In tumors, scientists can detect any number of point mutations and larger genomic alterations such as insertions, deletions, inversions and translocations, all of which make these diseased tissues dissimilar from healthy ones. Some mutations—driver mutations—lead a cancer to grow, spread and, often, take a patient’s life. Passenger mutations tend to not contribute to cancer growth.

The ability to discern between the two types of mutations can lead to a deeper understanding of cancer biology and empower the development of cancer therapeutics. But the complexity of cancer genomes does not make it easy for researchers to tell drivers and passengers apart. As second-generation sequencing matures, new tools and approaches are helping scientists discover what drives a given cancer.

Who could be driving?

There are around 100 genes that are known cancer drivers. When researchers look across the sequences of many tumor samples, they will find ‘mountains’, which are mutations occurring in many tumors. One such highly mutated driver is TP53, the gene encoding tumor protein p53; Kirsten rat sarcoma viral oncogene homolog (KRAS) is another.

Large-scale cancer genome sequencing projects such as The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) have created well-endowed gene catalogs and portals for the research community to use that render visible this variation in mutation frequency. As these projects reach the end of their first chapters, there are various ways to leverage these catalogs, hunt for signals of drivers in the data, and develop new methods and approaches.

Is there a rule for picking driver signals?

As a small lab, Kinzler says he, Bert Vogelstein and their colleagues do not sequence thousands of tumors at a time, nor do they have a large group of biostatisticians at their disposal. They have decided to focus on the genes that are “unequivocally, clearly driver genes,” Kinzler says.

They apply what they call the ratiometric rule, which is about mutation patterns as opposed to mutation frequencies. The rule distinguishes between oncogenes, which need to be hyperactive to cause cancer, and tumor suppressor genes, which cause cancer when they stop working. For an oncogene, 20% of the recorded mutations in the gene must occur at the same position and cause a single switched amino acid in the protein that the gene encodes. For a tumor suppressor gene, more than 20% of the mutations in the gene must be clearly inactivating.

Kinzler sees advantages to this ratiometric approach over other methods, which have “pretty significant false discovery rates”—perhaps even as high 10%, he says—which can skew a list of driver genes. “It’s a question of what you want your list to look like.”

Are there noncoding driver mutations?

Many approaches mainly analyze regions in the genome that encode proteins and that can be mutated to give overactive or defective forms. But recent studies indicate that noncoding regions of the genome, which can be responsible for regulating gene activity, might also harbor cancer drivers. Noncoding drivers could potentially outnumber coding ones, say Lawrence and Getz. But for now, the community is “completely blind to them” because whole-exome sequencing has been the workhorse to date.

The focus on coding regions has been a practical one. As a way to hold costs down, most cancer genome sequencing projects have focused on exome sequencing, says Lopez-Bigas. “Now with the focus and economics shifting to whole-genome sequencing, we’re all under the gun to get our act together beyond the splice sites,” say Lawrence and Getz.

A team of scientists at the Broad Institute, Dana-Farber Cancer Institute, Harvard Medical School and MD Anderson Cancer Center describe two highly recurrent mutations in melanoma that lie outside of protein-coding regions.

Specifically, they found two somatic mutations in a regulatory region, the promoter of the telomerase reverse transcriptase gene (TERT). They note that in addition to coding sequences, recurrent somatic mutations in regulatory genomic regions “may represent important driver events in cancer.”

Scientists can then launch their analysis from positive clinical response—a phenotype—and work their way back to these patients’ genome to hunt for reasons that explain these different, positive responses. Finding driver mutations in cancer is a challenge and will stay important. In some cases, finding these mutations can be exceptionally good news.

Reference:

Cancer genomes: discerning drivers from passengers. Nature Methods. 2014;11:375-379

Cancer Genome Landscapes.Science.2013;339:1546–1558.

Highly Recurrent TERT Promoter Mutations in Human Melanoma.Science.2013;339:957-959