Tumor initiation and progression is dependent on the acquisition and accumulation of multiple driver mutations that acti­vate and fuel oncogenic pathways and deactivate tumor suppressor networks. This complex continuum of non-stochastic genetic changes in accompaniment with error-prone mitoses largely explains why tumors are a mosaic of different cells. Contrary to the long-held notion that tumors are dominated by genetically-identical cells, tumors often contain many different subsets of cells that are remarkably diverse and distinct. The extent of this intratumor heterogeneity has bewildered cancer biologists’ and clinicians alike, as this partly illuminates why most cancer treatments fail. Unsurprisingly, there is no “wonder” drug yet available which can target all the different sub-populations including rare clones, and conquer the war on cancer. Breast tumors harbor ginormous extent of intratumoral heterogeneity, both within primary and metastatic lesions. This revelation essentially calls into question mega clinical endeavors such as the Human Genome Project that have sequenced a single biopsy from a large tumor mass thus precluding realization of the fact that a single tumor mass comprises of cells that present a variety of flavors in genotypic compositions. It is also becoming recognized that intratumor clonal heterogeneity underlies therapeutic resistance. Thus to comprehend the clinical behavior and therapeutic management of tumors, it is imperative to recognize and understand how intratumor heterogeneity arises. To this end, my research proposes to study two main features/cellular traits of tumors that can be quantitatively evaluated as “surrogates” to represent tumor heterogeneity at various stages of the disease: (a) centrosome amplification and clustering, and (b) mitotic frequency. This study aims at interrogating how a collaborative interplay of these “vehicles” support the tumor’s evolutionary agenda, and how we can glean prognostic and predictive information from an accurate determination of these cellular traits.