The Long Journey of Cancer Biomarkers from the Bench to the Clinic
Bibliographic record
Abstract
BACKGROUND: Protein cancer biomarkers serve multiple clinical purposes, both early and late, during disease progression. The search for new and better biomarkers has become an integral component of contemporary cancer research. However, the number of new biomarkers cleared by the US Food and Drug Administration has declined substantially over the last 10 years, raising concerns regarding the efficiency of the biomarker-development pipeline. CONTENT: We describe different clinical uses of cancer biomarkers and their performance requirements. We also present examples of protein cancer biomarkers currently in clinical use and their limitations. The major barriers that candidate biomarkers need to overcome to reach the clinic are addressed. Finally, the long and arduous journey of a protein cancer biomarker from the bench to the clinic is outlined with an example. SUMMARY: The journey of a protein biomarker from the bench to the clinic is long and challenging. Every step needs to be meticulously planned and executed to succeed. The history of clinically useful biomarkers suggests that at least a decade is required for the transition of a marker from the bench to the bedside. Therefore, it may be too early to expect that the new technological advances will catalyze the anticipated biomarker revolution any time soon.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".