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; Liewei Wang

doi:10.18632/oncotarget.16109

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

Journal article

Open access

Peer reviewed

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

Oncotarget, Vol.8(16), pp.27199-27215

04/18/2017

DOI: 10.18632/oncotarget.16109

PMCID: PMC5432329

PMID: 28423712

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https://doi.org/10.18632/oncotarget.16109View

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Abstract

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.

AMP-Activated Protein Kinases - metabolism

Breast Neoplasms - drug therapy

Breast Neoplasms - genetics

Breast Neoplasms - metabolism

Breast Neoplasms - pathology

cdc42 GTP-Binding Protein - genetics

Cell Line, Tumor

Cell Movement - drug effects

Cell Movement - genetics

Cluster Analysis

Computational Biology - methods

Female

Gene Expression Profiling

Gene Expression Regulation, Neoplastic - drug effects

Gene Knockdown Techniques

Humans

Metformin - pharmacology

Signal Transduction - drug effects

Unsupervised Machine Learning

Details

Title: Subtitle: Model-based unsupervised learning informs metformin-induced cell-migration inhibition through an AMPK-independent mechanism in breast cancer
Creators: Arjun P Athreya - University of Illinois Urbana-Champaign
Krishna R Kalari - Mayo Clinic in Florida
Junmei Cairns - Mayo Clinic
Alan J Gaglio - University of Illinois Urbana-Champaign
Quin F Wills - MRC Weatherall Institute of Molecular Medicine
Nifang Niu - University of Chicago
Richard Weinshilboum - Mayo Clinic
Ravishankar K Iyer - University of Illinois Urbana-Champaign
Liewei Wang - Mayo Clinic
Resource Type: Journal article
Publication Details: Oncotarget, Vol.8(16), pp.27199-27215
DOI: 10.18632/oncotarget.16109
PMID: 28423712
PMCID: PMC5432329
NLM abbreviation: Oncotarget
ISSN: 1949-2553
eISSN: 1949-2553
Publisher: Impact Journals LLC
Grant note: U19 GM061388 / NIGMS NIH HHS P50 CA116201 / NCI NIH HHS R01 CA196648 / NCI NIH HHS R01 GM028157 / NIGMS NIH HHS
Language: English
Date published: 04/18/2017
Academic Unit: Stead Family Department of Pediatrics; Medical Genetics and Genomics
Record Identifier: 9984701545402771

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