The cell-of-origin of high grade serous ovarian carcinoma (HGSOC) remains controversial, with fallopian tube epithelium (FTE) and ovarian surface epithelium (OSE) both considered candidates. Here, by using genetically engineered mouse models and organoids, we assessed the tumor-forming properties of FTE and OSE harboring the same oncogenic abnormalities. Combined RB family inactivation and Tp53 mutation in Pax8 + FTE caused Serous Tubal Intraepithelial Carcinoma (STIC), which metastasized rapidly to the ovarian surface. These events were recapitulated by orthotopic injection of mutant FTE organoids. Engineering the same genetic lesions into Lgr5 + OSE or OSE-derived organoids also caused metastatic HGSOC, although with longer latency and lower penetrance. FTE- and OSE-derived tumors had distinct transcriptomes, and comparative transcriptomics and genomics suggest that human HGSOC arises from both cell types. Finally, FTE- and OSE-derived organoids exhibited differential chemosensitivity. Our results comport with a dualistic origin for HGSOC and suggest that the cell-of-origin might influence therapeutic response.

We sought to characterize the variability of CCNE1 amplification among metastatic sites of CCNE1 amplified high grade serous carcinoma (HGSC) cases to investigate the feasibility of targeting this alteration for therapeutic purposes.

Conventional molecular recognition elements, such as antibodies, present issues for developing biomolecular assays for use in certain technologies, such as implantable devices. Additionally, antibody development and use, especially for highly multiplexed applications, can be slow and costly. We developed a perception-based platform based on an optical nanosensor array that leverages machine learning algorithms to detect multiple protein biomarkers in biofluids. We demonstrated this platform in gynecologic cancers, often diagnosed at advanced stages, leading to low survival rates. We investigated the detection of protein biomarkers in uterine lavage samples, which are enriched with certain cancer markers compared to blood. We found that the method enables the simultaneous detection of multiple biomarkers in patient samples, with F1-scores of ~0.95 in uterine lavage samples from patients with cancer. This work demonstrates the potential of perception-based systems for the development of multiplexed sensors of disease biomarkers without the need for specific molecular recognition elements.