卵泡性淋巴瘤（FL）占所有新淋巴瘤病例的大约20％。细胞学等级的增加是这种恶性肿瘤临床进展的特征，并且最终组织学转化（HT）到侵袭性弥漫性大B细胞淋巴瘤（DLBCL）在多达15％的患者中发生。目前尚未全面描述用于预测HT风险和时机的临床或遗传特征。在这项研究中，我们分析了423名患者的全基因组测序数据，比较了未转化的FL，转化后的FL和原发性DLBCL的蛋白质编码和非编码突变景观。这揭示了FL的两个基因上有所不同的亚组，我们将其命名为DLBCL-型（dFL）和受限FL（cFL）。每个亚组都具有独特的突变模式、异常的体细胞高度突变率、生物学和临床特征。我们采用机器学习推导的分类方法，根据其基因组特征将FL患者分为cFL和dFL亚组。通过不同的验证队列，我们证明了cFL状态，无论是使用该完整分类器还是单基因近似，都与HT的降低速率相关。这意味着cFL具有不同的生物学特征，限制了其演化，并强调了这种分类方法通过遗传特征在诊断时预测HT的潜力。版权所有©2023年美国血液学会。

Follicular lymphoma (FL) accounts for approximately 20% of all new lymphoma cases. Increases in cytological grade are a feature of the clinical progression of this malignancy, and eventual histologic transformation (HT) to the aggressive diffuse large B-cell lymphoma (DLBCL) occurs in up to 15% of patients. Clinical or genetic features to predict the risk and timing of HT have not been comprehensively described. In this study, we analyzed whole genome sequencing data from 423 patients to compare the protein coding and non-coding mutation landscapes of untransformed FL, transformed FL and de novo DLBCL. This revealed two genetically distinct subgroups of FL which we have named DLBCL-like (dFL) and constrained FL (cFL). Each subgroup has distinguishing mutational patterns, aberrant somatic hypermutation rates, biological, and clinical characteristics. We implemented a machine-learning-derived classification approach to stratify FL patients into cFL and dFL subgroups based on their genomic features. Using separate validation cohorts, we demonstrate that cFL status, whether assigned with this full classifier or a single-gene approximation, is associated with a reduced rate of HT. This implies distinct biological features of cFL that constrain its evolution and we highlight the potential for this classification to predict HT from genetic features present at diagnosis.Copyright © 2023 American Society of Hematology.