Search bioRxiv⌕ Search

Biology subjects

Cerezo, M.

Publications and source records attributed to Cerezo, M..

4 recordsLinked to original sources

Arginine synthesis pathway and ASS1 play a critical role in mRNA translation reprogramming and ICI resistance in cutaneous melanoma

Immune checkpoint inhibitors (ICI) have revolutionized cancer treatment, but their efficacy has now reached a plateau. ICIs are the first class of treatment targeting the crosstalk between immune and tumor cells, making it crucial to understand the complex interactions within the tumor microenvironment (TME) to enhance therapeutic responses. The elevated consumption of resources by cancer cells, coupled with limited vascularization, often results in a TME that is deficient in nutrients, leading to competition for resources between cancer and stromal cells. Consequently, targeting tumor metabolism has emerged as a promising strategy to improve the efficacy of ICIs. Through metabolomic analysis, we have identified metabolic alterations in melanoma cells that are resistant to ICIs, specifically an increase in arginine synthesis and upregulation of ASS1, the rate-limiting enzyme in this pathway. By using gain and loss of function models, as well as a pharmacological inhibitor specific for ASS1, we demonstrated that modulations in the expression or activity of ASS1 is associated with translational reprogramming, characterized by an inhibition of the cap-dependent mRNA translation mediated through mTORC1/4EBP1 axis. We also demonstrated that targeting ASS1 in vivo, resensitize tumors initially resistant to ICI. Taken together, our results highlight the interaction between modulations of arginine synthesis pathway, mRNA translation reprogramming, antitumor immunity, and restauration of sensitivity to anti-PD-1. Our work also demonstrates the therapeutic potential of targeting arginine synthesis pathway, and especially ASS1, to offer new treatments to patients suffering from cutaneous melanoma resistant to ICIs.

cancer biology↗

MELK controls tumor metabolism to promote resistance to melanoma therapy

Melanoma is the most aggressive form of skin cancer, and despite major advances in targeted and immune therapies, durable responses remain limited due to the emergence of resistance mechanisms. Metabolic reprogramming has emerged as a key driver of therapy resistance, allowing tumor cells to adapt to environmental and therapeutic pressures. Here, we identify the maternal embryonic leucine zipper kinase (MELK) as a critical mediator of resistance in melanoma. We demonstrate that MELK stimulates intracellular accumulation of amino acids, leading to activation of mTORC1 signaling and enhanced mitochondrial metabolism and biogenesis. This metabolic shift promotes resistance to immune checkpoint blockade. Importantly, preclinical MELK inhibition sensitizes resistant melanoma cells to immunotherapy, revealing a potential combinatorial strategy to overcome resistance. Our findings establish MELK as a central regulator of metabolic adaptation and therapeutic resistance in melanoma, highlighting its potential as a promising therapeutic target.

cancer biology↗

The NHGRI-EBI GWAS Catalog: standards for reusability, sustainability and diversity

The NHGRI-EBI GWAS Catalog serves as a vital resource for the genetic research community, providing access to the most comprehensive database of human GWAS results. Currently, it contains close to 7,000 publications for more than 15,000 traits, from which more than 625,000 lead associations have been curated. Additionally, 85,000 full genome-wide summary statistics datasets - containing association data for all variants in the analysis - are available for downstream analyses such as meta-analysis, fine-mapping, Mendelian randomisation or development of polygenic risk scores. As a centralised repository for GWAS results, the GWAS Catalog sets and implements standards for data submission and harmonisation, and encourages the use of consistent descriptors for traits, samples and methodologies. We share processes and vocabulary with the PGS Catalog, improving interoperability for a growing user group. Here, we describe the latest changes in data content, improvements in our user interface, and the implementation of the GWAS-SSF standard format for summary statistics. We address the challenges of handling the rapid increase in large-scale molecular quantitative trait GWAS and the need for sensitivity in the use of population and cohort descriptors while maintaining data interoperability and reusability.

genetics↗

A Standardized Framework For Representation Of Ancestry Data In Genomics Studies

BackgroundThe accurate description of ancestry is essential to interpret and integrate human genomics data, and to ensure that advances in the field of genomics benefit individuals from all ancestral backgrounds. However, there are no established guidelines for the consistent, unambiguous and standardized description of ancestry. To fill this gap, we provide a framework, designed for the representation of ancestry in GWAS data, but with wider application to studies and resources involving human subjects.\n\nResultHere we describe our framework and its application to the representation of ancestry data in a widely-used publically available genomics resource, the NHGRI-EBI GWAS Catalog. We present the first analyses of GWAS data using our ancestry categories, demonstrating the validity of the framework to facilitate the tracking of ancestry in big data sets. We exhibit the broader relevance and integration potential of our method by its usage to describe the well-established HapMap and 1000 Genomes reference populations. Finally, to encourage adoption, we outline recommendations for authors to implement when describing samples.\n\nConclusionsWhile the known bias towards inclusion of European ancestry individuals in GWA studies persists, African and Hispanic or Latin American ancestry populations contribute a disproportionately high number of associations, suggesting that analyses including these groups may be more effective at identifying new associations. We believe the widespread adoption of our framework will increase standardization of ancestry data, thus enabling improved analysis, interpretation and integration of human genomics data and furthering our understanding of disease.

genetics↗