Integrative Genomic and Transcriptomic Analysis Identified Candidate Genes Implicated in the Pathogenesis of Hepatosplenic T-cell Lymphoma

ABSTRACT: Hepatosplenic T-cell lymphoma (HSTL) is an aggressive lymphoma cytogenetically characterized by isochromosome 7q M-BM- [i(7)(q10)], of which the molecular consequences remain unknown. We report here results of an integrative genomic and transcriptomic (expression microarray and RNA-sequencing) study of six HSTL cases with i(7)(q10) and three cases with ring 7 M-BM- [r(7)], a rare variant aberration. Using high resolution array CGH, we prove that HSTL is characterized by the common loss of a 34.88 Mb region at 7p22.1p14.1 (3506316-38406226 bp) and duplication/amplification of a 38.77 Mb region at 7q22.11q31.1 (86259620-124892276 bp). Our data indicate that i(7)(q10)/r(7)-associated loss of 7p22.1p14.1 is a critical event in the development of HSTL, while gain of 7q sequences drives progression of the disease and underlies its intrinsic chemoresistance. Loss of 7p22.1p14.1 does not target a postulated tumor suppressor gene but unexpectedly enhances the expression of CHN2 from the remaining 7p allele, resulting in dysregulation of a pathway involving RAC1 and NFATC2 with a cell proliferation response. Gain of 7q leads to increased expression of critical genes, including RUNDC3B, PPP1R9A and ABCB1, a known multidrug resistance gene. RNA-sequencing did not identify any additional recurrent mutations or fusion, suggesting that i(7)(q10) is the only driver event in this tumor. Our study confirms the previously described gene expression profile of HSTL and identifies a set of 24 genes, including three located on chromosome 7 (CHN2, ABCB1 and PPP1R9A), distinguishing HSTL from other malignancies The fastq files of all samples were mapped to the reference human genome (assembly GRCh37.68). The mapping was performed allowing detection of insertions and deletions (indels). The mapped reads were used to calculate read counts and FPKM (Fragment Per Kilobase of exon model per Million of mapped read) per gene. The DESeq algorithm was applied to identify differentially expressed genes. The mapped reads were used to predict mutations and gene fusions.

ORGANISM(S): Homo sapiens

SUBMITTER: Julio Finalet 

PROVIDER: E-GEOD-55716 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress