HOCMO: A Tensor-based Higher-Order Correlation Model to Deconvolute Epigenetic Microenvironment in Breast Cancer

Abstract

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. To fill this gap, 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.

NeTOIF: A Network-based Approach for Time-Series Omics Data Imputation and Forecasting

“…In addition to missing or incomplete values, shortage of data is another problem, especially for time-series omics data collected from dynamic observations. For example, capturing the dynamic interactions among growth promoting ligands, signaling proteins, histone modifications, and genes in the breast cancer microenvironment requires integrating multiple time-series omics data (e.g., proteomics and genomics ) (Shi et al, 2020). Typically these time-series datasets do not align perfectly, i.e., each omic data only have observations in few time points.…”