Network Medicine and AI in Precision Cardio-Oncology:Learning for Cleveland Clinic Epic Systems
There are over 15.5 million cancer survivors in the United States (U.S.) alone; furthermore, cardiovascular disease is a leading cause of death and the second leading cause of mortality and morbidity in cancer survivors after recurrent malignancy in the U.S. Comorbidity between cardiovascular disease and cancer suggests an underlying shared disease etiologies, including genetic and environmental. One critical issue is that comorbidity is typically associated with various cancer treatments, termed cancer therapy-related cardiac dysfunction (CTRCD). However, there are no guidelines in terms of how to prevent and treat the new CTRCD in cancer survivors. In this talk, I will introduce a clinically relevant, network-based methodology for a comprehensive, unbiased network analysis of over 4,600 longitudinal cancer patients using clinical, laboratory and echocardiographic variables from our institutional, large-scale electronic medical records. Via network analysis, we identified four distinct subgroups who are statistically significantly correlated with incidence of CTRCD and patients’ mortality. Analysis of longitudinal patient-patient networks (20 years’ follow-up) reveals dosing-time-dependent (‘chronopharmacology’) CTRCD. Using clinical variable network analysis, we identified several clinically relevant predictors (i.e., Troponin-T and NT-proBNP) that are significantly associated with patients’ mortality. Compared to traditional machine learning approaches, network methodologies are more interpretable, visualizing the clinical decision boundary of cancer patients with CTRCD.
Feixiong Cheng, PhD, is a principal investigator with Cleveland Clinic’s Genomic Medicine Institute. Dr. Cheng is a computational and systems biologist by training, with expertise in analyzing, visualizing, and mining data from real world (e.g., electronic health records, and health care claims) and experiments that profile the molecular state of human cells and tissues by interactomics, transcriptomics, genomics, proteomics, and metabolomics for precision medicine drug discovery and patient care. Dr. Cheng is working to develop computational and experimental network medicine technologies for advancing the characterization of disease heterogeneity, thereby approaching the goal of coordinated, patient-centered strategies to innovative diagnostics and therapeutics development. The primary goal of Dr. Cheng’s lab is to combine tools from genomics, network medicine, bioinformatics, computational biology, chemical biology, and experimental pharmacology and systems biology assays (e.g., single cell sequencing and iPS-derived cardiomyocytes), to address the challenging questions toward understanding of various human complex diseases (e.g., cardio-oncology, pulmonary vascular diseases, and Alzheimer’s disease), which could have a major impact in identifying novel real-world data-driven diagnostic biomarkers and therapeutic targets for precision medicine. From 2013 to 2017, Dr. Cheng was trained as Postdoctoral Research Fellow in the field of pharmacogenomics and network medicine across Vanderbilt University Medical Center, Northeastern University, and Dana-Farber Cancer Institute. During 2017-2018, Dr. Cheng was promoted to Research Assistant Professor working with two of the world’s leading experts in the field of network medicine, Drs. Albert-Laszlo Barabasi and Joseph Loscalzo, with dual appointment at Northeastern University and Harvard Medical School. Dr. Cheng has received several awards, including NIH Pathway to Independence Award (K99/R00), SCI highly cited papers reward, and Vanderbilt Postdoc of the Year Honorable mention.