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 of understanding of various human complex diseases (e.g., cardio-oncology, pulmonary vascular diseases and cancer), which could have a major impact in identifying novel real-world data-driven diagnostic biomarkers and therapeutic targets for precision medicine.
Dr. Chen’s lab at Cleveland Clinic’s Genomic Medicine Institute has two major focus areas:
Building an Individualized Network Medicine Infrastructure for Precision Cardio-Oncology
The growing awareness of cardiac dysfunction by cancer treatment has led to the emerging field of Cardio-Oncology. However, there are no guidelines in terms of how to prevent and treat the new cardiotoxicity in cancer survivors due to the limited experimental assays. Network medicine – a discipline that seeks to redefine disease and therapeutics from an integrated perspective using systems biology and network science – offers a non-invasive way to identify actionable biomarkers for Cardio-Oncology. Supported by NIH Career Development Award (K99/R00), Dr. Cheng’s lab is working on developing state-of-the-art systems biology and network medicine approaches in Cardio-Oncology that focuses on screening, monitoring and treating cancer survivors with cardiac dysfunction resulting from cancer treatments. The central, unifying hypothesis is that an integrated, network-based, systems biology approach that incorporates not only genetic variations, but also gene expression, metabolomic, proteomic, interactomic (the human protein-protein interactome), radiomic, exposomic data and real-world data (e.g., patient longitudinal data), along with careful deep phenotypic data, will prove to be the most effective way to identify clinical actionable biomarkers and mechanisms responsible for Cardio-Oncology, thereby achieving the goal of coordinated, patient-centered strategies for treatment and long-term heart and vascular care (e.g., heart failure and pulmonary vascular disease) for cancer survivors.
Nodetic and Edgetic Network Perturbations in Personal Cancer Mutanomes
Although cancer is often described as a disease of the genome, it is perhaps more appropriate to describe cancer as a “disease of the interactome.” Cellular networks gradually rewire throughout cancer initiation, progression and maintenance, leading to progressive shifts of local and global network properties and systems states, all of which in turn underlie tumorigenesis and drug resistance. Dr. Cheng’s lab focuses on developing computational and experimental systems biology approaches to understand cancer biology and pharmacogenomics from the point-of-view of network perturbations: most genomic variants lead to both “nodetic and edgetic perturbations” of cellular network systems and tumorigenesis and drug responses results from the combined effect of multiple nodetic and edgetic perturbations in cancer cells.
In other words ...
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.
Huang Y, Fang J, Wang Z, Lu W, Wang Q, Hou Y, Jiang X, Reizes O, Lathia J, Nussinov R, Eng C, Cheng F (2019) A systems pharmacology approach uncovers wogonoside as a novel angiogenesis inhibitor of triple-negative breast cancer by targeting Hedgehog signaling. Cell Chemical Biology. 26: 1143-1158.
Cheng F, Liu C, Lu W, Fang J, Hou Y, Handy ED, Wang R, Zhao Y, Yang Y, Huang J, Hill ED, Vidal M, Eng C, Loscalzo J (2019) A genome-wide positioning systems network algorithm for in silico drug repurposing. Nature Communications. 10: 3476.
Cheng F, Kovacs I, Barabasi AL (2019) Network-based prediction of drug combinations. Nature Communications, 10: 1197.
Zeng X, Zhu, Liu X, Zhou Y, Nussinov R, Cheng F (2019) deepDR: A network-based deep learning approach to in silico drug repositioning. Bioinformatics. in press. doi: 10.1093/bioinformatics/btz418
Peng H, Zeng X, Zhang D, Nussinov R, Cheng F (2019) A components attribute clustering (COAC) algorithm for single cell RNA sequencing data analysis and potential pathobiological implications, PLoS Computational Biology, 15(2): e1006772
Cheng F, Liang H, Butte AJ, Eng C, Nussinov R (2019) Personal mutanomes meet modern oncology drug discovery and precision health. Pharmacological Reviews, 71:1-19.
Nussinov R, Jang H, Tsai JC, Cheng F (2019) Precision medicine review: rare driver mutations and their biophysical classification. Biophysical Reviews, 11(1):5-19.
NussinovR, Jang HB, Tsai CJ, Cheng F (2019) Conformational principles of precision medicine, singling and latent driver mutations: computational methods and functional assays for interpreting cancer driver mutations. PLoS Computational Biology, 15(3): e1006658.
Cheng F (2019) In silico Oncology Drug Repositioning and Polypharmacology. Methods in Molecular Biology, 1878:243-261.
