How are We Going to Discover New Cancer Biomarkers? a Proteomic Approach for Bladder Cancer (Editorials‪)‬

A handful of cancer biomarkers are currently used routinely for population screening, disease diagnosis, prognosis, monitoring of therapy, and prediction of therapeutic response. Unfortunately, most of these biomarkers suffer from low sensitivity, specificity, and predictive value, particularly when applied to rare diseases population screening programs. Thus, for the classic 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 cancer types. 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 a11 genes and proteins will lead to identification of many candidate biomarkers for cancer and other diseases. These predictions still need to be realized. The prevailing view among specialists is that the most powerful single cancer biomarkers may have already been discovered. Likely, in the future we will discover biomarkers that are less sensitive or specific but could be used in panels, in combination with powerful bioinformatic tools, to devise diagnostic algorithms with improved sensitivity and specificity. These efforts are currently in progress (1). Most of the currently used cancer biomarkers were discovered after development of novel analytical techniques such as immunologic assays and the monoclonal antibody technology. Animals were immunized with extracts from tumors or cancer cell lines, followed by screening of hybridomas for monoclonal antibodies that recognize "cancer-associated" antigens. More recently, and with the completion of the Human Genome Project, many researchers have hypothesized that the best cancer biomarkers will likely be secreted proteins (2). Approximately 20-25% of a11 cell proteins are secreted. However, this is not an absolute requirement because many classic cancer biomarkers, such as carcinoembryonic antigen (CEA) and Her2/neu, are bound to cell membranes, but their extracellular domains can be found, through shedding, in the circulation. Other groups are using bioinformatics such as digital differential display and in silico Northern blotting to compare gene expression between healthy and cancerous tissues to identify overexpressed genes (3). Although one of the prevailing hypotheses in new biomarker discovery is that the most promising biomarkers should be overproduced proteins, this is not generally true for some of the best-known cancer biomarkers, such as prostate-specific antigen (PSA) (4). Overexpressed genes are now identified experimentally by use of microarrays. Some of these genes have been proposed as candidate cancer biomarkers (5, 6). Despite this reasonable hypothesis, very few cancer biomarkers have been discovered by use of this approach.