Network analysis of skin tumor progression identifies a rewired genetic architecture affecting inflammation and tumor susceptibility
1 Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, 1450 Third St, San Francisco, CA 94158, USA
2 Thoracic Oncology Program, Department of Surgery, University of California San Francisco, 1600 Divisadero St, San Francisco, CA 94143, USA
3 Department of Laboratory Medicine, University of California San Francisco, 1521 Parnassus Ave, Room C255, Box 0451, San Francisco, CA 94143, USA
4 Instituto de Biología Molecular y Celular del Cáncer, CSIC/Universidad de Salamanca, Campus M Unamuno s/n, 37007-Salamanca, Spain
Genome Biology 2011, 12:R5 doi:10.1186/gb-2011-12-1-r5Published: 18 January 2011
Germline polymorphisms can influence gene expression networks in normal mammalian tissues and can affect disease susceptibility. We and others have shown that analysis of this genetic architecture can identify single genes and whole pathways that influence complex traits, including inflammation and cancer susceptibility. Whether germline variants affect gene expression in tumors that have undergone somatic alterations, and the extent to which these variants influence tumor progression, is unknown.
Using an integrated linkage and genomic analysis of a mouse model of skin cancer that produces both benign tumors and malignant carcinomas, we document major changes in germline control of gene expression during skin tumor development resulting from cell selection, somatic genetic events, and changes in the tumor microenvironment. The number of significant expression quantitative trait loci (eQTL) is progressively reduced in benign and malignant skin tumors when compared to normal skin. However, novel tumor-specific eQTL are detected for several genes associated with tumor susceptibility, including IL18 (Il18), Granzyme E (Gzme), Sprouty homolog 2 (Spry2), and Mitogen-activated protein kinase kinase 4 (Map2k4).
We conclude that the genetic architecture is substantially altered in tumors, and that eQTL analysis of tumors can identify host factors that influence the tumor microenvironment, mitogen-activated protein (MAP) kinase signaling, and cancer susceptibility.