Designing Transformative Clinical Trials in the Cancer Genome Era
Bibliographic record
Abstract
The incorporation of molecular profiling into routine clinical practice has already been adopted in some tumor types, such as human epidermal growth factor receptor 2 (HER2) testing in breast cancer and KRAS genotyping in colorectal cancer, providing a guide to treatment selection that is not afforded by histopathologic diagnosis alone. It is inevitable that over time, with rapid advances in scientific knowledge, bioinformatics, and technology to identify oncogenic drivers, molecular profiling will complement histopathologic data to influence management decisions. Emerging technologies such as multiplexed somatic mutation genotyping and massive parallel genomic sequencing have become increasingly feasible at point-of-care locations to classify cancers into molecular subsets. Because these molecular subsets may differ substantially between each other in terms of sensitivity or resistance to systemic agents, there is consensus that clinical trials should be more stratified for or be performed only in such molecularly defined subsets. This approach, however, poses challenges for clinical trial designs because smaller numbers of patients would be eligible for such trials, while the number of novel anticancer drugs warranting further clinical exploration is rapidly increasing. This article provides an overview of the emerging methodologic challenges in the cancer genome era and offers some potential solutions for transforming clinical trial designs so they can identify new active anticancer regimens in molecularly defined subgroups as efficiently as possible.
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 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 | 0.052 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".