Daniel J McGrail, PhD

Assistant Staff
Lerner Research Institute, Cleveland Clinic
Immune Oncology Program
Case Comprehensive Cancer Center

At every stage of my research career I have been highly productive, demonstrating the adaptability to succeed while pursuing diverse research topics. Integrating my expertise in both experimental and computational biology has resulted both in a robust publication record, but and success in obtaining research funding during my undergraduate (President’s Undergraduate Research Award, Petit Scholar), graduate (NSF GRFP), and postdoctoral (Susan G. Komen, NCI K99/R00) studies.

During my pre-doctoral work, I gained extensive experience using quantitative image analysis and other computational approaches for studying cell biophysics. I sought to build upon this during my postdoctoral studies to gain the skills necessary to harness on the abundance of “big data” being produced through efforts such as The Cancer Genome Atlas, as well as pursue more translational research, so joined the Department of Systems Biology at MD Anderson to accomplish this. Since joining MD Anderson I have gained this experience and used systems-level computational analyses to discover how proteome instability is a vulnerability in DNA mismatch repair deficient cancer, elucidate divergent causes of immunogenicity across cancers, elucidate ways to target deficient DNA replication stress, develop novel ways to schedule drugs and identify gene expression biomarkers to identify patients who may benefit from treatment. These studies have resulted in multiple high-impact publications in journals such as Cancer Cell, Nature Communications, Annals of Oncology, and Science Translational Medicine. My current research focus is on leveraging multi-omics technologies to improve immunomodulatory cancer therapies.

Research Information

Research Interests

Research in the McGrail lab integrates systems-level analyses with controlled experimental models in order to advance precision medicine in oncology and other human diseases. We use an array of large-scale data (e.g. genetic variation, epigenetic modifications, transcriptomics, proteomics, highly multiplexed imaging, and functional genomics) to inform our in vitro and in vivo systems.

We are particularly interested in applying this toolkit to better understand determinants of immune checkpoint blockade (ICB) sensitivity. We found that correlates of tumor immunogenicity and ICB response may be largely divergent between different types of cancers (Annals of Oncology 2021, Science Translational Medicine 2021, Nature Communications 2018). We hypothesize these dichotomous associations may indicate underlying fundamental differences in drivers of ICB sensitivity and resistance. By understanding these difference we hope to improve patient stratification for treatment with ICB and identify novel approaches to enhance anti-tumor immunity. Additional areas of interest include DNA damage response (Cancer Cell 2019, Cancer Cell 2020, npj Sys Bio and Appl 2017), functional genomics, tumor microbiome, and biomarker discovery.