Oncogenic composite mutations can be predicted by co-mutations and their chromosomal location.

IF 6.6 2区 医学 Q1 Biochemistry, Genetics and Molecular Biology
Molecular Oncology Pub Date : 2024-10-01 Epub Date: 2024-05-16 DOI:10.1002/1878-0261.13636
Asli Küçükosmanoglu, Carolien L van der Borden, Lisanne E A de Boer, Roel Verhaak, David Noske, Tom Wurdinger, Teodora Radonic, Bart A Westerman
{"title":"Oncogenic composite mutations can be predicted by co-mutations and their chromosomal location.","authors":"Asli Küçükosmanoglu, Carolien L van der Borden, Lisanne E A de Boer, Roel Verhaak, David Noske, Tom Wurdinger, Teodora Radonic, Bart A Westerman","doi":"10.1002/1878-0261.13636","DOIUrl":null,"url":null,"abstract":"<p><p>Genetic heterogeneity in tumors can show a remarkable selectivity when two or more independent genetic events occur in the same gene. This phenomenon, called composite mutation, points toward a selective pressure, which frequently causes therapy resistance to mutation-specific drugs. Since composite mutations have been described to occur in sub-clonal populations, they are not always captured through biopsy sampling. Here, we provide a proof of concept to predict composite mutations to anticipate which patients might be at risk for sub-clonally driven therapy resistance. We found that composite mutations occur in 5% of cancer patients, mostly affecting the PIK3CA, EGFR, BRAF, and KRAS genes, which are common precision medicine targets. Furthermore, we found a strong and significant relationship between the frequencies of composite mutations with commonly co-occurring mutations in a non-composite context. We also found that co-mutations are significantly enriched on the same chromosome. These observations were independently confirmed using cell line data. Finally, we show the feasibility of predicting compositive mutations based on their co-mutations (AUC 0.62, 0.81, 0.82, and 0.91 for EGFR, PIK3CA, KRAS, and BRAF, respectively). This prediction model could help to stratify patients who are at risk of developing therapy resistance-causing mutations.</p>","PeriodicalId":18764,"journal":{"name":"Molecular Oncology","volume":" ","pages":"2407-2422"},"PeriodicalIF":6.6000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11459034/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Molecular Oncology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/1878-0261.13636","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/5/16 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"Biochemistry, Genetics and Molecular Biology","Score":null,"Total":0}
引用次数: 0

Abstract

Genetic heterogeneity in tumors can show a remarkable selectivity when two or more independent genetic events occur in the same gene. This phenomenon, called composite mutation, points toward a selective pressure, which frequently causes therapy resistance to mutation-specific drugs. Since composite mutations have been described to occur in sub-clonal populations, they are not always captured through biopsy sampling. Here, we provide a proof of concept to predict composite mutations to anticipate which patients might be at risk for sub-clonally driven therapy resistance. We found that composite mutations occur in 5% of cancer patients, mostly affecting the PIK3CA, EGFR, BRAF, and KRAS genes, which are common precision medicine targets. Furthermore, we found a strong and significant relationship between the frequencies of composite mutations with commonly co-occurring mutations in a non-composite context. We also found that co-mutations are significantly enriched on the same chromosome. These observations were independently confirmed using cell line data. Finally, we show the feasibility of predicting compositive mutations based on their co-mutations (AUC 0.62, 0.81, 0.82, and 0.91 for EGFR, PIK3CA, KRAS, and BRAF, respectively). This prediction model could help to stratify patients who are at risk of developing therapy resistance-causing mutations.

可通过共突变及其染色体位置预测致癌复合突变。
当两个或更多独立的基因事件发生在同一个基因上时,肿瘤的基因异质性会表现出显著的选择性。这种现象被称为复合突变,指向一种选择性压力,经常导致对突变特异性药物的耐药性。由于复合突变已被描述为发生在亚克隆群体中,因此并不总能通过活检取样捕捉到。在此,我们提供了一种预测复合突变的概念验证,以预测哪些患者可能面临亚克隆驱动的耐药性风险。我们发现,5% 的癌症患者存在复合突变,主要影响 PIK3CA、EGFR、BRAF 和 KRAS 基因,这些基因是常见的精准医疗靶点。此外,我们还发现复合突变的频率与非复合背景下常见的共存突变之间存在着强烈而显著的关系。我们还发现,共突变在同一染色体上明显富集。这些观察结果通过细胞系数据得到了独立证实。最后,我们展示了根据共突变预测复合突变的可行性(EGFR、PIK3CA、KRAS 和 BRAF 的 AUC 分别为 0.62、0.81、0.82 和 0.91)。该预测模型有助于对有可能发生耐药突变的患者进行分层。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Molecular Oncology
Molecular Oncology Biochemistry, Genetics and Molecular Biology-Molecular Medicine
CiteScore
11.80
自引率
1.50%
发文量
203
审稿时长
10 weeks
期刊介绍: Molecular Oncology highlights new discoveries, approaches, and technical developments, in basic, clinical and discovery-driven translational cancer research. It publishes research articles, reviews (by invitation only), and timely science policy articles. The journal is now fully Open Access with all articles published over the past 10 years freely available.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信