S119 the Role of Recurrent Somatic Alterations in the Non-coding Genome With Functional Implications in Mm
Year: 2019
Type: conference-abstract
Abstract: Background: Multiple Myeloma (MM) is a heterogenous disease with distinct molecular and clinical characteristics. Recent large collaborative efforts have identified number of driver genes. However over 96% of all somatic alterations occur in non-coding regions and very little is known about how they affect the disease. Aims: Using our large dataset from uniformly treated newly diagnosed patinets as well as relpased patient samples, we investigated the somatic alterations in the non coding genome to identify critical hotspots. Methods: We performed a deep (average coverage > 80X) whole genome sequencing (WGS) on 376 MM samples (240 newly diagnosed, 52 first relapse and 84 rrMM) as well as RNAseq to comprehensively analyze recurrent somatic alterations in non-coding regions. Results: We detected median 9,649 (Range 3,194–126,935) mutations and indels per sample with overall more than 4 M total somatic mutations. Introns (> 2.5 mutations/per Mb) and intergenic regions (> 3 mutations/per Mb) had significantly higher number of mutations per megabase compared to Exons (∼2 mutations/per Mb) (p < 1e-5). Mutations in coding regions in our data was similar to published whole exome sequencing studies with increase in several driver mutations at relapse. We observed 57 [range 7 - 376] structural variants (SVs) per sample with > 98% involving non-coding regions. We identified 48 SV hotspots that are targeting tumor suppressor genes or key regulatory elements such as TCF3 and TNFSF10 and these SVs were also associated with significantly altered expression of the target genes. We have detected two less defined translocations targeting MAP3K14 and NFKB1 and driving the expressions of these genes. Our data showed that ∼80% of translocation breakpoints are in the close proximity of super enhancers and therefore Ig is not the only mechanism over expresses the target genes. We detected >500 single nucleotide hotspots in the con-coding genome some of which are targeting 5’UTR and introns of BCL7A, BCL6, CD93, CCND1, PAX5. Some of these hotspots were correlated with disease outcome. We also observed that overall mutational load is also associated with disease outcome. We detected chromothripsis in ∼25% of newly diagnosed samples; and kataegis hotspots on chromosome 3q27-3q28 (24%), 11q13 (5.8%) and 12q24 (5.3%). Summary/Conclusion: In conclusion, this large deep whole genome sequencing data from newly-diagnosed MM patients identifies a vast majority of non-coding mutations with potentially significant functional and biological role in MM. Our integrative approach using both WGS and RNA-seq data from the patients now provides us important tools to further characterize the impact of these mutations and develop opportunities for targeted therapeutics.
Source: HemaSphere
Language: English
Subfield: Hematology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,480
OpenAlex ID: W2950375640