Molecular Predictors of 3D Morphogenesis by Breast Cancer Cell Lines in 3D Culture

Correlative analysis of molecular markers with phenotypic signatures is the simplest model for hypothesis generation. In this paper, a panel of 24 breast cell lines was grown in 3D culture, their morphology was imaged through phase contrast microscopy, and computational methods were developed to segment and represent each colony at multiple dimensions. Subsequently, subpopulations from these morphological responses were identified through consensus clustering to reveal three clusters of round, grape-like, and stellate phenotypes. In some cases, cell lines with particular pathobiological phenotypes clustered together (e.g., ERBB2 amplified cell lines sharing the same morphometric properties as the grape-like phenotype). Next, associations with molecular features were realized through (i) differential analysis within each morphological cluster, and (ii) regression analysis across the entire panel of cell lines. In both cases, the dominant genes that are predictive of the morphological signatures were identified. Specifically, PPARγ has been associated with the invasive stellate morphological phenotype, which corresponds to triple-negative pathobiology. PPARγ has been validated through two supporting biological assays.Author Summary: Cell culture models are an important vehicle for understanding biological processes and evaluation of therapeutic reagents. More importantly, the literature suggests that tumor cells grown in 3D exhibit pronounced drug and radiation resistances that are remarkably similar to that of tumors in vivo. Therefore, the needs for quantifying 3D assays continue to grow. In this paper, we develop robust computational methods to integrate morphometric and molecular information for a panel of breast cancer cell lines that are grown in 3D. Specifically, morphometric traits are imaged through microscopy, and then quantified computationally. We then show that these morphometric traits can identify subtypes within this panel of breast cancer cell lines, and that the subtypes are clinically relevant in terms of being ERBB2 positive or triple negative. These subtypes and their representations are then associated with their molecular data to reveal PPARG as an important marker for triple-negative breast cancer. Finally, we design two independent experiments to show the validity of this marker in both 3D cell culture models and human breast cancer tissue.