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ccr-23-1248_supplementary_figure_s2_suppfs2.pdf (56.32 kB)

Supplementary Figure S2 from Tamoxifen Response at Single-Cell Resolution in Estrogen Receptor–Positive Primary Human Breast Tumors

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posted on 2023-12-01, 08:43 authored by 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

Cell type-specific tamoxifen regulated genes in normal human breast tissue.

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National Cancer Institute (NCI)

United States Department of Health and Human Services

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Society of University Surgeons Foundation

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ARTICLE ABSTRACT

In estrogen receptor–positive (ER+)/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. 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 μmol/L) or control media for 12 hours, and single-cell RNA libraries were generated using the 10X Genomics droplet-based kit. In total, we obtained and processed normal breast tissue from two women undergoing reduction mammoplasty and tumor tissue from 10 women with ER+/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 ER+ breast cancer patients and are enriched in endocrine therapy–resistant tumors. 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.

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