Enhancing Clinical Cancer Research Through Sharing of Data and Biospecimens
Bibliographic record
Abstract
Importance: Molecular analyses of biospecimens collected from study participants are essential for identifying biomarkers that can tailor treatments to specific subsets of patients who are most likely to benefit. Sharing of data and biospecimens from clinical trials enables personalized, patient-centric use of cancer therapies and accelerates the development of new treatments. Objective: To describe obstacles to sharing data and biospecimens and to propose strategies to enhance access and collaboration. Evidence Review: This is a Special Communication authored by 53 academic investigators and patient representatives from the breast cancer community with extensive experience in conducting clinical and translational research. The article also evaluates the impact of biomarker research on specifying responsive subpopulations in the 29 registrational clinical trials that have led to approval of a new drug for treatment of breast cancer between 2017 and 2024. Findings: Clinical trial participants are increasingly asked to provide tissue and/or body fluid biospecimens for biomarker research that is typically controlled by the sponsoring pharmaceutical company, but published biomarker studies are rare. Among 29 breast cancer registrational studies reported in the past 8 years, none resulted in biomarker research that restricted a drug's approved indication. Herein, strategies to maximize the value of clinical data and biospecimens contributed by participants are proposed, thereby supporting the shared goals of the pharmaceutical industry and academia to improve patient care. These strategies include (1) establishing coleadership structures involving academia and patients in clinical trial design and conduct, (2) ensuring that informed consent forms state that data and biospecimens will be shared with academia for future research, (3) requiring the sharing of clinical data as a condition for regulatory approval, and (4) enabling access to biospecimens and translational research data for independent studies on biomarkers that may indicate drug efficacy and toxicity. Conclusions and Relevance: Data and biospecimen sharing from registrational trials has been suboptimal. Improving clinical data, biospecimens, and biospecimens' related data sharing requires concrete actions and a multidimensional stakeholder approach to accelerate the impact of clinical cancer research on the quality of patient care.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".