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Identification of Key Processes Underlying Cancer Phenotypes Using Biologic Pathway Analysis

Cancer is recognized to be a family of gene-based diseases whose causes are to be found in disruptions of basic biologic processes. An increasingly deep catalogue of canonical networks details the specific molecular interaction of genes and their products. However, mapping of disease phenotypes to a...

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Detalles Bibliográficos
Autores principales: Efroni, Sol, Schaefer, Carl F., Buetow, Kenneth H.
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2007
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1855990/
https://www.ncbi.nlm.nih.gov/pubmed/17487280
http://dx.doi.org/10.1371/journal.pone.0000425
Descripción
Sumario:Cancer is recognized to be a family of gene-based diseases whose causes are to be found in disruptions of basic biologic processes. An increasingly deep catalogue of canonical networks details the specific molecular interaction of genes and their products. However, mapping of disease phenotypes to alterations of these networks of interactions is accomplished indirectly and non-systematically. Here we objectively identify pathways associated with malignancy, staging, and outcome in cancer through application of an analytic approach that systematically evaluates differences in the activity and consistency of interactions within canonical biologic processes. Using large collections of publicly accessible genome-wide gene expression, we identify small, common sets of pathways – Trka Receptor, Apoptosis response to DNA Damage, Ceramide, Telomerase, CD40L and Calcineurin – whose differences robustly distinguish diverse tumor types from corresponding normal samples, predict tumor grade, and distinguish phenotypes such as estrogen receptor status and p53 mutation state. Pathways identified through this analysis perform as well or better than phenotypes used in the original studies in predicting cancer outcome. This approach provides a means to use genome-wide characterizations to map key biological processes to important clinical features in disease.