Identification of gemcitabine-resistant populations using scRNA-sequencing in triple negative breast cancer patientderived xenograft

Abstract Despite major advances in the treatment of breast cancer (BC), it remains the most diagnosed and second most deadly cancer among American women. BC is a heterogeneous disease consisting of distinct subtypes, including hormone receptor-positive, HER2 receptor-positive and cancers that lack these receptors categorized as triple-negative breast cancers (TNBC). The TNBC subtype presents the worst outcome and the highest rates of recurrence and metastasis. The absence of targets prevents the use of established precision therapies in TNBC, and the standard of care remains neoadjuvant chemotherapy. While this is effective in some patients, about 50% develop resistance, leading to the development of metastasis. It is known that selective pressures exerted by chemotherapy treatment can promote the outgrowth of resistant tumor subclones. However, the diverse intra-tumoral population and the mechanisms that lead to chemotherapy resistance in TNBC are still poorly understood. We hypothesized that therapeutic regimens influence tumor plasticity by exerting selective pressures leading to the outgrowth of resistant subpopulations with the greatest survival advantage. To evaluate this hypothesis, we aimed to generate in vivo models of chemotherapy resistance and to investigate the plasticity of tumor cell subpopulations challenged with standard-of-care chemotherapies. To this end, we selected a multi-drug resistant (Doxorubicin, Cyclophosphamide, Cisplatin, and Paclitaxel) BC patient and developed a patient-derived xenograft (PDX) from the primary tumor and the lung metastasis. The metastasis PDX model was initially responsive to Gemcitabine (as observed in the BC patient) but eventually developed resistance. We challenged this metastasis PDX with several cycles of Gemcitabine and obtained residual, rebound, and resistant tumor samples. 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. Interestingly, our data shows that the rebound model presents greater similarity to the untreated metastasis, while the resistant model has significant differences in cell population expression profiles. We identified a hypoxic population in the primary tumor and its matched metastasis. This population was validated in these models by Nanostring GeoMx Digital Spatial Profiler. Our recent analyses have identified that this hypoxic population persists in the residual, rebound and resistant models. In addition, we have identified other populations that vary in these models. We 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, Hellen Kuasne, Paul Savage, Anne-Marie Fortier, Anie Monast, Atilla Omeroglu, Jamil Asselah, Nathaniel Bouganim, Sarkis Meterissian, Claudia Kleinman, Mark Basik, Morag Park. Identification of gemcitabine-resistant populations using scRNA-sequencing in triple negative breast cancer patient-derived xenograft [abstract]. In: Proceedings of the AACR Special Conference: Cancer Metastasis; 2022 Nov 14-17; Portland, OR. Philadelphia (PA): AACR; Cancer Res 2022;83(2 Suppl_2):Abstract nr A033.