Exploring the resistance mechanism of triple-negative breast cancer to paclitaxel through the scRNA-seq analysis

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. Then, the hallmark score of each cell was calculated by AUCell R package, the biological function of the highly expressed interest genes was analyzed by the DAVID database. Subsequently, we performed pseudotime analysis to explore the change patterns of drug resistance genes and SCENIC analysis to identify the key transcription factors (TFs). Finally, the inhibitors of which were also analyzed by the CTD database.

Results: We finally obtained 6 cell subsets from 2798 cells, which were marked as AKR1C3+, WNT7A+, FAM72B+, RERG+, IDO1+ and HEY1+HCC1143 cell subsets, among which the AKR1C3+, IDO1+ and HEY1+ cell subsets proportions increased with increasing treatment time, and then were regarded as paclitaxel resistance subsets. Hallmark score and pseudotime analysis showed that these paclitaxel resistance subsets were associated with the inflammatory response, virus and interferon response activation. In addition, the gene regulatory networks (GRNs) indicated that 3 key TFs (STAT1, CEBPB and IRF7) played vital role in promoting resistance development, and five common inhibitors targeted these TFs as potential combination therapies of paclitaxel were identified.

Conclusion: In this study, we identified 3 paclitaxel resistance relevant IFs and their inhibitors, which offers essential molecular basis for paclitaxel resistance and beneficial guidance for the combination of paclitaxel in clinical TNBC therapy.

Fig 1. Single cell profile and paclitaxel-resistant subsets analysis.

(A) UMAP dimensionality reduction map of HCC1143 cell clusters. (B) Violin plot of marker gene expression level in each cell subset. (C) The proportion of each cell subset in different samples. (D) The proportion of each cell subset in control, 24h treated, and 72h treated groups.

Fig 2. Function enrichment among paclitaxel-resistant subsets.

(A) Heatmap of the median hallmark enrichment score for each cell subset. (B) The biological process of highly expressed differential genes in AKR1C3+ HCC1143 cells. (C) The biological process of highly expressed differential genes in IDO1+ HCC1143 cells. (D) The biological process of highly expressed differential genes in HEY1+ HCC1143 cells.

Fig 3. Pseudotime analysis of AKR1C3+ HCC1143 cells.

(A) Differentiation trajectory of AKR1C3+HCC1143 cells from 24h control to 24h treatment to 72h treatment. (B) Heatmap of pseudotime related gene expression. (C) The Pearson correlation between the Hallmark score and pseudotime pathway. (D) Scatter plot of Pseudotime-related gene expression at different treatment groups.

Fig 4. Pseudotime analysis of IDO1+ HCC1143 cells.

(A) Differentiation trajectory of IDO1+HCC1143 cells from 24h control to 24h treatment to 72h treatment. (B) Heatmap of pseudotime related gene expression. (C) The Pearson correlation between the Hallmark score and pseudotime pathway. (D) Scatter plot of Pseudotime-related gene expression at different treatment groups.

Fig 5. Pseudotime analysis of HEY1+ HCC1143 cells.

(A) Differentiation trajectory of HEY1+HCC1143 cells from 24h control to 24h treatment to 72h treatment. (B) Heatmap of pseudotime related gene expression. (C) The Pearson correlation between the Hallmark score and pseudotime pathway. (D) Scatter plot of Pseudotime-related gene expression at different treatment groups.

Fig 6. Identifying of transcription factor among paclitaxel-resistant subsets.

(A) The Pearson correlation between regulon score and pseudotime pathway in the AKR1C3+HCC1143 cells. (B) The Pearson correlation between regulon score and pseudotime pathway in the IDO1+HCC1143 cells. (C) The Pearson correlation between regulon score and pseudotime pathway in the HEY1+HCC1143 cells. (D) The regulatory network of STAT1. (E) The regulatory network of CEBPB. (F) The regulatory network of IRF7.

Fig 7. Identifying of transcription factor inhibitors.

Venn plot of transcription factor inhibitors.