Abstract B067: Identification of Gemcitabine-resistant Populations using scRNA-Sequencing in Triple Negative Breast Cancer Patient-Derived Xenografts
Authors: Busque Sandrine , Ramirez Constanza Martinez , Aguirre Ariel Madrigal , Savage Paul , Monast Anie , Fortier Anne-Marie , Zapata Gerardo , Omeroglu Atilla , Asselah Jamil , Bouganim Nathaniel , Meterissian Sarkis , Tremblay Francine , Meguerditchian Ari , Alhosseini Yasser Riaz , Najafabadi Hamed S. , Kuasne Hellen , Park Morag 
Published: February 2024

Despite major advances in the treatment of breast cancer (BC), it remains the most diagnosed and second deadliest cancer among American women. BC consists of distinct clinical subtypes, including estrogen receptor or HER2 receptor-positive and triple-negative breast cancer (TNBC) that lacks these receptors preventing the use of established precision therapies and the standard of care remains neoadjuvant chemotherapy (NACT), followed by surgery, radiation and emerging combinations with immunotherapy. While NACT is effective in some patients, up to 50% of patients are intrinsically unresponsive or acquire resistance. These patients demonstrate the highest rates of recurrence and distant metastasis. It is known that chemotherapies can effectively eliminate proliferating tumor cells, but a small population of drug-tolerant persister (DTPs) cells can remain and are thought to play a key role in relapse and resistance. We hypothesized that chemotherapy influences TNBC tumor plasticity by exerting selective pressures leading to the outgrowth of resistant subpopulations with the greatest survival advantage. To evaluate this hypothesis, we generated in vivo models of disease progression and chemotherapy resistance and investigated the plasticity of tumor cell subpopulations by single-cell RNA sequencing. We developed PDX models from a multi-drug resistant primary TNBC treated with standard-of-care chemotherapy and its patient-matched lung metastasis. To model response and resistance at the metastatic stage, we challenged this metastasis PDX with several cycles of Gemcitabine. We obtained residual disease that relapsed and eventually developed resistance, mirroring the patient response. We performed single-cell RNA sequencing (scRNAseq) of these models using a droplet-based technology from 10X Genomics. This scRNAseq data was used to compare the changes in the proportions of cellular subpopulations in each model. Our data show that the rebound model presents greater similarity to the untreated metastasis, while the resistant model significantly differs in cell population expression profiles. Interestingly, the residual disease demonstrates an intermediated state with similarities to both the untreated metastasis and the resistant model. We identified populations with hypoxic signatures in the primary tumor, its matched metastasis and the residual, rebound and resistant models. Immunohistochemistry and Nanostring GeoMx Digital Spatial Profiler validated these populations in these models. In addition, we have identified other varying populations and are currently investigating their cellular mechanisms and gene expression patterns. Using scRNA-sequencing to understand the clonal expansion of resistant subpopulations following chemotherapy reveals distinctive resistant cell features enabling the identification of the vulnerabilities of these tumors.

Citation Format: Sandrine Busque, Constanza Martinez Ramirez, Ariel Madrigal Aguirre, Paul Savage, Anie Monast, Anne-Marie Fortier, Gerardo Zapata, Atilla Omeroglu, Jamil Asselah, Nathaniel Bouganim, Sarkis Meterissian, Francine Tremblay, Ari Meguerditchian, Yasser Riaz Alhosseini, Hamed S. Najafabadi, Hellen Kuasne, Morag Park. Identification of Gemcitabine-resistant Populations using scRNA-Sequencing in Triple Negative Breast Cancer Patient-Derived Xenografts [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr B067.