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. 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.
Competing Interests: AO has received honoraria for lectures and participation in advisory boards from Janssen and AstraZeneca. AO has received research grants from Roche and Celgene-BMS. JL has received honoraria for lectures and participation in advisory boards from Janssen, Abbey and Roche. JL has received research funds from Roche and Celgene-BMS. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer GS declared a shared affiliation with one of the authors CC, to the handling editor at time of review.
(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.)