9 results on '"Álvarez-Larrán A"'
Search Results
2. Cytogenetic Assessment and Risk Stratification in Myelofibrosis with Optical Genome Mapping
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Such, Álvaro Díaz-González, Elvira Mora, Gayane Avetisyan, Santiago Furió, Rosalía De la Puerta, José Vicente Gil, Alessandro Liquori, Eva Villamón, Carmen García-Hernández, Marta Santiago, Cristian García-Ruiz, Marta Llop, Blanca Ferrer-Lores, Eva Barragán, Silvia García-Palomares, Empar Mayordomo, Irene Luna, Ana Vicente, Lourdes Cordón, Leonor Senent, Alberto Álvarez-Larrán, José Cervera, Javier De la Rubia, Juan Carlos Hernández-Boluda, and Esperanza
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myelofibrosis ,optical genome mapping ,chromosome banding analysis ,prognosis - Abstract
Cytogenetic assessment in myelofibrosis is essential for risk stratification and patient management. However, an informative karyotype is unavailable in a significant proportion of patients. Optical genome mapping (OGM) is a promising technique that allows for a high-resolution assessment of chromosomal aberrations (structural variants, copy number variants, and loss of heterozygosity) in a single workflow. In this study, peripheral blood samples from a series of 21 myelofibrosis patients were analyzed via OGM. We assessed the clinical impact of the application of OGM for disease risk stratification using the DIPSS-plus, GIPSS, and MIPSS70+v2 prognostic scores compared with the standard-of-care approach. OGM, in combination with NGS, allowed for risk classification in all cases, compared to only 52% when conventional techniques were used. Cases with unsuccessful karyotypes (n = 10) using conventional techniques were fully characterized using OGM. In total, 19 additional cryptic aberrations were identified in 9 out of 21 patients (43%). No alterations were found via OGM in 4/21 patients with previously normal karyotypes. OGM upgraded the risk category for three patients with available karyotypes. This is the first study using OGM in myelofibrosis. Our data support that OGM is a valuable tool that can greatly contribute to improve disease risk stratification in myelofibrosis patients.
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- 2023
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3. Machine Learning Improves Risk Stratification in Myelofibrosis: An Analysis of the Spanish Registry of Myelofibrosis
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Mosquera Orgueira, Adrián, Pérez Encinas, Manuel, Hernández Sánchez, Alberto, González Martínez, Teresa, Arellano Rodrigo, Eduardo, Martínez Elicegui, Javier, Villaverde Ramiro, Ángela, Raya, José María, Ayala, Rosa, Ferrer Marín, Francisca, Fox, María Laura, Velez, Patricia, Mora, Elvira, Xicoy, Blanca, Mata Vázquez, María Isabel, García Fortes, María, Angona, Anna, Cuevas, Beatriz, Senín, María Alicia, Ramírez Payer, Angel, Ramírez, María José, Pérez López, Raúl, González de Villambrosía, Sonia, Martínez Valverde, Clara, Gómez Casares, María Teresa, García Hernández, Carmen, Gasior, Mercedes, Bellosillo Paricio, Beatriz, Steegmann, Juan Luis, Álvarez Larrán, Alberto, Hernández Rivas, Jesús María, Hernández Boluda, Juan Carlos, The Spanish MPN Group (GEMFIN)., Institut Català de la Salut, [Mosquera-Orgueira A, Pérez-Encinas M] Hospital Clínico Universitario, Santiago de Compostela, Spain. [Hernández-Sánchez A, González-Martínez T, Martínez-Elicegui J] Hospital Clínico, Salamanca, Spain. [Arellano-Rodrigo E] Hospital Clínic, Institut d’Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain. [Fox ML] Vall d’Hebron Institute of Oncology (VHIO), Barcelona, Spain. Vall d’Hebron Hospital Universitari, Barcelona, Spain, and Vall d'Hebron Barcelona Hospital Campus
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Intel·ligència artificial - Aplicacions a la medicina ,Pronòstic mèdic ,Mielofibrosi ,Immunology ,Myelofibrosis ,Neoplasms::Neoplasms by Histologic Type::Leukemia::Leukemia, Myeloid::Leukemia, Myeloid, Acute [DISEASES] ,enfermedades hematológicas y linfáticas::enfermedades hematológicas::enfermedades de la médula ósea::trastornos mieloproliferativos [ENFERMEDADES] ,Cell Biology ,Hematology ,Prognosis ,Biochemistry ,Ciencias de la información::metodologías computacionales::algoritmos::inteligencia artificial::aprendizaje automático [CIENCIA DE LA INFORMACIÓN] ,neoplasias::neoplasias por tipo histológico::leucemia::leucemia mieloide::leucemia mieloide aguda [ENFERMEDADES] ,Aprenentatge automàtic ,Machine learning ,Sang - Malalties ,Hemic and Lymphatic Diseases::Hematologic Diseases::Bone Marrow Diseases::Myeloproliferative Disorders [DISEASES] ,Information Science::Computing Methodologies::Algorithms::Artificial Intelligence::Machine Learning [INFORMATION SCIENCE] - Abstract
