Análisis de la heterogeneidad genética del adenocarcinoma ductal de páncreas y su relación con las características de la enfermedad

[ES]En la actualidad se conoce que el adenocarcinoma ductal de páncreas (ADCP) es una neoplasia histológica y genéticamente heterogénea. No obstante, la información disponible acerca de los patrones genéticos de evolución clonal y de los mecanismos moleculares implicados en la enfermedad (ya sea en su desarrollo y progresión biológica o en su transformación maligna) y su comportamiento clínico, sigue siendo limitado, especialmente en lo que se refiere a la identificación de biomarcadores con utilidad diagnóstica y de posibles dianas terapéuticas. Un número creciente de estudios indica que el comportamiento clínico y biológico de una neoplasia depende en gran medida de las alteraciones genéticas subyacentes y de las interacciones entre las células tumorales y el micromedioambiente que las rodea, traducidas a su vez en distintos perfiles de alteración genética y de expresión génica, que para el ADCP, siguen sin conocerse en profundidad, especialmente en lo que se refiere a las distintas vías de evolución clonal existentes a nivel intratumoral. En el presente trabajo doctoral nos hemos planteado como objetivo general profundizar en el conocimiento de las alteraciones genéticas y genómicas del ADCP, con especial énfasis en las vías de evolución clonal presentes a nivel intratumoral, y su impacto en el comportamiento clínico y biológico de la enfermedad. Para esto hemos analizado tumores de 55 pacientes con ADCP mediante técnicas que permiten el análisis de las alteraciones cromosómicas a nivel de células individuales (iFISH), en combinación con técnicas moleculares de análisis global del genoma y su perfil de expresión. Esta aproximación nos ha permitido profundizar en el conocimiento de la heterogeneidad genética y de las vías de evolución clonal existentes, tanto a nivel intratumoral en cada paciente, como a nivel intertumoral entre distintos pacientes con ADCP. Nuestros resultados nos indican que el ADCP presenta perfiles de aberración citogenética y de expresión génica altamente complejos, asociados a la alteración de múltiples genes. A través de estas características, definimos subgrupos genéticos de tumores asociados a características clínicas e histopatológicas de la enfermedad, como el estadio y el grado histológico de los tumores, así como la supervivencia de los pacientes. En base a estos hallazgos construimos un sistema de estratificación de los pacientes con ADCP que los clasifica en categorías con distinto pronóstico, cuya aplicación en la clínica podría ayudar a definir estrategias para el seguimiento y el tratamiento de los pacientes con esta enfermedad

Low-count monoclonal B-cell lymphocytosis persists after seven years of follow up and is associated with a poorer outcome

[EN]Low-count monoclonal B-cell lymphocytosis is defined by the presence of very low numbers of circulating clonal B cells, usually phenotypically similar to chronic lymphocytic leukemia cells, whose biological and clinical significance remains elusive. Herein, we re-evaluated 65/91 low-count monoclonal B-cell lymphocytosis cases (54 chronic lymphocytic leukemia-like and 11 non-chronic lymphocytic leukemialike) followed-up for a median of seven years, using high-sensitivity flow cytometry and interphase fluorescence in situ hybridization. Overall, the clone size significantly increased in 69% of low-count monoclonal B-cell lymphocytosis cases, but only one subject progressed to high-count monoclonal B-cell lymphocytosis. In parallel, the frequency of cytogenetic alterations increased over time (32% vs. 61% of cases, respectively). The absolute number of the major T-cell and natural killer cell populations also increased, but only among chronic lymphocytic leukemia-like cases with increased clone size vs. age- and sex-matched controls. Although progression to chronic lymphocytic leukemia was not observed, the overall survival of low-count monoclonal B-cell lymphocytosis individuals was significantly reduced vs. non-monoclonal Bcell lymphocytosis controls (P=0.03) plus the general population from the same region (P≤0.001), particularly among females (P=0.01); infection and cancer were the main causes of death in low-count monoclonal B-cell lymphocytosis. In summary, despite the fact that mid-term progression from low-count monoclonal B-cell lymphocytosis to high-count monoclonal B-cell lymphocytosis and chronic lymphocytic leukemia appears to be unlikely, these clones persist at increased numbers, usually carrying more genetic alterations, and might thus be a marker of an impaired immune system indirectly associated with a poorer outcome, particularly among females

Diagnosis of Genetic White Matter Disorders by Singleton Whole-Exome and Genome Sequencing Using Interactome-Driven Prioritization

