Daniela F. Quail


Daniela F. Quail, Ph.D., is an Assistant Professor at the Rosalind and Morris Goodman Cancer Institute and the Departments of Physiology and Experimental Medicine at McGill University (Montréal, Canada). She received a Ph.D. from the University of Western Ontario (London, Canada) where she studied microenvironmental regulation of cancer stemness and metastasis. She later completed a postdoctoral fellowship at Memorial Sloan Kettering Cancer Center (NY, USA). Her postdoctoral research was focused on how the immune microenvironment impacts cancer progression and prognosis, with particular interest in the myeloid compartment. She contributed to a body of research characterizing the effects of the innate immune system in cancer, including the role of macrophages in brain tumors, and neutrophils and monocytes in breast cancer metastasis. Since opening her lab at McGill, Dr. Quail served as the Early Career Representative for the American Association for Cancer Research Tumor Microenvironment Working Group (2017), she is the recipient of the Canadian Society for Immunology New Investigator Award (2023) and the McGill Principal’s Prize for Outstanding Emerging Researchers (2023), and she holds a Tier II Canada Research Chair in Tumor Microenvironment research (2018-2028). Currently, the Quail lab uses high-parameter tools to study the role of lifestyle factors on tumor immunology, including developmental regulation of the myeloid compartment and its impact on cancer progression and therapy response. 


There has been a surge of emerging single cell technologies in recent years that have enabled characterization of the tumour microenvironment in unprecedented detail. However, many of these technologies rely on dissociated tissues, preventing our ability to resolve cell-cell relationships that are critical to understand mechanisms of tumor immunity. In my presentation I will discuss my team’s application of imaging mass cytometry to study dynamic interactions of immune cells within the tumour niche. The datasets we have generated have been valuable in learning about new cell populations and their connections to clinical outcomes, and in predicting patients at risk of disease progression through machine learning approaches. Understanding the spatial contexture of cancer is the new direction for single cell technologies, and we have only scratched the surface of this exciting emerging field.