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CDCOCA: a statistical method to define complexity dependent co-occurring chromosomal aberrations

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Background

Copy number alterations (CNA) play a key role in cancer development and progression. In general, more than one CNA can be detected in any given tumor; therefore co-occurring genetic CNA may point to co-operating cancer related genes. Existing methods for co-occurrence evaluation so far have not considered the overall heterogeneity of CNAs per tumor, resulting in a preferential detection of frequent changes with limited specificity for each association owing to the frequently high genetic instability of the samples.

Results

We hypothesize that in cancer some linkage-independent CNAs might display a statistically non-random co-occurrence, and that these CNAs could be of pathogenetic relevance for the respective cancer. We also hypothesize that two CNAs co-occurring in samples with overall few changes (low complexity samples) represent a stronger association then coming from samples with a high number of changes. To verify our hypothesis, we here present a simulation based algorithm CDCOCA (complexity dependent co-occurring chromosomal aberrations). For an initial modeling approach, CNA data for bladder cancer and mantle cell lymphoma at cytogenetic band resolution was obtained from our Progenetix reference database (http://www.progenetix.net) and the CDCOCA was applied to them. A display of ~50 most frequent co-occurrences obtained after p value cut off along with selected cancer associated genes are shown here (Figure 1).

Figure 1
figure1

50 most frequent associations obtained after p value cut off of 0.02. 50 most frequent associations plotted using cytoscape. Green circles represent gains, orange represent losses. Red triangles represent apoptotic signaling genes and blue triangles represent TGF-beta receptor signaling genes located to these associations. Magenta triangles represent overlapping genes between both signaling pathways.

Conclusions

Our CDCOCA algorithm has constitutes a new approach to establish statistically significant co-occurring regional genomic imbalances from for example CGH data sets containing at least hundreds of individual copy number profiles. Along with finding CNAs from low/intermediate complexity samples, our algorithm points towards a generally low statistical specificity for co-occurrence of regional CNAs in a CNA rich samples, with a negative impact on pathway modeling approaches based on genomic copy number screening analyses derived from such data.

References

  1. 1.

    Baudis M, Cleary ML: Progenetix.net: an online repository for molecular cytogenetic aberration data. Bioinformatics. 2001, 17: 1228-1229. 10.1093/bioinformatics/17.12.1228.

  2. 2.

    Bredel M, Scholtens DM, Harsh GR, Bredel C, Chandler JP, Renfrow JJ, Yadav AK, Vogel H, Scheck AC, Tibshirani R, Sikic BI: A network model of a cooperative genetic landscape in brain tumors. JAMA. 2009, 302: 261-275. 10.1001/jama.2009.997.

  3. 3.

    Klijn C, Bot J, Adams DJ, Reinders M, Wessels L, Jonkers J: Identification of networks of co-occurring, tumor-related DNA copy number changes using a genome-wide scoring approach. PLoS Comput Biol. 2010, 6: e1000631-10.1371/journal.pcbi.1000631.

  4. 4.

    Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T: Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Network. Genome. Res. 2003, 13: 2498-2504. 10.1101/gr.1239303.

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Author information

Correspondence to Nitin Kumar.

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Kumar, N., Rehrauer, H., Ca, H. et al. CDCOCA: a statistical method to define complexity dependent co-occurring chromosomal aberrations. Genome Biol 11, P23 (2010) doi:10.1186/gb-2010-11-s1-p23

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Keywords

  • Mantle Cell Lymphoma
  • Copy Number Alteration
  • Cancer Related Gene
  • Genomic Copy Number
  • Genomic Imbalance