Iglesias, G. | bioRxiv Science

bioRxiv2026

The miR-199a-5p/XIAP axis defines cisplatin response, apoptotic control and spatial remodelling in High-grade serous ovarian cancer.

BackgroundHigh-grade serous ovarian cancer (HGSOC) is a leading cause of gynaecological cancer mortality, largely because of late diagnosis and platinum resistance. Impaired apoptotic execution is a defining feature of resistant disease, but the regulatory interactions underlying this phenotype remain incompletely understood. The relationship between miR-199a-5p and the anti-apoptotic factor XIAP across cisplatin-sensitive and cisplatin-resistant states was the focus of this study. MethodsWe investigated the miR-199a-5p/XIAP axis in paired cisplatin-sensitive (A2780) and cisplatin-resistant (A2780cis) ovarian cancer models and in human ovarian tissue using multiplex fluorescence in situ hybridisation and immunofluorescence. ResultsmiR-199a-5p did not show a uniform repressive relationship with XIAP across cellular states. In sensitive cells, miR-199a-5p was consistent with basal XIAP repression and enhanced apoptotic responses following cisplatin exposure. In resistant cells, this relationship was attenuated, suggesting uncoupling between XIAP and apoptotic responsiveness after cisplatin. In human FFPE specimens, spatial analysis identified tumour-associated reorganisation of this axis, with broader miR-199a-5p distribution, greater overlap with XIAP, and a distinct perinuclear pattern in HGSOC. ConclusionsThe miR-199a-5p/XIAP axis is context-dependent and altered in cisplatin-resistant ovarian cancer. These findings support spatially resolved analysis as a useful framework for identifying regulatory heterogeneity and tumour-associated phenotypes linked to platinum-resistant disease.

cancer biology↗

bioRxiv2025

On the utility of Deep Learning for model classification and parameter estimation on complex diversification scenarios.

Birth-Death models applied to dated phylogenies are a useful tool to study past diversification dynamics. Parameters in these stochastic models are typically inferred using likelihood-based methods such as Maximum Likelihood Estimation (MLE) or Bayesian Inference. However, these approaches exhibit computational tractability issues in the case of models of moderate to high complexity. One approach to increase model complexity while remaining computationally tractable in the context of birth-death modelling is machine learning. So far, these techniques have been explored in the context of serially-sampled phylogenies (phylodynamics) and trait-dependent birth-death models. Here, we explored the power of Convolutional Neural Networks (CNNs), a type of Deep Learning (DL) method, to solve classification and regression (parameter estimation) tasks under constant-rate and time-homogeneous, rate-variable birth-death models. In particular, we compared six diversification scenarios: Constant Birth-Death, High-Extinction, Mass-Extinction, Diversity-Dependent, Stasis-and-Radiate, and Waxing-and-Waning. We simulated 10, 000 phylogenetic trees under each diversification scenario, which were encoded using a vectorization procedure that captures the topology and branch length information. The encoded trees were used to train or test a set of CNNs models that were designed to tailor three empirical case studies differing in the number of tips. We compared CNNs performance with MLE inference. Our results show that CNNs exhibited classification accuracy levels of 93-78%, whereas maximum likelihood estimation achieved levels of 74-70%. The most difficult scenarios to predict for the CNNs were the high-extinction and mass-extinction scenarios, which were often misidentified as one another. For the regression tasks, mean average errors were comparable between CNNs models and MLE inference, and they also coincided in their difficulty estimating ratio parameters such as mass extinction survival and turnover. Finally, we applied our CNNs to three empirical studies (eucalypts, conifers and cetaceans) and discussed potential shortcomings and future avenues for improvement in the application of deep-learning birth-death modelling approaches.

evolutionary biology↗