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Comprehensive miRNA sequence analysis reveals survival differences in diffuse large B-cell lymphoma patients.

Lim EL, Trinh DL, Scott DW, Chu A, Krzywinski M, Zhao Y, Robertson AG, Mungall AJ, Schein J, Boyle M, Mottok A, Ennishi D, Johnson NA, Steidl C, Connors JM, Morin RD, Gascoyne RD, Marra MA - Genome Biol. (2015)

Bottom Line: Of these 25 miRNAs, six miRNAs are significantly associated with survival in our validation cohort.Abundant expression of miR-28-5p, miR-214-5p, miR-339-3p, and miR-5586-5p is associated with superior outcome, while abundant expression of miR-324-5p and NOVELM00203M is associated with inferior outcome.Our comprehensive sequence analysis of the DLBCL miRNome identifies candidate novel miRNAs and miRNAs associated with survival, reinforces results from previous mutational analyses, and reveals regulatory networks of significance for lymphomagenesis.

View Article: PubMed Central - PubMed

ABSTRACT

Background: Diffuse large B-cell lymphoma (DLBCL) is an aggressive disease, with 30% to 40% of patients failing to be cured with available primary therapy. microRNAs (miRNAs) are RNA molecules that attenuate expression of their mRNA targets. To characterize the DLBCL miRNome, we sequenced miRNAs from 92 DLBCL and 15 benign centroblast fresh frozen samples and from 140 DLBCL formalin-fixed, paraffin-embedded tissue samples for validation.

Results: We identify known and candidate novel miRNAs, 25 of which are associated with survival independently of cell-of-origin and International Prognostic Index scores, which are established indicators of outcome. Of these 25 miRNAs, six miRNAs are significantly associated with survival in our validation cohort. Abundant expression of miR-28-5p, miR-214-5p, miR-339-3p, and miR-5586-5p is associated with superior outcome, while abundant expression of miR-324-5p and NOVELM00203M is associated with inferior outcome. Comparison of DLBCL miRNA-seq expression profiles with those from other cancer types identifies miRNAs that were more abundant in B-cell contexts. Unsupervised clustering of miRNAs identifies two clusters of patients that have distinct differences in their outcomes. Our integrative miRNA and mRNA expression analyses reveal that miRNAs increased in abundance in DLBCL appear to regulate the expression of genes involved in metabolism, cell cycle, and protein modification. Additionally, these miRNAs, including one candidate novel miRNA, miR-10393-3p, appear to target chromatin modification genes that are frequent targets of somatic mutation in non-Hodgkin lymphomas.

Conclusions: Our comprehensive sequence analysis of the DLBCL miRNome identifies candidate novel miRNAs and miRNAs associated with survival, reinforces results from previous mutational analyses, and reveals regulatory networks of significance for lymphomagenesis.

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NMF Identifies two clusters of DLBCL patients with distinct miRNA and outcome profiles. We performed non-negative matrix factorization (NMF) clustering on 83 R-CHOP treated DLBCL patient samples, using the miRNA expression obtained from miRNA-seq data. (a) NMF yielded two clusters of patients (see Additional file 9: Figure S4) that had distinct differences in their outcomes. Patients in cluster 1 are indicated by dark gray bars, while patients in cluster 2 are indicated by light gray bars. Below the consensus matrix is a heatmap showing the expression of miR-148a and miR-21 in each patient. (b) Kaplan-Meier curves showing overall survival and progression-free survival of patients in both clusters. Patients in cluster 1 exhibit inferior outcome compared to those in cluster 2. (c) To identify which miRNAs were characteristic of each cluster, we identified the differentially expressed miRNA between the two clusters. The MA plot shows that miR-21 abundance is increased in cluster 2 patients, while miR-148a abundance is decreased in cluster 1 patients (Wilcoxon test q-value <0.05). (d) Expression patterns of miR-148a and miR-21 are discontinuous. miRNA expression in DLBCL patient samples is indicated with black squares, while expression in centroblast samples is indicated with orange diamonds.
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Fig6: NMF Identifies two clusters of DLBCL patients with distinct miRNA and outcome profiles. We performed non-negative matrix factorization (NMF) clustering on 83 R-CHOP treated DLBCL patient samples, using the miRNA expression obtained from miRNA-seq data. (a) NMF yielded two clusters of patients (see Additional file 9: Figure S4) that had distinct differences in their outcomes. Patients in cluster 1 are indicated by dark gray bars, while patients in cluster 2 are indicated by light gray bars. Below the consensus matrix is a heatmap showing the expression of miR-148a and miR-21 in each patient. (b) Kaplan-Meier curves showing overall survival and progression-free survival of patients in both clusters. Patients in cluster 1 exhibit inferior outcome compared to those in cluster 2. (c) To identify which miRNAs were characteristic of each cluster, we identified the differentially expressed miRNA between the two clusters. The MA plot shows that miR-21 abundance is increased in cluster 2 patients, while miR-148a abundance is decreased in cluster 1 patients (Wilcoxon test q-value <0.05). (d) Expression patterns of miR-148a and miR-21 are discontinuous. miRNA expression in DLBCL patient samples is indicated with black squares, while expression in centroblast samples is indicated with orange diamonds.