Background and Objectives Genetic white matter disorders (GWMD) are of heterogeneous origin, with >100 causal genes identified to date. Classic targeted approaches achieve a molecular diagnosis in only half of all patients. We aimed to determine the clinical utility of singleton whole-exome sequencing and whole-genome sequencing (sWES-WGS) interpreted with a phenotype- and interactome-driven prioritization algorithm to diagnose GWMD while identifying novel phenotypes and candidate genes. Methods A case series of patients of all ages with undiagnosed GWMD despite extensive standard-of-care paraclinical studies were recruited between April 2017 and December 2019 in a collaborative study at the Bellvitge Biomedical Research Institute (IDIBELL) and neurology units of tertiary Spanish hospitals. We ran sWES and WGS and applied our interactome-prioritization algorithm based on the network expansion of a seed group of GWMD-related genes derived from the Human Phenotype Ontology terms of each patient. Results We evaluated 126 patients (101 children and 25 adults) with ages ranging from 1 month to 74 years. We obtained a first molecular diagnosis by singleton WES in 59% of cases, which increased to 68% after annual reanalysis, and reached 72% after WGS was performed in 16 of the remaining negative cases. We identified variants in 57 different genes among 91 diagnosed cases, with the most frequent being RNASEH2B, EIF2B5, POLR3A, and PLP1, and a dual diagnosis underlying complex phenotypes in 6 families, underscoring the importance of genomic analysis to solve these cases. We discovered 9 candidate genes causing novel diseases and propose additional putative novel candidate genes for yet-to-be discovered GWMD. Discussion Our strategy enables a high diagnostic yield and is a good alternative to trio WES/WGS for GWMD. It shortens the time to diagnosis compared to the classical targeted approach, thus optimizing appropriate management. Furthermore, the interactome-driven prioritization pipeline enables the discovery of novel disease-causing genes and phenotypes, and predicts novel putative candidate genes, shedding light on etiopathogenic mechanisms that are pivotal for myelin generation and maintenance

ClinPrior: an algorithm for diagnosis and novel gene discovery by network-based prioritization

BackgroundWhole-exome sequencing (WES) and whole-genome sequencing (WGS) have become indispensable tools to solve rare Mendelian genetic conditions. Nevertheless, there is still an urgent need for sensitive, fast algorithms to maximise WES/WGS diagnostic yield in rare disease patients. Most tools devoted to this aim take advantage of patient phenotype information for prioritization of genomic data, although are often limited by incomplete gene-phenotype knowledge stored in biomedical databases and a lack of proper benchmarking on real-world patient cohorts.MethodsWe developed ClinPrior, a novel method for the analysis of WES/WGS data that ranks candidate causal variants based on the patient's standardized phenotypic features (in Human Phenotype Ontology (HPO) terms). The algorithm propagates the data through an interactome network-based prioritization approach. This algorithm was thoroughly benchmarked using a synthetic patient cohort and was subsequently tested on a heterogeneous prospective, real-world series of 135 families affected by hereditary spastic paraplegia (HSP) and/or cerebellar ataxia (CA).ResultsClinPrior successfully identified causative variants achieving a final positive diagnostic yield of 70% in our real-world cohort. This includes 10 novel candidate genes not previously associated with disease, 7 of which were functionally validated within this project. We used the knowledge generated by ClinPrior to create a specific interactome for HSP/CA disorders thus enabling future diagnoses as well as the discovery of novel disease genes.ConclusionsClinPrior is an algorithm that uses standardized phenotype information and interactome data to improve clinical genomic diagnosis. It helps in identifying atypical cases and efficiently predicts novel disease-causing genes. This leads to increasing diagnostic yield, shortening of the diagnostic Odysseys and advancing our understanding of human illnesses

Genomic heterogeneity of pancreatic ductal adenocarcinoma and its clinical impact

[EN]Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer death due to limited advances in recent years in early diagnosis and personalized therapy capable of overcoming tumor resistance to chemotherapy. In the last decades, significant advances have been achieved in the identification of recurrent genetic and molecular alterations of PDAC including those involving the KRAS, CDKN2A, SMAD4, and TP53 driver genes. Despite these common genetic traits, PDAC are highly heterogeneous tumors at both the inter- and intra-tumoral genomic level, which might contribute to distinct tumor behavior and response to therapy, with variable patient outcomes. Despite this, genetic and genomic data on PDAC has had a limited impact on the clinical management of patients. Integration of genomic data for classification of PDAC into clinically defined entities-i.e., classical vs. squamous subtypes of PDAC-leading to different treatment approaches has the potential for significantly improving patient outcomes. In this review, we summarize current knowledge about the most relevant genomic subtypes of PDAC including the impact of distinct patterns of intra-tumoral genomic heterogeneity on the classification and clinical and therapeutic management of PDAC.This research was funded by Gerencia Regional de Salud de Castilla y León, Valladolid, Spain (GRS2041/A/19, GRS2188/A/2020), RTICC and CIBERONC from the ISCIII, Madrid, Spain (RD12/0020/0035-FEDER, RD12/0036/0048-FEDER, CB16/12/00400), Junta Provincial de Sala- manca de la Asociación Española Contra el Cáncer, Salamanca, Spain (SAL16/004), and Fundación Memoria de Don Samuel Solórzano Barruso, Salamanca, Spain (FS/16-2016-2017; FS/22-2018; FS/31-2020). M.L.G was funded by “Stop fuga de Cerebros” grant from ROCHE FARMA SA, Madrid, Spain and Consejería de Educación de la Junta de Castilla y León (SA0109P20), Valladolid, Spain

