Cancer Biomarkers: Can We Turn Recent Failures into Success?
Bibliographic record
Abstract
Disease biomarkers are used widely in medicine. But very few biomarkers are useful for cancer diagnosis and monitoring. Over the past 15 years, major investments have been made to discover and validate cancer biomarkers. Despite such investments, no new major cancer biomarkers have been approved for clinical use for at least 25 years. In the last decade, many reports have described new cancer biomarkers that promised to revolutionize the diagnosis of cancer and the management of cancer patients. However, many initially promising biomarkers have not been validated for clinical use. In this commentary, a plethora of parameters before sample analysis, during sample analysis, and after sample analysis that can complicate biomarker discovery and validation and lead to "false discovery" are discussed. Several examples of biomarker discoveries that were published in high-profile journals are also presented, as well as why they were not validated and the lessons learned from these false discoveries, so that similar mistakes can be avoided in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.138 | 0.280 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.005 | 0.033 |
| Scholarly communication | 0.018 | 0.060 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.016 | 0.029 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".