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. 2015 Aug 28;5(8):e346.
doi: 10.1038/bcj.2015.69.

Whole-exome analysis reveals novel somatic genomic alterations associated with outcome in immunochemotherapy-treated diffuse large B-cell lymphoma

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Whole-exome analysis reveals novel somatic genomic alterations associated with outcome in immunochemotherapy-treated diffuse large B-cell lymphoma

A J Novak et al. Blood Cancer J. .

Abstract

Lack of remission or early relapse remains a major clinical issue in diffuse large B-cell lymphoma (DLBCL), with 30% of patients failing standard of care. Although clinical factors and molecular signatures can partially predict DLBCL outcome, additional information is needed to identify high-risk patients, particularly biologic factors that might ultimately be amenable to intervention. Using whole-exome sequencing data from 51 newly diagnosed and immunochemotherapy-treated DLBCL patients, we evaluated the association of somatic genomic alterations with patient outcome, defined as failure to achieve event-free survival at 24 months after diagnosis (EFS24). We identified 16 genes with mutations, 374 with copy number gains and 151 with copy number losses that were associated with failure to achieve EFS24 (P<0.05). Except for FOXO1 and CIITA, known driver mutations did not correlate with EFS24. Gene losses were localized to 6q21-6q24.2, and gains to 3q13.12-3q29, 11q23.1-11q23.3 and 19q13.12-19q13.43. Globally, the number of gains was highly associated with poor outcome (P=7.4 × 10(-12)) and when combined with FOXO1 mutations identified 77% of cases that failed to achieve EFS24. One gene (SLC22A16) at 6q21, a doxorubicin transporter, was lost in 54% of EFS24 failures and our findings suggest it functions as a doxorubicin transporter in DLBCL cells.

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Figures

Figure 1
Figure 1
CNA gains associated with failure to achieve EFS24. (a) Regions of chromosome gain, number of genes with P<0.05 and potential target genes in the regions. Graphical representation of individual genes (represented by a dot) plotted by chromosomal position and –Log10(P-value) on chromosomes (b) 3q13.12-3q29, (c) 11q23.1-11q23.3 and (d) 19q13.12-19q13.43. The position of BCL6, CBL and RELB is highlighted on individual graphs. Statistical analysis is described in the Materials and methods section.
Figure 2
Figure 2
CNA losses associated with failure to achieve EFS24. (a) Regions of chromosome loss, number of genes with P<0.05 and potential target genes in the regions. (b) Graphical representation of individual genes (represented by a dot) plotted by chromosomal position and –Log10(P-value) on chromosome 6q21-6q24.2. The position of SLC22A16 and PRDM1 is highlighted on the graphs. Statistical analysis is described in the Materials and methods section.
Figure 3
Figure 3
Genetic signature associated with failure to achieve EFS24. (a) Molecular features including COO, MYC-double hit (DH) status, the presence of driver mutations or mutations and CNAs associated with EFS24 failure are shown in individual patients. (b) Venn diagram showing integration of the EFS24 CNA and mutation data reveal that 77% patients who fail to achieve EFS24 can be identified using a combination of four variants (FOXO1 mutation and gains in 3q27.3 (BCL6), 11q23.3 (CBL) and 19q13.32 (RELB)).
Figure 4
Figure 4
Association of CNAs and mutations with DLBCL time to progression. Kaplan–Meier curves for the association of DLBCL time to progression with gains in (a) Chr 3q27.3 (BCL6, n=4), (b) 11q23.3 (CBL, n=3), (c) 19q13.32 (RELB, n=3), loss in (d) 6q21 (SLC22A16, n=11), a mutation in (e) FOXO1 (n=2) or (f) a combined analysis of the three gains.
Figure 5
Figure 5
Expression and functional characterization of SLC22A16 in DLBCL cells. (a) Expression of SLC22A16 mRNA levels was measured by quantitative real-time PCR in a panel of DLBCL tumors (n=10) and cell lines (n=5) as described in Materials and methods. (b) Analysis of OCI-LY7 cells expressing an empty vector control (EV) or HA-SLC22A16 was performed by western blot analysis with an anti-HA-specific antibody to confirm SLC22A16 expression (inset). Doxorubicin uptake was measured in OCI-LY7 EV (solid line, open triangle) or SLC22A16 (dashed line, square symbol) cells using 14C-doxorubicin as described in Materials and methods. The experiment shown is representative of three independent experiments and error bars for triplicate wells are shown. (c) Proliferation of OCI-LY7 EV (solid line) or SLC22A16 (dashed line) cells was measured in the presence of increasing doses of doxorubicin (0–10 μM) by 3H-TdR incorporation. Data from each cell line were normalized to its respective nil control and the experiment shown is representative of three independent experiments, error bars for triplicate wells are shown. (d) Bioinformatic modeling of SLC22A16 mutations shows their location in major facilitator superfamily domains, which were assigned by Pfam using a hidden Markov model. Tan regions are predicted to be trans-membrane helices and red residues (side chains shown) are the predicted pore. Each mutation site sits at the entrance/exit of the predicted translocation pore (yellow spheres).

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References

    1. Howlader NNA, Krapcho M, Garshell J, Neyman N, Altekruse SF, Kosary CL, et al. (eds). SEER Cancer Statistics Review, 1975–2010 National Cancer Institute: Bethesda, MD; Available from: : http://seer.cancer.gov/csr/1975_2010/ (last accessed November 2012). SEER data submission, posted to the SEER website, April2013
    1. Sant M, Allemani C, Tereanu C, De Angelis R, Capocaccia R, Visser O, et al. Incidence of hematologic malignancies in Europe by morphologic subtype: results of the HAEMACARE project. Blood. 2010;116:3724–3734. - PubMed
    1. A predictive model for aggressive non-Hodgkin's lymphoma The International Non-Hodgkin's Lymphoma Prognostic Factors Project. N Engl J Med. 1993;329:987–994. - PubMed
    1. Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A, et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature. 2000;403:503–511. - PubMed
    1. Wright G, Tan B, Rosenwald A, Hurt EH, Wiestner A, Staudt LM. A gene expression-based method to diagnose clinically distinct subgroups of diffuse large B cell lymphoma. Proc Natl Acad Sci USA. 2003;100:9991–9996. - PMC - PubMed

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