New algorithm ranks genes for their likelihood of contributing to cancerPublished On: Tue, Apr 8th, 2008 | Bioinformatics | By BioNews
April 8 : Researchers at Dana-Farber Cancer Institute and Memorial Sloan-Kettering Cancer Center have developed a new algorithm that may enable scientists to rank abnormal genes in accordance with their likelihood of contributing to cancer.
The researchers have, through experimentation, showed that a gene identified by the algorithm as a likely restrainer of tumour growth does indeed play that role in a common type of brain cancer, and is not a mere “bystander” to another restrainer gene.
“As the Human Cancer Genome Project begins to map the genetic alterations in different kinds of cancer, we need to be able to discriminate between alterations that truly are relevant to the disease and those that are not. Using a new algorithm developed in collaboration with Dr. Cameron Brennan of Memorial Sloan-Kettering, we were able to identify genes with too many or too few copies in cancer cells,” says Dr. Lynda Chin of Dana-Farber
The algorithm can be used to analyse similar genomic data generated by The Cancer Genome Atlas pilot project, a federally-led effort to explore genomic changes involved in human cancer. The research team is also submitting it to BioConductor, a collection of open-source computational tools for free download by researchers.
Describing their research in the Cancer Cell paper, the researchers said that they performed high-resolution genomic scans of glioblastoma (brain cancer) tumour samples and cell lines, which showed dozens of gene copy alterations, some of which had already been linked to the disease and some of which had not.
They ran the results through the new algorithm to determine which of the “suspect” alterations were most likely to contribute to cancer, and noted that one of the highest-scoring abnormalities involved a gene known as p18INK4C.
Scientists knew that a cousin of this gene called p16INK4A is missing in a majority of glioblastoma cases, though this gene itself was not known to be missing in the disease previously.
While both genes are thought to have similar functions, the researchers suspected that their disappearance together was more than a coincidence, and that the loss of p18INK4C plays a role in glioblastoma.
Based on the algorithmic analysis, the researchers looked at a connection between the two genes so as to determine what causes their joint disappearance.
It turns out that the loss of p16INK4A triggers a shutdown of a “pathway”—a series of interconnected genes called RB—and that, in turn, causes cell proliferation and a giant step toward cancer.
At that point, p18INK4C steps in as a backup system, pulling the reins on the hectic cell growth permitted by the loss of p16INK4A. If p18INK4C is lost, it is as though the emergency brake on growth is gone.
“We found that p16 and p18 are part of a ‘feedback’ loop that keeps the growth of normal glial cells in check. When p16 goes out of commission, p18 is signaled to pick up the slack. We demonstrated that the deletion of both genes is required for glioblastoma to develop,” Chin said.
The researcher says that the feedback loop is the latest evidence that cancer gene pathways are not as straightforward as earlier thought.
“Just a few years ago, the view was that pathways were largely linear. We’re increasingly coming to appreciate, however, that they operate in concert – that each one has multiple tentacles reaching out to other pathways and they function collectively as a network. When one pathway goes out of commission, another may switch on to compensate,” Chin said.
The researcher believes that the Genome-Topography-Scan algorithm, as it is called, can help investigators prioritise their search for cancer-related genes, and will be refined and improved as research continues.
“By pointing to genes with a high probability of being involved in cancer, the technique can speed the process by which new cancer genes are identified and therapies are developed to counter them,” she said. (ANI)