Mosquera Orgueira Adrián, Díaz Arías Jose Ángel, Cid López Miguel, Peleteiro Raíndo Andrés, López García Alberto, Abal García Rosanna, González Pérez Marta Sonia, Antelo Rodríguez Beatriz, Aliste Santos Carlos, Pérez Encinas Manuel Mateo, Fraga Rodríguez Máximo Francisco, Bello López José Luis
University Hospital of Santiago de Compostela (SERGAS), Spain.
Health Research Institute of Santiago de Compostela, Spain.
Hemasphere. 2022 Mar 25;6(4):e706. doi: 10.1097/HS9.0000000000000706. eCollection 2022 Apr.
Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin lymphoma. Despite notable therapeutic advances in the last decades, 30%-40% of affected patients develop relapsed or refractory disease that frequently precludes an infamous outcome. With the advent of new therapeutic options, it becomes necessary to predict responses to the standard treatment based on rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP). In a recent communication, we presented a new machine learning model (LymForest-25) that was based on 25 clinical, biochemical, and gene expression variables. LymForest-25 achieved high survival prediction accuracy in patients with DLBCL treated with upfront immunochemotherapy. In this study, we aimed to evaluate the performance of the different features that compose LymForest-25 in a new UK-based cohort, which contained 481 patients treated with upfront R-CHOP for whom clinical, biochemical and gene expression information for 17 out of 19 transcripts were available. Additionally, we explored potential improvements based on the integration of other gene expression signatures and mutational clusters. The validity of the LymForest-25 gene expression signature was confirmed, and indeed it achieved a substantially greater precision in the estimation of mortality at 6 months and 1, 2, and 5 years compared with the cell-of-origin (COO) plus molecular high-grade (MHG) classification. Indeed, this signature was predictive of survival within the MHG and all COO subgroups, with a particularly high accuracy in the "unclassified" group. Integration of this signature with the International Prognostic Index (IPI) score provided the best survival predictions. However, the increased performance of molecular models with the IPI score was almost exclusively restricted to younger patients (<70 y). Finally, we observed a tendency towards an improved performance by combining LymForest-25 with the LymphGen mutation-based classification. In summary, we have validated the predictive capacity of LymForest-25 and expanded the potential for improvement with mutation-based prognostic classifications.
弥漫性大B细胞淋巴瘤（DLBCL）是最常见的非霍奇金淋巴瘤类型。尽管在过去几十年中治疗取得了显著进展，但30%-40%的患者会出现复发或难治性疾病，这常常导致不良预后。随着新治疗方案的出现，有必要基于利妥昔单抗、环磷酰胺、阿霉素、长春新碱和泼尼松（R-CHOP）来预测对标准治疗的反应。在最近的一篇通讯中，我们提出了一种基于25个临床、生化和基因表达变量的新机器学习模型（LymForest-25）。LymForest-25在接受一线免疫化疗的DLBCL患者中实现了较高的生存预测准确性。在本研究中，我们旨在评估构成LymForest-25的不同特征在一个新的英国队列中的表现，该队列包含481例接受一线R-CHOP治疗的患者，可获得19个转录本中17个的临床、生化和基因表达信息。此外，我们基于整合其他基因表达特征和突变簇探索了潜在的改进方法。LymForest-25基因表达特征的有效性得到了证实，与起源细胞（COO）加分子高级别（MHG）分类相比，它在估计6个月、1年、2年和5年死亡率方面确实实现了更高的精度。事实上，该特征可预测MHG和所有COO亚组中的生存情况，在“未分类”组中准确性特别高。将该特征与国际预后指数（IPI）评分相结合可提供最佳的生存预测。然而，分子模型与IPI评分结合后性能的提高几乎完全局限于年轻患者（<70岁）。最后，我们观察到将LymForest-25与基于LymphGen突变的分类相结合有性能改善的趋势。总之，我们验证了LymForest-25的预测能力，并扩展了基于突变的预后分类的改进潜力。
