Abstract
AbstractTranscriptomics has revolutionized biomedical research and refined breast cancer subtyping and diagnostics. However, wider use in clinical practice is hampered for a number of reasons including the application of transcriptomic signatures as single sample predictors. Here, we present an embedding approach called EMBER that creates a unified space of 11,000 breast cancer transcriptomes and predicts phenotypes of transcriptomic profiles on a single sample basis. EMBER accurately captures the five molecular subtypes. Key biological pathways, such as estrogen receptor signaling, cell proliferation, DNA repair, and epithelial-mesenchymal transition determine sample position in the space. We validate EMBER in four independent patient cohorts and show with samples from the window trial, POETIC, that it captures clinical responses to endocrine therapy and identifies increased androgen receptor signaling and decreased TGFβ signaling as potential mechanisms underlying intrinsic therapy resistance. Of direct clinical importance, we show that the EMBER-based estrogen receptor (ER) signaling score is superior to the immunohistochemistry (IHC) based ER index used in current clinical practice to select patients for endocrine therapy. As such, EMBER provides a calibration and reference tool that paves the way for using RNA-seq as a standard diagnostic and predictive tool for ER+ breast cancer.
Funder
EC | Horizon 2020 Framework Programme
Breast Cancer Now
Publisher
Springer Science and Business Media LLC
Reference50 articles.
1. WORLD HEALTH ORGANIZATION: REGIONAL OFFICE FOR EUROPE. WORLD CANCER REPORT: Cancer Research for Cancer Development. (IARC, Place of publication not identified, 2020).
2. Anderson, W. F., Chatterjee, N., Ershler, W. B. & Brawley, O. W. Estrogen receptor breast cancer phenotypes in the Surveillance, Epidemiology, and End Results database. Breast Cancer Res Treat. 76, 27–36 (2002).
3. Allison, K. H. et al. Estrogen and Progesterone Receptor Testing in Breast Cancer: ASCO/CAP Guideline Update. J. Clin. Oncol. 38, 1346–1366 (2020).
4. Perou, C. M. et al. Molecular portraits of human breast tumours. Nature 406, 747–752 (2000).
5. Curtis, C. et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486, 346–352 (2012).