Application of signal processing techniques for estimating regions of copy number variations in human meningioma DNA.

We applied mode-decomposition and matched-filtering, both signal processing techniques used to increase the signal-to-noise ratio (SNR), to array CGH data of human meningioma DNA, in order to extract genomic regions of copy-number changes potentially associated with tumor progression. DNA segments from different chromosomes were decomposed into a small number of dominant components (modes), and low-amplitude modes were eliminated. The SNR of the entire segment was increased and it was possible to identify local changes in the data spatial structure, previously indistinguishable due to noise. We applied matched-filtering to the mode-reduced signals, using a normal DNA sequences (averaged over 50 healthy donors) as the template. The residual signals from this process were analyzed to identify disease-related copy number changes. We were able to identify distinct local changes at different chromosomes in patients with recurrent versus primary meningiomas.