Feixiong Cheng, PhD
Lerner Research Institute,
9500 Euclid Avenue, Cleveland, Ohio 44195
Phone: (216) 444-7654
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 iPSC-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 Precision Medicine 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 (Nature Genetics 2021; Nature Communications 2018 and 2019a) 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 (Genome Research 2021; PLOS Biology 2020; Nature Communications 2019b) 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 Intelligence methodologies (Lancet Digital Health 2020 [Cover]; Chemical Science 2020 [Cover]), for drug target identification and precision medicine drug discovery, by unique integration of big biomedical data, including genomics, transcriptomics, proteomics, metabolomics, radiomics, interactomics (e.g., protein-protein interactions [https://mutanome.lerner.ccf.org], Genome Biology 2021), 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.
Cheng Lab is developing and applying systems biology technologies and network medicine methodologies to predict drug targets and to identify mechanisms of disease, thereby approaching the goal of coordinated, patient-centered strategies to innovative diagnostics and therapeutics development.
We have 2-3 postdoc and multiple graduate student positions available for Network Medicine and Artificial Intelligence (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 network science, please send your cover letter (describing your interest in and qualifications for this position), curriculum vitae (including publications list), one research statement 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. Please apply before November 30, 2021.
Fang J, Zhang P, Zhou Y, Chiang WC, Tan J, Hou Y, Stauffer S, Li L, Pieper AA, Cummings J, Cheng F (2021) Endophenotype-based in-silico network medicine discovery combined with insurance records data mining identifies sildenafil as a candidate drug for Alzheimer’s disease. Nature Aging, in press, doi: 10.1038/s43587-021-00138-z.
Martin W, Sheynkman G, Lightstone FC, Nussinov R, Cheng F (2021) Interpretable Artificial Intelligence and Exascale Molecular Dynamics Simulations to Reveal Kinetics: Applications to Alzheimer’s Disease, Current Opinion in Structural Biology, 72:103-113.
Xu J, Zhang P, Huang Y, Zhou Y, Hou Y, Bekris L, Lathia JD, Chiang WC, Li L, Pieper AA, Leverenz BJ, Cummings J, Cheng F (2021) Multimodal single-cell/nucleus RNA-sequencing data analysis uncovers molecular networks between disease-associated microglia and astrocytes with implications for drug repurposing in Alzheimer’s disease. Genome Research, 31(10):1900-1912.
Hou Y, Zhou Y, Hussain M, Budd GT, Tang WHW, Abraham J, Xu B, Shah C, Moudgil R, Popovic Z, Watson C, Cho L, Chung M, Kanj M, Kapadia S, Griffin B, Svensson L, Collier P, Cheng F (2021) Cardiac risk stratification in cancer patients: A longitudinal patient-patient network analysis. PLOS Medicine, 18(8): e1003736.
Hou Y, Zhou Y, Gack UM, Lathia DJ, Kallianpur A, Mehra R, Chan T, Jung UJ, Jehi L, Eng C, Cheng F (2021) Multimodal single-cell omics analysis identifies epithelium-immune cell interactions and immune vulnerability associated with sex differences in COVID-19. Signal Transduction and Targeted Therapy, 6(1):292.
Zhou Y, Xu J, Hou Y, Leverenz B.J., Kallianpur A, Mehra MR, Liu Y, Yu H, Pieper AA, Jehi, L. & Cheng F (2021) Network medicine links SARS-CoV-2/COVID-19 infection to brain microvascular injury and neuroinflammation in dementia-like cognitive impairment. Alzheimer's Research & Therapy, 13:110.
