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Recurrent somatic mutations affecting B-cell receptor signaling pathway genes in follicular lymphoma

Kilannin Krysiak, Felicia Gomez, Brian S. White, Matthew Matlock, Christopher A. Miller, Lee Trani, Catrina C. Fronick, Robert S. Fulton, Friederike Kreisel, Amanda F. Cashen, Kenneth R. Carson, Melissa M. Berrien-Elliott, Nancy L. Bartlett, Malachi Griffith, Obi L. Griffith and Todd A. Fehniger

Data supplements

Article Figures & Data

Figures

  • Figure 1.

    Mutation numbers and spectrum within the FL discovery sample set. Baseline genomic features of FL are shown for the exome sequenced discovery cohort. Clinical features (upper) are indicated for all 28 samples sequenced from 24 individuals. Immediately below the clinical features is a row indicating the total number of mutations per sample. Mutations per megabase sequenced (middle) is based on the total mutations within the targeted exome capture space successfully covered in each sample (80% breadth, 20× depth), with the percentage of the sequenced target region covered in each sample indicated immediately below in green. Finally, the rate of transitions and transversions in the mutations observed in each individual are shown (bottom). Bulk fresh-frozen samples were sequenced unless indicated (*) as a flow-sorted sample. Brackets group multiple samples from a single individual. MB, megabase; Ti, transition; Tv, transversion

  • Figure 2.

    SMGs in FL. The frequency and type of mutations affecting 39 genes identified as significantly mutated in our cohort using MuSiC analysis (FDR < 0.05, convolution test method) are displayed in each row. Columns represent each patient in the cohort and are ordered by the presence of mutations in the most to least frequently mutated gene. The bar graph on the left corresponds to the frequency of mutations for that gene in the entire cohort. For genes with multiple mutations in a single patient, only 1 mutation type is shown with priority order indicated in the legend from the highest priority at the top to lowest at the bottom. For individuals with multiple samples, the union of mutations in all samples for that individual was used. The mutation waterfall plot was created using the “GenVisR” package in R.41

  • Figure 3.

    Histone gene mutations co-occur within individual patients with FL. Coding and splice site mutations in genes encoding the core histones (H2A, H2B, H3, H4) or histone linker (H1) often co-occur within patients. Each row represents a mutated histone gene, and each column represents a patient in this cohort. Histone mutations per patient are displayed at the top, indicating the total number of genes mutated (also summarized for the cohort in the bar graph on the left) and total number of mutations observed (includes multiple mutations per gene). The distribution of mutations and mutation types are indicated by colored boxes in the grid. For genes with multiple mutations in a single patient, only 1 mutation type is shown, with priority order indicated in the legend from the highest priority at the left to lowest at the right. Visualization created using GenVisR.41 (Inset) Histogram depicts the distribution of expected total histone gene mutation co-occurrences from 10 000 randomly permutated datasets with respect to the observed total co-occurrence in this cohort indicated by a red line (estimated P value < .0001). Although some patients had more than 1 mutation per histone gene, as indicated at the top, genes were considered mutated or not mutated for co-occurrence analysis. See supplemental Table 11 for a complete list of mutations. FS, frame shift; IF, in frame; SS, splice site.

  • Figure 4.

    Frequencies of mutations affecting the BCR/CXCR4 signaling pathways and SWI/SNF complex in patients with FL. (A) The interconnected BCR and CXCR4 signaling pathways are shown. Genes with nonsynonymous coding or splice site mutations are depicted in green, with SMGs in dark green and the mutation frequency observed in the entire cohort (N = 105) indicated. The total number and types of mutations observed are shown in the inset bar graph. (B) Recurrent mutations affecting both BAF (BRG1-associated factor) and PBAF (polybromo BRG1-associated factor) SWI/SNF complexes were observed in our cohort and annotated as in A. See supplemental Table 11 for a complete list of mutations. *Frequency includes 2 ARID1A variants rescued after ESP filtering.

  • Figure 5.

    Recurrent mutations in vacuolar ATPase genes and EGR1 in patients with FL. Hotspot mutations were identified in significantly mutated vacuolar ATPase-associated genes: (A) VMA21 (R148* in 4 of 5 mutated patients; ENST00000370361) and (B) ATP6V1B2 (R400Q in 6 of 9 mutated patients; ENST00000276390). (C) BLAST alignment results illustrating highly conserved yeast Vma2p (YBR127C) amino acid residues previously shown to abrogate ATPase catalytic activity when mutated (yellow) are orthologous to amino acid residues altered by mutations in human ATP6V1B2 (ENST00000276390.2) observed in our cohort (magenta). (D) EGR1 mutations observed in this cohort (N = 105), indicated above the protein diagram, were only observed near the N-terminus of the protein (ENST00000239938). EGR1 mutations previously reported for hematopoietic malignancies in COSMIC and selected papers are depicted below the protein diagram.8,23,24,68,69 See supplemental Table 11 for a complete list of V-ATPase complex and EGR1 mutations.

  • Figure 6.

    Mutations affecting PFS in treated patients with FL. Treatment-naive patients who received treatment within 1 year of diagnosis and sample collection (N = 59) were stratified by the presence or absence of coding or splice site mutations in SMGs, with a minimum of 5 mutations in this subset of patients (supplemental Table 6). Only groups showing significantly different survival are shown. (A) PFS was worse for patients harboring CREBBP mutations (P = .034; q = 0.884 after Benjamini-Hochberg correction for multiple hypothesis testing). (B) In contrast, patients with HVCN1 mutations had better PFS than those with wild-type HVCN1 (P = .033; q = 0.740).

Tables

  • Table 1.

    Clinical characteristics of patients used for genetic and clinical analysis

    CharacteristicGenetic analysis valueClinical analysis value
    Total patient number10582
    Female, %54.356.1
    Male, %45.743.9
    Age (median)5858.5
    Age range22-8728-87
    Stage, %
     I19.015.9
     II5.77.3
     III27.628.1
     IV44.848.8
     NA: no information2.9
    FLIPI score, %
     Low36.236.6
     Intermediate35.241.5
     High20.022.0
     NA: no information or tNHL8.6
    m7 FLIPI score, %
     Low75.287.7
     High11.412.4
     NA: no information or tNHL13.3
    Lymphoma type, %
     FL91.4
     Transformed lymphoma (tNHL)6.7
     NA: no information1.9
    Sequenced biopsy, %
     Treatment-naive FL80.0100.0
     Treated FL*11.4
     Transformed lymphoma (tNHL)6.7
     NA: no information1.9
    Treatment, %
     Rituximab containing regimen50.0
     Other treatment22.0
     Observation28.1
    Best response to treatment, %
     Complete remission66.1
     Partial remission28.8
     Stable disease1.7
     Progressive disease3.4
    • * One patient with both treatment-naive and treated biopsies was only counted as treatment-naive.

    • Other treatment includes: CHOP (cyclophosphamide, hydroxydaunomycin, oncovin, prednisolone), bendamustine-ofatumumab + ofatumumab, XRT (radiation therapy), bendamustine + ofatumumab, cyclophosphamide, CVP (cyclophosphamide, vincristine, prednisolone), CVP + genitope protocol vaccine, and CHOP + XRT.

    • This excludes patients who were observed.