TY - JOUR T1 - A Non-Negative Tensor Factorization Approach to Deconvolute Microenvironment in Breast Cancer JF - bioRxiv DO - 10.1101/2020.12.01.406249 SP - 2020.12.01.406249 AU - Min Shi AU - Liubou Klindziuk AU - Shamim Mollah Y1 - 2020/01/01 UR - http://biorxiv.org/content/early/2020/12/02/2020.12.01.406249.abstract N2 - Motivation An in-depth understanding of epithelial breast cell responses to the growth-promoting ligands is required to elucidate how the microenvironment (ME) signals affect cell-intrinsic regulatory networks and the cellular phenotypes they control, such as cell growth, progression, and differentiation. This is particularly important in understanding the mechanisms of breast cancer initiation and progression. However, the current mechanisms by which the ME signals influence these cellular phenotypes are not well established.Results We developed a high-order correlation method using proteomics data to reveal the regulatory dynamics among proteins, histones, and six growth-promoting ligands in the MCF10 cell line. In the proposed method, the protein-ligand and histone-ligand correlations at multiple time points are first encoded in two three-way tensors. Then, a non-negative tensor factorization model is used to capture and quantify the protein-ligand and histone-ligand correlations spanning all time points, followed by a partial least squares regression process to model the correlations between histones and proteins. Our method revealed the onset of specific protein-histone signatures in response to growth ligands contributing to distinct cellular phenotypes that are indicative of breast cancer initiation and progression.Contact smollah{at}wustl.eduCompeting Interest StatementThe authors have declared no competing interest. ER -