Dataset Information

Identification of cell-type-specific mutations in nodal T-cell lymphomas.

ABSTRACT: Recent genetic analysis has identified frequent mutations in ten-eleven translocation 2 (TET2), DNA methyltransferase 3A (DNMT3A), isocitrate dehydrogenase 2 (IDH2) and ras homolog family member A (RHOA) in nodal T-cell lymphomas, including angioimmunoblastic T-cell lymphoma and peripheral T-cell lymphoma, not otherwise specified. We examined the distribution of mutations in these subtypes of mature T-/natural killer cell neoplasms to determine their clonal architecture. Targeted sequencing was performed for 71 genes in tumor-derived DNA of 87 cases. The mutations were then analyzed in a programmed death-1 (PD1)-positive population enriched with tumor cells and CD20-positive B cells purified by laser microdissection from 19 cases. TET2 and DNMT3A mutations were identified in both the PD1+ cells and the CD20+ cells in 15/16 and 4/7 cases, respectively. All the RHOA and IDH2 mutations were confined to the PD1+ cells, indicating that some, including RHOA and IDH2 mutations, being specific events in tumor cells. Notably, we found that all NOTCH1 mutations were detected only in the CD20+ cells. In conclusion, we identified both B- as well as T-cell-specific mutations, and mutations common to both T and B cells. These findings indicate the expansion of a clone after multistep and multilineal acquisition of gene mutations.

SUBMITTER: Nguyen TB

PROVIDER: S-EPMC5301031 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of cell-type-specific mutations in nodal T-cell lymphomas.

Nguyen T B TB Sakata-Yanagimoto M M Asabe Y Y Matsubara D D Kano J J Yoshida K K Shiraishi Y Y Chiba K K Tanaka H H Miyano S S Izutsu K K Nakamura N N Takeuchi K K Miyoshi H H Ohshima K K Minowa T T Ogawa S S Noguchi M M Chiba S S

Blood cancer journal 20170106 1

Recent genetic analysis has identified frequent mutations in ten-eleven translocation 2 (TET2), DNA methyltransferase 3A (DNMT3A), isocitrate dehydrogenase 2 (IDH2) and ras homolog family member A (RHOA) in nodal T-cell lymphomas, including angioimmunoblastic T-cell lymphoma and peripheral T-cell lymphoma, not otherwise specified. We examined the distribution of mutations in these subtypes of mature T-/natural killer cell neoplasms to determine their clonal architecture. Targeted sequencing was ...[more]

PMID: 28157189

Similar Datasets

Project description:BackgroundThe diagnosis of T-cell lymphomas is typically established through a multiparameter approach that combines clinical, morphologic, immunophenotypic, and genetic features, utilizing a variety of histopathologic and molecular techniques. However, accurate diagnosis of such lymphomas and distinguishing them from reactive lymph nodes remains challenging due to their low prevalence and heterogeneous features, hence limiting the confidence of pathologists. We investigated the use of microRNA (miRNA) expression signatures as an adjunctive tool in the diagnosis and classification of T-cell lymphomas that involve lymph nodes. This study seeks to distinguish reactive lymph nodes (RLN) from two types of frequently occurring nodal T-cell lymphomas: nodal T-follicular helper (TFH) cell lymphomas (nTFHL) and peripheral T-cell lymphomas, not otherwise specified (nPTCL).MethodsFrom the formalin-fixed paraffin-embedded (FFPE) samples from a cohort of 88 subjects, 246 miRNAs were quantified and analyzed by differential expression. Two-class logistic regression and random forest plot models were built to distinguish RLN from the nodal T-cell lymphomas. Gene set enrichment analysis was performed on the target genes of the miRNA to identify pathways and transcription factors that may be regulated by the differentially expressed miRNAs in each subtype.ResultsUsing logistic regression analysis, we identified miRNA signatures that can distinguish RLN from nodal T-cell lymphomas (AUC of 0.92 ± 0.05), from nTFHL (AUC of 0.94 ± 0.05) and from nPTCL (AUC of 0.94 ± 0.08). Random forest plot modelling was also capable of distinguishing between RLN and nodal T-cell lymphomas, but performed worse than logistic regression. However, the miRNA signatures are not able to discriminate between nTFHL and nPTCL, owing to large similarity in miRNA expression patterns. Bioinformatic analysis of the gene targets of unique miRNA expression revealed the enrichment of both known and potentially understudied signalling pathways and genes in such lymphomas.ConclusionThis study suggests that miRNA biomarkers may serve as a promising, cost-effective tool to aid the diagnosis of nodal T-cell lymphomas, which can be challenging. Bioinformatic analysis of differentially expressed miRNAs revealed both relevant or understudied signalling pathways that may contribute to the progression and development of each T-cell lymphoma entity. This may help us gain further insight into the biology of T-cell lymphomagenesis.

Dataset Information

Identification of cell-type-specific mutations in nodal T-cell lymphomas.

Publications

Identification of cell-type-specific mutations in nodal T-cell lymphomas.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets