Latent factor analysis to discover pathway-associated putative segmental aneuploidies in human cancers.
Tumor microenvironmental stresses, such as hypoxia and lactic acidosis, play important roles in tumor progression. Although gene signatures reflecting the influence of these stresses are powerful approaches to link expression with phenotypes, they do not fully reflect the complexity of human cancers. Here, we describe the use of latent factor models to further dissect the stress gene signatures in a breast cancer expression dataset. The genes in these latent factors are coordinately expressed in tumors and depict distinct, interacting components of the biological processes. The genes in several latent factors are highly enriched in chromosomal locations. When these factors are analyzed in independent datasets with gene expression and array CGH data, the expression values of these factors are highly correlated with copy number alterations (CNAs) of the corresponding BAC clones in both the cell lines and tumors. Therefore, variation in the expression of these pathway-associated factors is at least partially caused by variation in gene dosage and CNAs among breast cancers. We have also found the expression of two latent factors without any chromosomal enrichment is highly associated with 12q CNA, likely an instance of "trans"-variations in which CNA leads to the variations in gene expression outside of the CNA region. In addition, we have found that factor 26 (1q CNA) is negatively correlated with HIF-1alpha protein and hypoxia pathways in breast tumors and cell lines. This agrees with, and for the first time links, known good prognosis associated with both a low hypoxia signature and the presence of CNA in this region. Taken together, these results suggest the possibility that tumor segmental aneuploidy makes significant contributions to variation in the lactic acidosis/hypoxia gene signatures in human cancers and demonstrate that latent factor analysis is a powerful means to uncover such a linkage.
Lucas
Joseph E
JE
Institute for Genome Sciences and Policy, Duke University, Durham, North Carolina, USA. joe@stat.duke.edu
Kung
Hsiu-Ni
HN
Chi
Jen-Tsan A
JT
D000140:Acidosis, Lactic; D000782:Aneuploidy; D001943:Breast Neoplasms / Q000235:genetics* / Q000378:metabolism; D015687:Cell Hypoxia; D045744:Cell Line, Tumor; D016000:Cluster Analysis; D055028:Comparative Genomic Hybridization; D019295:Computational Biology / Q000379:methods; D056915:DNA Copy Number Variations; D030541:Databases, Genetic; D005260:Female; D018628:Gene Dosage; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D015233:Models, Statistical; D009928:Organ Specificity; D012333:RNA, Messenger; D015398:Signal Transduction
D012333:RNA, Messenger
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