Wang Q, Chen R, Cheng F, Wei Q, Ji Y, Yang H, Zhong X, Tao R, Wen Z, Sutcliffe SJ, Liu C, Cook HE, Cox JN, Li B (2019) A Bayesian framework that integrates multi-omics data and gene networks predictes risk genes from Schizophreniz GWAS data. Nature Neuroscience. 22(5): 691-699.
Smith NI, Thacker S, Seyfi M, Cheng F, Eng C (2019) Novel structural communication and network perturbations by germline PTENmutations associated with autism compared to with cancer. The American Journal of Human Genetics, 104(5): 861-878.
Cheng F (2019) Cardio-Oncology: Network-based prediction of cancer therapy-induced cardiotoxicity. Methods in Molecular Biology. 30: 75-97.
Cheng F, Desai JR, Handy ED, Wang R, Schneeweiss S, Barabasi AL, Loscalzo J (2018) Network-based approach to prediction and population-based validation of in silicodrug repurposing. Nature Communications. 9: 2691.
Cheng F, Nussinov R (2018) KRAS Activating Signaling Triggers Arteriovenous Malformations. Trends Biochem Sci. 43(7):481-483.
Cai C, Fang J, Guo P, Wang Q, Hong H, Moslehi J, Cheng F (2018) In Silico Pharmacoepidemiologic Evaluation of Drug-Induced Cardiovascular Complications Using Combined Classifiers. J Chem Inf Model. 58(5):943-956.
Cai C, Guo P, Zhou Y, Zhou J, Wang Q, Zhang F, Fang J, Cheng F (2019) Deep learning-based prediction of cardiotoxicity. Journal of Chemical Information and Modeling. 59(3):1073-1084.
Fang J, Cai C, Chai Y, Zhou J, Huang Y, Gao L, Wang Q, Cheng F (2018) Quantitative and Systems Pharmacology 4. Network-based analysis of drug pleiotropy on coronary artery disease. European Journal of Medicinal Chemistry, 161: 192-204.
Peng X, Chen Z, Farshidfar F, Xu X, Lorenzi PL, Wang Y, Cheng F, Tan L, Mojumdar K, Du D, Ge Z, Li J, Thomas GV, Birsoy K, Liu L, Zhang H, Zhao Z, Marchand C, Weinstein JN; Cancer Genome Atlas Research Network., Bathe OF, Liang H (2018) Molecular Characterization and Clinical Relevance of Metabolic Expression Subtypes in Human Cancers. Cell Rep. 23(1):255-269.e4.
Jiang X, Lu W, Shen X, Wang Q, Lv J, Liu M, Cheng F (Co-corresponding author), Zhao Z, Pang X (2018) Repurposing sertraline sensitizes non-small cell lung cancer cells to erlotinib by inducing autophagy. The Journal of Clinical Investigation (JCI) Insight. 3(11): e98921.
Wu D, Chen W, Lian F, Wang W, Lang L, Huang Y, Xu Y, Zhang N, Liu M, Nussinov R, Cheng F (Co-corresponding author), Lu W, Huang J (2018) Pharmacologic inhibition of dihydroorotate dehydrogenase induces apoptosis and differentiation in acute myeloid leukemia cells. Haematologica. 103(9):1472-1483.
Cheng F, Loscalzo J (2018) Pulmonary Comorbidity in Lung Cancer. Trends Molecular Medicine. 24(3):239-241. doi: 10.1016/j.molmed.2018.01.005.
Zhao J, Cheng F, Jia P, Cox N, Denny JC, Zhao Z (2018) An integrative functional genomics framework for effective identification of novel regulatory variants in genome-phenome studies. Genome Medicine. 29;10(1):7.
Fang J, Wu Z, Cai C, Wang Q, Tang Y, Cheng F (2017) Quantitative and Systems Pharmacology. 1. In Silico Prediction of Drug-Target Interactions of Natural Products Enables New Targeted Cancer Therapy. J Chem Inf Model. 2017 Nov 27;57(11):2657-2671
Fang J, Gao L, Ma H, Wu Q, Wu T, Wu J, Wang Q, Cheng F (2017) Quantitative and Systems Pharmacology 3. Network-Based Identification of New Targets for Natural Products Enables Potential Uses in Aging-Associated Disorders. Fronts in Pharmacology. 18;8:747.
Lu W, Cheng F, Yan W, Li X, Yao X, Song W, Liu M, Shen X, Jiang H, Chen J, Li J, Huang J (2017) Selective targeting p53WT lung cancer cells harboring homozygous p53 Arg72 by an inhibitor of CypA. Oncogene. 36(33):4719-4731.
