Targeted panel data for newly diagnosed myeloma patients.

Genomic abnormalities in MM are common and can affect a patients outcome. Here we have performed targeted sequencing on xx patient tumor samples and matched control DNA. The targeted panel consists of ~160 genes and copy number regions, as well as key regions of chromosomal translocation including IGH, IGK, IGL and MYC. Using mutation, copy number, and translocation information we have been able to identify abnormalities that affect prognosis including bi-allelic inactivation of TP53 and rearrangements involving MYC.

Type: Other
Archiver: European Genome-Phenome Archive (EGA)

2 Datasets 4 Publications

Click on a Dataset ID in the table below to learn more, and to find out who to contact about access to these data

Dataset ID	Description	Technology	Samples
EGAD00001004117	Tumor DNA was extracted from 100 bone marrow aspirate samples where CD138+ selection had been performed to enrich plasma cells from patients with multiple myeloma. Patient matched control DNA from either peripheral blood leukocytes or CD34+ stem cell harvests was also isolated. Both tumor and control DNA underwent library preparation using the Hyperplus kit (KAPA Biosystems) and were hybridized to baits for a targeted SeqCap myeloma panel (Nimblegen) encompassing 129 genes, regions for SNPs for copy number determination, and the IGH, IGK, IGL loci, as well as approximately 5 Mb surrounding the MYC locus. Samples were sequenced on a HiSeq2500 using 100 bp paired end reads. Resulting BAM files were returned along with annotations for somatic events including single nucleotide mutations, indels and structural rearrangements.	Illumina HiSeq 2500	200
EGAD00010001577	RNA from the same tumor sample (n=98) was also processed using the 3' IVT kit (Affymetrix) and hybridized to U133 Plus 2.0 arrays (Affymetrix).	Affymetrix GeneChip Scanner 3000 7g	98

Publications	Citations
The level of deletion 17p and bi-allelic inactivation of <i>TP53</i> has a significant impact on clinical outcome in multiple myeloma. Thanendrarajan S, Tian E, Qu P, Mathur P, Schinke C, van Rhee F, Zangari M, Rasche L, Weinhold N, Alapat D, Bellamy W, Ashby C, Mattox S, Epstein J, Yaccoby S, Barlogie B, Hoering A, Bauer M, Walker BA, Davies FE, Morgan GJ. Haematologica 102: 2017 e364-e367	47
Microhomology-mediated end joining drives complex rearrangements and overexpression of <i>MYC</i> and <i>PVT1</i> in multiple myeloma. Mikulasova A, Ashby C, Tytarenko RG, Qu P, Rosenthal A, Dent JA, Ryan KR, Bauer MA, Wardell CP, Hoering A, Mavrommatis K, Trotter M, Deshpande S, Yaccoby S, Tian E, Keats J, Auclair D, Jackson GH, Davies FE, Thakurta A, Morgan GJ, Walker BA. Haematologica 105: 2020 1055-1066	32
TarPan: an easily adaptable targeted sequencing panel viewer for research and clinical use. Ashby C, Rutherford M, Bauer MA, Peterson EA, Wang Y, Boyle EM, Wardell CP, Walker BA. BMC Bioinformatics 21: 2020 144	2
Genomic Classification and Individualized Prognosis in Multiple Myeloma. Maura F, Rajanna AR, Ziccheddu B, Poos AM, Derkach A, Maclachlan K, Durante M, Diamond B, Papadimitriou M, Davies F, Boyle EM, Walker B, Hultcrantz M, Silva A, Hampton O, Teer JK, Siegel EM, Bolli N, Jackson GH, Kaiser M, Pawlyn C, Cook G, Kazandjian D, Stein C, Chesi M, Bergsagel L, Mai EK, Goldschmidt H, Weisel KC, Fenk R, Raab MS, Van Rhee F, Usmani S, Shain KH, Weinhold N, Morgan G, Landgren O. J Clin Oncol 42: 2024 1229-1240	15