Cancer genome evolution

Like any scientific advancement, new generation sequencing technologies have brought both excitement as well as challenges for cancer research. On one hand, the newly discovered, highly dynamic genetic and epigenetic landscapes of the cancer genome have finally explained why it is so hard to identify common gene mutations in clinical samples, as these dynamics are against the key prediction of the current gene mutation theory of cancer. On the other hand, massive amounts of data from these technologies have also generated confusion in the field (1). For example, despite the clinical reality that the majority of cancer cases display high heterogeneity, most basic researchers have focused on identifying commonly shared genetic patterns. This strategy is largely influenced by results generated over decades from various in vitro and in vivo experimental models, despite the fact that many model systems of cancer come with drastically reduced heterogeneity. However, the gap between basic research and clinical reality is rapidly increasing, and this is one of the key rationales for pushing the cancer genome sequencing project and unbiasedly map the cancer genome landscape and identify these common gene mutations once and for all (1,2). Unexpected by many, the cancer genome sequencing project has forcefully denied such rationale by presenting the highly complicated reality to the research community where every cancer is different, and there is no fixed genomic landscape (3,4). To face this daunting challenge, a new conceptual framework is needed that accounts for the abundant genetic/epigenetic diversity observed. This sentiment has also been shared by some leading researchers, who have admitted that it is not enough to simply continue collecting more sequencing data and suggested that a new paradigm is urgently needed to understand cancer in this age of massive quantities of diverse data (5,6). In contrast, others continue promoting the strategy of sequencing more samples. They are convinced that, by sequencing more samples and using more powerful mathematical and bioinformatics models, the mystery of cancer will ultimately be solved