Back

Stromal mediated DNA damage promotes high grade serous ovarian cancer initiation

Garcia, G. L.; Orellana, T.; Gorecki, G.; Frisbie, L. G.; Baruwal, R.; Goldfield, E.; Beddows, I.; MacFawn, I. P.; Britt, A. K.; Hale, M. M.; Shen, H.; Buckanovich, R.; Finkel, T.; Drapkin, R.; Soong, T. R.; Bruno, T. C.; Atiya, H. I.; Coffman, L. G.

2024-05-28 cancer biology
10.1101/2024.05.23.595550 bioRxiv
Show abstract

The fundamental steps in high-grade serous ovarian cancer (HGSOC) initiation are unclear, thus providing critical barriers to the development of prevention or early detection strategies for this deadly disease. Increasing evidence demonstrates most HGSOC starts in the fallopian tube epithelium (FTE). Current models propose HGSOC initiates when FTE cells acquire increasing numbers of mutations allowing cells to evolve into serous tubal intraepithelial carcinoma (STIC) precursors and then to full blown cancer. Here we report that epigenetically altered mesenchymal stem cells (termed high risk MSC-hrMSCs) can be detected prior to the formation of ovarian cancer precursor lesions. These hrMSCs drive DNA damage in the form of DNA double strand breaks in FTE cells while also promoting the survival of FTE cells in the face of DNA damage. Indicating the hrMSC may actually drive cancer initiation, we find hrMSCs induce full malignant transformation of otherwise healthy, primary FTE resulting in metastatic cancer in vivo. Further supporting a role for hrMSCs in cancer initiation in humans, we demonstrate that hrMSCs are highly enriched in BRCA1/2 mutation carriers and increase with age. Combined these findings indicate that hrMSCs may incite ovarian cancer initiation. These findings have important implications for ovarian cancer detection and prevention.

Matching journals

The top 11 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.