Biology subjects

Afenteva, D.

Publications and source records attributed to Afenteva, D..

5 recordsLinked to original sources

bioRxiv2025

Multi-Omics Characterization of ctDNA Release Mechanisms in Ovarian Cancer

Circulating tumor DNA (ctDNA) is a powerful biomarker capable to predict tumor dynamics and treatment response. Despite its importance, the biological mechanisms behind ctDNA release have remained unclear. The variability among patients is affected by cancer burden, histology and stage, but these factors cover only a part of the detected variability. Herein, we characterized molecular drivers of baseline ctDNA variability in a real-world cohort of 118 patients with ovarian high-grade serous carcinoma (HGSC). Genomic and transcriptomic analyses revealed a strong positive correlation between ctDNA levels and cellular proliferation, and an inverse relationship with immune activity. Particularly, low ctDNA tumors exhibited higher bulk expression of mucins and CIITA, suggesting that intrinsic immune responses and immune landscape are linked to ctDNA release. The low and high ctDNA levels were significantly associated with poor prognosis compared to medium level in unresectable HGSC patients treated with neoadjuvant chemotherapy. In summary, our results suggest that ctDNA release is modulated by cancer cell proliferation and tumor microenvironment.

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↗

bioRxiv2025

Identification of Monotonically Classifying Pairs of Genes for Ordinal Disease Outcomes

In this study, we extend an existing classification method for identifying pairs of genes whose joint expression is associated with binary outcomes to ordinal multi-class outcomes, such as overall survival or disease progression. Our approach is motivated by the need for interpretable classifiers that can provide insights into the underlying biological mechanisms. It can be easily adapted to different research questions, such as identifying gene pair signatures or functional enrichment. We demonstrate that our method is comparable to state-of-the-art classification approaches in terms of performance, while offering the benefit of higher interpretability and adaptability to solve different research questions. Our evaluation on two real-world use cases in glioblastoma and high-grade serous ovarian carcinoma shows that our approach can effectively predict ordinal outcomes and provide novel biological insights. The code is available at https://github.com/oceanefrqt/MBMC.

systems biology↗

bioRxiv2024

Multi-Omics Analysis Reveals the Attenuation of the Interferon Pathway as a Driver of Chemo-Refractory Ovarian Cancer

Ovarian high-grade serous carcinoma (HGSC) represents the deadliest gynecological malignancy, with 10-15% of patients exhibiting primary resistance to first-line chemotherapy. These primarily chemo-refractory patients have particularly poor survival outcomes, emphasizing the urgent need for developing predictive biomarkers and novel therapeutic approaches. Here, we show that interferon type I (IFN-I) pathway activity in cancer cells is a crucial determinant of chemotherapy response in HGSC. Through a comprehensive multi-omics analysis within the DECIDER observational trial (ClinicalTrials.gov identifier NCT04846933) cohort, we identified that chemo-refractory HGSC is characterized by diminished IFN-I and enhanced hypoxia pathway activities. Importantly, IFN-I pathway activity was independently prognostic for patient survival, highlighting its potential as a biomarker. Our results elucidate the heterogeneity of treatment response at the molecular level and suggest that augmentation of IFN-I response could enhance chemosensitivity in refractory cases. This study underscores the potential of the IFN-I pathway as a therapeutic target and advocates for the initiation of clinical trials testing external modulators of the IFN-I response, promising a significant stride forward in the treatment of refractory HGSC.

cancer biology↗

bioRxiv2024

Chemotherapy induces myeloid-driven spatial T-cell exhaustion in ovarian cancer

To uncover the intricate, chemotherapy-induced spatiotemporal remodeling of the tumor microenvironment, we conducted integrative spatial and molecular characterization of 97 high-grade serous ovarian cancer (HGSC) samples collected before and after chemotherapy. Using single-cell and spatial analyses, we identify increasingly versatile immune cell states, which form spatiotemporally dynamic microcommunities at the tumor-stroma interface. We demonstrate that chemotherapy triggers spatial redistribution and exhaustion of CD8+ T cells due to prolonged antigen presentation by macrophages, both within interconnected myeloid networks termed "Myelonets" and at the tumor stroma interface. Single-cell and spatial transcriptomics identifies prominent TIGIT-NECTIN2 ligand-receptor interactions induced by chemotherapy. Using a functional patient-derived immuno-oncology platform, we show that CD8+T-cell activity can be boosted by combining immune checkpoint blockade with chemotherapy. Our discovery of chemotherapy-induced myeloid-driven spatial T-cell exhaustion paves the way for novel immunotherapeutic strategies to unleash CD8+ T-cell-mediated anti-tumor immunity in HGSC.

cancer biology↗