弥漫性大B细胞淋巴瘤（DLBCL）是一种侵略性的非霍奇金淋巴瘤，对R-CHOP疗法反应较差，由于非常的异质性。根据基因表达，DLBCL被分为ABC和GCB两个亚型，其中ABC亚型与恶性结果有关。由于其与临床结果的关联，该分类也被称为细胞来源（COO），是预测对R-CHOP治疗反应的有效方式。以往的COO分类方法存在一些缺陷，例如训练数据集中的样本数量有限。这些缺陷挑战了这些方法的稳健性，并使它们难以推广到临床水平。为了解决以往方法的缺陷，我们使用20个基因的表达数据，在381名DLBCL患者的队列上开发了一个基于深度学习的分类器模型。我们使用多层感知器（MLP）来训练基于深度学习的分类器，名为MLP-COO。MLP-COO在训练和测试数据集中的10折交叉验证中，分别达到了99.70％和94.70％的准确率。我们还对294名DLBCL患者的独立数据集进行了评估。在独立数据集上，我们获得了95.90％的准确率，MCC为0.917。为了展示它的广泛适用性，我们使用该分类器使用两个大型DLBCL患者队列的生存数据来预测临床结果。在生存分析中，基于COO的MLP-COO重现了两个队列中DLBCL患者的生存概率。我们预计，本研究开发的MLP-COO模型将有助于准确预测DLBCL患者的COO和临床结果。© 作者（2022）。由牛津大学出版社发表。版权所有。请发送邮件至journals.permissions@oup.com获取权限。

Diffuse large B-cell lymphoma (DLBCL) is an aggressive form of non-Hodgkin lymphoma with poor response to R-CHOP therapy due to remarkable heterogeneity. Based on gene expression, DLBCL cases were divided into two subtypes, i.e. ABC and GCB, where ABC subtype is associated with poor outcomes. Due to its association with clinical outcome, this classification, also known as cell-of-origin (COO), is an efficient way to predict the response to R-CHOP therapy. Previous COO classification methods have some shortcomings, e.g. limited number of samples in the training dataset. These shortcomings challenge the robustness of methods and make it difficult to implicate these methods at clinical level. To overcome the shortcomings of previous methods, we developed a deep learning-based classifier model on a cohort of 381 DLBCL patients using expression data of 20 genes. We implemented multilayer perceptron (MLP) to train deep learning-based classifier, named MLP-COO. MLP-COO achieved accuracy of 99.70% and 94.70% on training and testing datasets, respectively, with 10-fold cross-validation. We also assessed its performance on an independent dataset of 294 DLBCL patients. On independent dataset, we achieved an accuracy of 95.90% with MCC of 0.917. To show its broader applicability, we used this classifier to predict the clinical outcome using survival data from two large cohorts of DLBCL patients. In survival analysis, MLP-COO recapitulates the survival probabilities of DLBCL patients based on their COO in both cohorts. We anticipate that MLP-COO model developed in this study will benefit in the accurate COO prediction of DLBCL patients and their clinical outcomes.© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.