Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, MA 02215, USA.
BMC Genomics. 2013 Oct 2;14:672. doi: 10.1186/1471-2164-14-672.
Multiple myeloma (MM) is a malignant proliferation of plasma B cells. Based on recurrent aneuploidy such as copy number alterations (CNAs), myeloma is divided into two subtypes with different CNA patterns and patient survival outcomes. How aneuploidy events arise, and whether they contribute to cancer cell evolution are actively studied. The large amount of transcriptomic changes resultant of CNAs (dosage effect) pose big challenges for identifying functional consequences of CNAs in myeloma in terms of specific driver genes and pathways. In this study, we hypothesize that gene-wise dosage effect varies as a result from complex regulatory networks that translate the impact of CNAs to gene expression, and studying this variation can provide insights into functional effects of CNAs.
We propose gene-wise dosage effect score and genome-wide karyotype plot as tools to measure and visualize concordant copy number and expression changes across cancer samples. We find that dosage effect in myeloma is widespread yet variable, and it is correlated with gene expression level and CNA frequencies in different chromosomes. Our analysis suggests that despite the enrichment of differentially expressed genes between hyperdiploid MM and non-hyperdiploid MM in the trisomy chromosomes, the chromosomal proportion of dosage sensitive genes is higher in the non-trisomy chromosomes. Dosage-sensitive genes are enriched by genes with protein translation and localization functions, and dosage resistant genes are enriched by apoptosis genes. These results point to future studies on differential dosage sensitivity and resistance of pro- and anti-proliferation pathways and their variation across patients as therapeutic targets and prognosis markers.
Our findings support the hypothesis that recurrent CNAs in myeloma are selected by their functional consequences. The novel dosage effect score defined in this work will facilitate integration of copy number and expression data for identifying driver genes in cancer genomics studies. The accompanying R code is available at http://www.canevolve.org/dosageEffect/.
多发性骨髓瘤（MM）是浆细胞的恶性增殖。基于反复出现的非整倍体，如拷贝数改变（CNAs），骨髓瘤分为两种亚型，具有不同的 CNA 模式和患者生存结局。非整倍体事件的发生方式，以及它们是否有助于癌细胞的进化，正在被积极研究。大量由于 CNA 产生的转录组变化（剂量效应）给确定 CNA 在骨髓瘤中的功能后果（特定驱动基因和途径）带来了巨大挑战。在这项研究中，我们假设基因剂量效应的变化是由于复杂的调控网络引起的，这些网络将 CNA 的影响转化为基因表达，研究这种变化可以深入了解 CNA 的功能效应。
我们提出了基因剂量效应评分和全基因组核型图作为工具，用于测量和可视化癌症样本中一致的拷贝数和表达变化。我们发现骨髓瘤中的剂量效应是广泛存在但可变的，并且与不同染色体上的基因表达水平和 CNA 频率相关。我们的分析表明，尽管在三倍体染色体中，高倍体 MM 和非高倍体 MM 之间的差异表达基因富集，但在非三倍体染色体中，剂量敏感基因的染色体比例更高。剂量敏感基因富集了具有蛋白质翻译和定位功能的基因，而剂量抗性基因富集了凋亡基因。这些结果表明，未来需要研究增殖和抗增殖途径的差异剂量敏感性和抗性及其在患者中的变异性，作为治疗靶点和预后标志物。
我们的发现支持这样一种假设，即骨髓瘤中的反复 CNA 是由其功能后果选择的。本工作中定义的新剂量效应评分将有助于整合拷贝数和表达数据，以确定癌症基因组学研究中的驱动基因。配套的 R 代码可在 http://www.canevolve.org/dosageEffect/ 上获得。
