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Polygenic Analysis May Aid Breast Cancer Diagnosis

NEW YORK (GenomeWeb News) – Combining traditional breast cancer screening with genetic risk assessment programs based on several alleles has the potential to improve diagnoses, new research suggests.
While much has been done to suss out genes that increase breast cancer risk, questions remain about how much diagnostic information common, low-penetrance risk alleles provide. In a paper published in today’s issue of the New England Journal of Medicine, a team of researchers from the United Kingdom analyzed a handful of these alleles to better understand their use, if any, in disease prevention. Their findings suggest that moderate-risk alleles may help delineate populations of women who require earlier or more vigilant screening.
Although many of the causes of breast cancer are still unknown, the disease often runs in families and is at least partly heritable. The most well-known single genes associated with high increases in breast cancer risk are BRCA1 and BRCA2.
But just a quarter of inherited breast cancer can be explained by such rare single mutations. And while it’s known that having several more common moderate-risk alleles can compound an individual’s overall risk of breast cancer, the overall usefulness of these genes for predicting cancer risk is poorly understood.
To address this, senior author Bruce Ponder, an oncology researcher at the University of Cambridge, led a team of researchers interested in determining the significance of common alleles, if any, for diagnosis and breast cancer prevention. They focused on seven recently discovered alleles that are thought to confer moderate increases in cancer risk based on genome-wide SNP studies.
Their analysis suggests that some 58 percent of breast cancer cases occur in women who are at the upper half of a risk distribution curve delineated by the seven risk alleles tested. At the far end of this curve, the ten percent of women with the highest risk represented 15 percent of breast cancer cases.
Those results suggest that the common alleles tested can’t necessarily be used as a prevention tool in individuals, but may be useful for identifying populations at increased risk that will benefit from closer scrutiny, including earlier mammography. Rather than using mammography screening on every woman over 50 years old, for instance, the researchers suggested that each woman’s breast screening should start at an age appropriate for her risk group — be it 40 years old or 60 years old.
“It would be possible to genotype every woman at all known susceptibility loci and, on the basis of her breast-cancer risk profile, offer a personalized screening program in which the starting age would vary,” Ponder and his team wrote.
And, they predicted, as more research turns up new risk alleles, the risk estimates within populations should continue improving. In addition, the researchers said, integrating information about other, individual lifestyle risks could further improve this prediction model.
“Although the clinical use of single, common low-penetrance genes is limited, a small number of susceptibility alleles could distinguish women at high risk for breast cancer from women at low risk, particularly in the context of population-screening programs,” the authors wrote.
Still, they noted that “[m]ost reported genetic associations have been false positive results and would be worthless for risk prediction.” Consequently, they emphasized the need for a solid understanding of susceptibility loci and the risk associated with each.
In an accompanying perspectives article published in the same issue of NEJM, Harvard researchers David Hunter and David Altschuler and University of Pennsylvania researcher Daniel Rader agreed that interpreting the risk associated with common alleles is promising but requires much more research and clinical validation.
“Our challenge will be to develop research methods that take us from genetic localization to medically useful application, as well as to support investigators who want to seize this opportunity and translate it into greater understanding of disease and better care for patients,” Hunter and his colleagues concluded.

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