Objective:Pancreatic ductal adenocarcinoma(PDAC)is an aggressive malignancy.CD8^(+)T cells,cancer stem cells(CSCs),and tumor budding(TB)have been significantly correlated with the outcome of patients with PDAC,but the...Objective:Pancreatic ductal adenocarcinoma(PDAC)is an aggressive malignancy.CD8^(+)T cells,cancer stem cells(CSCs),and tumor budding(TB)have been significantly correlated with the outcome of patients with PDAC,but the correlations have been independently reported.In addition,no integrated immune-CSC-TB profile for predicting survival in patients with PDAC has been established.Methods:Multiplexed immunofluorescence and artificial intelligence(AI)-based comprehensive analyses were used for quantification and spatial distribution analysis of CD8^(+)T cells,CD133^(+)CSCs,and TB.In vivo humanized patient-derived xenograft(PDX)models were established.Nomogram analysis,calibration curve,time-dependent receiver operating characteristic curve,and decision curve analyses were performed using R software.Results:The established‘anti-/pro-tumor’models showed that the CD8^(+)T cell/TB,CD8^(+)T cell/CD133^(+)CSC,TB-adjacent CD8^(+)T cell,and CD133^(+)CSC-adjacent CD8^(+)T cell indices were positively associated with survival of patients with PDAC.These findings were validated using PDX-transplanted humanized mouse models.An integrated nomogram-based immune-CSC-TB profile that included the CD8^(+)T cell/TB and CD8^(+)T cell/CD133^(+)CSC indices was established and shown to be superior to the tumor-nodemetastasis stage model in predicting survival of patients with PDAC.Conclusions:‘Anti-/pro-tumor’models and the spatial relationship among CD8^(+)T cells,CSCs,and TB within the tumor microenvironment were investigated.Novel strategies to predict the prognosis of patients with PDAC were established using AI-based comprehensive analysis and machine learning workflow.The nomogram-based immune-CSC-TB profile can provide accurate prognosis prediction for patients with PDAC.展开更多
基金supported by The Science&Technology Development Fund of Tianjin Education Commission for Higher Education(Grant No.2017KJ198)。
文摘Objective:Pancreatic ductal adenocarcinoma(PDAC)is an aggressive malignancy.CD8^(+)T cells,cancer stem cells(CSCs),and tumor budding(TB)have been significantly correlated with the outcome of patients with PDAC,but the correlations have been independently reported.In addition,no integrated immune-CSC-TB profile for predicting survival in patients with PDAC has been established.Methods:Multiplexed immunofluorescence and artificial intelligence(AI)-based comprehensive analyses were used for quantification and spatial distribution analysis of CD8^(+)T cells,CD133^(+)CSCs,and TB.In vivo humanized patient-derived xenograft(PDX)models were established.Nomogram analysis,calibration curve,time-dependent receiver operating characteristic curve,and decision curve analyses were performed using R software.Results:The established‘anti-/pro-tumor’models showed that the CD8^(+)T cell/TB,CD8^(+)T cell/CD133^(+)CSC,TB-adjacent CD8^(+)T cell,and CD133^(+)CSC-adjacent CD8^(+)T cell indices were positively associated with survival of patients with PDAC.These findings were validated using PDX-transplanted humanized mouse models.An integrated nomogram-based immune-CSC-TB profile that included the CD8^(+)T cell/TB and CD8^(+)T cell/CD133^(+)CSC indices was established and shown to be superior to the tumor-nodemetastasis stage model in predicting survival of patients with PDAC.Conclusions:‘Anti-/pro-tumor’models and the spatial relationship among CD8^(+)T cells,CSCs,and TB within the tumor microenvironment were investigated.Novel strategies to predict the prognosis of patients with PDAC were established using AI-based comprehensive analysis and machine learning workflow.The nomogram-based immune-CSC-TB profile can provide accurate prognosis prediction for patients with PDAC.
