E060 -Computational and genomic hematology (Grheco Xen)

Objectives

This research group has focused on the application of advanced information technologies in the field of oncohaematological tumors. These approaches, reflected in the diversity of our research, have focused on the following main objectives:

1. Identification of genes that drive oncogenesis and tumor progression in hematological malignancies.
2. Identification of recurrently altered signaling pathways in the genome of hematological tumors.
3. Development of machine learning prognostic models in different types of oncohaematological malignancies based on clinical and molecular information
4. Development of predictive models of response to drugs or drug combinations based on the integration of molecular and clinical variables.

Lines of research

1. Development of new prognostic models based on data from national and international collaborative groups.
2. Standardization of clinical and molecular models to improve risk stratification of patients with hematologic malignancies, particularly lymphomas, myelomas, and myeloid malignancies.
3. Development of new scores to predict the response to drugs in clinical trials.
4. Creation of new computer vision systems (computer vision) to infer prognostic patterns based on deep neural networks and learning transfer.
5. Optimization of preclinical drug development using computational tools, including machine learning models.

Leader
Mosquera Orgueira, Adrián

• Mosquera Orgueira A, Cid López M, Peleteiro Raíndo A, Abuín Blanco A, Díaz Arias JÁ, González Pérez MS, Antelo Rodríguez B, Bao Pérez L, Ferreiro Ferro R, Aliste Santos C, Pérez Encinas MM, Fraga Rodríguez MF, Cerchione C, Mozas P, Bello López JL. Personally Tailored Survival Prediction of Patients With Follicular Lymphoma Using Machine Learning Transcriptome-Based Models.  Front Oncol. 2022 Jan 10;11:705010; doi: 10.3389/fonc.2021.705010.  Impacto: 5,74. Decil: 7. Cuartil: 2. Artigo Orixinal. PMID: 35083135.
• Mosquera Orgueira A, Peleteiro Raindo A, Diaz Arias JA, Antelo Rodriguez B, Lopez Riñon M, Cerchione C, Gonzalez Perez MS, Martinelli G, Montesis Fernandez P, Perez Encinas MM. Evaluation of the Stellae-123 prognostic gene expression signature in Acute Myeloid Leukemia. Front. Oncol. 2022 Aug; doi: 10.3389/fonc.2022.968340. Impacto: 5,74. Decil: 7. Cuartil: 2. Artigo Orixinal. PMID: 36059646.
•  Mosquera Orgueira A, Díaz Arías JÁ, Cid López M, Peleteiro Raíndo A, López García A, Abal García R, González Pérez MS, Antelo Rodríguez B, Aliste Santos C, Pérez Encinas MM, Fraga Rodríguez MF, Bello López JL. Prognostic Stratification of Diffuse Large B-cell Lymphoma Using Clinico-genomic Models: Validation and Improvement of the LymForest-25 Model. Hemasphere. 2022 Mar 25;6(4):e706; doi: 10.1097/HS9.0000000000000706. Impacto: 8,3. Decil: 7. Cuartil: 2. Artigo Orixinal. PMID: 35392483.
• Mosquera Orgueira A, González Pérez MS, Díaz Arías JÁ, Antelo Rodriguez B and Mateos Manteca MV. Prognostic Stratification of Multiple Myeloma Using Clinicogenomic Models: Validation and Performance Analysis of the IAC-50 Model. Hemasphere. 2022 Aug 04;6(8):e760; doi: 10.1097/HS9.0000000000000760. Impacto: 8,3. Decil: 7. Cuartil: 2. Artigo Orixinal. PMID: 35935610.
• Mosquera Orgueira A, González Pérez MS, Diaz Arias J, Rosiñol L, Oriol A, Teruel AI, Martinez Lopez J, Palomera L, Granell M, Blanchard MJ, de la Rubia J, López de la Guia A, Rios R, Sureda A, Hernández MT, Bengoechea E, Calasanz MJ, Gutierrez N, Martin ML, Blade J, Lahuerta JJ, San Miguel J, Mateos MV; PETHEMA/GEM Cooperative Group. Unsupervised machine learning improves risk stratification in newly diagnosed multiple myeloma: an analysis of the Spanish Myeloma Group. Blood Cancer J. 2022 Apr 25;12(4):76; doi: 10.1038/s41408-022-00647-z. Impacto: 9,81. Decil: 9. Cuartil: 1. Artigo Orixinal. PMID: 35468898.
• Mosquera Orgueira A, González Pérez MS, Díaz Arias JÁ, Antelo Rodríguez B, Alonso Vence N, Bendaña López Á, Abuín Blanco A, Bao Pérez L, Peleteiro Raíndo A, Cid López M, Pérez Encinas MM, Bello López JL, Mateos Manteca MV. Survival prediction and treatment optimization of multiple myeloma patients using machine-learning models based on clinical and gene expression data. PMID: 34007046. Leukemia. 2021 Oct;35(10):2924-2935; doi: 10.1038/s41375-021-01286-2. Impacto: 12,90. Decil: 1. Cuartil: 1. Artigo Orixinal.
• Mosquera Orgueira A, Peleteiro Raíndo A, Cid López M, Díaz Arias JÁ, González Pérez MS, Antelo Rodríguez B, Alonso Vence N, Bao Pérez L, Ferreiro Ferro R, Albors Ferreiro M, Abuín Blanco A, Fontanes Trabazo E, Cerchione C, Martinnelli G, Montesinos Fernández P, Mateo Pérez Encinas M, Luis Bello López J. Personalized Survival Prediction of Patients With Acute Myeloblastic Leukemia Using Gene Expression Profiling. Front Oncol. 2021 Mar 29;11:657191; doi: 10.3389/fonc.2021.657191. Impacto: 5,74. Decil: 7. Cuartil: 2. Artigo Orixinal. PMID: 33854980
• Mosquera Orgueira A, Cid López M, Peleteiro Raíndo A, Díaz Arias JÁ, Antelo Rodríguez B, Bao Pérez L, Alonso Vence N, Bendaña López Á, Abuin Blanco A, Melero Valentín P, Ferreiro Ferro R, Aliste Santos C, Fraga Rodríguez MF, González Pérez MS, Pérez Encinas MM, Bello López JL. Detection of Rare Germline Variants in the Genomes of Patients with B-Cell Neoplasms.  Cancers. 2021; 13(6):1340; doi: 10.3390/cancers13061340. Impacto: 6,57. Decil: 8. Cuartil: 1. Artigo Orixinal. PMID: 33809641.
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