Deciphering the Increased Prevalence of TP53 Mutations in Metastatic Prostate Cancer

Author:

Zhang Wensheng1ORCID,Dong Yan2,Sartor Oliver3,Zhang Kun14ORCID

Affiliation:

1. Bioinformatics Core of Xavier NIH RCMI Center of Cancer Research, Xavier University of Louisiana, New Orleans, LA, USA

2. Department of Structural and Cellular Biology, Tulane University School of Medicine, Tulane Cancer Center, New Orleans, LA, USA

3. Department of Medicine, Tulane University School of Medicine, Tulane Cancer Center, New Orleans, LA, USA

4. Department of Computer Science, Xavier University of Louisiana, New Orleans, LA, USA

Abstract

The prevalence of TP53 mutations in advanced prostate cancers (PCa) is 3 to 5 times of the quantity in primary PCa. By an integrative analysis of the Cancer Genome Atlas and Catalogue of Somatic Mutations in Cancer data, we revealed the supporting evidence for 2 complementary hypotheses: H1 - TP53 abnormalities promote metastasis or therapy-resistance of PCa cells, and H2—part of TP53 mutations in PCa metastases occur after the diagnosis of original cancers. The plausibility of these hypotheses can explain the increased prevalence of TP53 mutations in PCa metastases. With H1 and H2 as the general assumptions, we developed mathematical models to decipher the change of the percentage frequency (prevalence) of TP53 mutations from primary tumors to metastases. The following results were obtained. Compared to TP53-normal patients, TP53-mutated patients had poorer biochemical relapse-free survival, higher Gleason scores, and more advanced t-stages ( P < .01). Single-nucleotide variants in metastases more frequently occurred on G bases of the coding sequence than those in primary cancers ( P = .03). The profile of TP53 hotspot mutations was significantly different between primary and metastatic PCa as demonstrated in a set of statistical tests ( P < .05). By the derived formulae, we estimated that about 40% TP53 mutation records collected from metastases occurred after the diagnosis of the original cancers. Our study provided significant insight into PCa progression. The proposed models can also be applied to decipher the prevalence of mutations on TP53 (or other driver genes) in other cancer types.

Funder

NIH

Publisher

SAGE Publications

Subject

Cancer Research,Oncology

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