Machine Learning-Driven Discovery of Quadruple-Negative Breast Cancer Subtypes from Gene Expression Data
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-5128-0_15
Reference21 articles.
1. Angajala, A., et al.: Quadruple negative breast cancers (QNBC) demonstrate subtype consistency among primary and recurrent or metastatic breast cancer. Transl. Oncol. 12, 493–501 (2019). https://doi.org/10.1016/j.tranon.2018.11.008
2. Bhattarai, S., Saini, G., Gogineni, K., Aneja, R.: Quadruple-negative breast cancer: novel implications for a new disease. Breast Cancer Res. 22 (2020). https://doi.org/10.1186/s13058-020-01369-5
3. Burstein, M.D., et al.: Comprehensive genomic analysis identifies novel subtypes and targets of triple-negative breast cancer. Clin. Cancer Res. 21, 1688–1698 (2014). https://doi.org/10.1158/1078-0432.ccr-14-0432
4. Davis, M., et al.: AR negative triple negative or “quadruple negative” breast cancers in African American women have an enriched basal and immune signature. PloS ONE 13, e0196909 (2018). https://doi.org/10.1371/journal.pone.0196909, https://www.ncbi.nlm.nih.gov/pubmed/29912871
5. Doane, A.S., et al.: An estrogen receptor-negative breast cancer subset characterized by a hormonally regulated transcriptional program and response to androgen. Oncogene 25, 3994–4008 (2006). https://doi.org/10.1038/sj.onc.1209415
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