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SIRAC: Supervised Identification of Regions of Aberration in aCGH datasets.

Lai C, Horlings HM, van de Vijver MJ, van Beers EH, Nederlof PM, Wessels LF, Reinders MJ - BMC Bioinformatics (2007)

Bottom Line: We first determine the DNA-probes that are important to distinguish the classes of interest, and then evaluate in a systematic and robust scheme if these relevant DNA-probes are closely located, i.e. form a region of amplification/deletion.SIRAC does not need any preprocessing of the aCGH datasets, and requires only few, intuitive parameters.The results on two breast cancer datasets show promising outcomes that are in agreement with previous findings, but SIRAC better pinpoints the dissimilarities between the classes of interest.

View Article: PubMed Central - HTML - PubMed

Affiliation: Bioinformatics group, Delft University, Delft, The Netherlands. c.lai@tudelft.nl

ABSTRACT

Background: Array comparative genome hybridization (aCGH) provides information about genomic aberrations. Alterations in the DNA copy number may cause the cell to malfunction, leading to cancer. Therefore, the identification of DNA amplifications or deletions across tumors may reveal key genes involved in cancer and improve our understanding of the underlying biological processes associated with the disease.

Results: We propose a supervised algorithm for the analysis of aCGH data and the identification of regions of chromosomal alteration (SIRAC). We first determine the DNA-probes that are important to distinguish the classes of interest, and then evaluate in a systematic and robust scheme if these relevant DNA-probes are closely located, i.e. form a region of amplification/deletion. SIRAC does not need any preprocessing of the aCGH datasets, and requires only few, intuitive parameters.

Conclusion: We illustrate the features of the algorithm with the use of a simple artificial dataset. The results on two breast cancer datasets show promising outcomes that are in agreement with previous findings, but SIRAC better pinpoints the dissimilarities between the classes of interest.

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Related in: MedlinePlus

Fridlyand dataset, summary tables results. Summary of the aberrations per chromosome arms for the Fridlyand dataset [15]. The deletions are depicted in red, and the amplification in green, the gray boxes indicates that the aberration was significant not in the class of interest but in the rest of the samples.
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Figure 6: Fridlyand dataset, summary tables results. Summary of the aberrations per chromosome arms for the Fridlyand dataset [15]. The deletions are depicted in red, and the amplification in green, the gray boxes indicates that the aberration was significant not in the class of interest but in the rest of the samples.

Mentions: We applied our algorithm to their data, analyzing each subtype against the remaining samples. Figure 6 summarizes our findings. We identified a loss on the q arms of Chromosomes 16 and 4 for the 1q16q and the Complex subtypes respectively, and the amplifications on Chromosomes 8, 16 and 20 for the Mixed amplifier subtype. The comparison with the conclusions of Fridlyand et al. [15] is not straightforward, since their goal was not to identify aberrations specific for one class. Their results consist in a frequency plot for each subtype of the copy number changes more frequently associated with it. More specifically, they show the frequency of the clone aberrations present in more than 50% of the samples of one subtype and in less than 30% of the samples in the other subtypes. This illustration is not clearly pointing out the differences between subtypes, since often a percentage of the same aberration is present in two or more subtypes. However, our findings show correspondences with the results of Fridlyand et al. [15]. They define the class 1q16q as exhibiting an amplification on Chromosome 1 and a deletion on Chromosome 16. We only detect the deletion on Chromosome 16. We think that the aberration on Chromosome 1, which is not detected by our algorithm, may be not specific for this class. From the data it is apparent that this amplification is present in all samples, i.e. not specific for the 1q16q subtype. Other aberrations detected by our algorithm reflect a pattern in the frequency plot of Fridlyand et al. [15], such as for the deletion in 4q of the Complex subtype and the amplification in 8q of the Mixed amplifier subtype. In other cases, such as the amplifications on Chromosomes 16 and 20 in the Mixed amplifier class, our findings are not reflected in the frequency plot of Fridlyand et al. [15]. In conclusion, the results of SIRAC and Fridlyand et al. [15] exhibit partial overlap. The advantage of our algorithm is that it better highlights the differences between subtypes and clearly points out the specific chromosomal aberrations.


