Comparative Whole Transcriptome Shotgun Sequencing (WTSS) of Myeloma at Diagnosis and at Drug-Resistant Relapse.

Abstract Abstract 603 Background: Acquired drug resistance often limits treatment efficacy in multiple myeloma, and may in part be caused by selection for cells bearing mutations conferring drug resistance that were either present in an extant subset of myeloma cells prior to treatment, or were acquired during therapy. We are applying whole transcriptome shotgun sequencing (WTSS) to myeloma BM at diagnosis and drug-resistant relapse, in order to identify gene expression and mutational signatures of drug resistance. We have an archive of frozen myeloma total bone marrow mononuclear cell fractions, as well as a smaller archive of CD138+ enriched specimens. The enriched specimens can be expected to contain purer populations of malignant cells, but may under-represent the heterogeneity of the disease. We are therefore interested in a comparison of the results of WTSS on these two different types of specimens. Methods: Bone marrow from a myeloma patient who responded to front line drug therapy and who subsequently experienced drug resistant disease relapse was identified from our biorepository. RNA was isolated from the total bone marrow mononuclear cell population and from a CD138+ enriched population at diagnosis and at relapse, yielding four samples for analysis (pre-treatment unprocessed, pre-treatment enriched, relapse unprocessed, relapse enriched). Poly-adenylated RNA from each of the four samples was converted to cDNA using a random primed approach, and paired-end sequences collected using Illumina instruments. Results: We sequenced the transcriptomes of the four samples by generating a total of 694,076,590 36- and 50-mer sequences (30,681,959,896 base-pairs). Of these, 325,998,000 (14,063,396,516 base-pairs) were mapped to reference genome or transcriptome databases using MAQ. Each base in 3,094 transcripts was represented at a minimum of 6 fold redundant coverage. 68.57 % (223,555,599) of the reads matched perfectly to reference sequences. Reads containing single nucleotide mismatches to the reference sequence were evaluated using a Bayesian mixture model to estimate the presence of single nucleotide variants (SNVs) which yielded 89,944 high confidence predictions divided amongst the four samples. Of these, 74,704 (83.1%) were identical to known SNVs present in dbSNP and other sequence datasets, and 13,073 represented non-coding or synonymous variants. The remaining 2,167 mismatches most likely represent undiscovered variants also present in the patient's normal DNA or somatic mutations. These two possibilities will be distinguished by sequencing the candidate variants using DNA purified from the patient's non-malignant cells. For candidate sequence variant positions well represented at the position and gene level in each of the two sample pairs we were comparing (pre- and post-treatment unprocessed, and pre- and post-treatment enriched) we found 85 unique to the pre-treatment enriched sample, 145 unique to the post-treatment enriched sample, 250 unique to the pre-treatment unprocessed sample and 51 unique tunique o the post-treatment unprocessed sample. Some of the genes affected by these candidate mutations include genes that have previously been observed in cancer, such as PIM2, SHC1 and SCRIB. Gene expression levels were estimated using the previously published RPKM (reads per kilobase of exon model per million mapped reads) metric. For the unprocessed samples, 108 genes were found to be up-regulated pre-treatment and 95 post-treatment. The enriched samples presented 80 genes with higher expression before treatment and 54 after treatment. Because we are using a paired end sequencing approach, the potential exists to identify candidate expressed gene fusions. Initial searches for candidate gene fusion events have resulted in the detection of a fusion involving IgH and MMSET, corresponding to a t(4;14) translocation which had previously been detected with standard methods. Discussion: It is possible to sequence and compare the expressed myeloma genome of patients at diagnosis and at relapse with WTSS, in order to gain insight into the evolution of myeloma under drug selection and its role in the development of treatment resistance. With the study of a larger cohort of patients and subsequent functional characterization of the candidate sequence variants, we hope to identify recurrent mechanisms of myeloma drug resistance which might be amenable to therapeutic intervention. Disclosures: No relevant conflicts of interest to declare.

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