Zhou Y, Fang J, Bekris L, Young H.K., Pieper AA, Leverenz J, Cummings J, Cheng F (2021) AlzGPS: A Genome-wide Positioning Systems Platform to Catalyze Multi-omics Findings for Alzheimer's Drug Discovery. Alzheimer's Research & Therapy, 13(1):24. AlzGPS website: https://alzgps.lerner.ccf.org
Shin KM, Vázquez-Rosa E, Koh YG, Dhar M, Chaubey K, Cintrón-Pérez JC, Barker S, Miller E, Franke K, Noterman M, Seth D, Allen SR, Motz TC, Rao R, Skelton AL, Pardue TM, Fliesler JS, Wang C, Tracy ET, Gan L, Liebl JD, Savarraj J, Torres LG, Ahnstedt H, McCullough DL, Kitagawa SR, Choi AH, Zhang P, Hou Y, Chiang WC, Li L, Ortiz F, Kilgore AJ, Williams SN, Whitehair CV, Gefen T, Flanagan EM, Stamler SJ, Jain KM, Kraus A, Cheng F, Reynolds DJ, Pieper AA (2021) Reducing tau acetylation is neuroprotective in brain injury, Cell, 184(10):2715-2732.e23.
Nussinov R, Jang H, Nir G, Tsai CJ, Cheng F (2021) A new precision medicine initiative at the dawn of exascale computing. Signal Transduction and Targeted Therapy, 6(1):3.
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.
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.
Martin W, Cheng F (2021) A rational design of a multi-epitope vaccine against SARS-CoV-2 which accounts for the glycan shield of the Spike glycoprotein. Journal of Biomolecular Structure and Dynamics. in press. 10.1080/07391102.2021.1894986.
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) Individualized genetic network analysis reveals new therapeutic vulnerabilities in 6,700 cancer genomes. 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). 9(23):e019628.
Castrillon JA, Eng C, Cheng F (2020) Pharmacogenomics for immunotherapy and immune-related cardiotoxicity. Human Molecular Genetics. 29(R2):R186-R196.
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. (Journal Cover)
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.
With this award, Dr. Cheng’s team will develop artificial intelligence and machine learning tools capable of identifying novel endophenotypes and actionable targets for drug repurposing in Alzheimer’s disease.
With a new $4 million grant, Drs. Cheng, Bekris and Leverenz will develop and utilize artificial intelligence tools to identify novel drug targets and repurposable drugs for Alzheimer’s disease.
A research team led by Drs. Cheng and Collier developed an artificial intelligence methodology to help identify cancer patients at risk for cancer therapy-related cardiac dysfunction.
Utilizing large-scale patient data and samples from the Cleveland Clinic COVID-19 registry, Dr. Cheng’s team identified clinical characteristics and immune-related mechanisms associated with sex differences in COVID-19 outcomes.
Dr. Cheng and Ms. Castrillon Lal received the Gilliam Fellowship for Advanced Study, and Dr. Smith received the MOSAIC Postdoctoral Career Transition Award to Promote Diversity.
Dr. Cheng’s team developed an artificial intelligence methodology to uncover molecular targets involved in neuroinflammation and identify candidate therapeutics for Alzheimer’s disease.
Utilizing network medicine methodologies, a research team led by Dr. Cheng linked COVID-19 to neuroinflammation and brain microvascular injury in Alzheimer’s disease-like cognitive impairment.
Dr. Cheng and team developed a personalized genomic medicine platform to identify clinically actionable mutations and accelerate the development of cancer precision medicine protocols.
An interdisciplinary team led by Drs. Cheng and Collier developed machine learning models that predict with promising accuracy the risk of cardiac dysfunction in cancer survivors and may be generalizable to clinical practice.
Cleveland Clinic Researchers Use “Big Data” Approach to Identify Melatonin as Possible COVID-19 Treatment
Dr. Cheng and colleagues developed a network medicine strategy to predict disease manifestations associated with COVID-19 and find existing drugs with the potential to be effective COVID-19 treatments.
Dr. Cheng and his team aim to identify repurposable drugs and combination regimens to treat COVID-19 in older adults
Dr. Cheng's team found a possible association between ACE2 and TMPRSS2 polymorphisms and COVID-19 susceptibility.
Dr. Cheng will develop and implement computational tools to identify and test novel repurposable drugs and drug combinations for Alzheimer’s disease.
By harnessing the powers of systems pharmacology and predictive modeling, Dr. Cheng identified 16 drugs and three drug combinations that may be candidates for repurposing as potential COVID-19 treatments.
A systems biology and network medicine expert, Dr. Cheng developed a deep learning methodology to more accurately predict drug-target interactions, which will help accelerate drug repurposing efforts.