The primary goal of Dr. Cheng’s lab is to combine tools from network medicine, Artificial Intelligence (AI), genomics, bioinformatics, computational biology, chemical biology, and experimental pharmacology and systems biology assays (e.g., single cell sequencing and iPS-derived cardiomyocytes and neurons) to address the challenging questions of 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 approaches for precision medicine.
Dr. Cheng’s lab at Cleveland Clinic’s Genomic Medicine Institute has several major focus areas:
1. Network Medcine Tools for Drug Discovery
Traditional drug discovery and development pipelines involve complex, expensive, and time-consuming processes. Many drug candidates with idealin vitroactivities are failed in phases II and III because of low efficacyin vivoor safety problems. One possible reason for this high clinical attrition rate might be the shortcoming if the classical hypothesis of ‘one drug, one gene, one disease’ in the traditional drug discovery paradigm. Supported by NIH grants, our team have invested a huge amount of efforts for the development of several integrated, network-based methodologies for drug repurposing. Those state-of-the-art network medicine tools (https://alzgps.lerner.ccf.org) present new and important methodologies fordeveloping broadly active therapeutics for various complex diseases (including COVID-19 and Alzheimer's disease)
2. Translation of Multi-Omics Discoveries to Disease Biology and Therapeutic Development
High-throughput omics (including genomics, transcriptomics (single-cell), proteomics, and metabolomics) offer power tools for human disease studies. However, how to translate multi-omics findings to disease pathobiology and therapeutic development remains a great challenge. Our team are actively developing and applying developed multiple network-based methodologies (i.e., Genome-wide Positioning Systems network (GPSnet) algorithm) to illustrate the pathobiology of disease and therapeutic discovery in various complex diseases, including Alzheimer’s disease, pulmonary vascular disease, and others.
3. Artificial Intelligence (AI) Methodologies for Target Identification and Therapeutic Discovery
Without foreknowledge of the complete drug target information, development of promising and affordable approaches for effective treatment of human diseases is challenging. Our team has developed multiple network-based,artificial Intelligencemethodologies, for drug target identification and precision medicine drug discovery, by unique integrationof big biomedical data, including genomics, transcriptomics, proteomics, metabolomics, radiomics, interactomics (e.g., protein-protein interactions [https://mutanome.lerner.ccf.org]), and electronic health records (EHRs).
4. 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, the human protein-protein interactome, 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.
In other words ...
Cheng Lab is developing and applying systems biology technologies and network medicine methodologies to predict drug targets and to understand mechanisms of disease, thereby approaching the goal of coordinated, patient-centered strategies to innovative diagnostics and therapeutics development.
We have 1-2 postdoc positions available for network medicine and AI projects. If you have PhD or MD in the field of systems biology, bioinformatics, machine learning, AI, natural language processing, mathematics, computational biology, and complex systems, please send your cover letter (describing your interest in and qualifications for this position), curriculum vitae (including publications list), one research statement (1-3 pages) that outlines both your research achievements and agenda, and your service and outreach activities and plans, and the names and contact information of three letter writers.
Zhou Y, Hou Y, Shen J, Huang Y, Martin W, Cheng F (2020) Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2. Cell Discovery, 6, 14.
Hou Y, Zhao J, Martin W, Kallianpur A, Chung KM, Jehi L, Sharifi N, Erzurum S, Eng C, Cheng F (2020) New insights into genetic susceptibility of COVID-19: an ACE2 and TMPRSS2 polymorphism analysis. BMC Medicine, 18, 216.
Zeng X, Song X, Ma T, Pan X, Zhou Y, Hou Y, Zhang Z, Karypis G, Cheng F (2020) Repurpose open data to discover therapeutics for COVID-19 using deep learning. Journal of Proteome Research, 19(11), 4624–4636 (Cover paper)
Martin W, Cheng F (2020) Repurposing of FDA-approved toremifene to treat COVID-19 by blocking the spike glycoprotein and NSP14 of SARS-CoV-2, Journal of Proteome Research, 19(11), 4670–4677.
Cheng F, Rao S, Mehra R (2020) COVID-19 treatment: Combining Anti-inflammatory and Antiviral Therapeutics using a Network–based Approach. Cleveland Clinic Journal of Medicine, in press, DOI: 10.3949/ccjm.87a.ccc037.
Martin W, Cheng F (2020) A rational design of a multi-epitope vaccine against SARS-CoV-2 which accounts for the glycan shield of the Spike glycoprotein. Published online August 8, 2020, ChemRxiv. DOI: https://doi.org/10.26434/chemrxiv.12770225.v1.
Fang J, Pieper AA, Lee G, Bekris L, Nussinov R, Leverenz BJ, Cummings J, Cheng F (2020) Harnessing endophenotypes and network medicine for Alzheimer’s drug repurposing, Medicinal Research Reviews, 40:2386–2426.
Zeng X, Zhu S, Lu W, Liu Z, Huang J, Zhou Y, Fnag J, Huang Y, Guo H, Li L, Trapp B, Nussinov R, Eng C, Loscalzo J, Cheng F (2020) Target identification among known drugs by deep learning from heterogeneous networks. Chemical Science, 11, 1775-1797. (Journal Cover Paper)
Liu C, Ma Y, Zhao J, Nussinov R, Zhang Y.C., Cheng F (co-corresponding author), Zhang Z (2020) Computational network biology: data, model, and application. Physics Reports 846 (3): 1-66.
