CDCOCA: A statistical method to define complexity dependence of co-occuring chromosomal aberrations
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- 作者：Nitin Kumar (1)
Hubert Rehrauer (2)
Haoyang Cai (1)
Michael Baudis (1) - 刊名：BMC Medical Genomics
- 出版年：2011
- 出版时间：December 2011
- 年：2011
- 卷：4
- 期：1
- 全文大小：7906KB
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43. The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1755-8794/4/21/prepub - 作者单位：Nitin Kumar (1)
Hubert Rehrauer (2)
Haoyang Cai (1)
Michael Baudis (1)
1. Institute of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, Zurich, Switzerland
2. Functional Genomics Center Zurich, University of Zurich, Winterthurerstrasse 190, Zurich, Switzerland - ISSN：1755-8794
文摘
Background Copy number alterations (CNA) play a key role in cancer development and progression. Since more than one CNA can be detected in most tumors, frequently co-occurring genetic CNA may point to cooperating cancer related genes. Existing methods for co-occurrence evaluation so far have not considered the overall heterogeneity of CNA per tumor, resulting in a preferential detection of frequent changes with limited specificity for each association due to the high genetic instability of many samples. Method We hypothesize that in cancer some linkage-independent CNA may display a non-random co-occurrence, and that these CNA could be of pathogenetic relevance for the respective cancer. We also hypothesize that the statistical relevance of co-occurring CNA may depend on the sample specific CNA complexity. We verify our hypotheses with a simulation based algorithm CDCOCA (complexity dependence of co-occurring chromosomal aberrations). Results Application of CDCOCA to example data sets identified co-occurring CNA from low complex background which otherwise went unnoticed. Identification of cancer associated genes in these co-occurring changes can provide insights of cooperative genes involved in oncogenesis. Conclusions We have developed a method to detect associations of regional copy number abnormalities in cancer data. Along with finding statistically relevant CNA co-occurrences, our algorithm points towards a generally low specificity for co-occurrence of regional imbalances in CNA rich samples, which may have negative impact on pathway modeling approaches relying on frequent CNA events.