Abstract
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-1α 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.
Highlights
The promise and challenge of cancer genomics Human cancers are extremely heterogeneous due to multiple mutations in oncogenes and tumor suppressor genes, varying environmental conditions, and a huge range of germline and somatic variations
We demonstrate how a latent factor model can improve the in vivo relevance of these pathway-associated gene signatures by dissecting them into co-regulated transcriptional components which better represent the structure in human cancer
We use this approach to analyze hypoxia and lactic acidosis gene signatures to identify latent factors that represent distinct, interacting components of the various biological processes which are in the initial gene signatures but poorly dissected
Summary
The promise and challenge of cancer genomics Human cancers are extremely heterogeneous due to multiple mutations in oncogenes and tumor suppressor genes, varying environmental conditions, and a huge range of germline and somatic variations. Experiments that have previously been performed one gene at a time can be done on the entire complement of transcribed genes This leads to a tremendous challenge of divining meaning behind the vast amounts of biological data and turning it into hypotheses and new understanding of the biology behind tumor heterogeneity. The expression signatures are portable and can be assayed in varied contexts, and so provide the capacity to link otherwise heterologous systems to provide a mechanism to link the defined biological processes with the complex phenotypes of human tumors These signatures can be used to recognize similar molecular features in human cancer samples in vivo and interrogate the relevance of particular biological processes and perturbations in human cancer and evaluate their relationship with other clinical and molecular features
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