The number of available cancer drugs is not unlimited and despite favorable clinical effect in many
patients, numerous therapies are not in use due to lack of adequate predictive biomarkers to define the responding subpopulations.
Absent use of some available therapies in a general population is often based on an imbalanced ratio of effect/side effects, social-economic factors, or a combination. These issues relate to both patients personally and societal considerations in health economy. Improved diagnostics in the form of novel clinical support tools is vital for precision cancer treatment, and we need to evolve current approaches to make our patients benefit the most of currently available cancer drugs and therapies. In our ongoing efforts with molecular signatures, we are using a focused tactic in which we develop biomarkers for specific treatments tested in clinical trials at Oslo University Hospital.
Bevacizumab is an anti-angiogenic therapy which is approved, but rarely used, for locally
advanced and metastatic breast cancer (BC). Using machine learning on clinical and molecular data, we have developed a molecular signature named ViRP (VEGF inhibition Response Predictor). The ViRP signature is able to predict which patients have good response to treatment with bevacizumab as supplement to chemotherapy, and is based on expression of nine independent proteins or their corresponding mRNA in tumor tissue samples and a formula for calculating a prediction score. A prospective clinical trial (NAPEER+) will now be started using our developed ViRP score for selecting breast cancer patients to treatment with bevacizumab in combination with chemotherapy.
To accomplish translation of our results into wide-ranging clinical value we are working to establish a strategy for commercial venture of ViRP usage. This further includes potential expansion of use into other cancer types than breast cancer, where bevacizumab has a clinical role.