Biology subjects

Martinez-Ruiz, C.

Publications and source records attributed to Martinez-Ruiz, C..

4 recordsLinked to original sources

bioRxiv2025

Agent-based simulations of lung tumour evolution suggest that ongoing cell competition drives realistic clonal expansions

Computational simulations of tumour evolution are increasingly used to infer the rules underlying cancer growth, with the goal of one day recommending tailored treatments. To make reliable inferences, such models must be able to reflect the properties of real tumours. Recent work has shown that lung tumours undergo frequent and late subclonal expansions, which are associated with poor prognosis. This paper tests three candidate simulations of three-dimensional tumour growth, which make different assumptions about the nature of competition between cells, for their ability to replicate these late expansions. Only a model which assumes stringent competition between tumour cells for existing tissue space, after the tumour has reached a fixed size, can produce multiregion sequencing data realistic to lung tumours. This work also assesses the influence of these assumptions on the inferred selection strength of individual tumours in a large cohort of lung cancers, and finds that inferences from a well-tailored model produce much higher estimates than poorly-tailored models of the effects of driver mutations on cell fitness. Table of Contents O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=175 SRC="FIGDIR/small/686578v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@40fae9org.highwire.dtl.DTLVardef@18f2d2org.highwire.dtl.DTLVardef@1edec55org.highwire.dtl.DTLVardef@136d1da_HPS_FORMAT_FIGEXP M_FIG Computational simulations of tumour evolution are increasingly used to infer the rules underlying cancer growth, with the goal of one day recommending tailored treatments. Here we show that the properties of lung cancer sequencing data are best replicated by a model which assumes that cells compete both to proliferate and survive. C_FIG

cancer biology↗

bioRxiv2025

Recovery of human upper airway epithelium after smoking cessation is driven by a slow-cycling stem cell population and immune surveillance

The upper airway epithelium in humans is maintained in homeostasis by a resident population of basal stem cells. In the presence of tobacco smoke these gain mutations that significantly increase their risk of transformation to lung squamous cell carcinoma. Previous studies show that a small proportion of stem cells avoid the mutational damage caused by carcinogens in tobacco and are more abundant in the lungs of former smokers than ongoing smokers, indicating unexplained tissue-level genomic recovery. This mirrors epidemiological risk, which falls rapidly after quitting smoking. Somatic evolutionary mechanistic hypotheses have been proposed to explain these observations. Here, we present a computational framework to model each of these hypotheses within the upper airway epithelial stem cell population over the entire patient lifetimes of a cohort with diverse smoking histories. Applying a mechanistic learning approach based on a set of biologically informed metrics to single cell-derived whole-genome sequencing data, we identified subtle differences between epithelia modelled under different combinations of hypotheses. A slow-cycling subpopulation of stem cells, combined with suppression of immune predation of highly mutated stem cells while smoking, best matched observed data, a result converged upon by multiple distinct machine learning methodologies. Our findings, drawing on an evolutionary model of mutagen exposure at a whole-lifetime scale that is not feasible to model in vivo, reveal the mechanisms behind reduction in lung squamous cell carcinoma risk on cessation of smoking and inform future therapeutic interventions to prevent lung cancer initiation.

evolutionary biology↗

bioRxiv2023

RNA allelic frequencies of somatic mutations encode substantial functional information in cancers

A central goal of cancer research is the identification of cancer genes that drive tumour growth and progression. Existing approaches to this problem typically leverage frequentist approaches based on patterns of somatic mutagenesis in DNA. Here, we interrogate RNA variant allele frequencies to identify putative cancer genes with a novel computational tool, RVdriver, from bulk genomic-transcriptomic data within 7,948 paired exomes and transcriptomes across 30 cancer types. An elevated RNA VAF reflects a signal from multiple biological features: clonal mutations; mutations retained or gained during somatic copy-number alterations; mutations favoured by allele-specific expression; and mutations in genes expressed preferentially by the tumour compartment of admixed bulk samples. RVdriver, a statistical approach that classifies RNA VAFs of nonsynonymous mutations relative to a synonymous mutation background, leverages this information to identify known, as well as putatively novel, cancer genes, with comparable performance to DNA-based approaches. Furthermore, we demonstrate RNA VAFs of individual mutations are able to distinguish driver from passenger mutations within established cancer genes. Low-RNA VAF EGFR mutations otherwise annotated as drivers of glioblastoma by DNA tools harbour a phenotype of reduced EGFR signalling, whilst high-RNA VAF KDM6A mutations otherwise annotated as passengers exhibit a driver-like H3K27me3 expression profile, demonstrating the value of our approach in phenotyping tumours. Overall, our study showcases a novel approach for cancer gene discovery, and highlights the potential value of multi-omic and systems-biology approaches in finding novel therapeutic vulnerabilities in cancer to bring about patient benefit.

cancer biology↗

bioRxiv2020

Genomic architecture and evolutionary conflict drive allele-specific expression in the social supergene of the red fire ant

Supergenes are genomic regions of suppressed recombination that underlie complex polymorphisms. Despite the importance of such regions, our empirical understanding of their early evolution is limited. The young "social" supergene of the fire ant Solenopsis invicta provides a powerful system for disentangling the roles of evolutionary conflict and the implications of suppressed recombination. We used population genomics to identify genetic differences between supergene variants and gene expression analyses across different populations, castes and body parts to characterize allelic expression differences for the hundreds of genes in the supergene. We find that the expression of most genes is independent of social form or supergene variant, in line with the young age of this system. Many of the genes with allelic expression differences, however, show a pattern consistent with gene degeneration due to suppressed recombination. In contrast, a small portion of the genes has the signature of evolutionary conflict between social forms.

molecular biology↗