BACKGROUND Multiple myeloma (MM) is a hematological cancer caused by abnormal accumulation of monoclonal plasma cells in bone marrow. With the increase in treatment options, risk-adapted therapy is becoming more and more important. Survival analysis is commonly applied to study progression or other events of interest and stratify the risk of patients. RESULTS In this study, we present the current state-of-the-art model for MM prognosis and the molecular biomarker set for stratification: the winning algorithm in the 2017 Multiple Myeloma DREAM Challenge, Sub-Challenge 3. Specifically, we built a non-parametric complete hazard ranking model to map the right-censored data into a linear space, where commonplace machine learning techniques, such as Gaussian process regression and random forests, can play their roles. Our model integrated both the gene expression profile and clinical features to predict the progression of MM. Compared with conventional models, such as Cox model and random survival forests, our model achieved higher accuracy in 3 within-cohort predictions. In addition, it showed robust predictive power in cross-cohort validations. Key molecular signatures related to MM progression were identified from our model, which may function as the core determinants of MM progression and provide important guidance for future research and clinical practice. Functional enrichment analysis and mammalian gene-gene interaction network revealed crucial biological processes and pathways involved in MM progression. The model is dockerized and publicly available at https://www.synapse.org/#!Synapse:syn11459638. Both data and reproducible code are included in the docker. CONCLUSIONS We present the current state-of-the-art prognostic model for MM integrating gene expression and clinical features validated in an independent test set.

中文翻译：

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整合基因表达和临床特征的多发性骨髓瘤进展的预后模型。

背景技术多发性骨髓瘤（MM）是一种由骨髓中单克隆浆细胞异常积累引起的血液癌症。随着治疗选择的增加，风险适应治疗变得越来越重要。生存分析通常应用于研究进展或其他感兴趣的事件并对患者的风险进行分层。结果在这项研究中，我们提出了当前最先进的 MM 预后模型和用于分层的分子生物标志物集：2017 年多发性骨髓瘤 DREAM 挑战赛子挑战 3 中的获胜算法。具体来说，我们构建了一个非-参数化完全风险排序模型，将右删失数据映射到线性空间，常见的机器学习技术（例如高斯过程回归和随机森林）可以在其中发挥作用。我们的模型整合了基因表达谱和临床特征来预测 MM 的进展。与传统模型（例如 Cox 模型和随机生存森林）相比，我们的模型在 3 个组内预测中取得了更高的准确率。此外，它在跨队列验证中显示出强大的预测能力。从我们的模型中确定了与 MM 进展相关的关键分子特征，这些特征可能作为 MM 进展的核心决定因素，并为未来的研究和临床实践提供重要指导。功能富集分析和哺乳动物基因-基因相互作用网络揭示了 MM 进展中涉及的关键生物过程和途径。该模型已进行 Docker 化并可在 https://www.synapse.org/#!Synapse:syn11459638 上公开获取。数据和可重现的代码都包含在 docker 中。结论 我们提出了当前最先进的 MM 预后模型，该模型整合了在独立测试集中验证的基因表达和临床特征。