Exploring the Resistance Mechanism of Triple-negative Breast Cancer to Paclitaxel Through the scRNA-seq Analysis
Wei Gao, Linlin Sun, Jinwei Gai, Yinan Cao, Shuqun Zhang
Abstract
Background
The triple negative breast cancer (TNBC) is the most malignant subtype of breast cancer with high aggressiveness. Although paclitaxel-based chemotherapy scenario present the mainstay in TNBC treatment, paclitaxel resistance is still a striking obstacle for cancer cure. So it is imperative to probe new therapeutic targets through illustrating the mechanisms underlying paclitaxel chemoresistance.
Methods
The Single cell RNA sequencing (scRNA-seq) data of TNBC cells treated with paclitaxel at different points were downloaded from the Gene Expression Omnibus (GEO) database. The Seurat R package was used to filter and integrate the scRNA-seq expression matrix. Cells were further clustered by the FindClusters function, and the gene marker of each subset was defined by FindAllMarkers function.
Introduction
Breast cancer is regarded as the most common malignancy diagnosed in women worldwide [1, 2], accounting for approximately 25% of all cancer cases [3]. Immunohistochemical analysis defined five major intrinsic or molecular subtypes of breast cancer based on the expression of estrogen and progesterone receptors (ER/PR) status [4], including the Luminal A (40%), Luminal B (20%), HER2-enriched (10–15%), Normal-like (2–8%) and Triple Negative (15–20%) [2, 5]. Among which, the triple negative breast cancer (TNBC) was characterized by the highest mortality and proliferative rate [6], higher early recurrences rate, distant metastases and poor outcomes [7], accompanied with lacked expressions of ER, PR and human epidermal growth factor receptor-2 (HER2) [8]. TNBC posed a greatly threat to women’s health due to the enormous heterogeneity and the absence of available molecular targets [9]. Due to this heterogeneity, large tumors may contain multiple cells with different molecular characteristics and displaying different sensitivity to treatment [10], which has been demonstrated to be the main reason for drug resistance in breast cancer therapy [11]. Clinically, TNBC tumor presents most commonly biological aggressive ductal carcinoma [12] and tend to be larger size, higher grade at diagnosis and involves lymph node [13]. Although the TNBC with aggressive feature, about 20% patients exhibited a pathologic complete response (pCR) after pre-operative chemotherapy [14]. However, TNBC patients without pCR suffering from early recurrence and metastatic death were several times of these non-TNBC patients [15].
Material and methods
Data acquisition
The dataset of GSE139129 was downloaded from the Gene Expression Omnibus database (GEO, https://www.ncbi.nlm.nih.gov/gds/) [33, 34], including the TNBC cell lines (HCC1143) treated with paclitaxel for 24 and 72 hours respectively were set as experimental groups. Cells treated with dimethyl sulfoxide (DMSO) for 24 and 72 hours respectively were set as control group. Then the samples were performed the single-cell RNA-sequencing (scRNA-seq) using the Illumina NextSeq 500. The informed consent was not required because this article does not contain any studies with human participants. And all data from publicly available databases.
Data preprocessing
The Seurat R package was used to read the scRNA-seq expression matrix [35], removing cells with a mitochondrial ratio > = 10%. SCTransform function was used to normalize the data [35], the harmony R package was used to remove batch effects between samples [36] after principal component analysis (PCA) dimensionality reduction. Then the top 20 principal components were used for Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP) [37], the FindNeighbors and FindClusters function (resulotion = 0.04) was further performed for the unsupervised clustering [38].
Results
Single cell profile and paclitaxel-resistant subsets analysis
A total of 6 cell subsets (including 2798 cells) were identified after that scRNA-seq data were filtered, normalized, integrated, clustered and annotated (Fig 1A). Based on the highlight expression genes (Fig 1B), they were defined as the AKR1C3+, WNT7A+, FAM72B+, RERG+, IDO1+ and HEY1+ HCC1143 cell subsets. We counted the proportion of each cell subset in control group and experimental group and found that the proportion of AKR1C3+, IDO1+ and HEY1+ cell subsets was higher in 72h treated group than that in 24h treated group (Fig 1C). Specially, the proportion of AKR1C3+HCC1143 cells increased markedly as the extension of paclitaxel treatment time (Fig 1D). Thus, the AKR1C3+, IDO1+ and HEY1+ HCC1143 cells were selected as paclitaxel-resistant subsets, and their dynamic changes of gene expression patterns were further analyzed.
Discussion
TNBC is the most malignant subtype of breast cancer with high degree of aggressiveness [58]. In this study, we conducted a comprehensive analysis of scRNA-seq data of TNBC cells treated with paclitaxel at different points (24 and 72h) to explore the potential resistance mechanisms of TNBC cells to paclitaxel. Firstly, 6 cell subsets AKR1C3+, WNT7A+, FAM72B+, RERG+, IDO1+ and HEY1+HCC1143 cells were annotated, among which AKR1C3+, IDO1+ and HEY1+ cells were regarded as paclitaxel resistance subsets. Finally, we identified STAT1, CEBPB and IRF7 as key TFs for paclitaxel resistance in TNBC therapy. And their inhibitors such as bisphenol A and Benzopyrene could be used as combination therapies of paclitaxel to improve the survival outcomes of patients with TNBC.
Conclusion
Collectively, three paclitaxel resistance relevantTFs STAT1, CEBPB and IRF7 were identified, and they shared 5 common inhibitors (Genistein, bisphenol A, Benzopyrene, Tetrachlorodibenzodioxin and monomethylarsonous acid). Our study provides fundamental molecular clues for the mechanism of paclitaxel resistance and helpful instrument for the combination of paclitaxel therapy to improve chemotherapy efficacy of TNBC patients.
Citation: Gao W, Sun L, Gai J, Cao Y, Zhang S (2024) Exploring the resistance mechanism of triple-negative breast cancer to paclitaxel through the scRNA-seq analysis. PLoS ONE 19(1): e0297260. https://doi.org/10.1371/journal.pone.0297260
Editor: Yunzhao Xu, Affiliated Hospital of Nantong University, CHINA
Received: October 12, 2023; Accepted: January 2, 2024; Published: January 16, 2024
Copyright: © 2024 Gao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The datasets generated and/or analysed during the current study are available in the [GSE139129] repository, [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE139129].
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Abbreviations: cds, CellDataSet; CML, Chronic Myeloid Leukemia; CTD, Comparative Toxicogenomics Database; DAVID, Database for Annotation, Visualization and Integrated Discovery; DEGs, Differentially expressed genes; DMSO, dimethyl sulfoxide; EGFR, Epidermal Growth Factor Receptor; ER, estrogen receptors; ERK, extracellular regulated protein kinases; GEO, Gene Expression Omnibus; GRNs, gene regulatory networks; HER2, human epidermal growth factor receptor-2; MAPK, mitogen activated protein kinases; MSigDB, Molecular Signatures Database; PCA, principal component analysis; PCR, pathologic complete response; PR, progesterone receptors; RTKs, receptor tyrosine kinases; SCENIC, Single-cell regulatory network inference and clustering; scRNA-seq, single-cell RNA-sequencing; TNBC, triple negative breast cancer; UMAP, Uniform Manifold Approximation and Projection for Dimension Reduction
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0297260#abstract0
