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Open Access Research article

The shaping and functional consequences of the dosage effect landscape in multiple myeloma

Mehmet K Samur12, Parantu K Shah1, Xujun Wang3, Stéphane Minvielle4, Florence Magrangeas4, Hervé Avet-Loiseau4, Nikhil C Munshi5* and Cheng Li1*

Author Affiliations

1 Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, MA 02215, USA

2 Department of Biostatistics and Medical Informatics, Akdeniz University, Antalya, Turkey

3 Department of Bioinformatics, School of Life Sciences and Technology, Tongji University, Shanghai, China

4 Inserm UMR892, CNRS 6299, Université de Nantes; Centre Hospitalier Universitaire de Nantes, Unité Mixte de Genomique du Cancer, Nantes, France

5 Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, VA Boston Healthcare System, Boston, MA 02215, USA

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BMC Genomics 2013, 14:672  doi:10.1186/1471-2164-14-672

Published: 2 October 2013

Abstract

Background

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.

Results

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.

Conclusions

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/ webcite.

Keywords:
Copy number alteration; Dosage effect; Multiple myeloma; Hyperdiploid; Integrative genomics