- Research article
- Open Access
- Open Peer Review
CDCOCA: A statistical method to define complexity dependence of co-occuring chromosomal aberrations
© Kumar et al; licensee BioMed Central Ltd. 2011
- Received: 26 July 2010
- Accepted: 3 March 2011
- Published: 3 March 2011
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.
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).
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.
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.
- Comparative Genomic Hybridization
- Mantle Cell Lymphoma
- Copy Number Alteration
- Genomic Interval
- Genomic Imbalance
Genetic alterations are an absolute requirement for malignant neoplasias in humans [1, 2]. Both kind of genetic alterations and order of occurrence are important for cancer development and progression . Additionally to sequential event models, large scale analysis of genomes from patient's tumors have shown that multiple genetic abnormalities can promote the development of one cancer entity . Alterations in cancer genome can range from subtle sequence changes (e.g. point mutations) over structural alterations with functional impact on the coding sequence (e.g. generation of fusion genes by chromosomal translocations) to regional or whole-chromosome copy number abnormalities (see e.g. [5–7]).
Through a gene dosage effect, genomic copy number alterations (CNA) may lead to insufficient expression of tumor suppressors or overexpression of proto-oncogenes, respectively. Recurrent CNA have been identified in nearly all cancer entities [8–10]). Comparative Genomic Hybridization (CGH) [11, 12] is a genome wide CNA screening technology which has been widely applied throughout the last two decades. Building on the reverse in situ hybridization principle developed for chromosomal CGH , genomic microarray technology (aCGH; [14, 15]) now utilizes intensity values from up to millions of short DNA sequences to derive regional copy number estimates.
Large data sets from copy number screening experiments should provide a powerful resource for oncogenomic data mining studies. In contrast to expression data, copy number data arises from the projection of discrete values into the experimental space. As such, a reduction of the (a)CGH data can result in the minimal information of segmental status (gain/loss/normal) and genomic position. This facilitates efforts to integrate data across large numbers of experimental series and derived from diverse tumor entities. So far, most of these efforts have been of descriptive nature [10, 16] or have been aimed at the definition of disease-specific genomic patterns and useful pattern descriptors ("markers", e.g. ). Other publications have attempted the reconstruction of relation and temporal order of oncogenetic events [18–20].
For some cancers types such as subsets of colorectal adenocarcinoma, presence of a limited number of genetic events including several CNA is critical for cancer development . Other neoplasias such as chronic lymphocytic leukemia (CLL) display a paucity of CNA, which however may be correlated to patient survival . These examples illustrate that the presence of certain CNA is not a chance phenomenon, but may either be necessary for cancer development or give a selective edge to affected clones. Previous publications have tried to address the cooperative nature of co-occurring CNA [23, 24]. So far, these approaches have not considered the high variability in the complexity of CNAs among individual malignant tumors. Here, we develop an algorithm CDCOCA for analysis of co-occurring oncogenomic CNA events which considers the genomic complexity of the individual samples. We use our approach for detection of CNA events in real-world example data sets. Furthermore, we compare the results from CDCOCA to a previously published method  (which we call "analysis 3" in this paper) and also to a modified version of CDCOCA which does not include the adjustment for genomic complexity.
Annotated copy number and associated data was selected from our Progenetix (a)CGH database (: http://www.progenetix.net; status as of 2010-03-01). For model development and testing, we choose one hematopoietic (MCL) and one solid tumor entity (BLCA) due to their overall intermediate genomic complexity, without consideration of their previously established genomic imbalance profiles or CNA subset analysis.
For analysis, copy number status data was determined for 320 genomic intervals based on corresponding cytogenetic bands. Sex chromosomes were removed due to possible bias in some of the published series (e.g. use as normalization control in (a)CGH experiments), resulting in 303 genomic intervals. For analysis by CDCOCA/CICOCA, gain and loss status of all genomic intervals were considered separately, leading to a data matrix with 606 categories. Only genomic intervals showing change in at least one sample were considered for analysis resulting in a CDCOCA/CICOCA input matrix with 593 categories for BLCA and 571 for MCL. For analysis 3, the original data matrix containing 303 genomic intervals was used. As a surrogate score for genomic complexity, a case specific score was calculated by adding each type of genomic imbalances (gain and/or loss) occurring on a chromosomal arm .
Binary matrix derived from CGH data.
Simulation of any genomic interval j is achieved by redistribution of the CNA status over all samples. For genomic interval j, we define as the corresponding vector representing the CNA status of simulated data. is obtained in a way so that .
number of samples with CNA in genomic intervals status, j and k.
number of samples with CNA in genomic interval status j but not k.
number of samples with CNA in genomic interval status k but not j.
F jk frequency of an overlap between genomic intervals status i and j.
number of samples having change in both genomic interval status i and j. n total number of samples in the data.
Initialize C = 0.
Calculate Jaccard's overlap J jk between genomic interval j and k.
- 3.For genomic interval j simulate the data to obtain as
Obtain a sample index r of size 1, from N = (1,....,n) using such that sample with maximum weight given has a higher probability of getting a change on permutation, update = 1.
Update N = N[-r].
