Welcome to the Dr. Ling Cai Lab!
Bioinformatics, data integration
Cancer metabolism
Inborn errors of metabolism
Lung neoplasm
Ling Cai, Fangjiang Wu, Qinbo Zhou, Ying Gao, Bo Yao, Ralph J. DeBerardinis, George K. Acquaah-Mensah, Vassilis Aidinis, Jennifer E. Beane, Shyam Biswal, Ting Chen, Carla P. Concepcion-Crisol, Barbara M. Grüner, Deshui Jia, Robert A. Jones, Jonathan M. Kurie, Min Gyu Lee, Per Lindahl, Yonathan Lissanu, Corina Lorz, David MacPherson, Rosanna Martinelli, Pawel K. Mazur, Sarah A. Mazzilli, Shinji Mii, Herwig P. Moll, Roger A. Moorehead, Edward E. Morrisey, Sheng Rong Ng, Matthew G. Oser, Arun R. Pandiri, Charles A. Powell, Giorgio Ramadori, Mirentxu Santos, Eric L. Snyder, Rocio Sotillo, Kang-Yi Su, Tetsuro Taki, Kekoa Taparra, Phuoc T. Tran, Yifeng Xia, J. Edward van Veen, Monte M. Winslow, Guanghua Xiao, Charles M. Rudin, Trudy G. Oliver, Yang Xie, John D. Minna
Cancer Res 15 May 2025; 85 (10): 1769–1783. https://doi.org/10.1158/0008-5472.CAN-24-1607
Ling Cai, Nia G. Hammond, Alpaslan Tasdogan, Massar Alsamraae, Chendong Yang, Robert B. Cameron, Peiran Quan, Ashley Solmonson, Wen Gu, Panayotis Pachnis, Mayher Kaur, Brianna K. Chang, Qin Zhou, Christopher T. Hensley, Quyen N. Do, Luiza Martins Nascentes Melo, Jessalyn M. Ubellacker, Akash Kaushik, Maia G. Clare, Isabel N. Alcazar, Katarzyna Kurylowicz, Joseph D. Marcuccilli, Gabriele Allies, Andrea Kutritz, Joachim Klode, Vijayashree Ramesh, Thomas J. Rogers, Aparna D. Rao, Hannah E. Crentsil, Hong Li, Fang Brister, Phyllis McDaniel, Xiaohong Xu, Bret M. Evers, Lauren G. Zacharias, Jessica Sudderth, Jian Xu, Thomas P. Mathews, Dwight Oliver, John D. Minna, John Waters, Sean J. Morrison, Kemp H. Kernstine, Brandon Faubert, Ralph J. DeBerardinis
Cancer Discov 1 April 2025; 15 (4): 702–716. https://doi.org/10.1158/2159-8290.CD-23-1319
Jui-Chung Chiang, Zengfu Shang, Tracy Rosales, Ling Cai, Wei-Min Chen, Feng Cai, Hieu Vu, John D Minna, Min Ni, Anthony J Davis, Robert D Timmerman, Ralph J DeBerardinis, Yuanyuan Zhang
Sci. Adv. 11, eadt1241(2025). DOI:10. 1126/sciadv. adt1241
Our lab develops scalable and user-friendly computational tools to support research. We build informatics platforms that enable researchers to navigate, analyze, and interpret complex omics datasets across human and preclinical models.
Projects and tools include:
• Lung Cancer Autochthonous Model Gene Expression Database (LCAMGDB): A web app for transcriptomic data from over 1,300 samples across > 50 labs, enabling cross-model comparisons.
- https://lccl.shinyapps.io/LCAMGDB/
- Cai et al., Cancer Res 2025 (PMID: 40298430)
• Lung Cancer Explorer (LCE): An open web portal for survival and co-expression analysis across > 6,700 lung cancer patients in 56 studies.
- https://lce.biohpc.swmed.edu/
- Cai et al., Oncogene 2019 (PMID: 30532070)
- Cai et al., Cancers 2019 (PMID: 31242643)
• Functional Data Consistency Explorer (FDCE): A web app evaluating consistency across drug and CRISPR/RNAi screens.
- https://lccl.shinyapps.io/FDCE/
- Cai et al., Oncogene 2019 (PMID: 30532070)
- Cai et al., Cancers 2019 (PMID: 31242643)
• CoMut: A co-mutation analysis tool using AACR Project GENIE pan-cancer patient data.
