Mutational burden and chromosomal aneuploidy synergistically predict survival from radiotherapy in non-small cell lung cancer.
Qingzhu JiaQian ChuAnmei ZhangJing YuFangfang LiuKaiyu QianYu XiaoXue WangYing YangYi ZhaoJi HeGuanghui LiYisong Y WanConghua XieBo ZhuPublished in: Communications biology (2021)
Therapeutic radiation can result in substantially different survival outcomes for patients with non-small cell lung cancer (NSCLC). Measures for identification of patients who can benefit most throughout radiotherapy remain limited. In this retrospective study, survival analysis was performed based on a discovery cohort from TCGA and a validation cohort from three independent hospitals. Tumor mutational burden (TMB) and chromosomal aneuploidy (ANE) were derived from the whole exome sequencing (WES) data from treatment-naïve tumors. Integrated risk scores were derived from TMB and ANE by a multivariate Cox proportional hazards model. TCGA reveal that TMB and ANE are associated positively and negatively, respectively, with survival throughout radiotherapy. Additionally, the synergistically predictive significance of these two genomic alterations, in differing responders and non-responders to radiotherapy is identified. These biomarkers may have clinical potential to improve personalized treatment management by rationally identifying highly likely responders to therapeutic radiation in patients with NSCLC.
Keyphrases
- early stage
- radiation induced
- locally advanced
- radiation therapy
- small cell lung cancer
- copy number
- healthcare
- free survival
- squamous cell carcinoma
- advanced non small cell lung cancer
- gene expression
- combination therapy
- electronic health record
- risk assessment
- machine learning
- dna methylation
- big data
- deep learning
- clinical evaluation