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A Non-Negative Tensor Factorization Approach to Deconvolute Microenvironment in Breast Cancer

Min Shi, Liubou Klindziuk, Shamim Mollah
doi: https://doi.org/10.1101/2020.12.01.406249
Min Shi
1Department of Genetics, Washington University School of Medicine, St. Louis, MO, 63110, USA
2Institute for Informatics, Washington University School of Medicine, St. Louis, MO, 63110, USA
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Liubou Klindziuk
2Institute for Informatics, Washington University School of Medicine, St. Louis, MO, 63110, USA
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Shamim Mollah
1Department of Genetics, Washington University School of Medicine, St. Louis, MO, 63110, USA
2Institute for Informatics, Washington University School of Medicine, St. Louis, MO, 63110, USA
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  • For correspondence: smollah@wustl.edu
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Abstract

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.edu

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted December 02, 2020.
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A Non-Negative Tensor Factorization Approach to Deconvolute Microenvironment in Breast Cancer
Min Shi, Liubou Klindziuk, Shamim Mollah
bioRxiv 2020.12.01.406249; doi: https://doi.org/10.1101/2020.12.01.406249
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A Non-Negative Tensor Factorization Approach to Deconvolute Microenvironment in Breast Cancer
Min Shi, Liubou Klindziuk, Shamim Mollah
bioRxiv 2020.12.01.406249; doi: https://doi.org/10.1101/2020.12.01.406249

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