Research Papers:

Model-based unsupervised learning informs metformin-induced cell-migration inhibition through an AMPK-independent mechanism in breast cancer

Arjun P. Athreya, Krishna R. Kalari, Junmei Cairns, Alan J. Gaglio, Quin F. Wills, Nifang Niu, Richard Weinshilboum, Ravishankar K. Iyer and Liewei Wang _

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Oncotarget. 2017; 8:27199-27215. https://doi.org/10.18632/oncotarget.16109

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Arjun P. Athreya1,*, Krishna R. Kalari2,*, Junmei Cairns3,*, Alan J. Gaglio4, Quin F. Wills5,7, Nifang Niu6, Richard Weinshilboum3, Ravishankar K. Iyer1, Liewei Wang3

1Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Champaign, IL, USA

2Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA

3Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA

4Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Champaign, IL, USA

5Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK

6Department of Pathology, University of Chicago, Chicago, IL, USA

7Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK

*These authors contributed equally to this work

Correspondence to:

Liewei Wang, email: [email protected].

Ravishankar K. Iyer, email: [email protected]

Keywords: single cell, RNA-seq, breast cancer, metformin, unsupervised learning

Received: November 10, 2016     Accepted: February 18, 2017     Published: March 10, 2017


We demonstrate that model-based unsupervised learning can uniquely discriminate single-cell subpopulations by their gene expression distributions, which in turn allow us to identify specific genes for focused functional studies. This method was applied to MDA-MB-231 breast cancer cells treated with the antidiabetic drug metformin, which is being repurposed for treatment of triple-negative breast cancer. Unsupervised learning identified a cluster of metformin-treated cells characterized by a significant suppression of 230 genes (p-value < 2E-16). This analysis corroborates known studies of metformin action: a) pathway analysis indicated known mechanisms related to metformin action, including the citric acid (TCA) cycle, oxidative phosphorylation, and mitochondrial dysfunction (p-value < 1E-9); b) 70% of these 230 genes were functionally implicated in metformin response; c) among remaining lesser functionally-studied genes for metformin-response was CDC42, down-regulated in breast cancer treated with metformin. However, CDC42’s mechanisms in metformin response remained unclear. Our functional studies showed that CDC42 was involved in metformin-induced inhibition of cell proliferation and cell migration mediated through an AMPK-independent mechanism. Our results points to 230 genes that might serve as metformin response signatures, which needs to be tested in patients treated with metformin and, further investigation of CDC42 and AMPK-independence’s role in metformin's anticancer mechanisms.

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