Pitfalls in Cancer Biomarker Discovery and Validation with Emphasis on Circulating Tumor DNA
Bibliographic record
Abstract
Despite significant investment of funds and resources, few new cancer biomarkers have been introduced to the clinic in the last few decades. Although many candidates produce promising results in the laboratory, deficiencies in sensitivity, specificity, and predictive value make them less than desirable in a patient setting. This review will analyze these challenges in detail as well as discuss false discovery, problems with reproducibility, and tumor heterogeneity. Circulating tumor DNA (ctDNA), an emerging cancer biomarker, is also analyzed, particularly in the contexts of assay specificity, sensitivity, fragmentation, lead time, mutant allele fraction, and clinical relevance. Emerging artificial intelligence technologies will likely be valuable tools in maximizing the clinical utility of ctDNA which is often found in very small quantities in patients with early-stage tumors. Finally, the implications of challenging false discoveries are examined and some insights about improving cancer biomarker discovery are provided.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".