Aprendizaje automático; Mielofibrosis Aprenentatge automàtic; Mielofibrosi Machine learning; Myelofibrosis Myelofibrosis (MF) is a myeloproliferative neoplasm (MPN) with heterogeneous clinical course. Allogeneic hematopoietic cell transplantation remains the only curative therapy, but its morbidity and mortality require careful candidate selection. Therefore, accurate disease risk prognostication is critical for treatment decision-making. We obtained registry data from patients diagnosed with MF in 60 Spanish institutions (N = 1386). These were randomly divided into a training set (80%) and a test set (20%). A machine learning (ML) technique (random forest) was used to model overall survival (OS) and leukemia-free survival (LFS) in the training set, and the results were validated in the test set. We derived the AIPSS-MF (Artificial Intelligence Prognostic Scoring System for Myelofibrosis) model, which was based on 8 clinical variables at diagnosis and achieved high accuracy in predicting OS (training set c-index, 0.750; test set c-index, 0.744) and LFS (training set c-index, 0.697; test set c-index, 0.703). No improvement was obtained with the inclusion of MPN driver mutations in the model. We were unable to adequately assess the potential benefit of including adverse cytogenetics or high-risk mutations due to the lack of these data in many patients. AIPSS-MF was superior to the IPSS regardless of MF subtype and age range and outperformed the MYSEC-PM in patients with secondary MF. In conclusion, we have developed a prediction model based exclusively on clinical variables that provides individualized prognostic estimates in patients with primary and secondary MF. The use of AIPSS-MF in combination with predictive models that incorporate genetic information may improve disease risk stratification.
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- 2023
4. Non-driver mutations in patients with JAK2V617F-mutated polycythemia vera or essential thrombocythemia with long-term molecular follow-up
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Senín, Alicia, Fernández-Rodríguez, Concepción, Bellosillo, Beatriz, Camacho, Laura, Longarón, Raquel, Angona, Anna, Besses, Carles, and Álvarez-Larrán, Alberto
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- 2017
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5. Clinical Characteristics and Outcomes of Patients with Primary and Secondary Myelofibrosis According to the Genomic Classification Using Targeted Next-Generation Sequencing.
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Garrote, Marta, López-Guerra, Mónica, Arellano-Rodrigo, Eduardo, Rozman, María, Carbonell, Sara, Guijarro, Francesca, Santaliestra, Marta, Triguero, Ana, Colomer, Dolors, Cervantes, Francisco, and Álvarez-Larrán, Alberto
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ANEUPLOIDY ,GENETIC mutation ,POLYCYTHEMIA vera ,SEQUENCE analysis ,MYELOFIBROSIS ,ONCOGENES ,HEALTH outcome assessment ,ALLELES ,GENOMICS ,DESCRIPTIVE statistics ,RESEARCH funding ,SEX chromatin ,SYMPTOMS - Abstract
Simple Summary: Myelofibrosis is a heterogeneous disease regarding its mutation landscape as well as its clinical presentation and outcome. A genomic classification with prognostic implications of myeloproliferative neoplasms has been previously proposed, however, this classification has hardly been implemented in myelofibrosis. The aim of our work is to evaluate this genomic classification in a large series of myelofibrosis patients from a single institution, taking into account whether cases are primary or secondary. We found that both primary and secondary myelofibrosis have distinctive molecular landscapes and that genomic profiling provides accurate information regarding prognosis and disease progression. Myelofibrosis (MF) is a heterogeneous disease regarding its mutational landscape, clinical presentation, and outcomes. The aim of our work is to evaluate the genomic classification of MF considering whether it is primary or secondary. One-hundred seventy-five patients, 81 with primary MF (PMF) and 94 with secondary MF (SMF) were hierarchically allocated into eight molecular groups. We found that TP53 disruption/aneuploidy (n = 16, 9%) was more frequent (12% versus 6%) and showed higher allele burden (57% versus 15%, p = 0.01) in SMF than in PMF, and was associated with shorter survival (median 3.5 years). Mutations in chromatin/spliceosome genes (n = 72, 41%) represented the most frequent genomic group in PMF. Homozygous JAK2 mutation (n = 40, 23%) was enriched with old patients with SMF after long-standing polycythemia vera, whereas MF with heterozygous JAK2 mutation (n = 22, 13%) was similarly distributed among PMF and SMF. MF with CALR mutation (n = 19, 11%) predominated in post-essential thrombocythemia MF. The remaining genomic groups were infrequent. TP53 disruption, chromatin/spliceosome mutation, and homozygous JAK2 mutation were associated with significantly shorter survival and higher risk of progression. In conclusion, genomic classification reveals different pathogenic pathways between PMF and SMF and provides relevant information regarding disease phenotype and outcomes. [ABSTRACT FROM AUTHOR]
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- 2023
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6. Impact of Individual Comorbidities on Survival of Patients with Myelofibrosis.
