Biology subjects

Gomez-Lopez, G.

Publications and source records attributed to Gomez-Lopez, G..

7 recordsLinked to original sources

bioRxiv2026

Clonucopya: An end-to-end workflow bridging tumor clonal architecture and drug prioritization

Motivation: Intra-tumor heterogeneity limits the effectiveness of anticancer treatments, as distinct clonal populations may respond differently to therapy. Although subclonal inference from sequencing data is well established, translating clonal architecture into actionable drug recommendations remains a major bottleneck. Results: We present Clonucopya, an end-to-end Snakemake workflow that integrates subclonal inference (PyClone-VI, PhyClone) with drug prioritization (PanDrugs2) to link tumor clonal architecture directly to candidate drugs. From genome sequencing data, Clonucopya reconstructs tumor phylogenies, quantifies clone-level prevalence across samples, and ranks FDA/EMA-approved and experimental therapeutic candidates against the somatic mutations detected in each clone. Outputs include clonality plots, per-clone variant and drug tables, and a summary ranking drugs by clonal coverage to highlight broadly active therapeutic candidates. We assess Clonucopya's utility through a real-world longitudinal, multi-region use case, revealing clinically actionable clonal dynamics across tumor progression.

bioinformatics↗

bioRxiv2026

A single-cell atlas linking intratumoral states to therapeutic vulnerabilities across cancers

Intratumoral heterogeneity (ITH) is a major determinant of therapeutic failure, yet its impact on drug response across cancers remains incompletely understood. Here, we present the Therapeutic Cancer Cell Atlas (TCCA), a pan-cancer single-cell resource integrating [~]1.8 million transcriptomes from 537 patients and 183 cancer cell lines spanning 34 tumor types. By combining single-cell transcriptomics with copy-number alteration inference and computational drug-response prediction, we systematically map therapeutic heterogeneity at subclonal resolution across cancers. Using this framework, we identify ten recurrent therapeutic clusters that capture conserved and context-specific drug vulnerabilities across tumor lineages. Notably, therapeutic heterogeneity is largely decoupled from genomic and transcriptomic diversity and instead arises from distinct functional transcriptional programs and tumor microenvironment (TME) states. Integration with transcriptional metaprograms and TME archetypes reveals how stress responses, proliferative states, lineage programs, and immune context shape drug sensitivity beyond tissue of origin. We further demonstrate the translational relevance of TCCA by linking therapeutic clusters to patient outcomes and validating predicted vulnerabilities using pharmacogenomic datasets, including clinically actionable examples in aggressive tumor subtypes. Together, TCCA provides a multidimensional atlas connecting subclonal states, microenvironmental context, and drug response, offering a scalable framework to guide therapeutic prioritization, drug repurposing, and combination strategies in precision oncology.

bioinformatics↗

bioRxiv2024

Spatial Transcriptomics in Breast Cancer Reveals Tumour Microenvironment-Driven Drug Responses and Clonal Therapeutic Heterogeneity

Breast cancer is a heterogeneous disease that has the highest incidence and mortality rate among cancers in women worldwide. Breast cancer patients are stratified into three clinical subtypes with different treatment strategies and prognostic values. The development of targeted therapies against the biomarkers that define these strata constitutes one of the precedents of precision oncology, which aims to provide tailored treatments to cancer patients by targeting the molecular alterations found in each tumour. Although this approach has increased patient outcomes, many treatment failure cases still exist. Drug ineffectiveness and relapse have been associated with the coexistence of several malignant subpopulations with different drug sensitivities within the same lesion, a phenomenon known as intratumor heterogeneity. This heterogeneity has been extensively studied from a tumour-centric view, but recently, it has become evident that the tumour microenvironment plays a crucial role in intratumor heterogeneity. However, few studies consider the tumour-microenvironment interplay and its influence on drug sensitivity. In this work, we predict the sensitivity of 10x Visium spatial transcriptomics data from 9 breast cancer patients to >1,200 drugs and verify different response patterns across the tumour, interphase and microenvironment regions. We uncover a sensitivity continuum from the tumour core to the periphery accompanied by a functional gradient. Moreover, we identify conserved therapeutic clusters with distinct response patterns within the tumour region. We link the specific drug sensitivities of each therapeutic cluster to different ligand-receptor interactions that underpin distinct biological functions. Finally, we demonstrate that genetically identical cancer spots may belong to different therapeutic clusters and that this therapeutic heterogeneity is related to their location at the edge or core of tumour ducts. These results highlight the importance of considering the distance to the tumour core and the microenvironment composition when identifying suitable treatments to target intratumor heterogeneity.

