A Comparative Analysis of the Molecular Characteristics of Canine and Human Gliomas

July 27, 2022 02:00 p.m. - 03:00 p.m. ET
Virtual

Register for the July Cancer Genomics Cloud (CGC) webinar where Dr. Nadia Lanman will present her research studying human gliomas using dogs as model organisms.

Spontaneous gliomas—a type of tumor that occurs in the brain and spinal cord—in dogs are being treated as a translational model for human glioma, though molecular characterization is not yet complete. This work utilizes publicly available data sets to characterize the molecular characteristics of canine gliomas. Dr. Lanman will discuss how key canonical pathways altered in human gliomas are likewise altered in canine gliomas, and how the canine tumor microenvironment (TME), like that in humans, appears to be immunosuppressive. Gene expression profiles of astrocytomas and oligodendrogliomas show alterations in a number of signaling pathways, including several immune-related and TME-specific pathways.

Dr. Lanman and her team will show how they developed a Naïve Bayes classifier that accurately classifies canine glioma pathologies based on gene expression profiles alone.

As one of the three Cloud Resources within the NCI Cancer Research Data Commons (CRDC), the Seven Bridges’ CGC provides researchers access to a wide variety of data sets, a catalog of tools to analyze and visualize the data directly from the browser, and scalable computational resources to perform large scale analysis on the cloud.

Nadia Lanman, Ph.D.

Dr. Lanman is a research assistant professor in the Department of Comparative Pathobiology at Purdue University. Dr. Lanman’s work at the cancer center focuses on managing the bioinformatics core, training, and data analysis. Her research is focused on utilizing large genomics data sets to expand our knowledge of the molecular basis of cancer, as well as immune and inflammatory diseases. Dr. Lanman is particularly interested in data integration and in developing methods for datasets that leverage temporal or spatial resolution.

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