Unraveling Cancer Diversity through the Geometry of its Data

Monica Nicolau
Stanford University Medical School

Monica Nicolau, PhD
Department of Microbiology & Immunology/Baxter Lab
Center for Cancer Systems Biology
Stanford School of Medicine
The advent of high throughput omic data is shedding light on a pervasive, fascinating yet difficult characteristic of cancer: the pathology of cancer shows immense diversity, raising a multitude of challenges. Early insights into this aspect of cancer were gained through clustering to identify distinct disease groups in the population. Now more
sophisticated data analysis methods are beginning to shed light on a wide range of distinctions in cancer types, from gradual disease progressions, to identifying the cells from which tumors originate. I will discuss these aspects of analysis, focusing on the concrete applications to cancer biology of these novel data analysis methods. These
examples include the discovery of a new clinically and biologically coherent breast cancer type with 100% survival, and distinct subgroups of acute myeloid leukemia types based on differences in the extent to which normal hematopoiesis is disrupted by the cancer.

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