Recent genetic and molecular classification of DLBCL has advanced our knowledge of disease biology, yet were not designed to predict early events and guide anticipatory selection of novel therapies. To address this unmet need, we used an integrative multiomic approach to identify a signature at diagnosis that will identify DLBCL at high risk of early clinical failure. Tumor biopsies from 444 newly diagnosed DLBCL were analyzed by WES and RNAseq. A combination of weighted gene correlation network analysis and differential gene expression analysis was used to identify a signature associated with high risk of early clinical failure independent of IPI and COO. Further analysis revealed the signature was associated with metabolic reprogramming and identified cases with a depleted immune microenvironment. Finally, WES data was integrated into the signature and we found that inclusion of ARID1A mutations resulted in identification of 45% of cases with an early clinical failure which was validated in external DLBCL cohorts. This novel and integrative approach is the first to identify a signature at diagnosis, in a real-world cohort of DLBCL, that identifies patients at high risk for early clinical failure and may have significant implications for design of therapeutic options.
DLBCL近期遗传学与分子分类的进展深化了我们对疾病生物学的理解，但这些分类方法并非为预测早期事件或指导新型疗法的前瞻性选择而设计。为填补这一空白，我们采用整合多组学分析方法，在诊断阶段识别出一个能筛选早期临床失败高风险DLBCL的特征标志。通过对444例新诊断DLBCL肿瘤活检样本进行全外显子测序和RNA测序分析，结合加权基因共表达网络分析与差异基因表达分析，我们鉴定出一个独立于国际预后指数和细胞起源分类的早期临床高风险特征标志。进一步分析显示该特征与代谢重编程相关，并识别出免疫微环境耗竭的病例。最后，将全外显子测序数据整合至该特征标志后，我们发现纳入ARID1A突变可使45%的早期临床失败病例被识别——该结果在外部DLBCL队列中得到验证。这种创新的整合分析方法首次在真实世界DLBCL队列中实现了诊断阶段的高风险特征识别，能有效筛选早期临床失败高风险患者，对治疗方案设计具有重要意义。
Multiomic analysis identifies a high-risk signature that predicts early clinical failure in DLBCL
肿瘤(癌症)患者之家