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García-Fortes, María, Hernández-Boluda, Juan C., Álvarez-Larrán, Alberto, Raya, José M., Angona, Anna, Estrada, Natalia, Fox, Laura, Cuevas, Beatriz, García-Hernández, María C., Gómez-Casares, María Teresa, Ferrer-Marín, Francisca, Saavedra, Silvana, Cervantes, Francisco, and García-Delgado, Regina
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RESEARCH ,THROMBOCYTOSIS ,HYPERTENSION ,SCIENTIFIC observation ,POLYCYTHEMIA vera ,MYELOFIBROSIS ,MULTIVARIATE analysis ,LUNG diseases ,HEPATITIS C ,HYPERLIPIDEMIA ,KIDNEY diseases ,TREATMENT effectiveness ,DESCRIPTIVE statistics ,COMORBIDITY ,LONGITUDINAL method - Abstract
Simple Summary: The coexistence of cancer with other chronic conditions has substantial implications for treatment decisions and outcomes for both neoplasms and chronic disease. Reports have demonstrated the impact of comorbidities on survival in different hematologic disorders. Myelofibrosis (MF) guidelines do not consider the complex interrelations between MF and comorbidity. Several works have shown how MF patients have a wide variety and high burden of comorbidities and demonstrated that the comorbidity burden was significantly associated with an unfavorable impact on survival. These previous studies about comorbidity on MF are retrospective and consider the cumulative rather than individual comorbidity burden. The influence of individual comorbidities on outcome in MF patients has not been studied. We sought to identify the comorbidities in MF patients at diagnosis and to assess the influence of those different comorbidities on survival. Considering them individually may contribute to the personalization of MF management and optimizing outcomes. The comorbidity burden is an important risk factor for overall survival (OS) in several hematological malignancies. This observational prospective study was conducted to evaluate the impact of individual comorbidities on survival in a multicenter series of 668 patients with primary myelofibrosis (PMF) or MF secondary to polycythemia vera (PPV-MF) or essential thrombocythemia (PET-MF). Hypertension (hazard ratio (HR) = 4.96, p < 0.001), smoking (HR = 5.08, p < 0.001), dyslipidemia (HR = 4.65, p < 0.001) and hepatitis C virus (HCV) (HR = 4.26, p = 0.015) were most adversely associated with OS. Diabetes (HR = 3.01, p < 0.001), pulmonary disease (HR = 3.13, p < 0.001) and renal dysfunction (HR = 1.82, p = 0.037) were also associated with an increased risk of death. Multivariate analysis showed that pulmonary disease (HR = 2.69, p = 0.001), smoking (HR = 3.34, p < 0.001), renal dysfunction (HR = 2.08, p = 0.043) and HCV (HR = 11.49, p = 0.001) had a negative impact on OS. When ruxolitinib exposure was included in the model, the effect of each comorbidity on survival was modified. Therefore, individual comorbidities should be taken into account in determining the survival prognosis for patients with MF. [ABSTRACT FROM AUTHOR]
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- 2022
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7. Non-driver mutations in patients with JAK2V617F-mutated polycythemia vera or essential thrombocythemia with long-term molecular follow-up.
