Development and Validation of a Novel TP53 Mutation Signature That Predicts Risk of Metastasis in Primary Prostate Cancer
Development and Validation of a Novel TP53 Mutation Signature That Predicts Risk of Metastasis in Primary Prostate Cancer
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DOI：
10.1016/j.clgc.2020.08.004
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发表时间：
2021-07-07
影响因子：
3.2
通讯作者：
Muralidhar, Vinayak
中科院分区：
文献类型：
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作者：
Chipidza, Fallon E.;Alshalalfa, Mohammed;Muralidhar, Vinayak
TP 53是癌症中最常见的突变基因。我们开发了一种检测潜在TP 53改变的分子特征。该特征识别了侵袭性肿瘤，在调整了已知的更差结果的预测因素后，该特征识别了可能发生转移的前列腺癌患者。这些发现可能有助于医生识别前列腺癌患者的侵袭性肿瘤，从而选择最佳的治疗。引言：TP 53基因突变的前列腺癌是分子异质性的，TP 53基因突变的存在与较差的结果有关。我们开发了一种基于RNA的基因签名，可以检测潜在的TP 53基因突变，并识别与TP 53突变肿瘤类似的野生型前列腺肿瘤。材料与方法：使用来自癌症基因组图谱的基因组表达谱，我们开发了一种突变特征评分，以预测具有与TP 53突变肿瘤相似的分子指纹的前列腺肿瘤。受试者工作特征曲线下面积评估模型预测TP 53突变的准确性，考克斯回归模型测量特征与无进展生存期和无转移生存期（MFS）之间的关联。结果如下：TP 53特征评分在训练中达到0.84的受试者工作特征曲线下面积，在验证队列中达到0.82的受试者工作特征曲线下面积，用于预测潜在突变。在三个回顾性队列中，高分是5年MFS差的预后因素：46%对81%（风险比[HR]，3.05; P < .0001;约翰霍普金斯大学队列），64%对83%（HR，2.77; P < .0001;马约诊所队列），71%对97%（HR，6.8; P = .0001;布里格姆妇女医院队列）。该特征还鉴定出TP 53野生型肿瘤在分子上类似于TP 53突变型肿瘤，其中高特征评分与更差的5年MFS相关（50%对82%; HR，3.05; P < .0001）。结论：这种新的突变特征预测了具有TP 53突变的肿瘤，鉴定了与突变型肿瘤类似的TP 53野生型肿瘤，并且与不良MFS独立相关。因此，该特征可用于加强现有的临床风险分层工具。(C)2020爱思唯尔公司All rights reserved.
TP53 is the most frequently mutated gene in cancer. We developed a molecular signature detecting underlying TP53 alterations. This signature identified aggressive tumors, and after adjusting for known predictors of worse outcomes, the signature identified patients with prostate cancer likely to develop metastases. These findings may help doctors identify patients with prostate cancer with aggressive tumors and hence, choose the optimal treatment.Introduction: Prostate tumors with TP53 gene mutations are molecularly heterogenous, and the presence of TP53 gene mutations has been linked to inferior outcomes. We developed an RNA-based gene signature that detects underlying TP53 gene mutations and identifies wild-type prostate tumors that are analogous to TP53-mutant tumors. Materials and Methods: Using genomic expression profiles from The Cancer Genome Atlas, we developed a mu-tation signature score to predict prostatic tumors with a molecular fingerprint similar to tumors with TP53 mutations. Area under the receiver operating characteristic curve assessed model accuracy in predicting TP53 mutations, and Cox regression models measured association between the signature and progression-free survival and metastasis-free survival (MFS). Results: The TP53 signature score achieved an area under the receiver operating characteristic curve of 0.84 in the training and 0.82 in the validation cohorts for predicting an underlying mutation. In three retro-spective cohorts, a high score was prognostic for poor 5-year MFS: 46% versus 81% (hazard ratio [HR], 3.05; P < .0001; Johns Hopkins University cohort), 64% versus 83% (HR, 2.77; P < .0001; Mayo Clinic cohort), and 71% versus 97% (HR, 6.8; P = .0001; Brigham and Women's Hospital cohort). The signature also identified TP53 wild-type tumors molecularly analogous to TP53 mutant tumors, wherein high signature score correlated with worse 5-year MFS (50% vs. 82%; HR, 3.05; P < .0001). Conclusions: This novel mutational signature predicted tumors with TP53 mutations, identified TP53 wild-type tumors analogous to mutant tumors, and was independently associated with poor MFS. This signature can therefore be used to strengthen existing clinical risk-stratification tools. (C) 2020 Elsevier Inc. All rights reserved.