Nicora G, Moretti F, Sauta E, Della Porta M, Malcovati L, Cazzola M, Quaglini S, Bellazzi R
Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy.
Cancer Center, Humanitas Research Hospital and Humanitas University, Milan, Italy.
J Biomed Inform. 2020 Apr;104:103398. doi: 10.1016/j.jbi.2020.103398. Epub 2020 Feb 26.
The integration of both genomics and clinical data to model disease progression is now possible, thanks to the increasing availability of molecular patients' profiles. This may lead to the definition of novel decision support tools, able to tailor therapeutic interventions on the basis of a "precise" patients' risk stratification, given their health status evolution. However, longitudinal analysis requires long-term data collection and curation, which can be time demanding, expensive and sometimes unfeasible. Here we present a clinical decision support framework that combines the simulation of disease progression from cross-sectional data with a Markov model that exploits continuous-time transition probabilities derived from Cox regression. Trajectories between patients at different disease stages are stochastically built according to a measure of patient similarity, computed with a matrix tri-factorization technique. Such trajectories are seen as realizations drawn from the stochastic process driving the transitions between the disease stages. Eventually, Markov models applied to the resulting longitudinal dataset highlight potentially relevant clinical information. We applied our method to cross-sectional genomic and clinical data from a cohort of Myelodysplastic syndromes (MDS) patients. MDS are heterogeneous clonal hematopoietic disorders whose patients are characterized by different risks of Acute Myeloid Leukemia (AML) development, defined by an international score. We computed patients' trajectories across increasing and subsequent levels of risk of developing AML, and we applied a Cox model to the simulated longitudinal dataset to assess whether genomic characteristics could be associated with a higher or lower probability of disease progression. We then used the learned parameters of such Cox model to calculate the transition probabilities of a continuous-time Markov model that describes the patients' evolution across stages. Our results are in most cases confirmed by previous studies, thus demonstrating that simulated longitudinal data represent a valuable resource to investigate disease progression of MDS patients.
由于分子患者档案越来越容易获取，现在将基因组学和临床数据整合以模拟疾病进展成为可能。这可能会促成新型决策支持工具的定义，这些工具能够根据患者的健康状况演变，基于“精确”的患者风险分层来定制治疗干预措施。然而，纵向分析需要长期的数据收集和整理，这可能耗时、昂贵，有时甚至不可行。在此，我们提出了一个临床决策支持框架，该框架将基于横断面数据的疾病进展模拟与一个马尔可夫模型相结合，该马尔可夫模型利用从Cox回归得出的连续时间转移概率。根据通过矩阵三分解技术计算出的患者相似度度量，随机构建不同疾病阶段患者之间的轨迹。这些轨迹被视为从驱动疾病阶段之间转变的随机过程中得出的实现。最终，应用于所得纵向数据集的马尔可夫模型突出显示了潜在相关的临床信息。我们将我们的方法应用于来自一组骨髓增生异常综合征（MDS）患者的横断面基因组和临床数据。MDS是异质性克隆性造血疾病，其患者具有不同的急性髓系白血病（AML）发生风险，由一个国际评分定义。我们计算了患者在AML发生风险增加及后续水平上的轨迹，并将Cox模型应用于模拟的纵向数据集，以评估基因组特征是否可能与疾病进展的较高或较低概率相关。然后，我们使用该Cox模型的学习参数来计算一个连续时间马尔可夫模型的转移概率，该模型描述了患者跨阶段的演变。我们的结果在大多数情况下得到了先前研究的证实，从而表明模拟的纵向数据是研究MDS患者疾病进展的宝贵资源。
