As the most common form of lymphoma, diffuse large B-cell lymphoma (DLBCL) is a clinical highly heterogeneous disease with variability in therapeutic outcomes and biological features. It is a challenge to identify of clinically meaningful tools for outcome prediction. In this study, we developed a prognosis model fused clinical characteristics with drug resistance pharmacogenomic signature to identify DLBCL prognostic subgroups for CHOP-based treatment. The expression microarray data and clinical characteristics of 791 DLBCL patients from two Gene Expression Omnibus (GEO) databases were used to establish and validate this model. By using univariate Cox regression, eight clinical or genetic signatures were analyzed. The elastic net-regulated Cox regression analysis was used to select the best prognosis related factors into the predictive model. To estimate the prognostic capability of the model, Kaplan–Meier curve and the area under receiver operating characteristic (ROC) curve (AUC) were performed. A predictive model comprising 4 clinical factors and 2 pharmacogenomic gene signatures was established after 1000 times cross validation in the training dataset. The AUC of the comprehensive risk model was 0.78, whereas AUC value was lower for the clinical only model (0.68) or the gene only model (0.67). Compared with low-risk patients, the overall survival (OS) of DLBCL patients with high-risk scores was significantly decreased (HR = 4.55, 95% CI 3.14–6.59, log-rank p value = 1.06 × 10−15). The signature also enables to predict prognosis within different molecular subtypes of DLBCL. The reliability of the integrated model was confirmed by independent validation dataset (HR = 3.47, 95% CI 2.42–4.97, log rank p value = 1.53 × 10−11). This integrated model has a better predictive capability to ascertain the prognosis of DLBCL patients prior to CHOP-like treatment, which may improve the clinical management of DLBCL patients and provide theoretical basis for individualized treatment.

中文翻译：

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CHOP样化疗后弥漫性大B细胞淋巴瘤患者的药物基因组基因特征和临床信息的综合预后模型。

弥漫性大B细胞淋巴瘤（DLBCL）作为淋巴瘤的最常见形式，是一种临床高度异质性疾病，在治疗结果和生物学特征方面存在差异。确定临床上有意义的预测结果的工具是一个挑战。在这项研究中，我们开发了一种将临床特征与耐药性药物基因组学特征相融合的预后模型，以鉴定基于CHOP的治疗的DLBCL预后亚组。来自两个Gene Expression Omnibus（GEO）数据库的791位DLBCL患者的表达微阵列数据和临床特征用于建立和验证该模型。通过使用单变量Cox回归，分析了八个临床或遗传特征。运用弹性净调节的Cox回归分析将最佳预后相关因素选择到预测模型中。为了评估模型的预后能力，执行了Kaplan–Meier曲线和接收器工作特征（ROC）曲线下面积（AUC）。在训练数据集中进行1000次交叉验证后，建立了包含4个临床因素和2个药物基因组学基因特征的预测模型。综合风险模型的AUC为0.78，而仅临床模型（0.68）或仅基因模型（0.67）的AUC值较低。与低风险患者相比，具有高风险评分的DLBCL患者的总体生存（OS）显着降低（HR = 4.55，95％CI 3.14–6.59，log-rank p值= 1.06×10-15）。该特征还能够预测DLBCL的不同分子亚型内的预后。集成模型的可靠性通过独立的验证数据集得到确认（HR = 3.47，95％CI 2）。42–4.97，对数秩p值= 1.53×10-11）。该综合模型具有更好的预测能力，可在CHOP样治疗之前确定DLBCL患者的预后，这可能会改善DLBCL患者的临床管理，并为个体化治疗提供理论依据。