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Personally Tailored Survival Prediction of Patients With Follicular Lymphoma Using Machine Learning Transcriptome-Based Models

Follicular Lymphoma (FL) has a 10-year mortality rate of 20%, and this is mostly related to lymphoma progression and transformation to higher grades. In the era of personalized medicine it has become increasingly important to provide patients with an optimal prediction about their expected outcomes....

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Autores principales: Mosquera Orgueira, Adrián, Cid López, Miguel, Peleteiro Raíndo, Andrés, Abuín Blanco, Aitor, Díaz Arias, Jose Ángel, González Pérez, Marta Sonia, Antelo Rodríguez, Beatriz, Bao Pérez, Laura, Ferreiro Ferro, Roi, Aliste Santos, Carlos, Pérez Encinas, Manuel Mateo, Fraga Rodríguez, Máximo Francisco, Cerchione, Claudio, Mozas, Pablo, Bello López, José Luis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8784530/
https://www.ncbi.nlm.nih.gov/pubmed/35083135
http://dx.doi.org/10.3389/fonc.2021.705010
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author Mosquera Orgueira, Adrián
Cid López, Miguel
Peleteiro Raíndo, Andrés
Abuín Blanco, Aitor
Díaz Arias, Jose Ángel
González Pérez, Marta Sonia
Antelo Rodríguez, Beatriz
Bao Pérez, Laura
Ferreiro Ferro, Roi
Aliste Santos, Carlos
Pérez Encinas, Manuel Mateo
Fraga Rodríguez, Máximo Francisco
Cerchione, Claudio
Mozas, Pablo
Bello López, José Luis
author_facet Mosquera Orgueira, Adrián
Cid López, Miguel
Peleteiro Raíndo, Andrés
Abuín Blanco, Aitor
Díaz Arias, Jose Ángel
González Pérez, Marta Sonia
Antelo Rodríguez, Beatriz
Bao Pérez, Laura
Ferreiro Ferro, Roi
Aliste Santos, Carlos
Pérez Encinas, Manuel Mateo
Fraga Rodríguez, Máximo Francisco
Cerchione, Claudio
Mozas, Pablo
Bello López, José Luis
author_sort Mosquera Orgueira, Adrián
collection PubMed
description Follicular Lymphoma (FL) has a 10-year mortality rate of 20%, and this is mostly related to lymphoma progression and transformation to higher grades. In the era of personalized medicine it has become increasingly important to provide patients with an optimal prediction about their expected outcomes. The objective of this work was to apply machine learning (ML) tools on gene expression data in order to create individualized predictions about survival in patients with FL. Using data from two different studies, we were able to create a model which achieved good prediction accuracies in both cohorts (c-indexes of 0.793 and 0.662 in the training and test sets). Integration of this model with m7-FLIPI and age rendered high prediction accuracies in the test set (cox c-index 0.79), and a simplified approach identified 4 groups with remarkably different outcomes in terms of survival. Importantly, one of the groups comprised 27.35% of patients and had a median survival of 4.64 years. In summary, we have created a gene expression-based individualized predictor of overall survival in FL that can improve the predictions of the m7-FLIPI score.
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spelling pubmed-87845302022-01-25 Personally Tailored Survival Prediction of Patients With Follicular Lymphoma Using Machine Learning Transcriptome-Based Models Mosquera Orgueira, Adrián Cid López, Miguel Peleteiro Raíndo, Andrés Abuín Blanco, Aitor Díaz Arias, Jose Ángel González Pérez, Marta Sonia Antelo Rodríguez, Beatriz Bao Pérez, Laura Ferreiro Ferro, Roi Aliste Santos, Carlos Pérez Encinas, Manuel Mateo Fraga Rodríguez, Máximo Francisco Cerchione, Claudio Mozas, Pablo Bello López, José Luis Front Oncol Oncology Follicular Lymphoma (FL) has a 10-year mortality rate of 20%, and this is mostly related to lymphoma progression and transformation to higher grades. In the era of personalized medicine it has become increasingly important to provide patients with an optimal prediction about their expected outcomes. The objective of this work was to apply machine learning (ML) tools on gene expression data in order to create individualized predictions about survival in patients with FL. Using data from two different studies, we were able to create a model which achieved good prediction accuracies in both cohorts (c-indexes of 0.793 and 0.662 in the training and test sets). Integration of this model with m7-FLIPI and age rendered high prediction accuracies in the test set (cox c-index 0.79), and a simplified approach identified 4 groups with remarkably different outcomes in terms of survival. Importantly, one of the groups comprised 27.35% of patients and had a median survival of 4.64 years. In summary, we have created a gene expression-based individualized predictor of overall survival in FL that can improve the predictions of the m7-FLIPI score. Frontiers Media S.A. 2022-01-10 /pmc/articles/PMC8784530/ /pubmed/35083135 http://dx.doi.org/10.3389/fonc.2021.705010 Text en Copyright © 2022 Mosquera Orgueira, Cid López, Peleteiro Raíndo, Abuín Blanco, Díaz Arias, González Pérez, Antelo Rodríguez, Bao Pérez, Ferreiro Ferro, Aliste Santos, Pérez Encinas, Fraga Rodríguez, Cerchione, Mozas and Bello López https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Mosquera Orgueira, Adrián
Cid López, Miguel
Peleteiro Raíndo, Andrés
Abuín Blanco, Aitor
Díaz Arias, Jose Ángel
González Pérez, Marta Sonia
Antelo Rodríguez, Beatriz
Bao Pérez, Laura
Ferreiro Ferro, Roi
Aliste Santos, Carlos
Pérez Encinas, Manuel Mateo
Fraga Rodríguez, Máximo Francisco
Cerchione, Claudio
Mozas, Pablo
Bello López, José Luis
Personally Tailored Survival Prediction of Patients With Follicular Lymphoma Using Machine Learning Transcriptome-Based Models
title Personally Tailored Survival Prediction of Patients With Follicular Lymphoma Using Machine Learning Transcriptome-Based Models
title_full Personally Tailored Survival Prediction of Patients With Follicular Lymphoma Using Machine Learning Transcriptome-Based Models
title_fullStr Personally Tailored Survival Prediction of Patients With Follicular Lymphoma Using Machine Learning Transcriptome-Based Models
title_full_unstemmed Personally Tailored Survival Prediction of Patients With Follicular Lymphoma Using Machine Learning Transcriptome-Based Models
title_short Personally Tailored Survival Prediction of Patients With Follicular Lymphoma Using Machine Learning Transcriptome-Based Models
title_sort personally tailored survival prediction of patients with follicular lymphoma using machine learning transcriptome-based models
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8784530/
https://www.ncbi.nlm.nih.gov/pubmed/35083135
http://dx.doi.org/10.3389/fonc.2021.705010
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