The genetic landscape of the murine 5T models for multiple myeloma

Multiple myeloma (MM) is a plasma cell malignancy which remains incurable in most cases. This is mainly attributed to the large genetic and clonal heterogeneity between patients and within individual patients. The mutational landscape of MM patients has led to the discovery of several potential driver mutations and copy number alterations reflecting this genetic heterogeneity. Genetic lesions affecting RAS/MAPK and NF-KB pathways, apoptosis and cell cycle signaling are the most commonly found in patients. Moreover, mutations disturbing the DNA damage response are linked with poor prognosis. The use of suitable murine MM models is important to gain understanding of the functional consequences of the genetic heterogeneity observed in patients. However, to date, the genetic landscape of murine MM models have not been analyzed.

In this study, we analyzed the copy-number alterations and the mutational landscape of 5T2, 5T33vv and 5TGM1 murine MM models and C57Bl/KaLwRij and C57Bl/6J germline samples using shallow whole genome sequencing and whole exome sequencing. Among the tested models, the 5T2 model displayed the most copy number alterations. Over the entire genome, 11% and 17% showed copy number alterations for the 5T33vv and 5TGM1 of which 6% is shared reflecting their clonal relationship. Overall, the copy-number alterations affects genes involved in RAS/MAPK, PI3K/AKT1 and JAK/STAT signaling, DNA damage response, cell cycle and epigenetic regulation. Exome sequencing revealed the presence of 417, 407 and 314 non-synonymous mutations and 8, 14 and 24 indels in the 5T33vv, 5TGM1 and 5T2 models, respectively. Moreover, a statistically significant overlap of mutated genes between the 5T33vv and 5T2 models and multiple myeloma patients from two large cohorts published by Lohr et al. and Walker et al. was observed (p<1E-8). Similar to MM patients, we identified damaging mutations in Trp53, Rb1, Pik3ca, Fat3, Kdm6a and Nf1.

In summary, our results show that the disturbed genetic landscape of the 5T models shows partial overlap with multiple myeloma patients and affects pathways known to be involved in multiple myeloma cell survival. The 5T models thus represent reliable models to study the characterized genetic defects.