UK researchers have developed a new biomarker-based test to classify breast tumours into one of seven subtypes and improve personalisation of treatments.
There is increasing realisation that single types of cancer such as breast cancer actually represent many different sub-types of the disease, characterised by specific combinations of genetic characteristics. For example, last year research suggested that genetic profiling could identify ten different forms (see previous news
Presently, genomic profiling is not routinely available for cancer patients; breast tumours are analysed for the presence of two protein biomarkers, the oestrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2). The presence of these proteins allows the use of biologically targeted drugs against the tumour cells. The new test, being commercially developed as the Nottingham Prognostic Index Plus, is also based on detection of protein biomarkers, but includes a further eight and can distinguish between seven different sub-types of breast cancer said to have different prognoses.
The developers, who have yet to validate the test in clinical trials, say it would be compatible with current clinical approaches to clinical investigation and guide treatment choices, as well as predicting outcomes.
Baroness Delyth Morgan, chief executive of research funder the Breast Cancer Campaign who funded the research, commented: "This new test could be a realistic step towards making the holy grail of personalised medicine a reality".
Personalised medicine is developing fastest within cancer, where there is increasing interest in tailoring treatment according to genetically determined tumour factors to improve outcomes. Whether this new test will prove to be the best option for breast cancer analysis is impossible to know at this stage, but it certainly has potential appeal for oncologists insofar as it fits with current clinical approaches to tumour analysis. However, it could be eclipsed by genome sequencing and analysis such as that to form part of the 100,000 Genomes Project (see previous news