Biology subjects

Sanz-Pamplona, R.

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

4 recordsLinked to original sources

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↗