The therapeutic paradigm of gliomas is changing from a general approach towards an individualized and targeted approach. Accordingly, the search for prognostic and predictive biomarkers, as well as the demand for quantitative, feasible and robust methods for biomarker analysis increases. We find that software classifiers can identify and quantify the expression of a given biomarker within different subcellular compartments and that such classifiers can exclude frequently occurring nontumor cells, thereby avoiding potential bias. The use of a quantitative approach provides a continuous measurement of the expression, allowing establishment of new cut-points and identification of patients with specific prognoses. However, some pitfalls must be noted. This article focuses on benefits and pitfalls of novel approaches for quantifying protein biomarkers in gliomas.