Deciphering genomic architecture is key to identifying novel disease drivers and understanding the mechanisms underlying myeloma initiation and progression. In this work, using the CoMMpass dataset, we show that structural variants (SV) occur in a nonrandom fashion throughout the genome with an increased frequency in the t(4;14), RB1, or TP53 mutated cases and reduced frequency in t(11;14) cases. By mapping sites of chromosomal rearrangements to topologically associated domains and identifying significantly upregulated genes by RNAseq we identify both predicted and novel putative driver genes. These data highlight the heterogeneity of transcriptional dysregulation occurring as a consequence of both the canonical and novel structural variants. Further, it shows that the complex rearrangements chromoplexy, chromothripsis and templated insertions are common in MM with each variant having its own distinct frequency and impact on clinical outcome. Chromothripsis is associated with a significant independent negative impact on clinical outcome in newly diagnosed cases consistent with its use alongside other clinical and genetic risk factors to identify prognosis.

破译基因组结构是识别新的疾病驱动因素和了解骨髓瘤发生和进展机制的关键。在这项工作中，使用 CoMMpass 数据集，我们表明结构变异 (SV) 以非随机方式在整个基因组中发生，其中 t(4;14)、RB1 或 TP53突变病例中的频率增加，而 t( 11;14) 案例。通过将染色体重排位点映射到拓扑相关域并通过 RNAseq 识别显着上调的基因，我们识别了预测的和新的推定驱动基因。这些数据强调了由于规范和新颖的结构变异而发生的转录失调的异质性。此外，它还表明复杂的染色体重排、染色体碎裂和模板化插入在多发性骨髓瘤中很常见，每种变异都有其独特的频率和对临床结果的影响。染色体碎裂对新诊断病例的临床结果具有显着的独立负面影响，这与其与其他临床和遗传风险因素一起用于确定预后的情况一致。