Identification of candidate tumour suppressor gene loci for Hodgkin and Reed-Sternberg cells by characterisation of homozygous deletions in classical Hodgkin lymphoma cell lines.

Several tumour suppressor genes (TSG) have been identified as a result of mapping homozygous deletions in cancer cells. To identify putative TSG involved in the pathogenesis of classical Hodgkin lymphoma (cHL), we investigated four cHL cell lines (L428, HDLM2, KMH2, L1236) using four different array-Comparative Genomic Hybridisation (array-CGH) platforms and focused on high resolution identification of homozygous deletions. Out of 79 candidate regions of bi-allelic loss identified by array-CGH, besides previously described regions, 28 novel regions of homozygous deletions could be verified by polymerase chain reaction. These regions ranged from 13 kb to 619 kb in size. Eleven of the 28 novel bi-allelic losses were putative copy number polymorphisms. This left 17 regions that might harbour novel tumour suppressors involved in Hodgkin lymphoma. Expression profiling with two different platforms confirmed lack of expression of the majority of the genes located in the homozygous deletions. Furthermore, analysis of ontology annotations of genes located in the homozygously deleted regions indicated an enrichment of genes involved in apoptosis and cell death. In summary, through the mapping of homozygous deletions in cell lines this study identified a series of genes, such as SEPT9, GNG7 and CYBB, which might encode candidate tumour suppressors involved in the pathogenesis of cHL.

Combining array-based approaches for the identification of candidate tumor suppressor loci in mature lymphoid neoplasms.

Tumor suppressor gene (TSG) inactivation by chromosomal deletions and/or mutations is a well-characterized genetic alteration in lymphomas. Array-based technologies have greatly increased the detection and characterization of chromosomal imbalances and regions with loss of heterozygosity (LOH), leading to the identification of a number of novel candidate TSG loci. In addition, microarray platforms for studying DNA methylation and histone modifications enable identifying epigenetic changes affecting gene expression of TSG. Combining these microarray technologies with gene expression profiling is a promising strategy to discover novel TSG in regions targeted by genetic or epigenetic alterations. In this review we present an outline of methodological aspects of the various microarray technologies, and discuss their potentials and restrictions. Furthermore, we survey research findings derived from these high-throughput techniques, which are allowing a deeper insight into the mechanisms of lymphomagenesis.

Mantle-cell lymphoma genotypes identified with CGH to BAC microarrays define a leukemic subgroup of disease and predict patient outcome.

To identify recurrent genomic changes in mantle cell lymphoma (MCL), we used high-resolution comparative genomic hybridization (CGH) to bacterial artificial chromosome (BAC) microarrays in 68 patients and 9 MCL-derived cell lines. Array CGH defined an MCL genomic signature distinct from other B-cell lymphomas, including deletions of 1p21 and 11q22.3-ATM gene with coincident 10p12-BMI1 gene amplification and 10p14 deletion, along with a previously unidentified loss within 9q21-q22. Specific genomic alterations were associated with different subgroups of disease. Notably, 11 patients with leukemic MCL showed a different genomic profile than nodal cases, including 8p21.3 deletion at tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) receptor gene cluster (55% versus 19%; P = .01) and gain of 8q24.1 at MYC locus (46% versus 14%; P = .015). Additionally, leukemic MCL exhibited frequent IGVH mutation (64% versus 21%; P = .009) with preferential VH4-39 use (36% versus 4%; P = .005) and followed a more indolent clinical course. Blastoid variants, increased number of genomic gains, and deletions of P16/INK4a and TP53 genes correlated with poorer outcomes, while 1p21 loss was associated with prolonged survival (P = .02). In multivariate analysis, deletion of 9q21-q22 was the strongest predictor for inferior survival (hazard ratio [HR], 6; confidence interval [CI], 2.3 to 15.7). Our study highlights the genomic profile as a predictor for clinical outcome and suggests that "genome scanning" of chromosomes 1p21, 9q21-q22, 9p21.3-P16/INK4a, and 17p13.1-TP53 may be clinically useful in MCL.