Leppiniemi, S. | bioRxiv Science

bioRxiv2025

Proteome profiling reveals HES1-driven mitotic catastrophe in ovarian serous carcinoma

Ovarian high-grade serous cancer (HGSC) is an aggressive subtype of epithelial ovarian cancer. Here, we identify BX-912, a phosphoinositide-dependent kinase 1 (PDPK1) inhibitor, as a promising therapeutic agent for HGSC. BX-912 suppressed HGSC growth as a single agent and synergized with olaparib independently of BRCA status. Unexpectedly, BX-912 treatment induced multinucleation, a phenotype not observed with other PDPK1 inhibitors. Proteome Integral Solubility Alteration (PISA) profiling revealed the transcription factor HES1 as a functional target of BX-912. Structural modeling showed that BX-912 binds the Orange domain of HES1, while its WRPW motif mediates interactions with protein partners, including the AP2 endocytic protein complex, coordinating their nuclear accumulation that leads to a mitotic catastrophe. Furthermore, cell cycle analyses showed that BX-912 combined with olaparib synergistically enhanced DNA damage and G2-M arrest. Our study demonstrates the value of proteomics for revealing hidden drug activities. It also identifies potential inhibition strategies for HES1, which is commonly overexpressed in HGSC. Additionally, this study proposes a novel strategy of targeting consecutive cell cycle phases to enhance treatment efficacy in HGSC.

cancer biology↗

bioRxiv2025

SegmentQTL: Identifying genetic variants influencing molecular phenotypes in copy number-driven cancers

MotivationMolecular quantitative trait loci (molQTL) analysis links genetic variants to molecular phenotypes, such as gene expression, but existing tools do not account for the structural complexity of copy number-driven cancers. High genomic instability of these cancers leads to chromosomal breaks (breakpoints), which disrupt the physical connection between genes and adjacent regulatory elements. Standard molQTL methods are unable to accommodate breakpoint information and would therefore indiscriminately test associations across breakpoints, leading to spurious signals and reduced biological relevance. To address these challenges, we developed SegmentQTL, a segmentation-aware molQTL analysis tool, designed to improve the accuracy of association testing in unstable cancer genomes by incorporating sample-specific break-point information. ResultsSegmentQTL applies an integrated purifying filtering step that removes associations spanning breakpoints, ensuring that only variants within the same segment as the phenotype are tested. This prevents false discoveries and reduces background noise. We evaluated SegmentQTL on selected genes from stable and unstable genomic regions and compared its results with a previously published state-of-the-art tool. In stable regions, SegmentQTL produced similar results, validating its approach. In unstable regions, however, the filtering step refined detected associations by shifting peak locations and removing artefactual signals that would arise if genomic instability were not properly accounted for. Availability and implementationhttps://github.com/HautaniemiLab/SegmentQTL

bioinformatics↗