Liu C, Zhao J, Lu W, Dai Y, Zhou Y, Hockings J, Nussinov R, Eng C, Cheng F (2020) Pan-cancer genetic network analysis reveals new therapeutic tumor vulnerabilities. PLoS Computational Biology, 16(2): e1007701.
Zhou Y, Hou Y, Hussain M, Watson C, Moudgil R, Shah C, Abraham J, Budd G.T., Tang W.H.W, Finet J.E., James, K; Estep J.D., Xu B, Hu B, Cremer P, Jellis C, Grimm R.A., Greenberg N, Popovic Z.B., Cho L, Desai M.Y., Nissen S.E., Kapadia S.R., Svensson L.G., Griffin B.P, Collier P, Cheng F (2020) Machine Learning Approaches to Cancer Therapy-related Cardiac Dysfunction Risk Stratification in 4,300 Longitudinal Cancer Patients. Journal of the American Heart Association (JAHA). In press.
Xu B, Kocyigit D, Griffin PB, Cheng F (2020) Applications of artificial intelligence in multimodality cardiovascular imaging: A state-of-the-art review. Progress in Cardiovascular Diseases. 63(3):367-376.
Bayik D, Zhou Y, Park C, Hong C, Vail D, Silver JD, Lauko JA, Roversi AG, Watson CD, Lo A, Alban JT, McGraw M, Sorensen DM, Grabowski MM, Otvos B, Vogelbaum AM, Horbinski MC, Kristensen WB, Khalil MA, Hwang HT, Ahluwalia SM, Cheng F, Lathia DJ (2020) Myeloid-derived suppressor cell subsets drive glioblastoma growth in a sex-specific manner, Cancer Discovery. 10(8):1210-1225.
Hu K, Li K, Lv J, Feng J, Chen J, Wu H, Cheng F, Jiang W, Wang J, Pei H, Chiao PJ, Cai Z, Chen Y, Liu M, Pang X (2020) Suppression of the SLC7A11/glutathione axis causes synthetic lethality in KRAS-mutant lung adenocarcinoma. Journal of Clinical Investigation. 130(4):1752-1766.
Akhavanfard S, Padmanabhan R, Yehia L, Cheng F, Eng C (2020) Comprehensive germline genomic profiles of children, adolescents and young adults with solid tumors. Nature Communications, 11: 2206.
Jin S, Zeng X, Lin J, Chan YS, Erzurum CS, Cheng F (2019) A network-based approach to infer microRNA-mediated disease comorbidities and potential pathobiological implications. npj Systems Biology and Applications, 5, 41.
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.
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.
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.
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.
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.
Cleveland Clinic Researchers Use “Big Data” Approach to Identify Melatonin as Possible COVID-19 Treatment
Results from a new Cleveland Clinic-led study suggest that melatonin, a hormone that regulates the sleep-wake cycle and is commonly used as an over-the-counter sleep aid, may be a viable treatment option for COVID-19.
Feixiong Cheng, PhD, Genomic Medicine Institute, has been awarded a one-year, $400,000 award from the National Institute on Aging, part of the National Institutes of Health, to find effective treatments for 2019 Novel Coronavirus (COVID-19) in older adults. This award is an administrative supplement to Dr. Cheng’s five-year, $3.3 million grant for the development of computational tools to identify novel repurposable drugs for Alzheimer’s disease.
A new Cleveland Clinic study has identified genetic factors that may influence susceptibility to coronavirus disease 2019 (COVID-19). Published today in BMC Medicine, the study findings could guide personalized treatment for COVID-19.
Feixiong Cheng, PhD, Genomic Medicine Institute, has been awarded a 5-year, $3.3M grant from the National Institute on Aging, part of the National Institutes of Health, to develop computational tools to identify novel repurposable drugs for Alzheimer’s disease (AD).
Human coronaviruses (HCoV), including Coronavirus Disease 2019 (COVID-19), can lead to epidemics with high morbidity and mortality. These epidemics emerge and mutate at such a rapid rate that traditional methods of drug discovery cannot keep up.
Although conventional drug development aims to design drugs that selectively target a single molecular entity (e.g., a disease-driving protein), drugs often are found to interact with more than one target. These off-target interactions can be problematic as they may result in adverse effects and suboptimal drug effectiveness. However, therapeutics with multiple targets provide opportunities for repurposing drugs to treat diseases that do not yet have effective therapies, as long as the molecular targets with which a drug will interact can be comprehensively identified.
When more than one disease occurs in a single individual (i.e., disease comorbidity), interactions between the diseases may worsen each other, resulting in poorer health outcomes and increased healthcare costs. In a recent study published in NPJ Systems Biology and Applications, a team of researchers led by Feixiong Cheng, PhD, Genomic Medicine Institute, developed a network-based methodology that may help predict relationships between multiple complex diseases as well as pinpoint potential therapeutic targets.
While specific genetic mutations are known to increase disease risk, the assumption of a linear genotype-phenotype connection often proves overly simplistic. Research has found that genes and their products interact with each other to form a system of complex networks within a cell. Known as the human protein-protein interactome, these molecular networks influence cellular function and can lead to system-wide changes in cells that influence disease mechanisms and treatment resistance.
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.