摘要：Current cancer biomarkers present variability in their predictive power and demonstrate limited clinical efficacy, possibly due to the lack of functional relevance of biomarker genes to cancer progression. To address this challenge, a biomarker discovery pipeline was developed to integrate gene expression profiles from The Cancer Genome Atlas and essential survival gene datasets from The Cancer Dependency Map, the latter of which catalogs genes driving cancer progression. By applying this pipeline to lung adenocarcinoma, lung squamous cell carcinoma, and glioblastoma, genes highly associated with cancer progression were identified and designated as progression gene signatures (PGSs). Analysis of area under the receiver operating characteristics curve revealed that PGSs predicted patient survival more accurately than previously identified cancer biomarkers. Moreover, PGSs stratified patients with high risk for progressive disease indicated by worse prognostic outcomes, increased frequency of cancer progression, and poor responses to chemotherapy. The robust performance of these PGSs were recapitulated in four independent microarray datasets from Gene Expression Omnibus and were further verified in six freshly dissected tumors from glioblastoma patients. Our results demonstrate the power of an integrated approach to cancer biomarker discovery and the possibility of implementing PGSs into clinical biomarker tests.
其他摘要：Abstract Current cancer biomarkers present variability in their predictive power and demonstrate limited clinical efficacy, possibly due to the lack of functional relevance of biomarker genes to cancer progression. To address this challenge, a biomarker discovery pipeline was developed to integrate gene expression profiles from The Cancer Genome Atlas and essential survival gene datasets from The Cancer Dependency Map, the latter of which catalogs genes driving cancer progression. By applying this pipeline to lung adenocarcinoma, lung squamous cell carcinoma, and glioblastoma, genes highly associated with cancer progression were identified and designated as progression gene signatures (PGSs). Analysis of area under the receiver operating characteristics curve revealed that PGSs predicted patient survival more accurately than previously identified cancer biomarkers. Moreover, PGSs stratified patients with high risk for progressive disease indicated by worse prognostic outcomes, increased frequency of cancer progression, and poor responses to chemotherapy. The robust performance of these PGSs were recapitulated in four independent microarray datasets from Gene Expression Omnibus and were further verified in six freshly dissected tumors from glioblastoma patients. Our results demonstrate the power of an integrated approach to cancer biomarker discovery and the possibility of implementing PGSs into clinical biomarker tests.
