SCIT Quarterly Seminar @ Zoom: https://stanford.zoom.us/j/98960758162?pwd=aHJJc3pDS3FONkZIc2FoZ0hqcXU1dz09
April 22, 2020 @ 10:00 am – 11:00 am
2020-04-22T10:00:00-07:00
2020-04-22T11:00:00-07:00
Zoom: https://stanford.zoom.us/j/98960758162?pwd=aHJJc3pDS3FONkZIc2FoZ0hqcXU1dz09
Contact:
Sofia Gonzales
“Tumor-Immune Interactions in TNBC Brain Metastases”
Maxine Umeh Garcia, PhD

ABSTRACT: It is estimated that metastasis is responsible for 90% of cancer deaths, with 1 in every 2 advanced staged triple-negative breast cancer patients developing brain metastases – surviving as little as 4.9 months after metastatic diagnosis. My project hypothesizes that the spatial architecture of the tumor microenvironment reflects distinct tumor-immune interactions that are driven by receptor-ligand pairing; and that these interactions not only impact tumor progression in the brain, but also prime the immune system (early on) to be tolerant of disseminated cancer cells permitting brain metastases. The main goal of my project is to build a model that recapitulates tumor-immune interactions in brain-metastatic triple-negative breast cancer, and use this model to identify novel druggable targets to improve survival outcomes in patients with devastating brain metastases.

“Classification of Malignant and Benign Peripheral Nerve Sheath Tumors With An Open Source Feature Selection Platform”
Michael Zhang, MD

ABSTRACT: Radiographic differentiation of malignant peripheral nerve sheath tumors (MPNSTs) from benign PNSTs is a diagnostic challenge. The former is associated with a five-year survival rate of 30-50%, and definitive management requires gross total surgical with wide negative margins in areas of sensitive neurologic function. This presentation describes a radiomics approach to pre-operatively identifying a diagnosis, thereby possibly avoiding surgical complexity and debilitating symptoms. Using an open-source, feature extraction platform and machine learning, we produce a radiographic signature for MPNSTs based on routine MRI.