Anna González-Neira, Head of our Human
Genotyping – CEGEN Unit, explains more about
the international study in which the most important genes that increase the risk of breast cancer were defined. At the CNIO, she has participated jointly with researchers Ana Osorio and Javier Benítez.
How will this study help to improve diagnostic tests and genetic counselling? This work will help improve diagnosis, as it has allowed us to identify which genes are most useful for breast cancer risk prediction tests. Moreover, through this study we were able to rule out a number of genes used in some diagnostic tests. Our data revealed they don’t increase the risk to develop breast cancer, so they should not be considered in risk estimates, at least at present. The risk of developing breast cancer depends partly on genetic inheritance, but determining which genes increase this risk and to what extent remained a challenge. We now have more information that will allow us to improve genetic counselling for patients and their families.
Do these kinds of findings have immediate applicability for patients or do we still have to wait for some time before they will be used? Yes, of course, this finding can be applied directly to patients; in fact, it has been done for years. Genetic testing for breast cancer susceptibility is widely used but for many genes, the evidence for association with breast cancer was weak and, in some cases, estimates of increased risk were imprecise. This study has allowed us to improve these risk estimates by defining which genes are clearly involved and fine-tuning their associated risk.
Apart from genetic factors, other factors, such as environmental factors, age, hormonal history, etc., also play a role in cancer. What can you tell us about the mathematical models that are being developed to integrate all risk factors and to individualise the clinical care of each patient? We have to bear in mind that the probability of developing breast cancer is not determined by genes alone. Other factors such as age, hormonal history and environmental factors play a very important role, and these, in turn, can also be modulated by the genes themselves. For this reason, it has been working over the past few years on the development of mathematical models that allow us to integrate information about each patient’s genes with other non-genetic factors to obtain the most accurate risk estimate possible and thus individualise their clinical care. Implementation of these new tools into breast cancer screening programmes, with a much more personalised approach, will allow earlier detection of breast cancer and thus reduce breast cancer mortality.