Analysis of gene expression in a developmental context emphasizes distinct biological leitmotifs in human cancers
1 Children's Hospital Informatics Program, Harvard-MIT Division of Health Sciences and Technology, Longwood Avenue, Boston, MA 02115, USA
2 The Jackson Laboratory, Main Street, Bar Harbor, ME 04609, USA
3 Department of Biomedical Engineering, Boston University, Cummington Street, Boston, MA 02215, USA
Genome Biology 2008, 9:R108 doi:10.1186/gb-2008-9-7-r108Published: 8 July 2008
In recent years, the molecular underpinnings of the long-observed resemblance between neoplastic and immature tissue have begun to emerge. Genome-wide transcriptional profiling has revealed similar gene expression signatures in several tumor types and early developmental stages of their tissue of origin. However, it remains unclear whether such a relationship is a universal feature of malignancy, whether heterogeneities exist in the developmental component of different tumor types and to which degree the resemblance between cancer and development is a tissue-specific phenomenon.
We defined a developmental landscape by summarizing the main features of ten developmental time courses and projected gene expression from a variety of human tumor types onto this landscape. This comparison demonstrates a clear imprint of developmental gene expression in a wide range of tumors and with respect to different, even non-cognate developmental backgrounds. Our analysis reveals three classes of cancers with developmentally distinct transcriptional patterns. We characterize the biological processes dominating these classes and validate the class distinction with respect to a new time series of murine embryonic lung development. Finally, we identify a set of genes that are upregulated in most cancers and we show that this signature is active in early development.
This systematic and quantitative overview of the relationship between the neoplastic and developmental transcriptome spanning dozens of tissues provides a reliable outline of global trends in cancer gene expression, reveals potentially clinically relevant differences in the gene expression of different cancer types and represents a reference framework for interpretation of smaller-scale functional studies.