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Paclitaxel resistance in TNBC

Anagha Shenoy · August 5, 2024


This case study was completed as part of the B-BRITE Computational Bootcamp (July 2024) led by Dr. Olga Nikolova and Dr. Zheng Xia.

Background

Data was generated by the Heiser Lab at OHSU and is available at GEO.

The study that generated this data is available as a preprint:

TNBC response to paclitaxel phenocopies interferon response which reveals cell cycle-associated resistance mechanisms (Calistri et al., 2024)

Data

  • HCC1143 cells grown in vitro (DMSO - control; Paclitaxel - treatment).
  • Control (-00) and treatment (-01) sequenced at 72 hr time point with 10x technology.

Summary

The primary investigation was anchored by the following questions: 1) how do HCC1143 cells respond to Paclitaxel treatment?, 2) how is gene expression impacted by exposure to Paclitaxel?

Conducting differential gene expression analysis demonstrates that there are significant differences in gene expression profiles between the two treatment groups, DMSO and Paclitaxel. There are two distinct gene expression profiles, here referred to as 1 and 2. 1 consists of PIF1, PLA2R1, FREM2, APCDD1, PLEKHS1, HIST1H4C, MUC16, PCLAF, HNRNPA1P48, and AP000527.1. In the context of the experiments conducted, these are expressed in tumor cells before treatment. 2 consists of ADM, CMPK2, IFIT1, IFI27, RAET1L, GADD45A, OAS1, CXCL8, and ATF3. In the context of the experiments conducted, these are expressed in tumor cells after treatment. Genes in group 1 were upregulated in the DMSO treatment group and downregulated in the PTX treatment group, while genes in group 2 were downregulated in the DMSO treatment group and upregulated in the PTX treatment group.

Notably, for cells that persisted after treatment with Paclitaxel, genes that are highly expressed are correlated with promoting an immunosuppressive microenvironment--for example, CXCL8, ATF3, and ADM (Huang et al., 2023; Borgoni et al., 2020; Nakamura et al., 2006). This suggests that these genes may be useful to investigate as potential targets for future iterations of drug development.

Acknowledgements

References

Borgoni, S., Sofyalı, E., Soleimani, M., Wilhelm, H., Müller-Decker, K., Will, R., Noronha, A., Beumers, L., Verschure, P. J., Yarden, Y., Magnani, L., van Kampen, A. H. C., Moerland, P. D., & Wiemann, S. (2020). Time-Resolved Profiling Reveals ATF3 as a Novel Mediator of Endocrine Resistance in Breast Cancer. Cancers, 12(10), 2918. https://doi.org/10.3390/cancers12102918

Huang, R., Wang, Z., Hong, J., Wu, J., Huang, O., He, J., Chen, W., Li, Y., Chen, X., & Shen, K. (2023). Targeting cancer-associated adipocyte-derived CXCL8 inhibits triple-negative breast cancer progression and enhances the efficacy of anti-PD-1 immunotherapy. Cell death & disease, 14(10), 703. https://doi.org/10.1038/s41419-023-06230-z

Nakamura, M., Han, B., Nunobiki, O., & Kakudo, K. (2006). Adrenomedullin: a tumor progression factor via angiogenic control. Current cancer drug targets, 6(7), 635–643. https://doi.org/10.2174/156800906778742442