Back

Mutant p53 Misfolding and Aggregation Precedes Transformation into High-Grade Serous Ovarian Carcinoma

Sartini, S.; Omholt, L.; Moatamed, N.; Soragni, A.

2024-09-18 cancer biology
10.1101/2024.09.17.612958 bioRxiv
Show abstract

High Grade Serous Ovarian Cancer (HG-SOC), the most prevalent and aggressive gynecological malignancy, is marked by ubiquitous loss of functional p53, largely due to point mutations that arise very early in carcinogenesis. These mutations often lead to p53 protein misfolding and subsequent aggregation, yet the alterations in intracellular p53 dynamics throughout ovarian cancer progression remain poorly understood. HG-SOC originates from the fallopian tube epithelium, with a well-documented stepwise progression beginning with early pre-malignant p53 signatures. These signatures represent largely normal cells that express and accumulate mutant p53, which then transform into benign serous tubal intraepithelial lesions (STIL), progress into late pre-malignant serous tubal intraepithelial carcinoma (STIC), and ultimately lead to HGSOC. Here, we show that the transition from folded, soluble to aggregated mutant p53 occurs during the malignant transformation of benign precursor lesions into HGSOC. We analyzed fallopian tube tissue collected from ten salpingo-oophorectomy cases and determined the proportion of cells carrying soluble versus mis-folded/mutant p53 through conformation-sensitive staining and quantification. Misfolded p53 protein, prone to aggregation, is present in STICs and HG-SOCs, but notably absent from preneoplastic lesions and surrounding healthy tissue. Overall, our results indicate that aggregation of mutant p53 is a structural defect that distinguishes preneoplastic early lesions from late premalignant and malignant ones, offering a potential treatment window for targeting p53 aggregation and halting ovarian cancer progression.

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.