Dataset Information

The genetic landscape of 5T models for multiple myeloma.

ABSTRACT: Murine models for multiple myeloma (MM) are often used to investigate pathobiology of multiple myeloma and disease progression. Unlike transgenic mice models, where it is known which oncogene is driving MM disease, the somatic aberrations of spontaneous syngeneic 5T models of MM have not yet been reported. Here, we analyzed the copy-number alterations (CNA) and mutational landscape of 5T2, 5T33vv and 5TGM1 murine MM models using whole-genome and whole-exome sequencing. Forty four percent of the genome of 5T2 cells is affected by CNAs while this was only 11% and 17% for 5T33vv and 5TGM1 cells, respectively. We found that up to 69% of the genes linked to gain of 1q or deletion of 13q in MM patients are present as respectively gains in 5T2 cells or deletions in 5T33 and 5TGM1 cells. Exome sequencing furthermore revealed mutations of genes involved in RAS/MAPK, PI3K/AKT1 and JAK/STAT signaling, DNA damage response, cell cycle, epigenetic regulation and extracellular matrix organization. We observed a statistically significant overlap of genes mutated in the 5T models and MM patients. Overall, the genetic landscape of the 5T models is heterogeneous with a high number of aberrations involving genes in various multiple myeloma-related pathways.

SUBMITTER: Maes K

PROVIDER: S-EPMC6177465 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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The genetic landscape of 5T models for multiple myeloma.

Maes Ken K Boeckx Bram B Vlummens Philip P De Veirman Kim K Menu Eline E Vanderkerken Karin K Lambrechts Diether D De Bruyne Elke E

Scientific reports 20181009 1

Murine models for multiple myeloma (MM) are often used to investigate pathobiology of multiple myeloma and disease progression. Unlike transgenic mice models, where it is known which oncogene is driving MM disease, the somatic aberrations of spontaneous syngeneic 5T models of MM have not yet been reported. Here, we analyzed the copy-number alterations (CNA) and mutational landscape of 5T2, 5T33vv and 5TGM1 murine MM models using whole-genome and whole-exome sequencing. Forty four percent of the ...[more]

PMID: 30301958

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Project description:Multiple Myeloma (MM) arises from malignant transformation and deregulated proliferation of clonal plasma cells (PCs) harbouring heterogeneous molecular anomalies. The effect of evolving mutations on clone fitness and their cellular prevalence shapes the progressing myeloma genome and impacts clinical outcomes. Although clonal heterogeneity in MM is well established, which subclonal mutations emerge/persist/perish with progression in MM and which of these can be targeted therapeutically remains an open question. In line with this, we have sequenced pairwise whole exomes of 62 MM patients collected at two time points, i.e., at diagnosis and on progression. Somatic variants were called using a novel ensemble approach where a consensus was deduced from four variant callers (Illumina's Dragen, Strelka2, SomaticSniper and SpeedSeq) and actionable/druggable gene targets were identified. A marked intraclonal heterogeneity was observed. Branching evolution was observed among 72.58% patients, of whom 64.51% had low TMBs (<10) and 61.29% had 2 or more founder clones. The hypermutator patients (with high TMB levels ≥10 to ≤100) showed a significant decrease in their TMBs from diagnosis (median TMB 77.11) to progression (median TMB 31.22). A distinct temporal fall in subclonal driver mutations was identified recurrently across diagnosis to progression e.g., in PABPC1, BRAF, KRAS, CR1, DIS3 and ATM genes in 3 or more patients suggesting such patients could be treated early with target specific drugs like Vemurafenib/Cobimetinib. An analogous rise in driver mutations was observed in KMT2C, FOXD4L1, SP140, NRAS and other genes. A few drivers such as FAT4, IGLL5 and CDKN1A retained consistent distribution patterns at two time points. These findings are clinically relevant and point at consideration of evaluating multi time point subclonal mutational landscapes for designing better risk stratification strategies and tailoring time to time risk adapted combination therapies in future.

Dataset Information

The genetic landscape of 5T models for multiple myeloma.

Publications

The genetic landscape of 5T models for multiple myeloma.

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