- http://lccl.shinyapps.io/comut/
- Cai et al., Cancers 2022 (PMID: 36077736)
• NEcalc: A web tool for developing a generalized neuroendocrine (NE) score for classifying tumors with NE features.
- https://lccl.shinyapps.io/NEcalc/
- Cai et al., iScience 2023 (PMID: 37378310)
• Genomic Regression Analysis of Co-Expression (GRACE): A web app facilitating co-expression analysis using denoised TCGA RNA-seq data (adjusted for DNA copy number).
- https://grace.biohpc.swmed.edu/
- Cai et al., Nat Commun 2017 (PMID: 29259170)
• Gazdar Neuroendocrine Transdifferentiation Explorer (GSNE): A web tool for small cell lung cancer and neuroblastoma transcriptomic comparisons in patient and preclinical model datasets.
- https://lccl.shinyapps.io/GSNE/
- Cai et al. (https://doi.org/10.1101/2022.08.01.502252)
Under development (feedback and data contribution welcomed):
• Lung Cancer Cell Line Explorer (LCCL): A web tool to explorer multi-omics lung cancer cell line data.
• Lung Model Pathology Viewer (LUMP): An access-controlled pathology image archive of in vivo lung cancer preclinical model pathology images.
We investigates metabolic heterogeneity and genotype-specific programs in cancer and rare disease. In collaboration with the DeBerardinis lab(https://cri.utsw.edu/faculty/ralph-deberardinis/), we integrate metabolomics, isotope tracing, transcriptomics, and imaging to uncover key metabolic dependencies, identify biomarkers of tumor aggressiveness, and support translational tool development.
Selected publication:
Lung Cancer
Kidney Cancer
Tool Development
In collaboration with SCLC investigators, we explored neuroendocrine (NE) transdifferentiation and its implications for immune evasion and molecular reclassification. Our work revealed parallels between SCLC and neuroblastoma, informed NE scoring systems, and helped reclassify YAP1⁺ SCLC lines as SMARCA4-deficient malignancies.
Selected publication:
We have leveraged public and institutional NSCLC datasets to generate integrative analyses and provide insights for collaborating investigators.
Selected publication:
Silencing of GRHL2 induces epithelial‑to‑mesenchymal transition in lung cancer cell lines with different effects on proliferation and clonogenic growth
📂 Pathology Image Archive (LUMP Viewer)
We are building an access-controlled digital pathology archive of H&E-stained sections from in vivo lung cancer preclinical models. This effort aims to enable cross-study comparisons of histologic subtypes and tumor microenvironments.
• 🔗 LUMP Viewer🧬 TMA and Xenium Profiling Initiative
We are assembling tissue microarrays (TMAs) of tumors from lung cancer autochthonous models for spatial transcriptomics profiling using 10x Xenium. This collaborative effort aims to develop preclinical model-specific digital pathology algorithms evaluate, tumor heterogeneity, cell-cell interactions, and model fidelity across lung cancer genotypes.
• Our first Xenium profiling will begin in mid-2025 using archived blocks and new TMAs.Dr. Ling Cai is an Assistant Professor in the Peter O’Donnell School of Public Health, the Children’s Research Institute, and a member of the Quantitative Biomedical Research Center (QBRC) and the Harold C. Simmons Cancer Center at UT Southwestern Medical Center (UTSW).
During her Ph.D. training in the Mechanism of Disease Track of the Integrative Biology Graduate Program at UTSW, she received comprehensive training in basic science, with a particular focus on cellular metabolism and disease mechanisms. This foundation has been instrumental in her research on understanding the intricate molecular mechanisms and metabolic pathways implicated in cancer development and progression. Her postdoctoral work focused on the integrated analysis of high-dimensional datasets from cancer metabolism research and developed various bioinformatics tools and databases for lung cancer research.
In her role as an Assistant Professor, Dr. Cai continues to delve into cancer metabolism and inborn errors of metabolism. Her research endeavors are centered around understanding the complex nature of tumors and the role of metabolic pathways in cancer and metabolic diseases. She is also actively involved in the creation and upkeep of data management and analysis systems specifically designed for lung cancer research.