Combined in vitro/in vivo genome-wide CRISPR screens in triple negative breast cancer identify cancer stemness regulators in paclitaxel resistance

The aminophospholipid transporter, ATP8B3, as a potential biomarker and target for enhancing the therapeutic effect of PD-L1 blockade in colon adenocarcinoma

BACKGROUND

Colon adenocarcinoma (COAD) is a prevalent malignant tumor globally, contributing significantly to cancer-related mortality. COAD guidelines label MSI (Microsatellite instability) and MSS (Microsatellite stability) subtypes as global classification criteria and treatment strategy selection criteria for COAD. Various combination therapies involving PD-L1 inhibitors and adjuvant therapy to enhance anti-tumor efficacy.

METHODS

Datasets from single-cell RNA sequencing and bulk RNA sequencing in the TCGA and GEO databases were utilized to identify differentially expressed genes (DEGs). Furthermore, the correlation between ATP8B3 and PD-L1 was validated using siRNA, shRNA, and western blot analysis. Additionally, the association between ATP8B3 and immune checkpoint blockade (ICB) therapy was investigated through immune infiltration analysis and flow cytometry in both in vivo and in vitro assays.

RESULTS

In the COAD patient group, ATP8B3 significantly contributed to the establishment of an immunosuppressive microenvironment. Inhibiting ATP8B3 led to a reduction in PD-L1 expression in colon cancer cell lines. Additionally, ATP8B3 expression levels could serve as a potential guide for PD-L1 treatment in MSI-H COAD patients, with higher ATP8B3 expression associated with increased sensitivity to PD-L1 therapy. However, due to the lack of immuno-killer cells in the microenvironment of MSS subtypes, elevated ATP8B3 expression couldn't increase the sensitivity of MSS COAD patients to PD-L1 inhibitors.

CONCLUSION

Our research results support that Inhibiting ATP8B3 could enhance TIL (tumor-infiltrating lymphocyte) infiltration by reducing PD-L1 expression in MSI-H COAD, thereby serving as an effective strategy to improve PD-L1 blocker efficacy. The treatment strategy of combining ATP8B3 inhibitors and immunotherapy for MSI/MSS COAD patients will be the best choice.

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

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.