Cheng F, Hong H, Yang S, Wei Y (2017) Individualized network-based drug repositioning infrastructure for precision oncology in the panomics era. Briefing in Bioinformmatics. 18(4):682-697.
Hanker AB, Estrada MV, Bianchini G, Moore PD, Zhao J, Cheng F, Koch JP, Gianni L, Tyson DR, Sánchez V, Rexer BN, Sanders ME, Zhao Z, Stricker TP, Arteaga CL (2017) Extracellular Matrix/Integrin Signaling Promotes Resistance to Combined Inhibition of HER2 and PI3K in HER2+ Breast Cancer. Cancer Res. 77(12):3280-3292.
Zhao J, Cheng F, Zhao Z (2017) Tissue-Specific Signaling Networks Rewired by Major Somatic Mutations in Human Cancer Revealed by Proteome-Wide Discovery. Cancer Research. 77(11):2810-2821.
Fang J, Liu C, Wang Q, Lin P, Cheng F (2017) In silico polypharmacology of natural products. Briefing in Bioinformatics. 19: 1153-1171.
Lu W, Yao X, Ouyang P, Dong N, Wu D, Jiang X, Wu Z, Zhang C, Xu Z, Tang Y, Zou S, Liu M, Li J, Zeng M, Lin P, Cheng F (Co-corresponding author), Huang J (2017) Drug Repurposing of Histone Deacetylase Inhibitors That Alleviate Neutrophilic Inflammation in Acute Lung Injury and Idiopathic Pulmonary Fibrosis via Inhibiting Leukotriene A4 Hydrolase and Blocking LTB4 Biosynthesis. Journal of Medicinal Chemistry. 60(5):1817-1828.
Cheng F, Loscalzo J. Autoimmune Cardiotoxicity of Cancer Immunotherapy. Trends in Immunology. 2017 Feb;38(2):77-78.
Shen Q, Cheng F, Song H, Lu W, Zhao J, An X, Liu M, Chen G, Zhao Z, Zhang J. Proteome-Scale Investigation of Protein Allosteric Regulation Perturbed by Somatic Mutations in 7,000 Cancer Genomes. American Journal of Human Genetics. 2017 Jan 5;100(1):5-20.
Cheng F, James M, Zhao J, Sheng J, Zhao Z, Rubin DH (2016) Systems biology-based investigation of cellular antiviral drug targets identified by gene-trap insertional mutagenesis. PLoS Computational Biology 12(9): e1005074.
Cheng F, Murray JL, Rubin DH (2016) Drug repurposing: new treatments for Zika virus infection. Trends in Molecular Medicine, 22(11): 919-921
Wang J, Hu K, Guo J,Cheng F, Lv J, Jiang W, Lu W, Liu J, Pang X, Liu M (2016) Combined inhibition of KRAS synthetic lethal partners, PLK1 and ROCK, suppresses KRAS-mutant cancers. Nature Communications7: 11363.
Cheng F, Liu C, Lin C-C, Zhao J, Jia P, Li W-H, Zhao Z (2015) A gene gravity model for the evolution of cancer genomes: A study of 3,000 cancer genomes across 9 cancer types. PLoS Computational Biology11(9): e1004497.
Cheng F, JiaP, WangQ, Lin C-C,Li W-H, ZhaoZ (2014) Studying Tumorigenesis through network evolution and somatic mutational perturbations in the cancer interactome. Molecular Biology and Evolution31(8): 2156-2169.
Zhao Y, Hu Q, Cheng F, Su N, Wang A, Zou Y, Hu H, Chen X, Zhou H, Huang X, Yang K, Zhu Q, Wang X, Yi J, Zhu L, Qian X, Chen L, Tang Y, Loscalzo J, Yang Y (2015) SoNar, a highly responsive NAD+/NADH sensor, allows high-throughput metabolic screening of anti-tumor agents. Cell Metabolism. 21, 777-789.
Thanks to remarkable scientific and technological advancements of late, researchers now have a deluge of sequencing data, which, with the right analysis, may help with the discovery and development of targeted cancer treatments. In a cover article published in Pharmacological Reviews, Cleveland Clinic’s Feixiong Cheng, PhD, Genomic Medicine, and collaborators review the current use of personal mutanomes in the discovery of modern oncology drugs, including therapies that are targeted to specific genomic or molecular profiles, as well as immunotherapies.