Integrated computational modeling of multi-scale biomedical data

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Pancreas cancer is a uniformly deadly disease due to the late stage at diagnosis and the lack of effective therapies. The proposed research program will develop new methods to analyze and integrate biomedical data at multiple scales to better elucidate the underlying mechanisms of pancreatic cancer where mutational analysis alone fails to define complete biological landscapes. The proposed program aims to bridge this critical barrier by integrating omics-based analyses of diagnostic imaging, histopathology slides, genomic data, and other sources to provide a unified, cogent approach to pancreatic cancer characterization. Substantial advances in machine learning algorithms combined with improvements in the resolution of diagnostic imaging, routine digitization of microscopy slides, and the influx of next generation sequencing has created an unrivaled opportunity that this program exploits. Diagnostic imaging, molecular profiling, and histologic imaging create a more complete biological picture than any individual modality. Our long-term goal is to harness data to change the way that patients are treated, utilizing novel computational methods to realize the promise of precision medicine - the greatest challenge in modern cancer care. Training of highly qualified personnel in this program will include experience in machine learning, biomedical data science, and computational modeling. Five PhD, three Master's, and four undergraduate students will receive training in the proposed program. HQP trained in machine learning and biomedical data science are in high demand in Canada and trainees will play a critical role in accelerating state-of-the-art approaches that will radically transform care for cancer patients by informing individualized treatments, at a reduced cost to Canadian healthcare.