publications

2026

1. Distinct H3K27 methylation states drive cellular responses to the histone demethylase inhibitor GSK-J4 in ovarian cancer cells

   Marcos Quintela, Lydia C. Powell, Agne Baseviciene, and 13 more authors

   Molecular Cancer Therapeutics, 2026

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   The deregulation of histone methylation has been implicated in the pathogenesis of multiple diseases, including ovarian cancer (OC), one of the most lethal gynaecological malignancies worldwide. The roles of the H3K27 demethylases UTX and JMJD3– key regulators of epigenetic homeostasis– remain incompletely characterised in OC. Here, we used the demethylase inhibitor GSK-J4 and siRNA-mediated knockdown of UTX and JMJD3 to investigate the functional impact of altered H3K27 methylation in OC in vitro models. Pharmacological modulation of H3K27me2/3 induced distinct cell type-specific phenotypes, including reduced proliferation, apoptosis and alterations in 3D spheroid architecture. These effects were accompanied by transcriptional downregulation of EMT- and ECM-associated genes. Notably, we identified the androgen receptor (AR) as a key upstream regulator of H3K27 methylation. In AR-expressing OC cells, AR inhibition increased H3K27me2/3 levels, revealing a novel epigenetic axis linking nuclear receptor signalling with histone methylation dynamics. Together, our findings uncover context-dependent vulnerabilities in OC cells and highlight a potential therapeutic interplay between AR signalling and epigenetic modulation.

2025

1. VenusGT: A Trajectory-Aware Graph Transformer for Rare-Cell Discovery in Single-Cell Multi-Omics

   Natalia Sikora, Rebecca Rees, Sean R. Holm, and 2 more authors

   In EurIPS 2025 (European version of NeurIPS 2025), Workshop on Multimodal Representation Learning for Healthcare (MMRL4H) , 2025
2. Perturbing LSD1 and WNT rewires transcription to synergistically induce AML differentiation

   Amir Hosseini, Abhinav Dhall, Nemo Ikonen, and 19 more authors

   Nature, Apr 2025

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   Impaired differentiation is a hallmark of myeloid malignancies1,2. Therapies that enable cells to circumvent the differentiation block, such as all-trans retinoic acid (ATRA) and arsenic trioxide (ATO), are by and large curative in acute promyelocytic leukaemia3, but whether ’differentiation therapy’ is a generalizable therapeutic approach for acute myeloid leukaemia (AML) and beyond remains incompletely understood. Here we demonstrate that simultaneous inhibition of the histone demethylase LSD1 (LSD1i) and the WNT pathway antagonist GSK3 kinase4 (GSK3i) robustly promotes therapeutic differentiation of established AML cell lines and primary human AML cells, as well as reducing tumour burden and significantly extending survival in a patient-derived xenograft mouse model. Mechanistically, this combination promotes differentiation by activating genes in the type I interferon pathway via inducing expression of transcription factors such as IRF7 (LSD1i) and the co-activator β-catenin (GSK3i), and their selective co-occupancy at targets such as STAT1, which is necessary for combination-induced differentiation. Combination treatment also suppresses the canonical, pro-oncogenic WNT pathway and cell cycle genes. Analysis of datasets from patients with AML suggests a correlation between the combination-induced transcription signature and better prognosis, highlighting clinical potential of this strategy. Collectively, this combination strategy rewires transcriptional programs to suppress stemness and to promote differentiation, which may have important therapeutic implications for AML and WNT-driven cancers beyond AML.

3. ColonScopeX: Leveraging Explainable Expert Systems with Multimodal Data for Improved Early Diagnosis of Colorectal Cancer

   Natalia Sikora, Robert L. Manschke, Alethea M. Tang, and 3 more authors

   In Proceedings of The First AAAI Bridge Program on AI for Medicine and Healthcare, 25 feb 2025

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   Colorectal cancer (CRC) ranks as the second leading cause of cancer-related deaths and the third most prevalent malignant tumour worldwide. Early detection of CRC remains problematic due to its non-specific and often embarrassing symptoms, which patients frequently overlook or hesitate to report to clinicians. Crucially, the stage at which CRC is diagnosed significantly impacts survivability, with a survival rate of 80-95% for Stage I and a stark decline to 10% for Stage IV. Unfortunately, in the UK, only 14.4% of cases are diagnosed at the earliest stage (Stage I). In this study, we propose ColonScopeX, a machine learning framework utilizing explainable AI (XAI) methodologies to enhance the early detection of CRC and pre-cancerous lesions. Our approach employs a multimodal model that integrates signals from blood sample measurements, processed using the Savitzky-Golay algorithm for fingerprint smoothing, alongside comprehensive patient metadata, including medication history, comorbidities, age, weight, and BMI. By leveraging XAI techniques, we aim to render the model’s decision-making process transparent and interpretable, thereby fostering greater trust and understanding in its predictions. The proposed framework could be utilised as a triage tool or a screening tool of the general population. This research highlights the potential of combining diverse patient data sources and explainable machine learning to tackle critical challenges in medical diagnostics.

4. Measuring Fairness in Financial Transaction Machine Learning Models

   Deniz Sezin Ayvaz, Lorenzo Belenguer, Hankun He, and 12 more authors

   25 feb 2025

2022

1. An observational cohort study to evaluate the use of serum Raman spectroscopy in a rapid diagnosis center setting

   Freya E.R. Woods, Susan Chandler, Natalia Sikora, and 7 more authors

   Clinical Spectroscopy, Dec 2022