Longitudinal single-cell RNA-seq data of metastatic ovarian cancer

We characterized transcriptional patterns of chemotherapy resistance in high-grade serous ovarian cancer (HGSOC) using patient-derived prospective tissue sample pairs before and after treatment at single-cell resolution. Our cohort consists of scRNA-seq data from treatment-naïve and post-neoadjuvant chemotherapy (post-NACT) pairs from 11 homogeneously treated HGSOC patients. After quality control, we obtained 51,786 cells, including 8,806 malignant epithelial (tumor), 8,045 stromal and 34,935 immune cells. Our unbiased analysis reveals how chemotherapy modulates cancer cell states by both subclonal selection and microenvironment boosted transcriptional induction across the homogeneously treated sample cohort. Our results define a cell state that allows biomarker-based prediction and targeting of chemoresistance. Expression profiles of single cells from treatment-naïve and post-NACT patient-derived prospective tissue sample pairs from 11 homogeneously treated HGSOC patients. Patients participating in this study are from the DECIDER cohort (NCT04846933), and treated at Turku University Hospital. *** Raw data were deposited in EGA (EGAD00001006922) due to patient privacy concerns.