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,Richard Weinshilboum,Liewei Wang,Nifang Niu,Quin F Wills,Alan J Gaglio,Ravishankar K Iyer

doi:10.18632/oncotarget.16109

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.

Highlights

The emergence of high-throughput, single-cell RNA sequencing has allowed investigators for the first time to determine levels of gene expression in hundreds of individual cells simultaneously [1]
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
Cells were treated with aminoimidazole-4-carboxyamide ribonucleoside (AICAR) or were co-transfected with AMPKα1 and AMPKα2 siRNAs followed by incubation with or without metformin (5 mM for SU86 and 1 mM for MDA-MB-231) for 48 hours

Summary

Introduction

The emergence of high-throughput, single-cell RNA sequencing (scRNA-seq) has allowed investigators for the first time to determine levels of gene expression in hundreds of individual cells simultaneously [1]. In comparison to conventional RNA-seq, which provides an aggregate view of the cells in a tissue sample, scRNAseq can simultaneously measure the expression level of the entire genome in all of the individual cells of the sample, allowing the characterization of all cell types and states present [1]. In this work we utilized the benefits of scRNA-seq to gain insights into metformin’s molecular mechanisms in MDA-MB231 triple-negative breast cancer www.impactjournals.com/oncotarget cells. Triple negative is the molecular subtype of breast cancer for which no highly effective targeted therapy currently exists [2]. The therapeutic effect of metformin in the treatment and prevention of TNBC remains unclear [7, 8], and there are no pharmacogenomic biomarkers for selecting responsive patients

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