Gene network Analysis Defines a Subgroup of Small Cell Lung Cancer patients With Short Survival

Clinical Lung Cancer(2022)

引用 0|浏览10
暂无评分
摘要
Background Small cell lung cancer (SCLC) is an aggressive tumor, and despite its sensitivity to chemotherapy and radiotherapy, patients usually have a short survival. There are no clinically relevant predictive factors of responses to therapies, and therapeutic options are still limited. Materials and Methods Clinical data and somatic mutations of genes included in the MSK-IMPACT panel were retrieved from cBioPortal for 108 SCLCs and analyzed to identify mutated gene networks. Results were validated in an independent cohort of 54 SCLCs, whose information was also available from cBioPortal. Results Different networks were observed in tumors of short and long survivors. Degree (K) and betweenness (B) are key features that characterize a gene in its network of related mutations. By comparing their B/K ratio, 2 signatures of mutated genes were identified, describing short (IL-7R, NTRK2, HNF-1A) and long survivors (NBN, PTPN-11, IRS-1, INPP-4A, PIK-3CG, HGF, LATS-2, SMARCA-4, FLT-3, EIF-4A2, SPEN, PAX-5, SH2-D1A, ARID-1A, HOXB-13, ERCC-4, FANCA, FH, FGFR-2, MST-1R, SMAD-4, DDR-2, IGF-1R, PIK-3CB). Patients with at least 1 mutated gene of the short signature had a worse median overall survival of 8 versus 28 months (P < .001). Patients with at least 1 mutated gene of the long signature had a better median overall survival of 39 versus 20 months (P = .004). The value of the short signature was further confirmed in an independent cohort of SCLCs. Conclusion The networks of mutated genes could help subclassify SCLCs based on their somatic mutations and aid in identifying a subset of tumors with poor prognosis.
更多
查看译文
关键词
Biomarkers,Co-occurring mutations,Gene network analysis,SCLC
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要