Student Presentation -- Katherine Aiello
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Ph.D. Dissertation Defense, Friday August 25, 2017 -- Comparative Spectral Decompositions for Predicting the Clinical Outcome of Astrocytoma

WEB 3780 (Evans Conference Room), 2:00 pm

Speaker: Katherine Aiello. Advisor: Dr. Orly Alter


As personalized medicine is integrated into clinical practice for the treatment of cancer, patient care will be centered around new methods of tumor diagnosis that are predictive of an individual patient's outcome based on a tumor's biology. Rather than prognosticating a tumor based solely on its observable anatomic features, the clinical and research communities recognize that it is important to consider the molecular features as well, which dictate outcome. This paradigm shift toward personalized diagnosis and treatment of tumors requires the identification and validation of robust molecular signatures that have high analytic and clinical validity. However, these fundamental patterns of biological variation that characterize a tumor's progression, and a patient's clinical outcome, are hidden in large, high-dimensional genomic datasets.

We develop comparative spectral decompositions, a set of universal mathematical frameworks that separate a signal into its underlying sources of variation, the same way a prism separates white light into its component colors. Rather than simplifying the data, as is commonly done, the decompositions make use of the complexity of the data in order to tease out the patterns within them. We recently demonstrated the effectiveness of these frameworks for modeling DNA copy-number data from glioblastoma (GBM) brain cancer patients, which revealed a genome-wide pattern of DNA copy-number aberrations (CNAs) that is predictive of patient survival and response to chemotherapy. Recurring DNA CNAs had been observed in GBM tumors' genomes for decades; however, copy-number subtypes that are predictive of patients' outcomes were not identified before, illustrating the universal ability of comparative spectral decompositions to find what other methods miss.

In this research, we build on those results by using comparative spectral decompositions to study lower-grade astrocytoma (LGA) patients' copy-number profiles, enabling prognostication of the LGA tumors and comparison of genomic aberrations that characterize the lower- and high-grade tumors. Additionally, demonstrate the analytic and clinical validity of the GBM pattern as a platform- and technology-independent prognostic predictor in the combined astrocytoma population, by classifying astrocytoma tumors based on genomic profiles measured by both microrarray and next generation sequencing technologies. The results reported here bring the GBM pattern a step closer to the clinic, where it can be implemented as a laboratory test and used to improve patient care.