Pathway-level mutational signatures predict breast cancer outcomes and reveal therapeutic targets.

IF 7.7 2区 医学 Q1 PHARMACOLOGY & PHARMACY
Máté Posta, Balázs Győrffy
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引用次数: 0

Abstract

Background and purpose: In order to significantly improve the therapeutic treatment of breast cancer, the exploration of underlying genetic and molecular differences is absolutely necessary. Here, our goal was to integrate mutational status of entire pathways to reveal molecular pathway interactions determining survival.

Experimental approach: A comprehensive analysis of breast cancer mutations was conducted by integrating data from three distinct databases with a total of 4586 samples encompassing over 25,000 genes. For each gene, we filtered mutations that disruptively affect the protein structure. Cox proportional hazard regression was employed to link altered pathways to outcome. We also identified the co-occurring and mutually exclusive disruptive mutations.

Key results: We identified 17 genes, the mutation status of which alone seriously affects relapse-free survival. The three most significant genes were TP53 (HR: 2.04, p: 4.65 × 10-33), CARD11 (HR: 2.59, p: 1.54 × 10-5) and PIK3R1 (HR: 2.27, p: 3.66 × 10-5). The five most significant biological processes and KEGG pathways affecting relapse-free survival include negative regulation of cell population proliferation, positive regulation of DNA-templated transcription, protein stabilisation, and MicroRNAs in cancer, hepatocellular carcinoma, and breast cancer. Co-mutation and mutual exclusivity analysis identified significant enrichment in 241 gene pairs. Finally, we also established an online platform to enable future analysis of the established cohort for any selected pathway.

Conclusions and implications: We assembled a comprehensive database of breast cancer samples and used this cohort to identify cancer-specific disruptive mutation signatures linked to altered survival outcomes.

通路水平的突变特征预测乳腺癌结果并揭示治疗靶点。
背景与目的:为了显著提高乳腺癌的治疗效果,探索潜在的遗传和分子差异是绝对必要的。在这里,我们的目标是整合整个通路的突变状态,以揭示决定生存的分子通路相互作用。实验方法:通过整合来自三个不同数据库的数据,总共4586个样本,超过25,000个基因,对乳腺癌突变进行了全面分析。对于每个基因,我们都过滤掉了对蛋白质结构有破坏性影响的突变。采用Cox比例风险回归将改变的通路与结果联系起来。我们还确定了共同发生和互斥的破坏性突变。关键结果:我们鉴定了17个基因,这些基因的突变状态严重影响无复发生存。三个最显著的基因是TP53 (HR: 2.04, p: 4.65 × 10-33)、CARD11 (HR: 2.59, p: 1.54 × 10-5)和PIK3R1 (HR: 2.27, p: 3.66 × 10-5)。影响无复发生存的五个最重要的生物学过程和KEGG途径包括癌症、肝细胞癌和乳腺癌中细胞群增殖的负调控、dna模板转录的正调控、蛋白质稳定和microrna。共突变和互排他性分析发现241对基因对显著富集。最后,我们还建立了一个在线平台,以便将来对任何选择的途径进行已建立的队列分析。结论和意义:我们收集了一个全面的乳腺癌样本数据库,并使用该队列来识别与生存结果改变相关的癌症特异性破坏性突变特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
15.40
自引率
12.30%
发文量
270
审稿时长
2.0 months
期刊介绍: The British Journal of Pharmacology (BJP) is a biomedical science journal offering comprehensive international coverage of experimental and translational pharmacology. It publishes original research, authoritative reviews, mini reviews, systematic reviews, meta-analyses, databases, letters to the Editor, and commentaries. Review articles, databases, systematic reviews, and meta-analyses are typically commissioned, but unsolicited contributions are also considered, either as standalone papers or part of themed issues. In addition to basic science research, BJP features translational pharmacology research, including proof-of-concept and early mechanistic studies in humans. While it generally does not publish first-in-man phase I studies or phase IIb, III, or IV studies, exceptions may be made under certain circumstances, particularly if results are combined with preclinical studies.
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