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The Cancer Genome Atlas (TCGA) is a rapidly expanding resource that is accelerating discovery in cancer by providing the research community with minable genomics and clinical outcome data. Recently pathology images, from H&E stained samples, have been added to complement the molecular and clinical data. However, utilization of whole slide images is substantially hindered by the batch effects, biological heterogeneity, and tumor composition. A computational pipeline is presented that overcome these complexities for revealing intrinsic subtypes from morphometric signature of a cohort of 250 GBM patients. Subsequently, molecular correlates of each subtype are constructed for targeted therapy. In addition to computed morphometric subtypes, tumor heterogeneity is also computed to evaluate if heterogeneity is more virulent in predicting the outcome.
Dr. Bahram Parvin is a principal scientist at the Lawrence Berkeley National Laboratory and has an adjunct appointment with the EE Department at the U.C. His laboratory focuses on technology development for realization of pathway pathology, elucidating molecular signature of aberrant morphogenesis in engineered matrices, and screening for probes for labeling and cargo delivery. He has published over 100 papers and was the General Chair of the IEEE Int. Symp. On Biomedical Imaging: from nano to macro in 2013. He is also an Associate Editor for IEEE Transactions on Medical Imaging, a member of the steering committee for IEEE Bioimaging and Signal Processing and IEEE Bioengineering and Health Care.