cancer biology↗

bioRxiv2021

Beyondcell: targeting cancer therapeutic heterogeneity in single-cell RNA-seq

We present Beyondcell (https://gitlab.com/bu_cnio/beyondcell/), a computational methodology for identifying tumour cell subpopulations with distinct drug responses in single-cell RNA-seq data and proposing cancer-specific treatments. Our method calculates an enrichment score in a collection of drug signatures, delineating therapeutic clusters (TCs) within cellular populations. Additionally, Beyondcell determines therapeutic differences among cell populations, and generates a prioritised ranking of the differential sensitivity drugs between chosen conditions to guide drug selection. We performed Beyondcell analysis in four single-cell datasets to validate our score and to demonstrate that TCs can be exploited to target malignant cells both in cancer cell lines and tumour patients.

bioinformatics↗

bioRxiv2020

DREIMT: a drug repositioning database and prioritization tool for immunomodulation.

MotivationDrug immunomodulation modifies the response of the immune system and can be therapeutically exploited in pathologies such as cancer and autoimmune diseases. ResultsDREIMT is a new hypothesis-generation web tool which performs drug prioritization analysis for immunomodulation. DREIMT provides significant immunomodulatory drugs targeting up to 70 immune cells subtypes through a curated database that integrates 4,960 drug profiles and ~2,6K immune gene expression signatures. The tool also suggests potential immunomodulatory drugs targeting user-supplied gene expression signatures. Final output includes drug-signature association scores, FDRs and downloadable plots and results tables. Availabilityhttp://www.dreimt.org Contactfalshahrour@cnio.es; ggomez@cnio.es

bioinformatics↗

bioRxiv2020

A comprehensive and centralized database for exploring omics data in Autoimmune Diseases

Autoimmune diseases are heterogeneous pathologies with difficult diagnosis and few therapeutic options. In the last decade, several omics studies have provided significant insights into the molecular mechanisms of these diseases. Nevertheless, data from different cohorts and pathologies are stored independently in public repositories and a unified resource is imperative to assist researchers in this field. Here, we present ADEx (https://adex.genyo.es), a database that integrates 82 curated transcriptomics and methylation studies covering 5609 samples for some of the most common autoimmune diseases. The database provides, in an easy-to-use environment, advanced data analysis and statistical methods for exploring omics datasets, including meta-analysis, differential expression or pathway analysis.

bioinformatics↗

bioRxiv2017

Distinct roles of cohesin-SA1 and cohesin-SA2 in 3D chromosome organization

In addition to mediating sister chromatid cohesion, cohesin plays a central role in DNA looping and segmentation of the genome into contact domains (TADs). Two variant cohesin complexes that contain either STAG/SA1 or SA2 are present in all cell types. Here we addressed their specific contribution to genome architecture in non-transformed human cells. We found that cohesin-SA1 drives stacking of cohesin rings at CTCF-bound sites and thereby contributes to the stabilization and preservation of TAD boundaries. In contrast, a more dynamic cohesin-SA2 promotes cell type-specific contacts between enhancers and promoters within TADs independently of CTCF. SA2 loss, a condition frequently observed in cancer cells, results in increased intra-TAD interactions, likely altering the expression of key cell identity genes.

genomics↗