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Angiomirs expression profiling in diffuse large B-Cell lymphoma.

ABSTRACT: Despite advances in treatment, 30% of diffuse large B-cell lymphoma (DLBCL) cases are refractory or relapse after chemoimmunotherapy. Currently, the relationship between angiogenesis and angiomiRs in DLBCL is unknown. We classified 84 DLBCL cases according to stromal signatures and evaluated the expression of pro- and antiangiomiRs in paraffin embedded tissues of DLBCL and correlated them with microvascular density (MVD). 40% of cases were classified as stromal-1, 50% as stromal-2 and 10% were not classified. We observed increased expression of proangiomiRs Let-7f, miR-17, miR-18a, miR-19b, miR-126, miR-130a, miR-210, miR-296 and miR-378 in 14%, 57%, 30%, 45%, 12%, 12%, 56%, 58% and 48% of the cases, respectively. Among antiangiomiRs we found decreased expression of miR-16, miR-20b, miR-92a, miR-221 and miR-328 in, respectively, 27%, 71%, 2%, 44% and 11%. We found association between increased expression of proangiomiRs miR-126 and miR-130a and antiangiomiR miR-328 and the subtype non-GCB. We found higher levels of the antiangiomiRs miR-16, miR-221 and miR-328 in patients with low MVD and stromal-1 signature. IPI and CD34 confirmed independent impact on survival of the study group. None of the above angiomiRs showed significance as biomarker in an independent serum samples cohort of patients and controls. In conclusion, we confirmed association between antiangiomiRs miR-16, miR-221 and miR-328 and stromal-1 signature. Four angiomiRs emerged as potential therapeutic targets: proangiomiRs miR-17, miR-210 and miR-296 and antiangiomiR miR-20b. Although the four microRNAs seem to be important in DLBCL pathogenesis, they were not predictive of DLBCL onset or relapse in the serum independent cohort.

SUBMITTER: Borges NM

PROVIDER: S-EPMC4826244 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature

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Angiomirs expression profiling in diffuse large B-Cell lymphoma.

Borges Natália M NM do Vale Elias Marcela M Fook-Alves Veruska L VL Andrade Tathiana A TA de Conti Marina Lourenço ML Macedo Mariana Petaccia MP Begnami Maria Dirlei MD Campos Antônio Hugo J F M AH Etto Leina Yukari LY Bortoluzzo Adriana Bruscato AB Alves Antonio C AC Young Ken H KH Colleoni Gisele W B GW

Oncotarget 20160101 4

Despite advances in treatment, 30% of diffuse large B-cell lymphoma (DLBCL) cases are refractory or relapse after chemoimmunotherapy. Currently, the relationship between angiogenesis and angiomiRs in DLBCL is unknown. We classified 84 DLBCL cases according to stromal signatures and evaluated the expression of pro- and antiangiomiRs in paraffin embedded tissues of DLBCL and correlated them with microvascular density (MVD). 40% of cases were classified as stromal-1, 50% as stromal-2 and 10% were n ...[more]

PMID: 26683099

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Project description:BackgroundThirty to forty percent of patients with Diffuse Large B-cell Lymphoma (DLBCL) have an adverse clinical evolution. The increased understanding of DLBCL biology has shed light on the clinical evolution of this pathology, leading to the discovery of prognostic factors based on gene expression data, genomic rearrangements and mutational subgroups. Nevertheless, additional efforts are needed in order to enable survival predictions at the patient level. In this study we investigated new machine learning-based models of survival using transcriptomic and clinical data.MethodsGene expression profiling (GEP) of in 2 different publicly available retrospective DLBCL cohorts were analyzed. Cox regression and unsupervised clustering were performed in order to identify probes associated with overall survival on the largest cohort. Random forests were created to model survival using combinations of GEP data, COO classification and clinical information. Cross-validation was used to compare model results in the training set, and Harrel's concordance index (c-index) was used to assess model's predictability. Results were validated in an independent test set.ResultsTwo hundred thirty-three and sixty-four patients were included in the training and test set, respectively. Initially we derived and validated a 4-gene expression clusterization that was independently associated with lower survival in 20% of patients. This pattern included the following genes: TNFRSF9, BIRC3, BCL2L1 and G3BP2. Thereafter, we applied machine-learning models to predict survival. A set of 102 genes was highly predictive of disease outcome, outperforming available clinical information and COO classification. The final best model integrated clinical information, COO classification, 4-gene-based clusterization and the expression levels of 50 individual genes (training set c-index, 0.8404, test set c-index, 0.7942).ConclusionOur results indicate that DLBCL survival models based on the application of machine learning algorithms to gene expression and clinical data can largely outperform other important prognostic variables such as disease stage and COO. Head-to-head comparisons with other risk stratification models are needed to compare its usefulness.

Dataset Information

Angiomirs expression profiling in diffuse large B-Cell lymphoma.

Publications

Angiomirs expression profiling in diffuse large B-Cell lymphoma.

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