Biology subjects

Jimenez-Sanchez, A.

Publications and source records attributed to Jimenez-Sanchez, A..

6 recordsLinked to original sources

bioRxiv2026

Carbenoxolone disrupts cell migration by inhibiting the SERCA pump

Collective cell migration is a fundamental process driving tissue repair, angiogenesis, and vascular homeostasis. This coordinated movement requires both intercellular communication via gap junctions and precise intracellular Ca{superscript 2} signaling, largely regulated by the sarco(endo)plasmic reticulum Ca2+ ATPase (SERCA) pump within the endoplasmic reticulum (ER). Historically, carbenoxolone (CBX)--a synthetic derivative of glycyrrhetinic acid--has been widely utilized as a pharmacological tool to inhibit gap junctions and dissect their role in collective cell motility. However, its molecular specificity remains highly controversial. In the present study, using different cellular models, we found that CBX drastically reduces collective cell migration by a previously undescribed function for CBX: a fast, potent, and reversible inhibition of the SERCA pump, which provokes a passive leak of the luminal ER Ca{superscript 2} store. Our findings suggest that the effect of CBX over many cellular responses including cell migration and communication, previously only attributed to gap junction blockade, are indeed the consequence of the disruption of intracellular Ca{superscript 2} homeostasis. One Sentence Summarycarbenoxolone blocks cell migration by inhibiting SERCA

physiology↗

bioRxiv2025

Transcriptomic plasticity is a hallmark of metastatic pancreatic cancer

Metastasis is the leading cause of cancer deaths; nonetheless, how tumor cells adapt to vastly different organ contexts is largely unknown. To investigate this question, we generated a transcriptomic atlas of primary tumor and diverse metastatic samples from a patient with pancreatic ductal adenocarcinoma who underwent rapid autopsy. Unsupervised archetype analysis identified both shared and site-specific gene programs, including lipid metabolism and gastrointestinal programs prevalent in peritoneum and stomach wall lesions, respectively. We developed a probabilistic approach for inferring clonal phylogeny from single-cell and matched whole-exome data. Distantly related genetic clones in the peritoneum express the lipid metabolism program, likely due to signaling by the adipocyte-rich peritoneum environment, and cells in most clones express multiple programs, suggesting that transcriptomic plasticity is a prevalent feature of metastatic cells. These deeply annotated analyses using a patient-centric platform provide a model for investigating metastatic mechanisms and plasticity in advanced cancer.

bioinformatics↗

bioRxiv2019

Multi-site clonality analyses uncovers pervasive subclonal heterogeneity and branching evolution across melanoma metastases

Metastatic melanoma carries a poor prognosis despite modern systemic therapies. Understanding the evolution of the disease could help inform patient management. Through whole-genome sequencing of 13 melanoma metastases sampled at autopsy from a treatment naive patient and by leveraging the analytical power of multi-sample analyses, we reveal that metastatic cells may depart the primary tumour very early in the disease course and follow a branched pattern of evolution. Truncal UV-induced mutations that often swamp downstream analyses of heterogeneity, were found to be replaced by APOBEC-associated mutations in the branches of the evolutionary tree. Multi-sample analyses from a further 7 patients confirmed that branched evolution was pervasive, representing an important mode of melanoma dissemination. Our analyses illustrate that combining cancer cell fraction estimates across multiple metastases provides higher resolution phylogenetic reconstructions relative to single sample analyses and highlights the limitations of accurately inferring inter-tumoural heterogeneity from a single biopsy.

