Biological signatures of International Prognostic Index in diffuse large B-cell lymphoma

国际预后指标 弥漫性大B细胞淋巴瘤 淋巴瘤 美罗华 生发中心 乳酸脱氢酶 队列 B症状 医学 内科学 胃肠病学 肿瘤科 病理 生物 免疫学 B细胞 抗体 生物化学
作者
Yue Wang,Qing Shi,Zi‐Yang Shi,Shuang Tian,Mu‐Chen Zhang,Rong Shen,Di Fu,Lei Dong,Hongmei Yi,Binshen Ouyang,Rongji Mu,Shu Cheng,Li Wang,Pengpeng Xu,Weili Zhao
出处
期刊:Blood Advances [Elsevier BV]
卷期号:8 (7): 1587-1599
标识
DOI:10.1182/bloodadvances.2023011425
摘要

Abstract Diffuse large B-cell lymphoma (DLBCL) is a highly aggressive subtype of lymphoma with clinical and biological heterogeneity. The International Prognostic Index (IPI) shows great prognostic capability in the era of rituximab, but the biological signatures of IPI remain to be discovered. In this study, we analyzed the clinical data in a large cohort of 2592 patients with newly diagnosed DLBCL. Among them, 1233 underwent DNA sequencing for oncogenic mutations, and 487 patients underwent RNA sequencing for lymphoma microenvironment (LME) alterations. Based on IPI scores, patients were categorized into 4 distinct groups, with 5-year overall survival of 41.6%, 55.3%, 71.7%, and 89.7%, respectively. MCD-like subtype was associated with age of >60 years, multiple extranodal involvement, elevated serum lactate dehydrogenase (LDH), and IPI scores ranging from 2 to 5, whereas ST2-like subtype showed an opposite trend. Patients with EZB-like MYC+ and TP53Mut subtypes exhibited poor clinical outcome independent of the IPI; integrating TP53Mut into IPI could better distinguish patients with dismal survival. The EZB-like MYC−, BN2-like, N1-like, and MCD-like subtypes had inferior prognosis in patients with IPI scores of ≥2, indicating necessity for enhanced treatment. Regarding LME categories, the germinal center–like LME was more prevalent in patients with normal LDH and IPI scores of 0 to 1. The mesenchymal LME served as an independent protective factor, whereas the germinal center–like, inflammatory, and depleted LME categories correlated with inferior prognosis for IPI scores of 2 to 5. In summary, our work explored the biological signatures of IPI, thus providing useful rationale for future optimization of the IPI-based treatment strategies with multi-omics information in DLBCL.
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