A handful of cancer biomarkers are currently used routinely for population screening, disease diagnosis, prognosis, monitoring of therapy, and prediction of therapeutic response. Some established biomarkers are listed in Table I. Although it is highly desirable to have biomarkers suitable for population screening and early diagnosis, none of the biomarkers listed in Table I has adequate sensitivity, specificity, and predictive value for population screening. Even prostate-specific antigen (PSA), which has been approved for population screening by the Food and Drug Administration, is not universally accepted for this application. The reasons for biomarker failure in population screening settings are multiple and fall outside the scope of this review. It will suffice to mention that poor specificity leads to many false-positive results. In population screening, disease prevalence is another important parameter; diseases of low prevalence (like ovarian cancer) will require outstanding diagnostic test specificity (>99%) for the test to be considered viable (18). It can be concluded that none of the individual biomarkers currently at hand can fulfill the requirements of population screening for cancer. Biomarkers are clinically recommended mainly for monitoring the effectiveness of therapeutic interventions. Some biomarkers are also invaluable tools for early diagnosis of cancer relapse, which may trigger additional treatments before the appearance of clinical symptoms.
With current cancer biomarkers, much is left to be desired in terms of clinical applicability. We need new cancer biomarkers that will further enhance our ability to diagnose, prognose, and predict therapeutic response in many types of cancer. Because biomarkers can be analyzed relatively noninvasively and economically, it is worth investing in discovering more biomarkers in the future. The completion of the Human Genome Project has raised expectations that the knowledge of all genes and proteins will lead to the identification of many candidate biomarkers for cancer and other diseases. This prediction still needs to be realized. Among specialists in the field, the prevailing view is that the most powerful single cancer biomarkers may have already been discovered (e.g.
those shown in Table I
). Likely, we are now bound to discover biomarkers that are less sensitive or specific but that could be used in panels, in combination with powerful bioinformatic tools (such as artificial neural networks, logistic regression, etc.), to devise diagnostic algorithms with improved sensitivity and specificity (19
). These efforts are currently ongoing.