. This study aimed to construct a DDR-related immune activation(DRIA) signature and evaluate the predictive accuracy of the DRIA signature for response to immune checkpoint inhibitor(ICI) therapy in gastrointestinal(GI) cancer.Methods: A DRIA signature was established based on two previously reported DNA damage immune response assays. Clinical and gene expression data from two published GI cancer cohorts were used to assess and validate the association between the DRIA score and response to ICI therapy. The predictive accuracy of the DRIA score was validated based on one ICI-treated melanoma and three pan-cancer published cohorts.Results: The DRIA signature includes three genes(CXCL10, IDO1, and IFI44L). In the discovery cancer cohort, DRIA-high patients with gastric cancer achieved a higher response rate to ICI therapy than DRIA-low patients(81.8% vs. 8.8%;P < 0.001), and the predictive accuracy of the DRIA score [area under the receiver operating characteristic curve(AUC) = 0.845] was superior to the predictive accuracy of PD-L1 expression, tumor mutational burden, microsatellite instability, and Epstein–Barr virus status. The validation cohort demonstrated that the DRIA score identified responders with microsatellite-stable colorectal and pancreatic adenocarcinoma who received dual PD-1 and CTLA-4 blockade with radiation therapy. Furthermore, the predictive performance of the DRIA score was shown to be robust through an extended validation in melanoma, urothelial cancer, and pan-cancer.Conclusions: The DRIA signature has superior and robust predictive accuracy for the efficacy of ICI therapy in GI cancer and pancancer, indicating that the DRIA signature may serve as a powerful biomarker for guiding ICI therapy decisions.
