Tamoxifen Response at Single-Cell Resolution in Estrogen Receptor–Positive Primary Human Breast Tumors

Hyunsoo Kim, Austin A. Whitman, Kamila Wisniewska, Rasha T. Kakati, Susana Garcia-Recio, Benjamin C. Calhoun, Hector L. Franco, Charles M. Perou, Philip M. Spanheimer

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Purpose: In estrogen receptor–positive (ERþ)/HER2- breast cancer, multiple measures of intratumor heterogeneity are associated with a worse response to endocrine therapy. We sought to develop a novel experimental model to measure heterogeneity in response to tamoxifen treatment in primary breast tumors. Experimental Design: To investigate heterogeneity in response to treatment, we developed an operating room-to-laboratory pipeline for the collection of live normal breast specimens and human tumors immediately after surgical resection for processing into single-cell workflows for experimentation and genomic analyses. Live primary cell suspensions were treated ex vivo with tamoxifen (10 mmol/L) or control media for 12 hours, and single-cell RNA libraries were generated using the 10X Genomics droplet-based kit. Results: In total, we obtained and processed normal breast tissue from two women undergoing reduction mammoplasty and tumor tissue from 10 women with ERþ/HER2- invasive breast carcinoma. We demonstrate differences in tamoxifen response by cell type and identify distinctly responsive and resistant subpopulations within the malignant cell compartment of human tumors. Tamoxifen resistance signatures from resistant subpopulations predict poor outcomes in two large cohorts of ERþ breast cancer patients and are enriched in endocrine therapy–resistant tumors. Conclusions: This novel ex vivo model system now provides the foundation to define responsive and resistant subpopulations within heterogeneous human tumors, which can be used to develop precise single cell–based predictors of response to therapy and to identify genes and pathways driving therapeutic resistance.

Original languageEnglish (US)
Pages (from-to)4894-4907
Number of pages14
JournalClinical Cancer Research
Issue number23
StatePublished - 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
©2023 The Authors; Published by the American Association for Cancer Research.

PubMed: MeSH publication types

  • Journal Article


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