Biology subjects

Sanz-Pamplona, R.

Publications and source records attributed to Sanz-Pamplona, R..

8 recordsLinked to original sources

bioRxiv2025

Integrative multi-omics reveals PARP14 as a key IFNγ-regulated mediator of metastatic progression in Ewing sarcoma.

Metastatic Ewing sarcoma (EwS) remains a major clinical challenge, highlighting the need to identify molecular drivers of disease dissemination. Through integrated transcriptomic, functional, and mechanistic analyses, we identify PARP14 as a key regulator of EwS metastatic progression. PARP14 is consistently upregulated in metastatic lesions across spontaneous and xenograft mouse models as well as in patient samples. Genetic and pharmacologic depletion of PARP14 (via siRNA knockdown, CRISPR-Cas9 knockout, or PROTAC-mediated degradation) significantly impaired EwS cell migration and invasion in vitro, without affecting proliferation or clonogenic capacity. In vivo, PARP14 loss delayed tumor growth and reduced metastatic burden, supporting a functional role in disease progression. Conversely, IFN{gamma}-induced PARP14 upregulation and stable overexpression enhanced EwS cell migration and increased lung metastasis formation. Transcriptomic profiling of PARP14-deficient cells revealed downregulation of invasive signatures and enrichment of inflammatory and p53-related pathways, together with gene sets associated with chromatin remodeling. Subcellular fractionation and interactome analyses showed that PARP14 localizes to cytoplasmic, nuclear, and chromatin-associated compartments, and interacts with chromatin, cytoskeletal and ECM regulators. ChlP analyses further demonstrated PARP14 binding to promoters enriched for NRF1 motifs, suggesting a role as a transcriptional co-regulator influencing gene networks relevant to EwS progression. Together, our results establish PARP14 as a contributor to EwS dissemination and uncover a previously unappreciated chromatin-associated role for this protein, supporting its potential as both a biomarker and therapeutic target in metastatic EwS. STATEMENT OF SIGNIFICANCEWe identify PARP14 as a novel driver of Ewing sarcoma metastasis through integrated multi-omics profiling. Genetic and pharmacological interrogation establishes PARP14 as a promising therapeutic target for this understudied malignancy.

cancer biology↗

bioRxiv2023

Unraveling the Power of NAP-CNB's Machine Learning-enhanced Tumor Neoantigen Prediction

In this study, we present a proof-of-concept classical vaccination experiment that validates the in silico identification of tumor neoantigens (TNAs) using a machine learning-based platform called NAP-CNB. Unlike other TNA predictors, NAP-CNB leverages RNAseq data to consider the relative expression of neoantigens in tumors. Our experiments show the efficacy of NAP-CNB. Predicted TNAs elicited potent antitumor responses in vivo following classical vaccination protocols. Notably, optimal antitumor activity was observed when targeting the antigen with higher expression in the tumor, which was not the most immunogenic. Additionally, the vaccination combining different neoantigens resulted in vastly improved responses compared to each one individually, showing the worth of multiantigen-based approaches. These findings validate NAP-CNB as an innovative TNA-identification platform and make a substantial contribution to advancing the next generation of personalized immunotherapies

immunology↗

bioRxiv2022

Driver mutations in GNAQ and GNA11 genes as potential targets for precision immunotherapy in uveal melanoma patients

Uveal melanoma (UM) is the most common ocular malignancy in adults. Nearly 95% of UM patients carry the mutually exclusive mutations in the homologous genes GNAQ (aminoacid change Q209L/Q209P) and GNA11 (aminoacid change Q209L). UM is located in an immunosuppressed organ and do not suffer immunoediting. Therefore, we hypothesize that driver mutations in GNAQ/11 genes could be recognized by the immune system. Genomic and transcriptomic data for primary uveal tumors was collected from TCGA-UM dataset (n=80). The immunogenic potential for GNAQ/GNA11 Q209L/Q209P mutations was assessed using a variety of tools and HLA types information. The immune microenvironment was characterized using gene expression data. All prediction tools showed stronger GNAQ/11 Q209L binding to HLA. The immunogenicity analysis revealed that Q209L is likely to be presented by more than 73% of individuals in 1000G database whereas Q209P is only predicted to be presented in 24% of individuals. GNAQ/11 Q209L showed higher likelihood to be presented by HLA-I molecules than almost all driver mutations analyzed. Samples carrying Q209L had a higher immune-reactive phenotype: (i) expression of antigen presenting genes HLA-A (p=0.009) and B2M (p=0.043); (ii) immunophenoscore (p=0.008); (iii) infiltration of immune system cells NK (p=0.002) and CD8+ T lymphocytes (p=0.02). Results suggest a high potential immunogenicity of the GNAQ/11 Q209L variant that could allow the generation of novel therapeutic tools to treat UM like neoantigen vaccinations.

