Genetic Testing Helps Breast Cancer Patients Make Surgery Decisions
Testing women newly diagnosed with breast cancer for the genetic mutations that are associated with an increased risk of the disease affects the decisions they make about treatment, according to a new study.
Breast cancer patients with BRCA1 or BRCA2 gene mutations are up to 60% more likely to develop cancer in the opposite breast, known as contralateral breast cancer, for at least 10 years after their original diagnosis. These women may wish to decrease this risk by having a double mastectomy, but there are barriers to testing women after diagnosis with breast cancer.
Marc D. Schwartz, M.D., of Georgetown University, and colleagues offered genetic testing and counseling to 167 women newly diagnosed with breast cancer who had at least a 10% chance of carrying one of the mutations. Of the 31 women who tested positive for the BRCA1 or BRCA2 mutations, 48% chose to have a double mastectomy, while only 24% of the 136 women who had no mutation or an unknown mutation chose the procedure.
Despite its usefulness in decision-making, genetic testing for all newly diagnosed breast cancer patients may be difficult because of financial and institutional barriers and the long timeup to 3 monthsthat it can take to receive results. Increasing genetic testing, however, so that women at the highest risk can reduce their chance of contralateral breast cancer could ultimately increase survival rates, wrote Mary B. Daly, M.D., Ph.D., of the Fox Chase Cancer Center in Philadelphia, in an editorial that accompanies the article, both of which appear in the Early Release section of the online version of the Journal of Clinical Oncology.
Survival after Colorectal Cancer Keyhole Surgery Same as that for Conventional Surgery
Patients who undergo laparoscopyor "keyhole surgery"for treatment of colorectal cancer have the same survival rates and a shorter recovery time than those who undergo conventional surgery, according to a new study.
Keyhole surgery has been used for treating colorectal cancer since 1991. Despite the fact that patients had a quicker recovery and less surgical stress after keyhole surgery, there was concern about long-term survival and whether the method was able to fully clear tumors, so it was recommended for use only in clinical trials and not for routine clinical practice.
Ka Lau Leung, FRCS, of the Prince of Wales Hospital in Hong Kong, and colleagues randomly assigned about 400 patients at the hospital to receive either keyhole or conventional open surgery and monitored them until 2003. Their results are published in the April 10 issue of The Lancet.
Five years after surgery, both groups had about the same chance of survival and of being disease-free. The group that had received the keyhole surgery reported less pain and a faster recovery, but the surgery took longer to perform and was more expensive.
See News, Vol. 93, No. 12, p. 897, "Surgical Oncology Focusing on Minimally Invasive Surgery, More Randomized Clinical Trials."
Scientists Develop Tool to Predict Cancer Survival from Gene Expression
A new study shows that a computer model that combines gene expression data and clinical history to identify cancer subtypes may be used to predict patient survival.
Two patients with the same type of cancer can respond differently to the same treatment and have very different outcomes, in part because their tumors may in fact be different diseases at the molecular level. When these subtypes are known, a variety of techniques can be used to identify a patients cancer subtype, but most of these methods cannot identify new subtypes.
One of the goals of DNA microarray research has been to develop a method for diagnosing cancer more accurately. In the April issue of PLoS Biology, Eric Bair, M.S., and Robert Tibshirani, Ph.D., of Stanford University, report the development of a computer model that uses clinical data to identify the patterns of expressed genes from microarrays that correspond to each of these factors. Their model could then place patients into subgroups and accurately predict their survival. The authors conclude that this tool may one day be used to identify new cancer subgroups, predict expected patient survival, and help suggest appropriate treatments.
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