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.

New/Revised Entities in Gastrointestinal Lymphoproliferative Disorders

The gastrointestinal tract is a common extranodal site of involvement by lymphomas. These may be diagnostically challenging because they can mimic a variety of benign conditions and may be difficult to subclassify when malignant. The classification of gastrointestinal lymphomas is an evolving area with some recent changes. Although some of these entities are rare, they are important to recognize because of the variable clinical presentations, comorbidities, and treatment implications. This article explores new and revised entities in gastrointestinal lymphoproliferative disorders.