genomics↗

bioRxiv2022

MmCMS: Mouse models' Consensus Molecular Subtypes of colorectal cancer

BACKGROUNDColorectal cancer (CRC) primary tumours are molecularly classified into four consensus molecular subtypes (CMS1-4). Genetically engineered mouse models aim to faithfully mimic the complexity of human cancers and, when appropriately aligned, represent ideal pre-clinical systems to test new drug treatments. Despite its importance, dual-species classification has been limited by the lack of a reliable approach. Here we utilise, develop and test a set of options for human-to-mouse CMS classifications of CRC tissue. METHODSUsing transcriptional data from established collections of CRC tumours, including human (TCGA cohort; n=577) and mouse (n=57 across n=8 genotypes) tumours with combinations of random forest and nearest template prediction algorithms, alongside gene ontology collections, we comprehensively assess the performance of a suite of new dual-species classifiers. RESULTSWe developed three approaches: MmCMS-A; a gene-level classifier, MmCMS-B; an ontology-level approach and MmCMS-C; a combined pathway system encompassing multiple biological and histological signalling cascades. Although all options could identify tumours associated with stromal-rich CMS4-like biology, MmCMS-A was unable to accurately classify the biology underpinning epithelial-like subtypes (CMS2/3) in mouse tumours. CONCLUSIONSWhen applying human-based transcriptional classifiers to mouse tumour data, a pathway-level classifier, rather than an individual gene-level system, is optimal. Our R package with three options helps researchers select suitable mouse models of human CRC subtype for their experimental testing.

cancer biology↗

bioRxiv2021

Copy-number intratumor heterogeneity contributes to predict relapse in chemotherapy-naive stage II colon cancer

Optimal selection of high-risk patients with stage II colon cancer is crucial to ensure clinical benefit of adjuvant chemotherapy after surgery. Here, we investigate the prognostic and predictive value of genomic intratumor heterogeneity and aneuploidy for disease recurrence. We combined SNP arrays, targeted next-generation sequencing, fluorescence in situ hybridization and inmunohistochemistry on a retrospective cohort of 84 untreated stage II colon cancer patients. We assessed the subclonality of copy-number alterations (CNAs) and mutations, CD8+ lymphocyte infiltration and their association with time to recurrence (TTR). Prognostic factors were included in machine learning analysis to evaluate their ability to predict individual relapse risk. Tumors from recurrent patients (N = 38) exhibited greater proportion of CNAs compared with non-recurrent (N = 46) (mean 31.3% vs. 23%, respectively; P = 0.014), which was confirmed in an independent cohort. Candidate chromosome-specific aberrations included the gain of the chromosome arm 13q (P = 0.02; HR, 2.67) and loss of heterozygosity at 17q22-q24.3 (P = 0.05; HR, 2.69), both associated with shorter TTR. CNA load positively correlated with intratumor heterogeneity (R = 0.52; P < 0.0001), indicating ongoing chromosomal instability. Consistently, subclonal copy-number heterogeneity was associated with elevated risk of relapse (P = 0.028; HR, 2.20), which we did not observe for subclonal mutations. The clinico-genomic model rated an area under the curve of 0.83, achieving a 10% incremental gain compared to clinicopathological markers. In conclusion, tumor aneuploidy and copy-number heterogeneity were predictive of a poor outcome in early-stage colon cancer, and improved discriminative performance in comparison to clinicopathological data.

genomics↗

bioRxiv2020

NAP-CNB: Bioinformatic pipeline to predict MHC-I-restricted T cell epitopes in mice

