Breastfeeding is a way to reduce the risk of breast cancer, even if women first become mothers at a relatively old age, according to a new American-European meta-analysis of clinical studies disclosed at a Israel Cancer Association (ICA) workshop. Studies have shown that the later in life a woman has her first child and the fewer children she has, the higher the risk of breast cancer. But the meta-analysis conducted at the Universities of Oslo and Southern California looked at the influence of breastfeeding in 10 studies published between 1995 and 2006, and was recently published in Breast Cancer Research. Two-thirds of breast-cancer victims have cells with estrogen or progesterone receptors; these are like antennae, as female hormones send signals through them that tell breast cancer cells to grow. After a breast tumor is removed, the cancer cells are tested to see if they have hormone receptors. The more such receptors are present, the more likely that hormonal therapy will work. Every baby born to a woman reduces the risk of contracting hormone-receptor-positive (HR+) breast cancer by 11 percent, while women who have their first baby late have an increased risk of 27%. The number of deliveries or the age of a woman at her first delivery wasn't connected to a risk of HR-negative cancer, according to ICA director-general Miri Ziv, who spoke at the workshop. But getting one's first menstrual period relatively late and breastfeeding reduced the risk of both HR+ and HR- breast cancer. Pregnancy, suggested the researchers, has a protective effect against breast cancer because it reduces a woman's exposure to estrogen, which is also reduced during breastfeeding. Thus breastfeeding reduces and even negates the breast cancer risk among women who had their first baby after 25, and had their first menstrual period early. IBM-HAIFA HELPS FIGHT HIV A consortium that includes IBM researchers in Haifa is working to develop EuResist, a European integrated system for clinical management of anti-retroviral drug resistance. The system will provide clinicians with a prediction of response to anti-retroviral treatment, thus helping HIV patients choose the best drugs and drug combinations for any given HIV genetic variant. HIV infection no longer means automatic death from AIDS, and the number of approved treatments grows each year. But the virus is becoming resistant to an increasing number of drugs and is spinning off different strains. Many treatments for HIV-infected patients fail due to this drug resistance, so the best way to use anti-HIV drugs is in combinations or "cocktails" prescribed as the patient's individual virus progresses and as resistance to the drugs changes. Although there are new standardized systems to monitor the development of drug-resistant mutations, there is a vital need for a method that will help doctors decide which cocktail has the best chance of success for each patient. "Monitoring the history of treatments and the progress of the virus itself is crucial to successful patient care," notes Boaz Carmeli, manager of healthcare and life science at the IBM Haifa Research Lab. "Tapping into knowledge garnered from a huge collection of data will help doctors take into account the patient, the virus, the viral mutations and the current stage of the disease." EuResist is a European Union project that uses an innovative approach to predict the efficacy of anti-retroviral drug regimens against a specific HIV, based on viral genotype data integrated with treatment response data collected from some of the largest HIV databases in Europe. The project's biomedical information integration technology gathers data from three large genotype-response databases, namely the Italian ARCA database (one of the biggest in the world), the German AREVIR database, and data coming from Sweden's Karolinska infectious diseases and clinical virology department. The data include treatment histories, treatment response, and the sequence of the relevant part of the HIV genome (genotype). The resulting EuResist integrated data set is expected to be the largest in the world. "If we look closely at a patient's blood work, virus stage, family history and race, and then compare it to the thousands of people who have been treated over the years, we can see what was done, what worked and what didn't," notes Prof. Mauricio Zazzi at the University of Siena. On the basis of this historical data, EuResist can predict how the virus will respond to a certain cocktail. "This method not only provides a huge savings in costs, it also means a patient's chances for successful treatment are not dependant on their doctor's individual knowledge." With EuResist, this interaction is done through the web, where physicians can input a patient's information and status and then get a summary of what is known about this specific virus stage along with a prediction of what treatment has a good chance of helping. For example, a doctor in Bolivia who may not have expertise in AIDS treatment or access to recent research can use the knowledge accumulated in the EuResist system to treat patients. "This access to shared knowledge greatly increases our chances of fighting AIDS, and can provide a vital contribution to world healthcare," continues Zazzi.