How Are We Going to Discover New Cancer Biomarkers? A Proteomic Approach for Bladder Cancer
Bibliographic record
Abstract
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 in 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 all 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 all cell proteins are secreted. However, this is not an absolute requirement because many classic cancer biomarkers, such as carcinoembryonic …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".