Lack of a dedicated integrated pipeline for neoantigen discovery in mice hinders cancer immunotherapy research. Novel sequential approaches through recurrent neural networks can improve the accuracy of T-cell epitope immunogenicity predictions in mice, and a simplified variant selection process can reduce operational requirements. We have developed a web server tool (NAP-CNB) for a full and automatic pipeline based on recurrent neural networks, to predict putative neoantigens from tumoral RNA sequencing reads. The developed software can estimate H-2 peptide ligands, with an AUC of 0.95, directly from tumor samples. As a proof-of-concept, we used the B16 melanoma model to test the systems predictive capabilities, and we report its putative neoantigens. NAP-CNB web server is freely available at http://biocomp.cnb.csic.es/NeoantigensApp/ with scripts and datasets accessible through the download section.

bioinformatics↗

bioRxiv2020

High Cysteinyl Leukotriene Receptor 1 Expression Correlates with Poor Survival of Uveal Melanoma Patients and Cognate Antagonist Drugs Modulate the Growth, Cancer Secretome, and Metabolism of Uveal Melanoma Cells.

Uveal melanoma (UM) is a rare, but often lethal, form of ocular cancer arising from melanocytes within the uveal tract. UM has a high propensity to spread hematogenously to the liver, with up to 50% of patients developing liver metastases. Unfortunately, once liver metastasis occurs, patient prognosis is extremely poor with as few as 8% of patients surviving beyond two years. There are no standard-of-care therapies available for the treatment of metastatic uveal melanoma, hence it is a clinical area of urgent unmet need. Here, the clinical relevance and therapeutic potential of cysteinyl leukotriene receptors (CysLT1 and CysLT2) in UM was evaluated. High expression of CYSLTR1 or CYSLTR2 transcripts is significantly associated with poor disease-free survival and poor overall survival in UM patients. Digital pathology analysis identified high expression of CysLT1 in primary UM is associated with reduced disease-specific survival (p = 0.012) and overall survival (p = 0.011). High CysLT1 expression shows a statistically significant (p = 0.041) correlation with ciliary body involvement, a poor prognostic indicator in UM. Small molecule drugs targeting CysLT1 were vastly superior at exerting anti-cancer phenotypes in UM cell lines and zebrafish xenografts than drugs targeting CysLT2. Quininib, a selective CysLT1 antagonist, significantly inhibits survival (p < 0.0001), long-term proliferation (p < 0.0001), and oxidative phosphorylation (p < 0.001), but not glycolysis, in primary and metastatic UM cell lines. Quininib exerts opposing effects on the secretion of inflammatory markers in primary versus metastatic UM cell lines. Quininib significantly downregulated IL-2 and IL-6 in Mel285 cells (p < 0.05), but significantly upregulated IL-10, IL-1{beta}, IL-2 (p < 0.0001), IL-13, IL-8 (p < 0.001), IL-12p70 and IL-6 (p < 0.05) in OMM2.5 cells. Finally, quininib significantly inhibits tumour growth in orthotopic zebrafish xenograft models of UM. These preclinical data suggest that antagonism of CysLT1, but not CysLT2, may be of therapeutic interest in the treatment of UM.

cancer biology↗

bioRxiv2020

Detection of Merkel cell polyomavirus using whole exome sequencing data

Merkel cell carcinoma (MCC) is a highly malignant neuroendocrine tumor of the skin in which Merkel cell polyomavirus (MCV) DNA virus insertion can be detected in 75-89% of cases. Etiologic and phenotypic differences exist between MCC tumors with and without the inserted virus, thus it is important to distinguish between MCV+ MCC and MCV-MCC cases. Currently, MCV insertions in MCC genomes are detected using laboratory techniques. Here we report a freely available bioinformatics methodology to identify MCV+ MCC tumors using whole exome sequencing (WES) data. WES data could be also used to infer the virus insertion site into the tumor genome. Our method has been validated in a set of MCC samples previously characterized in the laboratory as MCV+ or MCV-, achieving 100% sensitivity and 62,5% specificity. Thus, with enough depth of sequencing, it is possible to use WES to the presence of MCV insertions in cancer samples.

bioinformatics↗