Identify Non-mutational p53 Functional Deficiency in Human Cancers.

Qianpeng Li, Yang Zhang, Sicheng Luo, Zhang Zhang, Ann L Oberg, David E Kozono, Hua Lu, Jann N Sarkaria, Lina Ma, Liguo Wang
Author Information
  1. Qianpeng Li: National Genomics Data Center, China National Center for Bioinformation, Beijing 100101, China. ORCID
  2. Yang Zhang: National Genomics Data Center, China National Center for Bioinformation, Beijing 100101, China. ORCID
  3. Sicheng Luo: National Genomics Data Center, China National Center for Bioinformation, Beijing 100101, China. ORCID
  4. Zhang Zhang: National Genomics Data Center, China National Center for Bioinformation, Beijing 100101, China. ORCID
  5. Ann L Oberg: Division of Computational Biology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA. ORCID
  6. David E Kozono: Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women's Hospital, Boston, MA 02215, USA. ORCID
  7. Hua Lu: Department of Biochemistry & Molecular Biology and Tulane Cancer Center, Tulane University School of Medicine, New Orleans, LA 70112, USA. ORCID
  8. Jann N Sarkaria: Department of Radiation Oncology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA. ORCID
  9. Lina Ma: National Genomics Data Center, China National Center for Bioinformation, Beijing 100101, China. ORCID
  10. Liguo Wang: Division of Computational Biology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA. ORCID

Abstract

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 (TP53WT) 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 TP53WT tumors actually had compromised p53 function. Additional analyses uncovered that these genetically intact but functionally impaired (termed as predictively reduced function of p53 or TP53WT-pRF) tumors exhibited genomic and pathophysiologic features akin to TP53-mutant tumors: heightened genomic instability and elevated levels of hypoxia. Clinically, patients with TP53WT-pRF tumors experienced significantly shortened overall survival or progression-free survival compared to those with predictively normal function of p53 (TP53WT-pN) tumors, and these patients also displayed increased sensitivity to platinum-based chemotherapy and radiation therapy.

Keywords

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Grants

  1. U10 CA180882/NCI NIH HHS

MeSH Term

Humans
Tumor Suppressor Protein p53
Neoplasms
Support Vector Machine
Mutation
Prognosis

Chemicals

Tumor Suppressor Protein p53
TP53 protein, human

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