P840: ASSESSMENT OF CONVENTIONAL TRANSLOCATIONS AND IG REARRANGEMENTS IN THE DIAGNOSIS OF MULTIPLE MYELOMA PATIENTS USING A TARGETED CAPTURE-HYBRIDIZATION RNA SEQUENCING PANEL.

Topic: 13. Myeloma and other monoclonal gammopathies - Biology & Translational Research Background: Multiple myeloma (MM) is a malignant plasma cell disorder characterized by genomic heterogeneity. Cytogenetics testing by fluorescence in situ hybridization (FISH) yields important prognostic information, nevertheless novel strategies could define better a patient’s risk at the time of diagnosis such as targeted sequencing approaches. Translocations affecting the IGH locus have been proven to predict response to treatment and may be considered as a predictive biomarker involving t(4;14), t(14;16), t(11;14), and t(6;14). These translocations usually drive overexpression of the partner genes, FGFR3, MAF, CCND1, and CCND3, respectively. In this study, we illustrate that targeted capture-hybridization RNA sequencing (tchRNA-Seq) may emerge as a reliable diagnostic tool for a routine diagnostic approach. Aims: Evaluating the value of the tchRNA-Seq in the identification of potential biomarkers in the molecular diagnosis of MM patients. Methods: We focused on patients with newly diagnosed MM who are not candidates for transplantation and who have not received previous treatment. As a preliminary test, cDNA of 16 RNA samples obtained at diagnosis from CD138+ plasma cells (PC) and CD138- as germline control from BM aspirates was used. Our tchRNA panel was aimed to detect most relevant aberrations known in MM, covering the coding regions of over 100 genes involved in MM progression, drug resistance and immunotherapy; the canonical IGH, IGK and IGL loci for IG rearrangements; and regions to cover traditional translocations and gene partners. Capture libraries were generated with SureSelect Reagent kits (Agilent Technologies) applying an input of 60-145 ng and final libraries were run on Ion Torrent platform. A specific bioinformatics pipeline was applied to perform differential expression, mutational screening, and IG rearrangements. Results: Analysis of 16 samples sequenced in duplicate identified at least one rearrangement corresponding to the heavy (IGH) or light (IGL or IGK) chain in all cases. Specifically, in 81% (13/16) of the patients, both a heavy and a light chain were expressed, with IGH/IGL being the predominant combination (9/16), whereas 25% presented an IGH/IGK set (Fig 1A). Regarding differential gene expression assays, the whole transcriptome approach revealed that the weight of the clustering was on the IG genes, as expected due to the biological differences. To eliminate this biological bias, we repeated the analysis selecting the functional genes from our panel and observed three main clusters: samples with t(4;14), CD138- and the last one CD138+ PC. We performed unsupervised clustering using the 50 most differentially expressed genes, observing in 4 out of 5 cases a high concordance between cytogenetics and expression patterns, as overexpression of FGFR3 and CCND1 was clearly associated with t(4;14) and t(11;14) respectively; and we also observed a downregulation of TP53 in two patients with del17p reported by FISH. We detected cases of downregulation of TP53 not related to del17p and are implementing assays to confirm this (Fig 1B). Finally, ongoing mutational screening is performed in genes frequently mutated in MM and we have identified mutations in KRAS, TENT5C or TGFBR2, confirming the expression of these mutations. Summary/Conclusion: In view of the high concordance found between tchRNA-Seq and FISH, our approach could provide additional relevant molecular information and could be useful for the identification of new biomarkers at diagnosis or relapse. The clustering of patients into cytogenetically defined subgroups by studying up- or down-regulated genes could improve the genetic risk stratification. Nevertheless, it is necessary to increase the cohort size to validate these promising results.Keywords: IgH rearrangment, Multiple myeloma, RNA-seq