Despite advancements in both treatment and detection, breast cancer remains the second leading cause of cancer-related deaths in women. While breast cancer patients are often stratified through the tissue-wide assessment of histopathological markers, recent evidence has shown that tumors consist of multiple cell subpopulations with varied proliferative, invasive, and therapeutic resistive properties that determine patient outcomes. Historically, studies on the development of different tumor cell phenotypes, otherwise known as tumor cell heterogeneity, have focused on oncogenic mutations that increase cellular fitness in response to environmental pressures. However, phenotypic heterogeneity can develop through transient changes in the transcriptome, phosphoproteome, and metabolic profile, along with varied interactions with the extracellular matrix. Thus, understanding the drivers of heterogeneity will require multidisciplinary approaches that integrate results from both computational and experimental studies that characterize both molecular and biophysical properties of tumor cells and their surroundings. Overall, the goal of the work presented in this dissertation is to establish a framework for integrative approaches for studying tumor cell heterogeneity that considers effects from the physicochemical properties of the extracellular matrix. The results presented here demonstrate how insights from multiple omics technologies, data science approaches, imaging, and engineered extracellular matrix models can converge to identify regulators of tumor cell subpopulations like the cancer stem-like phenotype. Altogether, the results of this doctoral work highlight the interdependence of metabolism, invasion, and the stem-like phenotype. Further, extracellular matrix models complement cancer biology studies, but also emphasize the extent of heterogeneity that can exist. Additional studies are needed to corroborate the observed interplay between biochemical and physical properties of the extracellular matrix with the molecular and biophysical cellular phenotypes. However, the collective results of this doctoral work provide an important basis for taking a systems biology approach that considers heterogeneity in both the extracellular matrix and cell phenotypes to understand tumor progression.