Mentions: We next sought to determine whether DLBCL patients could be stratified using their miRNA expression profiles. Unsupervised non-negative matrix factorization (NMF) consensus clustering (Additional file 9: Figure S4), using only the miRNA expression profiles of the 83 R-CHOP treated patients, identified an optimum of two groups of patients (Figure 6a) with distinct outcome correlations (Figure 6b) and miRNA expression patterns (Figure 6c). These two groups did not differ based on any clinical characteristics, including age, sex, LDH level, number of extranodal sites, cell-of-origin subtype, or other parameters such as presence of a chromosomal break-apart at BCL2, BCL6, or MYC (Chi-square test P value >0.05). However, two miRNAs were significantly differentially expressed between the groups. In the cluster of patients with poorer outcome, miR-148a was increased in abundance and miR-21 was decreased in abundance compared to the cluster of patients with superior outcome (Figure 6a).Figure 6


Comprehensive miRNA sequence analysis reveals survival differences in diffuse large B-cell lymphoma patients.

Lim EL, Trinh DL, Scott DW, Chu A, Krzywinski M, Zhao Y, Robertson AG, Mungall AJ, Schein J, Boyle M, Mottok A, Ennishi D, Johnson NA, Steidl C, Connors JM, Morin RD, Gascoyne RD, Marra MA - Genome Biol. (2015)

