"Looking at many risky genes may be better able to predict one's chance of getting breast cancer than looking at any single index alone," comments Debu Tripathy, clinical professor of internal medicine at University of Texas Southwestern Medical Center.
If a woman were found to have multiple low-risk genes, her doctor might advise her to undergo early screening and prophylactic treatment for breast cancer. But since 75 percent of breast cancer's genetic risk remains unexplained, such tests are still largely speculative.
When scientists first began searching for disease-causing genes, they usually had to start out knowing where to look.
The approach used to find the four new genes departed from older methods in that it allowed scientists to "Google" the genome, zeroing in on genes that appeared most in women with breast cancer.
"Rather than fish for new genes one at a time with a rod and line, we have trawled the pool," notes author Professor Bruce Ponder, director of the Cancer Research UK's Cambridge Research Institute, in the press release.
"This is not only a more efficient approach, it gets round the bias of previous studies in which scientists only examined genes they already know something about."
By contrast, Easton and his colleagues did not have to begin with any preconceived notions of where to look. This allowed them to discover genes in areas of the genome not previously investigated.
Dr. Kenneth Offit, chief of clinical genetics at Memorial Sloan-Kettering Cancer Center, acknowledges the strengths of this approach, but also highlights the questions it brings with it.
"This study is a tour de force of enormous size addressing an important question in cancer genetics," he said. "Its major significance is in the new scientific questions it raises rather than the questions resolved."
Indeed, what do these genes do? How do they increase a woman's risk of breast cancer? Dr. Marisa Weiss, president and founder of Breastcancer.org, notes, "This technique is good at finding possible genetic leads -- but it doesn't tell you why or how or what the culprit might actually have done to cause the problem."
Dr. Anthony Elias, Medical Director of the Breast Cancer Program at the University of Colorado, noted that we need to interpret these data with caution.
"Until we can put together a model of breast cancer risk by manipulating these [genetic variations] and demonstrating causality," he said, "we will not understand whether the association if causality, chance, statistical fluke, or as an innocent adjacent or related bystander."