解读转移性前列腺癌中TP53突变增加的患病率

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Wensheng Zhang, Yan Dong, O. Sartor, Kun Zhang
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引用次数: 3

摘要

晚期前列腺癌(PCa)中TP53突变的发生率是原发性前列腺癌的3-5倍。通过对癌症基因组图谱和癌症体细胞突变目录数据的综合分析,我们揭示了两个互补假设的支持证据:H1-TP53异常促进PCa细胞的转移或耐药性,而PCa转移中TP53的H2-部分突变发生在原发癌症诊断后。这些假设的合理性可以解释前列腺癌转移中TP53突变发生率的增加。以H1和H2为一般假设,我们开发了数学模型来解读TP53突变从原发肿瘤到转移瘤的百分比频率(患病率)的变化。获得以下结果。与TP53正常患者相比,TP53突变患者的生化无复发生存率较差,Gleason评分较高,t分期更晚期(P < .01)。转移癌中的单核苷酸变异发生在编码序列的G碱基上的频率高于原发癌(P = .03)。TP53热点突变在原发性和转移性前列腺癌之间有显著差异,如一组统计检验所示(P < .05)。根据推导的公式,我们估计从转移中收集的大约40%的TP53突变记录发生在诊断出原始癌症之后。我们的研究为前列腺癌的进展提供了重要的见解。所提出的模型也可用于解读其他癌症类型中TP53(或其他驱动基因)突变的流行率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deciphering the Increased Prevalence of TP53 Mutations in Metastatic Prostate Cancer
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.
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来源期刊
Cancer Informatics
Cancer Informatics Medicine-Oncology
CiteScore
3.00
自引率
5.00%
发文量
30
审稿时长
8 weeks
期刊介绍: The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.
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