Identify Non-mutational p53 Functional Deficiency in Human Cancers  

作　　者：Qianpeng Li Yang Zhang Sicheng Luo Zhang Zhang Ann L.Oberg David E.Kozono Hua Lu Jann N.Sarkaria Lina Ma Liguo Wang 

机构地区：[1]National Genomics Data Center,China National Center for Bioinformation,Beijing 100101,China [2]Beijing Institute of Genomics,Chinese Academy of Sciences,Beijing 100101,China [3]University of Chinese Academy of Sciences,Beijing 100049,China [4]Division of Computational Biology,Mayo Clinic College of Medicine and Science,Rochester,MN 55905,USA [5]Department of Radiation Oncology,Dana-Farber Cancer Institute and Brigham and Women's Hospital,Boston,MA 02215,USA [6]Department of Biochemistry&Molecular Biology and Tulane Cancer Center,Tulane University School of Medicine,New Orleans,LA 70112,USA [7]Department of Radiation Oncology,Mayo Clinic College of Medicine and Science,Rochester,MN 55905,USA [8]Department of Biochemistry and Molecular Biology,Mayo Clinic College of Medicine and Science,Rochester,MN 55905,USA [9]Bioinformatics and Computational Biology Graduate Program,University of Minnesota Rochester,Rochester,MN 55904,USA

出　　处：《Genomics, Proteomics & Bioinformatics》2024年第5期85-98,共14页基因组蛋白质组与生物信息学报(英文版)

基　　金：supported by the National Institutes of Health(Grant No.U10-CA180882-07)and the Mayo Clinic Center for Individualized Medicine,USA,as well as the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB38030400);the Youth Innovation Promotion Association of Chinese Academy of Sciences(Grant No.2019104),China.

摘　　要：An accurate assessment of p53's functional statuses is critical for cancer genomic medicine.However,there is a significant challenge in identifying tumors with non-mutational p53 inactivation which is not detectable through DNA sequencing.These undetected cases are often misclassified as p53-normal,leading to inaccurate prognosis and downstream association analyses.To address this issue,we built the support vector machine(SVM)models to systematically reassess p53's functional statuses in TP53 wild-type(TP53^(WT))tumors from multiple The Cancer Genome Atlas(TCGA)cohorts.Cross-validation demonstrated the good performance of the SVM models with a mean area under the receiver operating characteristic curve(AUROC)of 0.9822,precision of 0.9747,and recall of 0.9784.Our study revealed that a significant proportion(87%-99%)of TP53^(WT) tumors actually had compromised p53 function.Additional analyses uncovered that these genetically intact but functionally impaired(termed as predictively reduced function of p53 or TP53^(WT)-pRF)tumors exhibited genomic and pathophysiologic features akin to TP53-mutant tumors:heightened genomic instability and elevated levels of hypoxia.Clinically,patients with TP53^(WT)-pRF tumors experienced significantly shortened overall survival or progression-free survival compared to those with predictively normal function of p53(TP53^(WT)-pN)tumors,and these patients also displayed increased sensitivity to platinum-based chemotherapy and radiation therapy.

关 键 词：CANCER Composite expression DNA mutation Machine learning p53 deficiency 

分 类 号：R730.2[医药卫生—肿瘤]