OpenLB
Open Library of Bioscience
Advanced Search
Genomics, proteomics & bioinformatics
Dec 03, 2024;22 (5):

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
PMID: 39325855 DOI: 10.1093/gpbjnl/qzae064

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

Cancer Composite expression DNA mutation Machine learning p53 deficiency

References

Cell. 1997 Feb 7;88(3):323-31 [PMID: 9039259]
Int J Mol Sci. 2016 Dec 02;17(12): [PMID: 27918441]
Clin Cancer Res. 2020 Mar 1;26(5):1094-1104 [PMID: 31852831]
Am J Pathol. 1995 Sep;147(3):790-8 [PMID: 7677190]
Proc Natl Acad Sci U S A. 1993 Jun 15;90(12):5742-6 [PMID: 8516323]
Genes Dev. 2018 Mar 1;32(5-6):430-447 [PMID: 29549180]
Genes Dev. 2012 Jun 15;26(12):1268-86 [PMID: 22713868]
BMC Bioinformatics. 2013 Jan 16;14:7 [PMID: 23323831]
Sci Transl Med. 2014 Mar 26;6(229):229ra42 [PMID: 24670686]
Cell Death Differ. 2022 May;29(5):895-910 [PMID: 35087226]
Clin Cancer Res. 2015 Aug 15;21(16):3667-77 [PMID: 25904749]
Cancer Cell. 2006 Nov;10(5):437-49 [PMID: 17097565]
Genes Dev. 2000 Jan 1;14(1):34-44 [PMID: 10640274]
Cell Rep. 2019 Jul 30;28(5):1370-1384.e5 [PMID: 31365877]
Nature. 2009 Dec 24;462(7276):1005-10 [PMID: 20033038]
Cancer Res. 2017 May 1;77(9):2292-2305 [PMID: 28280037]
Br J Cancer. 2010 Jan 19;102(2):428-35 [PMID: 20087356]
Nat Genet. 2020 Feb;52(2):177-186 [PMID: 32015526]
Nat Rev Mol Cell Biol. 2007 Apr;8(4):275-83 [PMID: 17380161]
Lancet. 2002 Sep 14;360(9336):852-4 [PMID: 12243922]
PLoS Comput Biol. 2008 Nov;4(11):e1000217 [PMID: 18989396]
Proc Natl Acad Sci U S A. 2021 Jun 8;118(23): [PMID: 34088837]
Cold Spring Harb Perspect Med. 2016 Aug 01;6(8): [PMID: 27329033]
Genome Biol. 2014;15(12):550 [PMID: 25516281]
Cell Rep. 2019 Jan 8;26(2):496-506.e3 [PMID: 30625331]
Cell Death Differ. 2006 Jun;13(6):1003-16 [PMID: 16543940]
Nat Commun. 2015 Dec 04;6:8971 [PMID: 26634437]
Cell Death Differ. 2003 Apr;10(4):400-3 [PMID: 12719715]
Oncogene. 2000 Feb 3;19(5):649-60 [PMID: 10698510]
Science. 2013 Mar 29;339(6127):1546-58 [PMID: 23539594]
Proc Natl Acad Sci U S A. 2010 Apr 6;107(14):6334-9 [PMID: 20308559]
Science. 1994 Nov 4;266(5186):807-10 [PMID: 7973635]
Nat Rev Mol Cell Biol. 2019 Apr;20(4):199-210 [PMID: 30824861]
EMBO J. 2009 Jul 22;28(14):2100-13 [PMID: 19536131]
Oncogene. 2001 Aug 2;20(34):4601-12 [PMID: 11498783]
Nat Rev Mol Cell Biol. 2005 Jan;6(1):44-55 [PMID: 15688066]
Annu Rev Biochem. 2016 Jun 2;85:375-404 [PMID: 27145840]
Proc Natl Acad Sci U S A. 2012 Apr 3;109(14):5316-21 [PMID: 22431589]
J Biol Chem. 2009 Jan 30;284(5):3250-3263 [PMID: 19033443]
Nat Commun. 2021 Mar 19;12(1):1740 [PMID: 33741950]
BMC Bioinformatics. 2005 Sep 12;6:225 [PMID: 16156896]
Subcell Biochem. 2014;85:133-59 [PMID: 25201193]
Nat Struct Mol Biol. 2017 Oct;24(10):840-847 [PMID: 28825732]
Nature. 2004 Nov 18;432(7015):353-60 [PMID: 15525938]
J Natl Cancer Inst. 2015 Nov 17;108(1): [PMID: 26577528]
Cold Spring Harb Perspect Biol. 2010 Feb;2(2):a001107 [PMID: 20182618]
J Clin Oncol. 2009 Mar 10;27(8):1160-7 [PMID: 19204204]
Cancer Lett. 2000 Mar 31;150(2):191-9 [PMID: 10704742]
Cell Growth Differ. 1998 Jul;9(7):545-55 [PMID: 9690622]
Clin Cancer Res. 2012 Jan 1;18(1):290-300 [PMID: 22090360]
Proc Natl Acad Sci U S A. 1995 May 9;92(10):4407-11 [PMID: 7753819]
JCO Precis Oncol. 2022 May;6:e2200085 [PMID: 35613413]
Nature. 1992 Jul 2;358(6381):15-6 [PMID: 1614522]
Cell Death Dis. 2011 May 26;2:e164 [PMID: 21614094]
Nature. 2009 Nov 5;462(7269):108-12 [PMID: 19847166]
Oncogene. 1993 Oct;8(10):2653-8 [PMID: 8378077]
Acta Neuropathol. 2005 Aug;110(2):178-84 [PMID: 16025287]
Oncogene. 2017 Jul 13;36(28):3943-3956 [PMID: 28288132]
Proc Natl Acad Sci U S A. 1992 Aug 1;89(15):7262-6 [PMID: 1353891]
Elife. 2015 May 12;4: [PMID: 25965177]
Oncogene. 2020 Apr;39(18):3638-3649 [PMID: 32157215]
Int J Cancer. 2020 Jul 15;147(2):472-477 [PMID: 31359406]
Nature. 2013 Aug 22;500(7463):415-21 [PMID: 23945592]
Science. 1991 Jul 5;253(5015):49-53 [PMID: 1905840]
Cell Death Differ. 2019 Jan;26(2):199-212 [PMID: 30538286]
J Biol Chem. 2019 Jul 5;294(27):10720-10736 [PMID: 31113863]
Cancers (Basel). 2018 May 25;10(6): [PMID: 29799511]
Clin Cancer Res. 2014 Sep 1;20(17):4419-21 [PMID: 24916693]
Bioinformatics. 2009 Aug 15;25(16):2078-9 [PMID: 19505943]
Nucleic Acids Res. 2020 Jan 8;48(D1):D180-D188 [PMID: 31665499]
Science. 2015 May 8;348(6235):648-60 [PMID: 25954001]
Nat Rev Cancer. 2014 May;14(5):359-70 [PMID: 24739573]
Nature. 1997 May 15;387(6630):296-9 [PMID: 9153395]
Cancer Res. 2018 May 15;78(10):2721-2731 [PMID: 29490944]
Cancers (Basel). 2021 Feb 11;13(4): [PMID: 33670160]
PLoS Med. 2007 Mar;4(3):e90 [PMID: 17388661]
Proc Natl Acad Sci U S A. 2003 Jul 8;100(14):8424-9 [PMID: 12826609]

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
Journal Article

Word Cloud

Similar Articles

Cited By