Automated screening for genomic imbalances using matrix-based comparative genomic hybridization

Genome-wide screening for chromosomal imbalances using comparative genomic hybridization (CGH) revealed a wealth of data on previously unrecognized tumor-specific genomic alterations. CGH to microarrays of DNA, an approach termed matrix-CGH, allows detection of genomic imbalances at a much higher resolution. We show that matrix CGH is also feasible from small tissue samples requiring universal amplification of genomic DNA. Because widespread application of matrix-CGH experiments using large numbers of DNA targets demands a high degree of automation, we have developed a protocol for a fully automated procedure. The use of specialized instrumentation for the generation of DNA chips, their hybridization, scanning, and evaluation required numerous alterations and modifications of the initial protocol. We here present the elaboration and testing of automated matrix-CGH. A chip consisting of 188 different genomic DNA fragments, cloned in bacterial artificial chromosome (BAC) or P1-derived artificial chromosome (PAC) vectors and immobilized in replicas of 10, was used to assess the performance of the automated protocol in determining the gene dosage variations in tumor cell lines COLO320-HSR, HL60, and NGP. Although ratios of matrix-CGH were highly concordant with results of chromosomal CGH (85%), the dynamic range of the matrix-CGH ratios was highly superior. Investigation of the two amplicons on 8q24 in COLO320-HSR and HL60, containing the MYC gene, revealed a homogeneous amplicon in COLO320-HSR but a heterogeneous amplification pattern in HL60 cells. Although control clones for normalization of the signal ratios can be predicted in cases with defined chromosomal aberrations, in primary tumors such data are often not available, requiring alternative normalization algorithms. Testing such algorithms in a primary high-grade B-cell lymphoma, we show the feasibility of this approach. With the matrix-CGH protocol presented here, robust and reliable detection of genomic gains and losses is accomplished in an automated fashion, which provides the basis for widespread application in tumor and clinical genetics.