A major component of cancer’s complexity lies in its heterogeneity. Because cancer heterogeneity can manifest across multiple spatiotemporal and biological scales1–4, comprehensive characterization is challenging. Assays that allow controlled experimentation to determine the mechanisms behind cancer heterogeneity remain underdeveloped. Many existing technologies rely on exploitation of predetermined characteristics5–7, which precludes exploration of phenotypes that are ill-defined. Other platforms begin with blind genomics, using post-sequencing experiments for validation8,9. These approaches inherently require significant differences in the genomics underlying heterogeneity to elucidate potential mechanisms, since unsupervised clustering relies on deploying mathematics to obtain clear separations10–12. While successful when involving multiple cell types, since their biological profiles lie in separable state spaces6,8,13,14, unsupervised analysis has limitations when evaluating more similar cells, such as within-cell-type heterogeneity, as it becomes difficult to separate profiles that are highly alike. A study on this specialized scope has not yet been done, probably due to the challenges of parsing a more subtly heterogeneous sample.This dissertation describes the development and application of a platform that enables detailed interrogation of within-cell-type heterogeneous, 3D collective cancer cell migration phenotypes. Collective migration is a process where multiple cells coordinate their movement15,16. Recent studies point to the importance of collective cell migration in cancer metastasis17–20. Although various phenotypes have been identified, factors that regulate collective behavior are not fully understood15,16,21. Using a flexible, microscopy-based platform, I identified and isolated cells that exhibit invasive and non-invasive collective migration. By first defining functional subpopulations, I deployed a supervised approach to determine the mechanisms behind this aspect of heterogeneity. I found that the collectively invasive phenotype is associated with upregulated proliferation, increased stress responses, and the ductal carcinoma subtype, while the collectively non-invasive phenotype was associated with immune-related processes and the luminal carcinoma subtype. Functional perturbation of differentially expressed genes resulted in shifts in migration phenotypes. These results validate the platform I developed for identifying mechanisms of within-cell-type heterogeneity. Furthermore, the results demonstrate a link between migration regulation, stress response, proliferation, and immune response and indicate potential value in exploring how collective invasion may be controlled through these associated modules.