Proposal and validation of a method to classify genetic subtypes of diffuse large B cell lymphoma

Lucía Pedrosa, Ismael Fernández-Miranda, David Pérez-Callejo, Cristina Quero, Marta Rodríguez, Paloma Martín-Acosta, Sagrario Gómez, Julia González-Rincón, Adrián Santos, Carlos Tarin, Juan F García, Francisco R García-Arroyo, Antonio Rueda, Francisca I Camacho, Mónica García-Cosío, Ana Heredero, Marta Llanos, Manuela Mollejo, Miguel Piris-Villaespesa, José Gómez-Codina, Natalia Yanguas-Casás, Antonio Sánchez, Miguel A Piris, Mariano Provencio, Margarita Sánchez-Beato, Lucía Pedrosa, Ismael Fernández-Miranda, David Pérez-Callejo, Cristina Quero, Marta Rodríguez, Paloma Martín-Acosta, Sagrario Gómez, Julia González-Rincón, Adrián Santos, Carlos Tarin, Juan F García, Francisco R García-Arroyo, Antonio Rueda, Francisca I Camacho, Mónica García-Cosío, Ana Heredero, Marta Llanos, Manuela Mollejo, Miguel Piris-Villaespesa, José Gómez-Codina, Natalia Yanguas-Casás, Antonio Sánchez, Miguel A Piris, Mariano Provencio, Margarita Sánchez-Beato

Abstract

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease whose prognosis is associated with clinical features, cell-of-origin and genetic aberrations. Recent integrative, multi-omic analyses had led to identifying overlapping genetic DLBCL subtypes. We used targeted massive sequencing to analyze 84 diagnostic samples from a multicenter cohort of patients with DLBCL treated with rituximab-containing therapies and a median follow-up of 6 years. The most frequently mutated genes were IGLL5 (43%), KMT2D (33.3%), CREBBP (28.6%), PIM1 (26.2%), and CARD11 (22.6%). Mutations in CD79B were associated with a higher risk of relapse after treatment, whereas patients with mutations in CD79B, ETS1, and CD58 had a significantly shorter survival. Based on the new genetic DLBCL classifications, we tested and validated a simplified method to classify samples in five genetic subtypes analyzing the mutational status of 26 genes and BCL2 and BCL6 translocations. We propose a two-step genetic DLBCL classifier (2-S), integrating the most significant features from previous algorithms, to classify the samples as N12-S, EZB2-S, MCD2-S, BN22-S, and ST22-S groups. We determined its sensitivity and specificity, compared with the other established algorithms, and evaluated its clinical impact. The results showed that ST22-S is the group with the best clinical outcome and N12-S, the more aggressive one. EZB2-S identified a subgroup with a worse prognosis among GCB-DLBLC cases.

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Mutational prevalence in genes and pathways. (A) Most frequently mutated genes and pathways in the whole cohort. (B) Mutational prevalence for refractory/relapsed (R/R) and sensitive (S) cases for genes with more than four samples mutated.
Figure 2
Figure 2
Statistical analysis of genes and pathways in the PdH cohort. Mutated genes and pathways with significant p-values in the univariate Cox proportional-hazards model analysis for (A) progression-free (PFS) and (B) overall survival (OS). Error bars represent the 95% confidence intervals for the hazard ratios. FDR p-values for Benjamini–Hochberg correction are shown.
Figure 3
Figure 3
Genetic subtypes and association with overall survival (OS) and progression-free survival (PFS). Genetic classification in the five defined genetic subtypes of DLBCL. Clustering was performed using alterations in genes (rows) from 84 DLBCL samples (columns). The OS and event-free survival status, and the ABC/GCB classification based on Lymph2cx and Hans are represented at the top. The phi coefficient and Fisher's exact test significance are represented on the right of the figure (*p 

Figure 4

Comparison of the classifiers. Subtypes…

Figure 4

Comparison of the classifiers. Subtypes assigned to the HMRN cohort by LymphGen, AIC…

Figure 4
Comparison of the classifiers. Subtypes assigned to the HMRN cohort by LymphGen, AIC cluster and the two-step classifier.

Figure 5

Progression-free survival (PFS) and overall…

Figure 5

Progression-free survival (PFS) and overall survival (OS) according to the genetic subtypes of…

Figure 5
Progression-free survival (PFS) and overall survival (OS) according to the genetic subtypes of the two-step, AIC cluster and LymphGen classifiers in the HMRN cohort. Kaplan–Meier analysis of genetic subtypes from (A) two-step, (B) AIC cluster and (C) LymphGen methods. Tables on the right of the figure show the hazard ratio (HR) values from the Cox proportional-hazards model of genetic subtypes for PFS and OS status. Error bars represent the 95% confidence intervals. Significance: *p < 0.05; **p < 0.01.

Figure 6

Kaplan–Meier analysis of progression-free survival…

Figure 6

Kaplan–Meier analysis of progression-free survival (PFS) and overall survival (OS) for EZB 2-S…

Figure 6
Kaplan–Meier analysis of progression-free survival (PFS) and overall survival (OS) for EZB2-S-GCB and other GCB cases in the HMRN cohort when applying the two-step classifier.
Figure 4
Figure 4
Comparison of the classifiers. Subtypes assigned to the HMRN cohort by LymphGen, AIC cluster and the two-step classifier.
Figure 5
Figure 5
Progression-free survival (PFS) and overall survival (OS) according to the genetic subtypes of the two-step, AIC cluster and LymphGen classifiers in the HMRN cohort. Kaplan–Meier analysis of genetic subtypes from (A) two-step, (B) AIC cluster and (C) LymphGen methods. Tables on the right of the figure show the hazard ratio (HR) values from the Cox proportional-hazards model of genetic subtypes for PFS and OS status. Error bars represent the 95% confidence intervals. Significance: *p < 0.05; **p < 0.01.
Figure 6
Figure 6
Kaplan–Meier analysis of progression-free survival (PFS) and overall survival (OS) for EZB2-S-GCB and other GCB cases in the HMRN cohort when applying the two-step classifier.

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