Biology subjects

Villoslada-Blanco, P.

Publications and source records attributed to Villoslada-Blanco, P..

3 recordsLinked to original sources

bioRxiv2026

Learning from Drops: AI-Guided Integration of Liquid Biopsy Features in Cancer Studies

Cancer is a major global health issue with rising incidence and mortality. Early detection, tumor characterization, and disease surveillance are crucial for timely and effective treatment, ultimately reducing mortality rates. Liquid biopsy (LB) has emerged as a valuable detection tool offering a non-invasive method to determine tumor-derived biomarkers in body fluids with demonstrated translational potential. To increase biomarker sensitivity, high-throughput sequencing platforms deliver massive volumes of data. Artificial Intelligence (AI) is pivotal in enabling huge and complex data integration. This contribution aims to assess the current state of integrative AI-based research in the LB field and provide methodological guidance. First, we conducted a PubMed search and found that the literature is sparse in studies integrating LB features, particularly by applying AI. When adopting the latter approach, defining the study objectives is crucial to guide the subsequent methodological aspects, including study design, patient selection criteria, sample size, nature of the LB features, and metadata to collect. Specifically, we propose strategies and tools for data preprocessing, including normalization and batch correction, as well as handling outliers and missing data. Furthermore, we recommend various Machine/Deep Learning approaches for feature selection techniques to ensure model robustness, and we highlight the importance of undergoing rigorous internal and external validations of the selected models. Assessing clinical utility and interpretability is often overlooked but fundamental for real-world implementation. In conclusion, we provide the LB scientific community with an AI-based methodological guidance to bridge the two fields and enhance the integrative analysis of LB features. Graphical abstractWorkchart for multiomics integrative studies in the liquid biopsy field. Note: CTCs, circulating tumor cells; ctDNA, circulating tumor-DNA; TEPs, tumor-educated platelets; miRNA, microRNA; cfRNAs, cell-free RNAs. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=159 SRC="FIGDIR/small/724535v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@1f250b2org.highwire.dtl.DTLVardef@18fe36corg.highwire.dtl.DTLVardef@19c02b9org.highwire.dtl.DTLVardef@176f6e0_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

DEVELOPMENT OF A CONSENSUS MOLECULAR CLASSIFIER FOR PANCREATIC DUCTAL ADENOCARCINOMA

Pancreatic ductal adenocarcinoma (PDAC) presents a significant challenge, with a five-year survival rate of approximately 10%. Tumor heterogeneity contributes to the limited effectiveness of treatments. Several tumor and stroma molecular classifiers have attempted to clarify this heterogeneity with moderate agreement. Recognizing the complexity introduced by this extensive array of taxonomies, this study aims to develop a consensus molecular classifier by including both tumor and stroma features. We integrated gene expression data through Virtual Microdissection and classified the training samples to apply Machine Learning algorithms for each previous classifier. The consensus classifier was then derived using a Markov Clustering Algorithm, and its association with overall survival was assessed. The results indicated that Elastic-Net emerged as the superior model. We identified two classes for tumor components (Consensus Classical and Consensus Non-classical) and stroma components (Consensus Normal-Immune and Consensus Activated-ECM). The consensus Random Forest achieved a balanced accuracy of 96.33% and 98.92%, respectively. While not consistent across retrospective series, the algorithm (PDAConsensus) independently predicted overall survival. We developed a robust consensus classifier for PDAC that integrates tumor and stroma features and made it accessible through the R package PDACMOC (PDACMolecularOmniClassifier, https://github.com/pavillos/PDACMOC) and a Shiny app (https://pdacmoc.cnio.es/).

bioinformatics↗

bioRxiv2022

Impact of HIV infection and integrase strand transfer inhibitors-based treatment on gut virome

Viruses are the most abundant components of the microbiome in human beings with a significant impact on health and disease. However, the impact of human immunodeficiency virus (HIV) infection on gut virome has been scarcely analyzed. On the other hand, several studies suggested that not all antiretrovirals for treating HIV infection exert similar effects on the gut bacteriome, being the integrase strand transfers inhibitors (INSTIs) --first-choice treatment of naive HIV-infected patients nowadays-- those associated with a healthier gut. Thus, the aim of this study was to evaluate the effects of HIV infection and INSTIs in first line of treatment on gut virome composition. To accomplish this objective, 26 non-HIV-infected volunteers, 15 naive HIV-infected patients and 15 INSTIs-treated HIV-infected patients were recruited and gut virome composition was analysed using shotgun sequencing. The results showed that bacteriophages are the most abundant and diverse viruses in the gut independent from the HIV-status and the use of treatment. HIV infection was accompanied by a decrease in phage richness which was reverted after INSTIs-based treatment (p<0.01 naive vs. control Richness index and p<0.05 naive vs. control Fishers alpha index). {beta}-diversity of phages revealed that samples from HIV-infected samples clustered separately from those belonging to the control group (padj<0.01 naive vs. control and padj<0.05 INSTIs vs. control). However, it is worth mentioning that samples coming from INSTIs-treated patients were more grouped than those from naive patients. Differential abundant analysis of phages showed an increase of Caudoviricetes class in the naive group compared to control the group (padj<0.05) and a decrease of Malgrandaviricetes class in the INSTIs-treated group compared to the control group (padj<0.001). Besides, it was observed that INSTIs-based treatment was not able to reverse the increase of lysogenic phages associated with HIV infection (p<0.05 vs. control) or to modify the decrease observed on the relative abundance of Proteobacteria-infecting phages (p<0.05 vs. control). To sum up, our study describes for the first time the impact of HIV and INSTIs on gut virome and demonstrates that INSTIs-based treatments are able to partially restore gut dysbiosis not only at bacterial but also at viral level, which opens several opportunities for new studies focused on microbiota-based therapies. Author summaryThe impact of human immunodeficiency virus (HIV) infection and the effects of integrase strand transfer inhibitors (INSTIs)-based treatments --first-choice treatment of naive HIV-infected patients nowadays-- on gut virome are unknown. In this study, we have confirmed that phages are the most abundant viral component of the human gut virome. Besides, we have described for the first time that INSTIs-based treatments are able to partially restore gut dysbiosis induced by HIV infection not only at bacteria but also at viral level. This fact opens new opportunities for future studies and approaches focused on microbiota-based therapies in the context of HIV infection and treatment.

microbiology↗