Deciphering the Increased Prevalence of TP53 Mutations in Metastatic Prostate Cancer.
Wensheng ZhangYan DongOliver SartorKun ZhangPublished in: Cancer informatics (2022)
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: H 1 - TP53 abnormalities promote metastasis or therapy-resistance of PCa cells, and H 2 -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 H 1 and H 2 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.
Keyphrases
- prostate cancer
- risk factors
- ejection fraction
- free survival
- squamous cell carcinoma
- small cell lung cancer
- newly diagnosed
- papillary thyroid
- stem cells
- radical prostatectomy
- induced apoptosis
- mesenchymal stem cells
- transcription factor
- squamous cell
- single cell
- machine learning
- genome wide
- cell proliferation
- big data