Differential and Limited Expression of Mutant Alleles in Multiple Myeloma

Multiple Myeloma (MM) is a heterogeneous disease but the hallmark genetic changes involve large numbers of genomic rearrangements. Recent studies have focused on attempts to identify individual driver mutations that might provide both prognostic information and unique therapeutic targets. Whole genome and exome sequencing of increasingly large numbers of patient samples have identified a number of commonly mutated genes in MM patients. However, none of these mutations are found in more than one quarter of patients and most are found in less than 10% of samples sequenced. We recently reported a large cohort of MM exome sequences involving 84 samples from 67 patients (Nat Commun. 2014;5:2997). We defined a diverse set of gene mutations with significant heterogeneity across our cohort with a median of 52 (range 21-488) mutations identified per sample. Although computational approaches can be used to prioritize mutations that are expected to alter protein structure and function, it is more challenging to determine which mutations are likely to be clinically meaningful. As a first step towards that understanding, here we report the frequency of expression of mutant alleles in Multiple Myeloma.

In this study we report RNA-seq (100 million paired end reads on Illumina HiSeq) data on 14 samples from 10 MM patients for which we have previously performed exome sequencing and correlate allele-specific expression to the DNA mutant allele frequency. We find that a minority, average 27% (range 11-48%), of previously identified DNA mutations are expressed at detectable levels in MM patients. We also compared the allele frequency found in the RNA-seq to that from our exome sequencing to identify genes that demonstrate differential allelic expression and show that this is a common phenomenon in MM patients. We identified 42 such mutations in our analysis supported by at least 10 RNA-seq reads that showed a significant difference as determined by Bayesian hypothesis testing. For instance, the CCND1 mutant allele is expressed at a higher level than would be predicted based on exome-seq frequencies. Another gene showing a similar pattern of increased expression of the mutant allele in one patient was PARP4 (87% in RNA-seq vs 49% in exome-seq). Conversely, the mutant allele frequency of EIF1AX was lower than would be expected suggesting that the mutant allele may be suppressed in our patient (15% in RNA-seq vs 67% in exome-seq). Moreover, among a subset of genes previously identified as recurrently mutated within our patient samples we see that 8/11 (73%) express the mutant allele, providing further evidence that these genes may in fact be important in disease pathogenesis. Therefore, while a large number of mutations have been described in MM, only a small fraction of the mutant alleles have detectable expression and are likely to be biologically relevant. Unbalanced allelic expression of mutant alleles appears to be a relatively common occurrence in MM patients and may help explain why patients with the same identified mutation do not always behave in a similar fashion. This analysis for the first time highlights the important issue that DNA-based reporting of mutations may have significant limitations. It will be important in the future to study expression of mutant alleles in order to understand the biology, generate prognostic models and develop targeted therapies.