A Stemness and EMT Based Gene Expression Signature Identifies Phenotypic Plasticity and is A Predictive but Not Prognostic Biomarker for Breast Cancer

Abstract

Aims: Molecular heterogeneity of breast cancer results in variation in morphology, metastatic potential and response to therapy. We previously showed that breast cancer cell line sub-groups obtained by a clustering approach using highly variable genes overlapped almost completely with sub-groups generated by a drug cytotoxicity-profile based approach. Two distinct cell populations thus identified were CSC(cancer stem cell)-like and non-CSC-like. In this study we asked whether an mRNA based gene signature identifying these two cell types would explain variation in stemness, EMT, drug sensitivity, and prognosis in silico and in vitro. Main methods: In silico analyses were performed using publicly available cell line and patient tumor datasets. In vitro analyses of phenotypic plasticity and drug responsiveness were obtained using human breast cancer cell lines. Key findings: We find a novel gene list (CNCL) that can generate both categorical and continuous variables corresponding to the stemness/EMT (epithelial to mesenchymal transition) state of tumors. We are presenting a novel robust gene signature that unites previous observations related either to EMT or stemness in breast cancer. We show in silico, that this signature perfectly predicts behavior of tumor cells tested in vitro, and can reflect tumor plasticity. We thus demonstrate for the first time, that breast cancer subtypes are sensitive to either Lapatinib or Midostaurin. The same gene list is not capable of predicting prognosis in most cohorts, except for one that includes patients receiving neo-adjuvant taxene therapy. Significance: CNCL is a robust gene list that can identify both stemness and the EMT state of cell lines and tumors. It can be used to trace tumor cells during the course of phenotypic changes they undergo, that result in altered responses to therapeutic agents. The fact that such a list cannot be used to identify prognosis in most patient cohorts suggests that presence of factors other than stemness and EMT affect mortality.

A computational systems biology approach identifies SLUG as a mediator of partial Epithelial-Mesenchymal Transition (EMT)

“…In all these scenarios, the scores calculated across these methods correlated well with one another -while KS and MLR scores were positively correlated, 76GS scores correlated negatively with KS and with MLR scores, as expected (Fig S1A-C). Next, for GSE43495 and GSE40690 datasets, we also calculated the stemness scores using a gene list corresponding to EMT and stemness for breast cancer cells (CNCL) [Akbar et al, 2020]. Cells overexpressing SNAI1 were found to show a stronger enrichment of this gene signature as compared to those overexpressing SNAI2 and/or TWIST (Fig 1D; i, ii).…”

“…The stemness score was calculated using CNCL as described [Akbar et al, 2020]. To identify the stem-cell-like (CS/M) and non-stem-cell like (NS/E) cells, the samples were clustered based on the expression of CSC/non-CSC gene list (CNCL).…”

Plasticity as an Underlying Mechanism of Tumor Heterogeneity in Breast Cancer

“…In this paper we address this switching as plasticity. Additionally, this subtype plasticity is also seen among pre-and post-treated tumors due to epithelial to mesenchymal transition [8].…”

Renin angiotensin system genes are biomarkers for personalized treatment of acute myeloid leukemia with Doxorubicin as well as etoposide

“…In the AMLCG 1999 cohort, patients were treated with TAD: Thioguanine, Cytarabine and Daunorubicin, or HAM: Cytarabine, Mitoxantrone protocols followed by the TAD protocol. We used an in-house R script (https://github.com/muratisbilen/ LRMC.git) (Log Rank Multiple Cutoff, LRMC) by which log-rank test-based p-values associated with hazard ratio (HR) could be obtained using all possible cutoff values representing each sample in a given dataset and best cutoff is selected as in [39,40]. Using this approach, we selected best cutoffs for IGF2R, ATP6AP2 and CTSA genes to be used for clinical correlation studies and Kaplan-Meier plots.…”