A Next Generation Sequencing-Based Approach to Detect Gene Mutations, Copy Number Changes and IGH Translocations in Multiple Myeloma

The standard approach to multiple myeloma relies on marrow morphology, serum and urine biochemistry and skeletal imaging for diagnosis. Conventional karyotyping and FISH are performed to assess prognosis but with limited impact on therapeutic decisions. Recently, several recurrent mutated genes have been described, some of which are clinically actionable, but their prognostic value remains to be prospectively investigated. Therefore, strategies to investigate the landscape of chromosomal and gene lesions of a large number of myeloma samples in a robust fashion will soon be needed. In this study, we developed a target-enrichment strategy to streamline simultaneous analysis of gene mutations, copy number changes and IGH translocations in multiple myeloma in a high-throughput fashion.

We designed Agilent SureSelect cRNA pull down baits to target 246 genes implicated in myeloma and/or cancer, 2538 single nucleotide polymorphisms to detect copy number and allelic ratio at the single-gene and whole-genome level, and we tiled the whole IGH locus to detect IGH translocations and V(D)J rearrangements. As a pilot, we sequenced 13 myeloma cell lines and 10 control haematopoietic cells lines in HiSeq2000 with 75-bp paired end protocol and used standard algorithms developed at the Sanger Institute for analysis.

With a mean coverage of the target region of 130x we identified 530 likely oncogenic substitutions and indels, 98.7% of which were validated as real by independent whole exome sequencing. Mutation spectrum of myeloma cell lines was different from patient samples, with mutations in PCLO being the most frequent (8/13) followed by TP53 (7/13), NEB and LTB (6/13). Other genes canonically mutated in myeloma (KRAS, NRAS, BRAF, DIS3, FAM46C) were found at lower frequencies. As expected in a cell line study, most mutations were fully clonal. Using normalized coverage data we were able to study genes and regions showing recurrent copy number changes. With this approach we identified losses in CDKN2C, FAM46C, BIRC2, BIRC3, RB1, DIS3, TRAF3, CYLD and TP53. Furthermore, we could detect deletions in 1p32.3, 1p12, 12p13.31 16q12 and 17p13, as well as amplifications in 1q21.1 and 1q23.3. Validation by SNP-array confirmed an overall positive predictive value of 98% for sequencing data with regards to gain and losses of genes and small chromosomal regions. Analysis of read pairs where mates are aligned to different chromosomes allowed identification and near-bp mapping of the t(11;14) and t(8;14) translocations in 3 cases each, and t(4;14) in two cases, always consistent with the published karyotype for the cell line. No events were found in control cell lines, but our algorithm missed a t(4;14) and a cryptic t(11;14) in two cell lines. Last, we identified reads spanning the V(D)J rearrangement site in most myeloma cell lines, and we are developing tools to reconstruct the tumor-specific rearrangement sequence that could be employed in minimal residual disease monitoring in the future. This panel is now being applied to a cohort of 500 myeloma patient samples with clinical annotation for prognostication.

In conclusion, we describe sequencing techniques and analysis tools that can be easily implemented in diagnostic and research laboratories and can be deployed in the study of myeloma pathogenesis, diagnosis, prognosis and as minimal residual disease detection.