Researchers have found that integration of the mutational status of seven genes with clinical risk factors improves prognostication for patients with follicular lymphoma receiving first-line immunochemotherapy, and is a promising approach to identify the subset at highest risk of treatment failure, a recent study published in The Lancet has shown.

DNA deep sequencing was performed retrospectively to analyze the mutation status of 74 genes in 151 follicular biopsy specimens that were obtained from patients within 1 year before beginning imunochemotherapy consisting of rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP).

Patients were recruited between May 4, 2000, and October 20, 2010, as part of a phase 3 trial. Patient eligibility included previously untreated symptomatic, advanced stage follicular lymphoma.

Mutations and clinical factors were incorporated into a risk model for failure-free using multivariable L1-penalised Cox Regression.


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Researchers validated the risk model in an independent population-based cohort of 107 patients with symptomatic follicular lymphoma considered ineligible for curative irradiation. Pretreatment biopsies were taken between February 24, 2004, and November 24, 2009.

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Findings established a clinicogenetic risk model that included the mutation status of seven genes (EZH2, ARIDIA, MEF2B, EP300, FOX01, CREBBP, and CARD11), the Follicular Lymphoma International Prognostic Index (FLIPI), and the Eastern Cooperative Oncology Group (ECOG) performance status.

In the training cohort, m7-FLIPI defined a high-risk group with 5-year failure-free survival 29% versus 21% for the low-risk group. In the validation cohort, risk stratification by m7-FLIPI outperformed FLIPI alone and FLIPI combined with ECOG performance status.

Reference

  1. Pastore A, Jurinovic V, Kridel R, et al. Integration of gene mutations in risk prognostication for patients receiving first-line immunochemotherapy for follicular lymphoma: a retrospective analysis of a prospective clinical trial and validation in a population-based registry. The Lancet. 2015. [epub ahead of print]. doi: 10.1016/S1470-2045(15)00169-2.