Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

0

Integrated DNA methylation and RNA expression in Small Intestinal neuroendocrine tumors


ABSTRACT: Background and Aims Small intestinal neuroendocrine tumours (SINETs) are the commonest malignancy of the small intestine; however underlying pathogenic mechanisms remain poorly characterised. Whole genome and exome sequencing has demonstrated that SINETs are mutationally quiet with the most frequent known mutation in the cyclin dependent kinase inhibitor 1B gene (CDKN1B) occurring in only ~8% of tumours, suggesting that alternative mechanisms may drive tumourigenesis. The aim of this study is to perform genome-wide molecular profiling of SINETs in order to identify pathogenic drivers based on molecular profiling. This study represents the largest unbiased integrated genomic, epigenomic, and transcriptomic analysis undertaken in this tumour type. Methods Here we present data from integrated molecular analysis of SINETs (n=97) including whole exome or targeted CDKN1B sequencing (n=29), HumanMethylation450 BeadChip (Illumina) array profiling (n=69), methylated DNA immunoprecipitation sequencing (n=16), copy number variance analysis (n=47) and Whole Genome-DASL (Illumina) expression array profiling (n=43). Results Based on molecular profiling SINETs can be classified in to three groups which demonstrate significantly different progression-free survival after resection of primary tumour (not reached at 10 years vs 56 months vs 21 months, p=0.04). Epimutations were found at a recurrence rate of up to 85% and 21 epigenetically dysregulated genes were identified, including CDX1 (86%), CELSR3 (84%), FBP1 (84%) and GIPR (74%). Conclusions This is the first comprehensive integrated molecular analysis of SINETs. We have demonstrated that these tumours are highly epigenetically dysregulated. Furthermore, we have identified novel molecular subtypes with significant impact on progression free survival. Background and Aims Small intestinal neuroendocrine tumours (SINETs) are the commonest malignancy of the small intestine; however underlying pathogenic mechanisms remain poorly characterised. Whole genome and exome sequencing has demonstrated that SINETs are mutationally quiet with the most frequent known mutation in the cyclin dependent kinase inhibitor 1B gene (CDKN1B) occurring in only ~8% of tumours, suggesting that alternative mechanisms may drive tumourigenesis. The aim of this study is to perform genome-wide molecular profiling of SINETs in order to identify pathogenic drivers based on molecular profiling. This study represents the largest unbiased integrated genomic, epigenomic, and transcriptomic analysis undertaken in this tumour type. Methods Here we present data from integrated molecular analysis of SINETs (n=97) including whole exome or targeted CDKN1B sequencing (n=29), HumanMethylation450 BeadChip (Illumina) array profiling (n=69), methylated DNA immunoprecipitation sequencing (n=16), copy number variance analysis (n=47) and Whole Genome-DASL (Illumina) expression array profiling (n=43). Results Based on molecular profiling SINETs can be classified in to three groups which demonstrate significantly different progression-free survival after resection of primary tumour (not reached at 10 years vs 56 months vs 21 months, p=0.04). Epimutations were found at a recurrence rate of up to 85% and 21 epigenetically dysregulated genes were identified, including CDX1 (86%), CELSR3 (84%), FBP1 (84%) and GIPR (74%). Conclusions This is the first comprehensive integrated molecular analysis of SINETs. We have demonstrated that these tumours are highly epigenetically dysregulated. Furthermore, we have identified novel molecular subtypes with significant impact on progression free survival. This study included 97 tumour samples from 85 individuals, this included both primary and metastatic tumour samples. 25 normal small intestinal samples were analysed.

ORGANISM(S): Homo sapiens

SUBMITTER: Christian Thirlwell 

PROVIDER: E-GEOD-73832 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

Similar Datasets

2015-04-02 | E-GEOD-63315 | biostudies-arrayexpress
2022-11-30 | E-MTAB-11962 | biostudies-arrayexpress
2022-11-30 | E-MTAB-11965 | biostudies-arrayexpress
2020-11-30 | E-MTAB-7924 | biostudies-arrayexpress
2016-02-15 | E-GEOD-73950 | biostudies-arrayexpress
2016-03-16 | E-GEOD-77445 | biostudies-arrayexpress
2018-11-29 | E-MTAB-7431 | biostudies-arrayexpress
2015-09-17 | E-GEOD-73103 | biostudies-arrayexpress
2023-09-23 | E-MTAB-12250 | biostudies-arrayexpress
2015-12-31 | E-GEOD-55479 | biostudies-arrayexpress