Lijunlab

Welcome to Lijun Cheng Lab

• This repository houses the Lijun Lab website, which is shared among members through Github and hosted by Dr.Lijun Cheng.
• Lab members should keep their own pages current, as well as contribute to the lab news feed and update research themes and study systems as appropriate.

Lijun Cheng Ph.D

01/2018 - present, Assistant Professor, Department of Biomedical Informatics, College of Medicine, Ohio State University (OSU)

• Introduction on OSU webpage
• Contact: 1800 Cannon Drive, Columbus, OH 43210, United State
• Email: lijun.cheng@osumc.edu
• The full journal publication list NCBI MyBibliography
• All papers list on google scholar

AREA OF EXPERTISE

Artificial intelligence in drug discovery and development

• The aim of “precision medicine” is to devise individualized treatment strategies and therapies. Artificial Intelligence (AI) methods play important roles in predicting the efficacy of drugs from clinical study data, based on patient characteristics. Dr. Cheng focuses on AI mathematical methods development that efficiently identify the characteristics relevant to molecular mechanisms of drug resistance, disease progression and an optimal drug recommendation in the disease treatment. Dr.Cheng's work has substantially advanced our understanding of the molecular mechanisms of cancers, neurodegenerative diseases, and other disorders by using AI technology and strategy. With respect to cancer specifically, her research adapts patient genomic feature data to study molecularly characterized tumors as an alternative to traditional histologic identification. The novel AI computational technology will enable to improve drug selection and courses of treatment for different types of cancer patients depending on their specific genetic and genomics variations.

Molecular mechanisms of drug resistance, and efficacy therapeutics to overcome

• Dr.Cheng developed a series of resources and methods to facilitate data mining of drug resistance mechanism and new drug combination from various data sources.
• Dr. Cheng’s pioneering work led to 52 publications in renowned journals, such as Nature genetics(Impact factor:26.7), Advanced Science (Impact factor:16.8, Co-first authors), Molecular Cancer (Impact factor: 25.55), Cancer Research(Impact factor: 12.8, Co-first authors), Oncogene (Impact factor: 9.86), Clinical Pharmacology Therapy (Impact factor: 6.544, Co-first), Cancers (Impact factor: 6.16, co-first author ), Journal of Clinical Immunology( Impact factor:6.780), Journal of biological chemistry (Impact factor: 4.238 ), as well as AI professional journals, such as IEEE Transaction on neural network and learning systems (impact factor: 12.85, Co-first). Neurocomputing (impact factor: 4.38, Co-first), Plos computational biology (impact factor:4.92, Co-first).

Breakthrough

• PDAC_Immune platform optimal target cell prediction to increasing immune therapy activity in pancreatic cancer. Zhang, Zhuangzhuang#, Lijun Cheng#, Jie Li#, Qi Qiao, Anju Karki, Derek B. Allison, Nuha Shaker et al. "Targeting Plk1 sensitizes pancreatic cancer to immune checkpoint therapy." Cancer Research 82, no. 19 (2022):3532-3548. (#Co-first authors, High Impact Factor of 12.8 ) (This study illustrates the molecular mechanism of how how cold effect changes to hot effect to immune PDL1 inhibitor during pancreatic cancer progression. Plk1 inhibitor resistance will cause immune checkpoint gene activated. Combination drug treatment PDL1 inhibitor and plk1 inhibitor can overcome the ‘rewiring’ to stop pancreatic cancer patients'progression)
• XDeath shinyapps cloud for therapeutic target identification. Zhuangzhuang Zhang#, Lijun Cheng #, Qiongsi Zhang, Yifan Kong, Daheng He, Kunyu Li, Matthew Rea, Jianling Wang, Ruixin Wang, Jinghui Liu, Zhiguo Li, Chongli Yuan, Enze Liu, Yvonne N. Fondufe-Mittendorf, Lang Li, Chi Wang and Xiaoqi Liu*. Co-targeting Plk1 and DNMT3a in advanced prostate cancer. Advanced Science. 2021 Jul;8(13):e2101458. doi: 10.1002/advs.202101458. (#Co-first authors, High Impact Factor of 16.8 ) (This study illustrates the PLK1 signaling pathway switching mechanism with DNMT3A signaling pathway. Drug combination inhibition both on DNMT3A and PLK1 can overcome the ‘rewiring’ to stop prostate cancer patients'progression)
• Zhuangzhuang Zhang, Lijun Cheng , Jie Li, Elia Farah, Nadia M Atallah, Pete E Pascuzzi, Sanjay Gupta, Xiaoqi Liu. Inhibition of the Wnt/β-catenin pathway overcomes resistance to enzalutamide in castration-resistant prostate cancer. Cancer research. 2018, 78 (12): 3147-3162. ( High Impact Factor of 12.8 . This study illustrates the Wnt/ β-catenin pathway switching mechanism with AR signaling pathways and seek new drug combination to overcome the Wnt pathway activated after enzalutamide resistance.)
• Jinghui Liu, Daheng He, Lijun Cheng , Changkun Huang, Yanquan Zhang, Xiongjian Rao, Yifan Kong, Chaohao Li, Zhuangzhuang Zhang, Jinpeng Liu, Karrie Jones, Dana Napier, Eun Y Lee, Chi Wang, Xiaoqi Liu. p300/CBP inhibition enhances the efficacy of programmed death-ligand 1 blockade treatment in prostate cancer. Oncogene. 2020, 39:3939-3951. ( High Impact Factor of 9.86 , this study illustrates the cell death immunology pathway switching mechanism with AR signaling pathways and seek new drug combination to overcome the new pathway activate.)

Latest funding granted in molecular mechanism and drug development

• R01 ES032026, In utero endocrine disruption causes cell type specific alterations that promote breast cancer, co-investigator
• R01 GM135234, Mitochondrial metabolism in microbial sepsis, co-investigator
• U01, An informatics bridge over the valley of death for cancer Phase I trials for drug combination therapies, co-investigator
• R01, Maternal and pediatric precision in therapeutics data, model, knowledge, and research coordination center (IU-OSU MPRINT DMKRCC), co-investigator
• P54 HD090215, Optimization of therapeutic approaches for children with relapsed sarcomas using precision medicine, co-investigator

PREVIOUS POSITIONS

• 05/2015 – 12/2017 Assistant Researcher Professor, Department of Medical and Molecular Genetics, Indiana University, Indianapolis, Indiana, U.S.
• 04/2014 – 05/2015 Postdoctoral Fellow, Department of Medical and Molecular Genetics, Indiana University, Indianapolis, IN, U.S.
• 12/2012 – 03/2014 Assistant Researcher, Shanghai Jiao Tong University, State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, P. R. China
• 07/2008 – 11/2012 Donghua University, P.R. China, and Concordia University, Canada, Joint Ph.D. , Artificial Intelligence Pattern Identification, Electrical Engineering and Computer Science

DEVELOPPED SOFTWARE

Drug response prediction in precision medicine, DRPM interactive website platform

• DRPM is for predicting an individual's response to cancer target therapy by patient gene expression profile. To drug resistance patient, DRPM will recommend optional drug treatment.
• A novel artificial intelligence modeling (layer optimal pattern matching) between cancer cells and tumors based on both gene expression profiles and drug response to seek optional experimental cells and drugs for individual patients.
• The DRPM connected the evidence from patients to the basic science experiment to generate a therapeutic hypothesis providing a strong theoretical basis.
• User evaluation and feedback, usability surveys to DRPM
• How to cite: Cheng, Lijun , Abhishek Majumdar, Daniel Stover, Shaofeng Wu, Yaoqin Lu, and Lang Li. 2020. "Computational Cancer Cell Models to Guide Precision Breast Cancer Medicine" Genes 11, no. 3: 263. https://doi.org/10.3390/genes11030263

TrilogPM platform for precision medicine– “the right treatment, for the right patient, at the right time."

• TrilogPM, a comprehensive evidence knowledgebase in precision cancer medicine. It is a Shiny web server currently for searching drug, target, and genome variation (biomarkers) in copy number variation, mutation and fusion, and cancer type and associated drug treatment. The system integrated the most famous six precision medicine database together to provide a comprehensive evidence knowledgebase in precision cancer medicine for clinical practice and clinical trial generate hypothesis.
• TrilogyPM software is freely available to biomedical researchers and educators in the non-profit sector.
• User evaluation and feedback, usability surveys to TrilogyPM

PDAC_Immune

• How to turn cold tumors into hot tumors by regulating immune checkpoint expression.
• Although targeting Plk1 to treat pancreatic cancer (PDAC) has been attempted in clinical trials, the results were not promising, and the mechanisms of resistance to Plk1 inhibition is poorly understood. In addition, the role of Plk1 in PDAC progression requires further elucidation.
• Our latest results show combination immunotherapy and inhibition of the cell cycle can improve immunotherapy effective.
• PDACImmune is developed to disclose the relationship between cell/gene expression variation and survi...