Biology subjects

Sabroso-Lasa, S.

Publications and source records attributed to Sabroso-Lasa, S..

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↗

bioRxiv2024

BPTF cooperates with MYCN and MYC to link neuroblastoma cell cycle control to epigenetic cellular states

The nucleosome remodeling factor BPTF is required for the deployment of the MYC-driven transcriptional program. Deletion of one Bptf allele delays tumor progression in mouse models of pancreatic cancer and lymphoma. In neuroblastoma, MYCN cooperates with the transcriptional core regulatory circuitry (CRC). High BPTF levels are associated with high-risk features and decreased survival. BPTF depletion results in a dramatic decrease of cell proliferation. Bulk RNA-seq, single-cell sequencing, and tissue microarrays reveal a positive correlation of BPTF and CRC transcription factor expression. Immunoprecipitation/mass spectrometry shows that BPTF interacts with MYCN and the CRC proteins. Genome-wide distribution analysis of BPTF and CRC in neuroblastoma reveals a dual role for BPTF: 1) it co-localizes with MYCN/MYC at the promoter of genes involved in cell cycle and 2) it co-localizes with the CRC at super-enhancers to regulate cell identity. The critical role of BPTF across neuroblastoma subtypes supports its relevance as a therapeutic target.

genomics↗