SIRAC: Supervised Identification of Regions of Aberration in aCGH datasets.

Lai C, Horlings HM, van de Vijver MJ, van Beers EH, Nederlof PM, Wessels LF, Reinders MJ - BMC Bioinformatics (2007)

Fridlyand dataset, summary tables results. Summary of the aberrations per chromosome arms for the Fridlyand dataset [15]. The deletions are depicted in red, and the amplification in green, the gray boxes indicates that the aberration was significant not in the class of interest but in the rest of the samples.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2198922&req=5

Figure 6: Fridlyand dataset, summary tables results. Summary of the aberrations per chromosome arms for the Fridlyand dataset [15]. The deletions are depicted in red, and the amplification in green, the gray boxes indicates that the aberration was significant not in the class of interest but in the rest of the samples.
Mentions: We applied our algorithm to their data, analyzing each subtype against the remaining samples. Figure 6 summarizes our findings. We identified a loss on the q arms of Chromosomes 16 and 4 for the 1q16q and the Complex subtypes respectively, and the amplifications on Chromosomes 8, 16 and 20 for the Mixed amplifier subtype. The comparison with the conclusions of Fridlyand et al. [15] is not straightforward, since their goal was not to identify aberrations specific for one class. Their results consist in a frequency plot for each subtype of the copy number changes more frequently associated with it. More specifically, they show the frequency of the clone aberrations present in more than 50% of the samples of one subtype and in less than 30% of the samples in the other subtypes. This illustration is not clearly pointing out the differences between subtypes, since often a percentage of the same aberration is present in two or more subtypes. However, our findings show correspondences with the results of Fridlyand et al. [15]. They define the class 1q16q as exhibiting an amplification on Chromosome 1 and a deletion on Chromosome 16. We only detect the deletion on Chromosome 16. We think that the aberration on Chromosome 1, which is not detected by our algorithm, may be not specific for this class. From the data it is apparent that this amplification is present in all samples, i.e. not specific for the 1q16q subtype. Other aberrations detected by our algorithm reflect a pattern in the frequency plot of Fridlyand et al. [15], such as for the deletion in 4q of the Complex subtype and the amplification in 8q of the Mixed amplifier subtype. In other cases, such as the amplifications on Chromosomes 16 and 20 in the Mixed amplifier class, our findings are not reflected in the frequency plot of Fridlyand et al. [15]. In conclusion, the results of SIRAC and Fridlyand et al. [15] exhibit partial overlap. The advantage of our algorithm is that it better highlights the differences between subtypes and clearly points out the specific chromosomal aberrations.

Bottom Line: We first determine the DNA-probes that are important to distinguish the classes of interest, and then evaluate in a systematic and robust scheme if these relevant DNA-probes are closely located, i.e. form a region of amplification/deletion.SIRAC does not need any preprocessing of the aCGH datasets, and requires only few, intuitive parameters.The results on two breast cancer datasets show promising outcomes that are in agreement with previous findings, but SIRAC better pinpoints the dissimilarities between the classes of interest.

View Article: PubMed Central - HTML - PubMed

Affiliation: Bioinformatics group, Delft University, Delft, The Netherlands. c.lai@tudelft.nl

ABSTRACT

Background: Array comparative genome hybridization (aCGH) provides information about genomic aberrations. Alterations in the DNA copy number may cause the cell to malfunction, leading to cancer. Therefore, the identification of DNA amplifications or deletions across tumors may reveal key genes involved in cancer and improve our understanding of the underlying biological processes associated with the disease.

Results: We propose a supervised algorithm for the analysis of aCGH data and the identification of regions of chromosomal alteration (SIRAC). We first determine the DNA-probes that are important to distinguish the classes of interest, and then evaluate in a systematic and robust scheme if these relevant DNA-probes are closely located, i.e. form a region of amplification/deletion. SIRAC does not need any preprocessing of the aCGH datasets, and requires only few, intuitive parameters.

Conclusion: We illustrate the features of the algorithm with the use of a simple artificial dataset. The results on two breast cancer datasets show promising outcomes that are in agreement with previous findings, but SIRAC better pinpoints the dissimilarities between the classes of interest.

Show MeSH
Related in: MedlinePlus