Cancer Gene Resequencing Collaboration to Select 8 Researchers
Many mutations of normal human DNA have been identified and implicated in tumorigenesis over the past several decades of cancer research, and in-depth studies of the genes and pathways affected by these mutations have greatly increased our understanding of some of the molecular details underlying cancer. More recently, low resolution genome-wide studies have begun to catalog additional changes, such as epigenetic and copy-number variations, illuminating the variety of inherited and somatic mutations that can lead to cancer. These data have also made it evident that only a modest fraction of the molecular targets involved in tumorigenesis have been identified, and that cancer is a very heterogeneous disease that can result from many different mutations, environmental influences, and interactions between the two. In order to develop effective targeted therapies, it will be important to correlate a particular mutational profile with specific disease phenotypes and treatments, and to discriminate causative mutations from passenger mutations. Importantly, the last few years have given birth to a variety of rapidly developing and novel sequencing technologies that enable researchers to obtain sequence information much more quickly and inexpensively, allowing the cataloging of genomic changes associated with cancer from multiple tumor types at different developmental stages and rendering these changes accessible to study and targeted therapy.
Most, if not all, tumor cells accumulate a plethora of mutations along the path leading to disregulated growth. In order to provide meaningful results, a sequencing strategy must allow the identification of the mutations directly implicated in cancer origin and metastasis within the context of the background mutations that accumulate as a consequence of the neoplastic state. In addition, because the spectrum of genetic alterations (e.g., point mutations or small nucleotide changes, deletions, translocations, amplifications) associated with cancer processes is broad, the most appropriate tools for analysis must be considered. Comprehensive, targeted sequencing provides an unbiased view of the cancer genome. This approach entails sequencing a sufficient number of genomes from the same tumor type to have the statistical power to distinguish clinically significant changes from background mutations. This methodology is well-supported, straightforward, rapid, and cost-effective. Current cancer research and genomic sequencing have reached a point of confluence; as the cost and speed of DNA sequencing continue to decrease, large-scale sequencing has become a viable tool for cancer discovery.
The cancer and genomics communities together have both the means and motivation to embark on a project to describe the universe of genetic changes associated with cancer. This investigation will then allow the identification of numerous molecular targets for diagnosis, prevention, and therapeutic intervention. The cancer community currently has the technological basis to perform a comprehensive characterization of genomic mutations and epigenetic changes associated with cancer, as well as directly related functional pathways. This “wholesale” approach to cancer research will require the scientific community to adopt a strategy that capitalizes on new technologies and optimizes the analysis and application of the data thus generated. Results such as those obtained using targeted sequencing could potentially lead to the development of more effective diagnostic and therapeutic strategies to treat cancer.
In order to empower the cancer research community with tools that enable a deeper understanding of the molecular basis of disease, RainDance Technologies has entered into collaboration with Expression Analysis in the sponsorship of a program to work directly with leading cancer researchers to define a set of high-priority genomic targets in order to facilitate large-scale targeted sequencing studies of cancer samples. The studies will help characterize rare mutations that would otherwise prove difficult or impossible to identify using alternative methodologies.
