Novel clinico-genome network modeling for revolutionizing genotype-phenotype-based personalized cancer care.

Although cancer heterogeneity, even within individual tumors with different treatment responses of subcloncal cells populations, suggests the need for personalized medicine, most funding and efforts go to conventional single gene-based research and comparative-effectiveness research. Cancer arises from changes in the DNA sequence in the genomes of cancer cells. These accelerating somatic mutations dysregulate signaling pathways, including EGFR, Wnt/Notch, Hedgehog and others, with a central role in cell growth, proliferation, survival, angiogenesis and metastasis. All of these genetic alterations can now be discovered using next-generation DNA sequencing technology. This high-throughput technology can achieve two major goals: first, to complete the catalogue of driver mutations, including point mutations, rearrangements and copy-number changes, by full and targeted sequencing; and second, to explore the functional role of cancer genes and their interactions by genome-wide RNA, serial analysis of gene expression, microRNAs, protein-DNA interactions, and comprehensive analyses of transcriptomes and interactomes. This review article discusses the challenges, including costs, in completing the catalogue of driver mutations for each cancer type and understanding how cancer genomes operate as whole biological systems. Now high-quality clinical treatment and outcomes (death or survival) data from biobanks, and extensive genetics and genomics data for some common tumors, including breast, colorectal and pancreatic cancer, are available. In this article, we will describe how all these clinical and genetics data could be integrated into reverse engineering-based network modeling to approach the extremely complex genotype-phenotype map. This clinico-genome systems model, published for the first time, opens the way for the discovery of new molecular innovations, both predictive markers and therapies, towards personalized treatment of cancer. Instead of the comparative-effectiveness research or personalized medicine debate, harmonization of both can revolutionize cancer management.