genomics↗

bioRxiv2018

Lack of detectable neoantigen depletion in the untreated cancer genome

Somatic mutations in cancer can result in the presentation of mutated peptides on the cell surface, eliciting an immune response. Mutant peptides are presented via HLA molecules and are known as neoantigens. It has been suggested that selection acts against the underlying mutations, leading to neoantigen depletion. Knowing the extent of this specific form of immunoediting may provide fundamental insights into tumour-immune interactions during tumour evolution. Here, we quantified the extent of neoantigen depletion in a wide range of human cancers by studying somatic mutations in the HLA-binding annotated exome, i.e. genomic regions that can be translated into presented peptides. We initially observed reduced non-synonymous mutation rates in presented regions, suggestive of neoantigen depletion. However, when compared to the expected mutation rates from a trinucleotide-based mutational signature model, depletion signals were negligible. This is explained by correlative relationships between the likelihood of mutagenesis in different nucleotide sequences and predicted HLA affinities for corresponding peptides. Our results suggest that signals of immunogenic negative selection are weak or absent in cancer genomics data and that other mechanisms to escape immune responses early during tumour evolution might be more efficient.

genomics↗

bioRxiv2018

Unraveling Tumor-Immune Heterogeneity in Advanced Ovarian Cancer Uncovers Immunogenic Effect of Chemotherapy

In metastatic cancer, the role of heterogeneity at the tumor-immune microenvironment, its molecular underpinnings and clinical relevance remain largely unexplored. To understand tumor-immune dynamics at baseline and upon chemotherapy treatment, we performed unbiased pathway and cell type-specific immunogenomics analysis of treatment-naive (38 samples from 8 patients) and paired chemotherapy treated (80 paired samples from 40 patients) high-grade serous ovarian cancer (HGSOC) samples. Whole transcriptome analysis and image-based quantification of T cells from treatment-naive tumors revealed ubiquitous variability in immune signaling and distinct immune microenvironments co-existing within the same individuals and within tumor deposits at diagnosis. To systematically explore cell type composition of the tumor microenvironment using bulk mRNA, we derived consensus immune and stromal cell gene signatures by intersecting state-of-the-art deconvolution methods, providing improved accuracy and sensitivity when compared to HGSOC immunostaining and leukocyte methylation data sets. Cell-type deconvolution and pathway analyses revealed that Myc and Wnt signaling associate with immune cell exclusion in untreated HGSOC. To evaluate the effect of chemotherapy on the intrinsic tumor-immune heterogeneity, we compared site-matched and site-unmatched tumors before and after neoadjuvant chemotherapy. Transcriptomic and T-cell receptor sequencing analyses showed that site-matched samples had increased cytotoxic immune activation and oligoclonal expansion of T cells after chemotherapy, which was not seen in site-unmatched samples where heterogeneity could not be accounted for. These results demonstrate that the tumor-immune interface in advanced HGSOC is intrinsically heterogeneous, and thus requires site-specific analysis to reliably unmask the impact of therapy on the tumor-immune microenvironment.

cancer biology↗

bioRxiv2018

Comprehensive Benchmarking and Integration of Tumour Microenvironment Cell Estimation Methods

The tumour microenvironment comprises complex cellular compositions and interactions between cancer, immune, and stromal components which all play crucial roles in cancer. Various computational approaches have been developed during the last decade that estimate the relative abundance of different cell types in an unbiased manner using bulk tumour RNA data. However, a comparison that objectively evaluates the performance of these approaches against one another has not been conducted. Here we benchmarked six widely used tools and gene sets: Bindea et al. gene sets, Davoli et al. gene sets, CIBERSORT, MCP-counter, TIMER, and xCell. We also introduce ConsensusTME, a consensus approach that uses the union of genes that the six tools used for cell estimation, and corrects for tumour-type specificity. We benchmarked the seven tools using TCGA DNA-derived purity scores (33 tumour-types), methylation-derived leukocyte scores (30 tumour-types), and H&E deep learning derived lymphocyte counts (13 tumour types), and individual benchmark data sets (PBMCs and 2 tumour types). Although none of the seven tools outperformed others in every benchmark, ConsensusTME ranked consistently well in all cancer-related benchmarks making it the top performing method overall. Computational methods that provide robust and accurate estimates of non-cancerous cell populations in the tumour microenvironment from tumour bulk expression data are important tools that can advance our understanding of tumour, immune, and stroma interactions, with potential clinical application if high accuracy estimates are achieved.

bioinformatics↗