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Wading Through the Mountains of Data Culled From Cancer Genome Projects

In 2008, an entity known as the International Cancer Genome Consortium (ICGC) was created. The goal of this Consortium – which combines the forces of 12 different countries – is to work together to map the genetic codes of various types of cancer. By scrutinizing various genetic mutations and other data gleaned from these cancer genomes, it is hoped that improvements in cancer therapies and treatments can be made.

However, such a task is proving remarkably complex and difficult. This is largely in part to the sheer numbers that are associated with mapping genomes. Each specific cancer has thousands of genes within its unique genetic code. Within these thousands of genes, there are dozens or even hundreds of mutations that may or may not be pertinent to the cancer’s ability to accelerate growth, thwart immune system responses or metastasize in new regions of the body.

So, while gene mapping may be helping to identify mutations within a specific type of cancer, the sheer number of them frequently makes it difficult to pinpoint which genes are worth focusing on.

Exacerbating the issue, is the fact that some mutations might appear to be relatively innocuous, when actually the opposite is the case. For example, a mutation related to colorectal cancer (IDH1) appeared in only one reviewed case out of 300. Due to this fact, the importance of the gene was seen as inconsequential and put aside in favor of more prevalent mutations.

However, later research indicated that IDH1 turned up in a number of other types of cancers. For example, researchers mapping brain cancer discovered the unique mutation in 12 percent of all samples. It also appeared in eight percent of acute myeloid leukemia cases. Due to these higher percentages, further investigations into the mutation were warranted, and it was found to be a major contributor to the promotion of cancer growth.

Clearly, the example of IDH1 proves that genomic sequencing of cancer cells can be beneficial. Still, with about 75 cancer genomes already completed, and hundreds scheduled for completion within the year, the glut of data can often be daunting for cancer researchers.

Another issue facing the ICGC (as well as other cancer genome projects), is the fairly small number of samples used to find patterns within mutations. As Joe Gray, a researcher with the Lawrence Berkeley National Laboratory in California, puts it, ” In the early days, I thought that doing a few hundred tumors would probably be sufficient. (However) even at the level of 1,000 samples, I think we’re probably not going to have the statistics we want.”

Without these larger samples to draw from, it becomes more difficult to identify the most prevalent or impactful mutations associated with a cancer’s growth. This is one of the reasons that IDH1 was initially neglected as a mutation worth studying.

Even with larger sample groups, the route to finding genetic drivers for a cancer’s success can be like searching for a needle in a haystack. In some cases, strong drivers have been identified in less than one percent of all cancer samples. While not as prevalent as other mutations, understanding how these less-common genes affect cancer growth may still be worth investigation.

So, as one might guess, the process for going line by line through thousands of mutations to find the ones that are worth targeting is a painfully drawn out process. And in the cases where a notable mutation is found, the discovery may prove meaningless unless a molecular inhibitor can be found that ostensibly switches this mutation “off.” Then, and only then, can researchers begin the process of developing a drug treatment that effectively targets the mutated gene.

While the process is clearly difficult and complex, the potential gains of mapping a cancer’s genome prove too beneficial to ignore. With small successes like IDH1 helping to spur on research, scientists will continue to dig through the genomic maps in an effort to identify causative genes and create medications that can vastly improve survival rates.

Resource:

http://wavefunction.fieldofscience.com/2010/04/it-not-mutation-stupid.html

http://www.nature.com/news/2010/100414/full/464972a.html