Cancer clinical trials in the era of genomic signatures: Biomedical innovation, clinical utility, and regulatory-scientific hybrids
Bibliographic record
Abstract
The paper examines two large-scale, North American and European clinical trials designed to validate two commercially available genomic tumor signatures that predict a patient's risk of breast cancer recurrence and response to chemotherapy. The paper builds on empirical evidence from the two trials to explore the emergence of diverse regulatory-scientific hybrids; that is, the paper discusses configurations of genomic practice and bioclinical work that depend on linkages between technical, commercial, patient, clinical, and legal interests and institutions. The development of the genomic signatures for each trial--Oncotype DX and MammaPrint--has followed quite different routes. Oncotype began as a commercial platform: the company that produced it did not discover a signature but rather constructed it by asking users at every step what clinical question they wanted the signature to answer and what data would be credible in that regard. The test has been designed to minimally disrupt existing clinical workflows. MammaPrint, on the other hand, began as a breast cancer signature: the researchers who discovered it, at the Netherlands Cancer Institute (NKI), established a company to commercialize it as a test after the fact. MammaPrint requires a change in pathologists' routines. Thus, while these two trials signify a new departure for clinical cancer trials on a number of levels--they both incorporate new models of interaction between biotech companies and public research, and they both aim to establish the clinical relevance of genomic markers--they also embody different socio-technical scripts: one attempts to accommodate established routines, while the other openly challenges prevailing evidential hierarchies and existing biomedical configurations.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.407 | 0.368 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.059 |
| Scholarly communication | 0.029 | 0.040 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".