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.

最近 DLBCL 的遗传和分子分类提高了我们对疾病生物学的了解，但并非旨在预测早期事件和指导新疗法的预期选择。为了满足这一未满足的需求，我们使用了一种综合多组学方法来识别诊断时的特征，该特征将识别出早期临床失败风险较高的 DLBCL。通过 WES 和 RNAseq 分析 444 例新诊断 DLBCL 的肿瘤活检。加权基因相关网络分析和差异基因表达分析的组合用于识别与独立于 IPI 和 COO 的早期临床失败高风险相关的特征。进一步分析显示该特征与代谢重编程有关，并确定了免疫微环境耗尽的病例。最后，将 WES 数据整合到签名中，我们发现包含 ARID1A 突变导致识别出 45% 的早期临床失败病例，这在外部 DLBCL 队列中得到了验证。这种新颖的综合方法是第一个在真实世界的 DLBCL 队列中识别诊断时特征的方法，该方法可以识别早期临床失败的高风险患者，并可能对治疗方案的设计产生重大影响。