A very important breakthrough article on interpreting genome data of cancer cells. It seems that scientists are generating far more data than they can easily interpret. June 5th 2013 was a “breakthrough” for simplifying and interpreting genome data bearing evidence of mutations, such as those that characterize specific cancers.
Research scientists from Cold Spring Harbor Laboratory, NY. Wednesday, 05 June 2013
Mathematical technique de-clutters cancer-cell data, revealing tumor evolution, treatment leads
“Today, two scientists from Cold Spring Harbor Laboratory (CSHL) publish a mathematical method of simplifying and interpreting genome data bearing evidence of interpreting genome data bearing evidence of, such as those that characterize specific cancers. Not only is the technique highly accurate; it has immediate utility in efforts to parse tumor cells, in order to determine a patient’s prognosis and the best approach to treatment. An example of an interval would be a segment of DNA that is missing in the genetic sequence of one or more cells sampled from the tumor. Tumor cells are often missing DNA that should normally be present; or conversely, they often have genome intervals in which the normal DNA sequence is amplified – it appears in multiple copies. Such deletions and amplifications are called copy-number variations, or CNVs.
When genome sequence data from 100 cells sampled from a single human tumor is analyzed, and the mathematical algorithm devised by Krasnitz and Wigler is applied, the rich structure of the data emerges. This is a “heat map” in which each horizontal row contains data from 1 of the 100 sampled cells; and each vertical column contains information about the presence (black) or absence (no mark) of a “CORE.” Each core represents a place in the genome where a particular cell either has amplified DNA (blue bar, top) or deleted DNA (red bar, top). From the mass of data underlying these phenomena, signatures of 4 subpopulations of tumor cells now become visible. The four groups and their evolutionary relation is shown along the left vertical axis: about half are “green,” and their genomes are the least changed by the disease; cells in the remaining three subpopulations harbor multiple mutations in their genomes.
“In cancer,” says Krasnitz, “we find intervals in the genome that are hit again and again. You might see this in many cells coming from a single patient’s tumor; or you may see these repeating patterns in cells sampled from many patients with a similar cancer type.”
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