Biological convergence of cancer signatures.

Gene expression profiling has identified cancer prognostic and predictive signatures with superior performance to conventional histopathological or clinical parameters. Consequently, signatures are being incorporated into clinical practice and will soon influence everyday decisions in oncology. Howe...

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Autores principales: Xavier Solé, Núria Bonifaci, Núria López-Bigas, Antoni Berenguer, Pilar Hernández, Oscar Reina, Christopher A Maxwell, Helena Aguilar, Ander Urruticoechea, Silvia de Sanjosé, Francesc Comellas, Gabriel Capellá, Víctor Moreno, Miguel Angel Pujana
Formato: article
Lenguaje:EN
Publicado: Public Library of Science (PLoS) 2009
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Acceso en línea:https://doaj.org/article/20d0f19f31fa4a788ea07ceecd3a2d37
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Sumario:Gene expression profiling has identified cancer prognostic and predictive signatures with superior performance to conventional histopathological or clinical parameters. Consequently, signatures are being incorporated into clinical practice and will soon influence everyday decisions in oncology. However, the slight overlap in the gene identity between signatures for the same cancer type or condition raises questions about their biological and clinical implications. To clarify these issues, better understanding of the molecular properties and possible interactions underlying apparently dissimilar signatures is needed. Here, we evaluated whether the signatures of 24 independent studies are related at the genome, transcriptome or proteome levels. Significant associations were consistently observed across these molecular layers, which suggest the existence of a common cancer cell phenotype. Convergence on cell proliferation and death supports the pivotal involvement of these processes in prognosis, metastasis and treatment response. In addition, functional and molecular associations were identified with the immune response in different cancer types and conditions that complement the contribution of cell proliferation and death. Examination of additional, independent, cancer datasets corroborated our observations. This study proposes a comprehensive strategy for interpreting cancer signatures that reveals common design principles and systems-level properties.