NMF Identifies two clusters of DLBCL patients with distinct miRNA and outcome profiles. We performed non-negative matrix factorization (NMF) clustering on 83 R-CHOP treated DLBCL patient samples, using the miRNA expression obtained from miRNA-seq data. (a) NMF yielded two clusters of patients (see Additional file 9: Figure S4) that had distinct differences in their outcomes. Patients in cluster 1 are indicated by dark gray bars, while patients in cluster 2 are indicated by light gray bars. Below the consensus matrix is a heatmap showing the expression of miR-148a and miR-21 in each patient. (b) Kaplan-Meier curves showing overall survival and progression-free survival of patients in both clusters. Patients in cluster 1 exhibit inferior outcome compared to those in cluster 2. (c) To identify which miRNAs were characteristic of each cluster, we identified the differentially expressed miRNA between the two clusters. The MA plot shows that miR-21 abundance is increased in cluster 2 patients, while miR-148a abundance is decreased in cluster 1 patients (Wilcoxon test q-value <0.05). (d) Expression patterns of miR-148a and miR-21 are discontinuous. miRNA expression in DLBCL patient samples is indicated with black squares, while expression in centroblast samples is indicated with orange diamonds.
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Fig6: NMF Identifies two clusters of DLBCL patients with distinct miRNA and outcome profiles. We performed non-negative matrix factorization (NMF) clustering on 83 R-CHOP treated DLBCL patient samples, using the miRNA expression obtained from miRNA-seq data. (a) NMF yielded two clusters of patients (see Additional file 9: Figure S4) that had distinct differences in their outcomes. Patients in cluster 1 are indicated by dark gray bars, while patients in cluster 2 are indicated by light gray bars. Below the consensus matrix is a heatmap showing the expression of miR-148a and miR-21 in each patient. (b) Kaplan-Meier curves showing overall survival and progression-free survival of patients in both clusters. Patients in cluster 1 exhibit inferior outcome compared to those in cluster 2. (c) To identify which miRNAs were characteristic of each cluster, we identified the differentially expressed miRNA between the two clusters. The MA plot shows that miR-21 abundance is increased in cluster 2 patients, while miR-148a abundance is decreased in cluster 1 patients (Wilcoxon test q-value <0.05). (d) Expression patterns of miR-148a and miR-21 are discontinuous. miRNA expression in DLBCL patient samples is indicated with black squares, while expression in centroblast samples is indicated with orange diamonds.
Mentions: We next sought to determine whether DLBCL patients could be stratified using their miRNA expression profiles. Unsupervised non-negative matrix factorization (NMF) consensus clustering (Additional file 9: Figure S4), using only the miRNA expression profiles of the 83 R-CHOP treated patients, identified an optimum of two groups of patients (Figure 6a) with distinct outcome correlations (Figure 6b) and miRNA expression patterns (Figure 6c). These two groups did not differ based on any clinical characteristics, including age, sex, LDH level, number of extranodal sites, cell-of-origin subtype, or other parameters such as presence of a chromosomal break-apart at BCL2, BCL6, or MYC (Chi-square test P value >0.05). However, two miRNAs were significantly differentially expressed between the groups. In the cluster of patients with poorer outcome, miR-148a was increased in abundance and miR-21 was decreased in abundance compared to the cluster of patients with superior outcome (Figure 6a).Figure 6

Bottom Line: Of these 25 miRNAs, six miRNAs are significantly associated with survival in our validation cohort.Abundant expression of miR-28-5p, miR-214-5p, miR-339-3p, and miR-5586-5p is associated with superior outcome, while abundant expression of miR-324-5p and NOVELM00203M is associated with inferior outcome.Our comprehensive sequence analysis of the DLBCL miRNome identifies candidate novel miRNAs and miRNAs associated with survival, reinforces results from previous mutational analyses, and reveals regulatory networks of significance for lymphomagenesis.

View Article: PubMed Central - PubMed

ABSTRACT

Background: Diffuse large B-cell lymphoma (DLBCL) is an aggressive disease, with 30% to 40% of patients failing to be cured with available primary therapy. microRNAs (miRNAs) are RNA molecules that attenuate expression of their mRNA targets. To characterize the DLBCL miRNome, we sequenced miRNAs from 92 DLBCL and 15 benign centroblast fresh frozen samples and from 140 DLBCL formalin-fixed, paraffin-embedded tissue samples for validation.

Results: We identify known and candidate novel miRNAs, 25 of which are associated with survival independently of cell-of-origin and International Prognostic Index scores, which are established indicators of outcome. Of these 25 miRNAs, six miRNAs are significantly associated with survival in our validation cohort. Abundant expression of miR-28-5p, miR-214-5p, miR-339-3p, and miR-5586-5p is associated with superior outcome, while abundant expression of miR-324-5p and NOVELM00203M is associated with inferior outcome. Comparison of DLBCL miRNA-seq expression profiles with those from other cancer types identifies miRNAs that were more abundant in B-cell contexts. Unsupervised clustering of miRNAs identifies two clusters of patients that have distinct differences in their outcomes. Our integrative miRNA and mRNA expression analyses reveal that miRNAs increased in abundance in DLBCL appear to regulate the expression of genes involved in metabolism, cell cycle, and protein modification. Additionally, these miRNAs, including one candidate novel miRNA, miR-10393-3p, appear to target chromatin modification genes that are frequent targets of somatic mutation in non-Hodgkin lymphomas.

Conclusions: Our comprehensive sequence analysis of the DLBCL miRNome identifies candidate novel miRNAs and miRNAs associated with survival, reinforces results from previous mutational analyses, and reveals regulatory networks of significance for lymphomagenesis.

Show MeSH
Related in: MedlinePlus