Biomarker Research

The search for biomarkers for response to a therapy consists of several steps. It starts with the acquisition of data and biological samples from a number of patients prior to the therapy. The collection of these data and samples form a biobank. After an appropriate time following the therapy, the response data are integrated in the biobank and employed to distinguish responders from non-responders. In the next step, the samples from all patients are analyzed, resulting in the identification of some features specific to responders and other features specific to non-responders. All these features are considered to be biomarker candidates. In a further step, the biomarker candidates are evaluated in validation studies involving new patients; if they succeed in predicting therapy response in these studies, they can be employed in the future as biomarkers for response. A similar workflow is used in the search of biomarkers for prognosis and for adverse reactions.

In recent years, many biomarker candidates for cancer prognosis, therapy response, and adverse reactions of a therapy have been identified. Unfortunately, the majority of them have failed in validation studies due to their poor reproducibility and/or their poor accuracy in the medical prediction1,2. There are good reasons for this:

  • The quality of samples is often inferior because the exogenous factors that distort the molecular features of the cells during sample acquisition are not eliminated or not standardized.
  • Prognosis, response, and adverse reactions depend on the molecular attributes of both components of the cancer disease: the cancer cells and the patient's organism. Biomarker candidates that have been identified in one of these components are often evaluated in validation studies without involving the other component.
  • In cancer, a high genomic heterogeneity exists at several levels: among the cancer cells of the same patient, among the cancer cells of several patients sharing similar clinico-pathological characteristics, and among different patients' organisms. This heterogeneity is often not considered in the identification of biomarker candidates and/or in the validation studies.
  1. Alymani NA, Smith MD, Williams DJ & Petty RD (2010) Predictive biomarkers for personalized anti-cancer drug use: discovery to clinical implementation. Eur J Cancer 46: 869-879.
  2. Maxim B et al. (2012), Impact of Collection and Storage of Lung Tumor Tissue on Whole Genome Expression Profiling. JMD 12(2): 140-148.