A new big data triclustering approach for extracting three-dimensional patterns in precision agriculture

Precision agriculture focuses on the development of site-specific harvest considering the variability of each crop area. Vegetation indices allow the study and delineation of different characteristics of each field zone, generally invisible to the naked-eye. This paper introduces a new big data triclustering approach based on evolutionary algorithms. The algorithm shows its capability to discover three-dimensional pat-terns on the basis of vegetation indices from vine crops. Different vegetation indices have been tested to find different patterns in the crops. The results reported using a vineyard crop located in Portugal depicts four areas with different moisture stress particularities that can lead to changes in the management of the vineyard. Furthermore, scalability studies have been performed, showing that the proposed algorithm is suitable for dealing with big datasets.Ministerio de Ciencia e Innovación PID2020-117954RBJunta de Andalucía PY20-00870Junta de Andalucía UPO-138516Fundação para a Ciência e a Tecnologia (FCT) UIDB/00066/202

X chromosome inactivation does not necessarily determine the severity of the phenotype in Rett syndrome patients

Rett syndrome (RTT) is a severe neurological disorder usually caused by mutations in the MECP2 gene. Since the MECP2 gene is located on the X chromosome, X chromosome inactivation (XCI) could play a role in the wide range of phenotypic variation of RTT patients; however, classical methylation-based protocols to evaluate XCI could not determine whether the preferentially inactivated X chromosome carried the mutant or the wild-type allele. Therefore, we developed an allele-specific methylation-based assay to evaluate methylation at the loci of several recurrent MECP2 mutations. We analyzed the XCI patterns in the blood of 174 RTT patients, but we did not find a clear correlation between XCI and the clinical presentation. We also compared XCI in blood and brain cortex samples of two patients and found differences between XCI patterns in these tissues. However, RTT mainly being a neurological disease complicates the establishment of a correlation between the XCI in blood and the clinical presentation of the patients. Furthermore, we analyzed MECP2 transcript levels and found differences from the expected levels according to XCI. Many factors other than XCI could affect the RTT phenotype, which in combination could influence the clinical presentation of RTT patients to a greater extent than slight variations in the XCI pattern

ClinPrior: an algorithm for diagnosis and novel gene discovery by network-based prioritization

Abstract Background Whole-exome sequencing (WES) and whole-genome sequencing (WGS) have become indispensable tools to solve rare Mendelian genetic conditions. Nevertheless, there is still an urgent need for sensitive, fast algorithms to maximise WES/WGS diagnostic yield in rare disease patients. Most tools devoted to this aim take advantage of patient phenotype information for prioritization of genomic data, although are often limited by incomplete gene-phenotype knowledge stored in biomedical databases and a lack of proper benchmarking on real-world patient cohorts. Methods We developed ClinPrior, a novel method for the analysis of WES/WGS data that ranks candidate causal variants based on the patient’s standardized phenotypic features (in Human Phenotype Ontology (HPO) terms). The algorithm propagates the data through an interactome network-based prioritization approach. This algorithm was thoroughly benchmarked using a synthetic patient cohort and was subsequently tested on a heterogeneous prospective, real-world series of 135 families affected by hereditary spastic paraplegia (HSP) and/or cerebellar ataxia (CA). Results ClinPrior successfully identified causative variants achieving a final positive diagnostic yield of 70% in our real-world cohort. This includes 10 novel candidate genes not previously associated with disease, 7 of which were functionally validated within this project. We used the knowledge generated by ClinPrior to create a specific interactome for HSP/CA disorders thus enabling future diagnoses as well as the discovery of novel disease genes. Conclusions ClinPrior is an algorithm that uses standardized phenotype information and interactome data to improve clinical genomic diagnosis. It helps in identifying atypical cases and efficiently predicts novel disease-causing genes. This leads to increasing diagnostic yield, shortening of the diagnostic Odysseys and advancing our understanding of human illnesses

Delaying surgery for patients with a previous SARS-CoV-2 infection

Not availabl