To accuractely determine the outcome of clinical trials careful analysis of the biomarkers is required. In recent years this has become more and more complex due to quantity of biomarkers that has to be assessed in new clinical trials. Furthermore, as we move to more personalized therapy, we need to be able to measure even subtle changes in biomarker expression, putting a larger emphasis on accurate and precise biomarker quantification. These changes have made manual assessment of biomarker expression tedious, time-consuming, and, often, inaccurate.
Within this project, funded by the Humboldt Foundation, we set out to develop machine learning tools to automate the quantification and discovery of biomarkers in a variety of clinical trials. Specifically, we looked at applications in prostate cancer immunotherapy, lung cancer prognosis, and MAGE-expression in head and neck cancers.