Image_1_Personally Tailored Survival Prediction of Patients With Follicular Lymphoma Using Machine Learning Transcriptome-Based Models.jpeg (65.79 kB)
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posted on 10.01.2022, 04:13 by Adrián Mosquera Orgueira, Miguel Cid López, Andrés Peleteiro Raíndo, Aitor Abuín Blanco, Jose Ángel Díaz Arias, Marta Sonia González Pérez, Beatriz Antelo Rodríguez, Laura Bao Pérez, Roi Ferreiro Ferro, Carlos Aliste Santos, Manuel Mateo Pérez Encinas, Máximo Francisco Fraga Rodríguez, Claudio Cerchione, Pablo Mozas, José Luis Bello López

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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