Finding exclusively deleted or amplified genomic areas in lung adenocarcinomas using a novel chromosomal pattern analysis

Abstract

Abstract Background Genomic copy number alteration (CNA) that are recurrent across multiple samples often harbor critical genes that can drive either the initiation or the progression of cancer disease. Up to now, most researchers investigating recurrent CNAs consider separately the marginal frequencies for copy gain or loss and select the areas of interest based on arbitrary cut-off thresholds of these frequencies. In practice, these analyses ignore the interdependencies between the propensity of being deleted or amplified for a clone. In this context, a joint analysis of the copy number changes across tumor samples may bring new insights about patterns of recurrent CNAs. Methods We propose to identify patterns of recurrent CNAs across tumor samples from high-resolution comparative genomic hybridization microarrays. Clustering is achieved by modeling the copy number state (loss, no-change, gain) as a multinomial distribution with probabilities parameterized through a latent class model leading to nine patterns of recurrent CNAs. This model gives us a powerful tool to identify clones with contrasting propensity of being deleted or amplified across tumor samples. We applied this model to a homogeneous series of 65 lung adenocarcinomas. Results Our latent class model analysis identified interesting patterns of chromosomal aberrations. Our results showed that about thirty percent of the genomic clones were classified either as "exclusively" deleted or amplified recurrent CNAs and could be considered as non random chromosomal events. Most of the known oncogenes or tumor suppressor genes associated with lung adenocarcinoma were located within these areas. We also describe genomic areas of potential interest and show that an increase of the frequency of amplification in these particular areas is significantly associated with poorer survival. Conclusion Analyzing jointly deletions and amplifications through our latent class model analysis allows highlighting specific genomic areas with exclusively amplified or deleted recurrent CNAs which are good candidate for harboring oncogenes or tumor suppressor genes.