Repeat step 3a and 3b F j times to obtain simulated vector .
For genomic interval k simulate the data using step 3 to obtain .
Recompute Jaccard's overlap increase C = C + 1.
Repeat step 3, 4 and 5 for S times.
At the end of S (5000 in our case) permutations calculate p value as, .
The p-value obtained after step 7 represent the probability of co-occurrence of two CNAs in absence of any other CNA in sample. A low p-value cut off will help in enriching for CNAs which occur together even in less heterogenous samples.
We here propose a methodology named CDCOCA (Complexity dependence of co-occurring chromosomal aberrations) that defines highly correlated pairs of CNA in cancer samples while correcting for the overall degree of genomic instability.
The performance of CDCOCA depends on the number of tumor samples, number of genomic intervals and number of iterations. CDCOCA produces a matrix of p values for all possible associations in the data matrix which are then used to enrich for associations dependent on sample complexity. The algorithm is implemented in the R statistical framework and is available through R package "CDCOCA" provided on the Progenetix website .
Our results show that most of the CNA data for both cancers are derived on a background of multiple and extended CNA. The total number of genetic associations in both cancer types remains beyond scope of the current analysis. However, with CDCOCA we are able to focus on a defined set of statistically relevant, specific changes.
For estimating the performance of our methodology in relation to otherwise discussed models we compared CDCOCA to a modified version "CICOCA" (see supplement) and a previously published method . Both the later algorithms do not include an estimate of sample complexity and primarily identify associations with a high frequency. CICOCA and analysis 3 use different methods to compute overlap resulting in slightly different but overall concordant results.
Statistic of associations in BLCA
The abundance of 8p losses, 8q gains, 5q losses, 5p gains, 3q gains, 4q losses points towards the importance of these CNA in tumors carrying them. Genes from TGF-beta receptor signaling (blue triangles) and cellular apoptotic pathways (red triangles) located to the co-occurring changes are shown in Figure 7. The presence of genes from the same pathways on co-occurring CNA point towards a possible cooperative action of these genes. CDC23 (5q31), CASP6 (4q25) and PMAIP1 (18q21) are among TGF-receptor cascade genes with well established role in cancer [29, 30] Other possible targets for genetic cooperation include PMSD2, PAK2, BCL2L1 and FNTA. Genes from apoptotic signaling pathways mapped to these regions include CDC23 (5q31), SMAD2 (18q21), SMAD4 (18q21) and SMAD7 (18q21) which have been shown defective in several cancer entities . As possible target on 5p, loss of SKP2 had been shown to cause cell senescence . On 5q, loss of function mutations including copy number losses of both APC and MCC have been associated with a variety of epithelial neoplasias [33–36].
Mantle cell lymphoma
Statistic of associations in MCL
As candidate targets, TNF-signaling genes (red triangles) and T-cell receptor signaling genes (blue triangles) are marked on their corresponding band locations in Figure 9. The role of genes such as MDM2 (12p15), TNFRSF1A (12p13), MALT1 (18q21) for neoplastic transformation and/or progression has already been well established [37–39]. Other examples for cancer relevant genes mapping to those regions are STAT2 (12q13), and STAT3 (17q) [40, 41].
We have developed a method CDCOCA to define complexity dependence of co-occurring CNA in cancer samples. In contrast to methods published previously  and a modified algorithm which does not include the complexity adjustment step, CDCOCA does not simply focus on the most frequent co-occurrences of regional genomic copy number changes in cancer entities. Here, we determine statistically relevant co-occurring CNA through accounting for the CNA "background noise", introduced e.g. through chromosome scale imbalances (e.g. isochromosomes, chromosomal aneuploidy). In theory, this procedure should highlight specific but comparatively rare CNA events.
As indicated by our analysis of BLCA and MCL, two unrelated cancer entities with overall intermediate copy number complexity, the relevant CNA associations in many specimen are obscured due to the large number and/or extension of regional CNA. When correcting for genomic background heterogeneity most of the associations involving highly recurring CNA were removed. This indicates that many high frequency changes may be related to the overall genomic instability and therefore cannot unanimously be assigned a causative role in oncogenesis. Especially regarding the large number of genes affected by complex genomic imbalances, some of the cancer type specific CNA patterns may represent an epiphenomenon of disturbed genomic maintenance processes rather than the expression of copy number dependent target gene modifications.
However, when accounting for the overall complexity, CNA associations may point towards connected events and/or preferred pathways activated during carcinogenesis. Based on our CNA associations, we found multiple genes from single well defined cancer pathways to be a effected in sample subsets. Alteration of more than one gene in a pathway may potentiate the effect on pathway function and be responsible for a specific clonal phenotype.
CDCOCA should prove to be a powerful tool for defining mutual associations at gene level and to gain insights into cellular mechanisms relevant for oncogenesis. Although we applied our method to CGH data at band resolution, there is no practical obstacle against use with segmented data from high resolution genomic array experiments. In fact, this should facilitate a gene centric analysis and automatic integration with functional data sources.
NK is supported through a grant by the Krebsliga Schweiz (Swiss Cancer League). Haoyang Cai is supported through a grant from the China Scholarship Council.
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