Back

The prion-like protein Doppel: A soluble biomarker steering ovarian cancers peritoneal to circulatory dissemination

Al-Hilal, T.; Azam, M. Z.; Zhang, X.; Wahab, R.; Hasan, M. M.; Kang, B.; Hassan, M. M.; Karim, M.; Choi, J. U.; Rana, M.; Zhang, J.-Y.; Roy, S.; Byun, Y.; Kim, I.-S.; Song, J. Y.; Alam, F.; Toy, E. P.; Reddy, S. Y.

2024-07-29 cancer biology
10.1101/2024.07.26.605386 bioRxiv
Show abstract

Detecting ovarian cancer (OC) early using existing biomarkers, e.g., cancer antigen 125 (CA125), is challenging due to its ubiquitous expression in many tissues. Doppel, a prion-like protein, expresses in male reproductive organ but absent in female reproductive systems and healthy tissues, but plays an important role in neoangiogenesis. Here, we have shown two platforms, soluble Doppel in sera/ascites and Doppel expressed circulating tumor cells (Dpl+CTC) in the whole blood, to detect subsets of epithelial OC (EOC). Increased level of Doppel in the sera of OC patients, in three different cohorts, confirm Doppel as OC specific biomarker. Serum Doppel level distinguishes EOC subtypes and early stages HGSOCs from non-cancerous conditions with high sensitivity and specificity. Stratifying the EOCs based on Doppel level, we categorized them into Doppel-high (Dplhi) and Doppel-low (Dpllow) groups. Using ascites-derived organoids and single cell sequencing of whole ascites of Dplhi and Dpllow patients, we identify that Doppel induces epithelial-mesenchymal transition (EMT) and creates an immunosuppressive microenvironment, respectively. Doppel levels in the sera/ascites correlate with the changes of Dpl+CTC number in whole blood, highlighting the association of Doppel-induced EMT with CTC dissemination in circulation. Thus, Doppel-based detection of EOC subtypes could be a promising platform as clinical biomarker and link Doppel-axis with OC dissemination.

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.