Proteomic time course of breast cancer cells highlights enhanced sensitivity to Stat3 and Src inhibitors prior to endocrine resistance development
To prevent the development of endocrine-resistant breast cancer, additional targeted therapies are increasingly being trialled in combination with endocrine therapy. The molecular mechanisms facilitating cancer cell survival during endocrine treatment remain unknown but could help direct selection of additional targeted therapies. We present a novel proteomic timecourse dataset, profiling potential drug targets in a population of MCF7 cells during 1 year of tamoxifen treatment. Reverse phase protein arrays profiled >70 proteins across 30 timepoints. A biphasic response to tamoxifen was evident, which coincided with changes in growth rate. Tamoxifen strongly impeded cell growth for the first 160 days, followed by gradual growth recovery and eventual resistance development. The growth-impeded phase was distinguished by the phosphorylation of Stat3 (y705) and Src (y527). Tumour tissue from patients treated with neo-adjuvant endocrine therapy (<4 months) also displayed increased Stat3 and Src signalling. Inhibitors of Stat3 (napabucasin) and Src (dasatinib), were effective at killing tamoxifen-treated MCF7 and T47D cells. Sensitivity to both drugs was significantly enhanced once tamoxifen had induced the growth-impeded phase. This novel proteomic resource identifies key mechanisms enabling cell survival during tamoxifen treatment. It provides valuable insight into potential drug combinations and timing that may prevent the development of endocrine resistance.
Breast Cancer Ireland
The Physiological Society
CommentsThe original article is available at https://www.nature.com/
Published CitationMadden SF, Cremona M, Farrelly AM, Low WH, McBryan J. Proteomic time course of breast cancer cells highlights enhanced sensitivity to Stat3 and Src inhibitors prior to endocrine resistance development. Cancer Gene Ther. 2023;30(2):324-334.
Publication Date20 October 2022
- Beaumont Hospital
- Data Science Centre
- Molecular Medicine
- Neurological and Psychiatric Disorders
- Gynaecology, Obstetrics and Perinatal Health
PublisherNature Publishing Group
- Published Version (Version of Record)