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Senín, Alicia, Fernández-Rodríguez, Concepción, Bellosillo, Beatriz, Camacho, Laura, Longarón, Raquel, Angona, Anna, Besses, Carles, and Álvarez-Larrán, Alberto
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POLYCYTHEMIA vera ,THROMBOCYTOSIS ,JANUS kinases ,GENETIC mutation ,HYDROXYUREA ,PATIENTS ,ALLELES ,AMINO acids ,BONE marrow diseases ,CYTOGENETICS ,GENES ,LONGITUDINAL method ,PHENYLALANINE ,PROTEINS ,VALINE ,DISEASE progression ,NEOPLASTIC cell transformation - Abstract
JAK2V617F monitoring and NGS of non-driver genes was performed in 100 patients with polycythemia vera (PV) or essential thrombocythemia (ET) with long molecular follow-up. Patients who did not progress to myelofibrosis (MF) or acute myeloid leukemia (AML) after more than 10 years (n = 50) showed a low frequency of mutations at first sample (18%) and an incidence rate of 1.7 new mutations × 100 person-years. Mutations were detected at first sample in 83% of PV/ET patients who later progressed to AML (n = 12) with these patients having a rate of 25.6 mutations × 100 person-years. Presence of mutations at diagnosis was the unique risk factor for acquiring a new genetic event (HR 2.7, 95% CI 1.1-6.8, p = 0.03) after correction for age, PV diagnosis, and total duration of hydroxyurea (HU) exposure. Patients with additional mutation at first sample showed a higher probability of developing cytopenia under HU therapy and a higher risk of AML (HR 12.2, 95% CI 2.6-57.1, p = 0.001) with mutations in ASXL1 (p < 0.0001), TP53 (p = 0.01), SRSF2 (p < 0.0001), IDH1/2 (p < 0.0001), and RUNX1 (p < 0.0001) being associated with a higher probability of AML. Myelofibrotic transformation was more frequent in patients with additional mutations, especially in SF3B1 (p = 0.02) and IDH1/2 (p < 0.0001) although a persistently high or a progressive increase of the JAK2V617F allele burden while receiving cytoreduction was the strongest predictor of MF transformation (HR 10.8, 95% CI 2.4-49.1, p = 0.002). In conclusion, NGS may be useful to identify a minority of PV and ET patients with high genetic instability and increased risk of AML transformation. [ABSTRACT FROM AUTHOR]
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- 2018
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8. TET2, ASXL1, IDH1, IDH2, and c-CBL genes in JAK2- and MPL-negative myeloproliferative neoplasms.
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Martínez-Avilés, Luz, Besses, Carlos, Álvarez-Larrán, Alberto, Torres, Erica, Serrano, Sergi, and Bellosillo, Beatriz
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GENETIC mutation ,MYELOPROLIFERATIVE neoplasms ,TUMORS ,HAPLOTYPES ,MYELOFIBROSIS ,THROMBOCYTOSIS - Abstract
Mutations in the TET2 and ASXL1 genes have been described in approximately 14% and 8% of patients, respectively, with classic myeloproliferative neoplasms (MPN), but their role as possible new diagnostic molecular markers is still inconclusive. In addition, other genes such as IDH1, IDH2, and c-CBL have also been reported in several myeloid neoplasms. We have studied the mutational status of TET2 (complete coding region), ASXL1 (exon12), IDH1 (R132), IDH2 (R140 and R172), and c-CBL (exons 8 and 9) in 62 MPN patients (52 essential thrombocythemia (ET), five polycythemia vera (PV), and five primary myelofibrosis (PMF)) negative for both JAK2 (V617F and exon 12) and MPL (exon 10) mutations. Pathogenic alterations in the TET2 gene were detected in three out 52 ET cases (4.8%). ASXL1 gene pathogenic mutations were also detected in three cases (two ET and one PMF). One ET patient harbored, simultaneously, one TET2 and one ASXL1 mutations. Mutations in the TET2 and ASXL1 genes showed no association with the JAK2 46/1 haplotype. Analysis of a JAK2V617F-positive cohort of 50 ET patients showed no mutations in either the TET2 or ASXL1 genes. Regarding IDH1, IDH2, and c-CBL genes, no mutations were found in any patient. In conclusion, TET2 and ASXL1 pathogenic mutations are found in 8% of MPN lacking JAK2 and MPL mutations, whereas IDH1, IDH2, and c-CBL mutations are not detected in this subset of patients. [ABSTRACT FROM AUTHOR]
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- 2012
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9. Mutations in the RNA splicing machinery genes in myelofibrotic transformation of essential thrombocythaemia and polycythaemia vera.
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Martínez‐Avilés, Luz, Besses, Carles, Álvarez‐Larrán, Alberto, Camacho, Laura, Pairet, Silvia, Fernández‐Rodríguez, Concepción, Serrano, Sergi, and Bellosillo, Beatriz
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GENETIC mutation ,MESSENGER RNA ,MYELOID leukemia ,LYMPHOCYTIC leukemia ,POLYMERASE chain reaction ,NUCLEOTIDE sequence ,GENETICS - Abstract
The article focuses on the analysis of mutations in messenger RNA splicing machinery genes such as SF3B1, SRSF2 and U2AF1 which have been identified in lymphoid and myeloid haematopoietic malignancies. Topics discussed include determination of SF3B1 gene modifications in thrombocythaemia and primary myelofibrosis, analysis of mutations in patients with polycythaemia vera disease, and use of polymerase chain reaction amplification and Sanger sequencing in the analysis of mutations.
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- 2014
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