Despite advances and standardization of surgery, radiotherapy and chemotherapy, solid cancers kill million of patients worldwide. In 2020, cancer will be the top health problem. How could cancer mortality be improved?
In the early 2000s, targeted therapy and multigene expression profiling research raised overentousiasm for highly effective personalized treatment strategies. But with a few exceptions, such as trastuzumab for HER2-positive cancer, and perhaps two gene expression-based signatures, the expectations have not been met.
Absolutely no surprising. Extensive genetic studies reveal now multiple genetic alterations underlying tumorigenesis. Solid cancers including the most common lung, breast, prostate and colorectal cancer are extremely complex and highly heterogeneous diseases. It becomes increasingly clear we should move away from single gene-based approaches to causal network-based approaches.
Genome-wide association (GWA) studies, genetics and genomics research provide now a plethora of data. But how could the extremely complex “genotype-phenotype cancer map” can be displayed?
This perspective article describe how quantitative genetics, personal genomics, bioinformatics, systems biology and in silico could be integrated into comprehensive causal network approaches. These systematic approaches represent the major hope for a new generation of drugs and response prediction biomarkers.