Next-Generation Sequencing to Guide Clinical Trials
Bibliographic record
Abstract
Rapidly accruing knowledge of the mutational landscape of malignant neoplasms, the increasing facility of massively parallel genomic sequencing, and the availability of drugs targeting many "driver" molecular abnormalities have spurred the oncologic community to consider how to use these new tools to improve cancer treatment. In order to assure that assignment of patients to a particular targeted treatment is likely to be beneficial to the patient, it will be necessary to conduct appropriate clinical research. It is clear that clinical (histology and stage) eligibility criteria are not sufficient for most clinical trials using agents that target mutations that are present in only a minority of patients. Recently, several clinical trial designs have been suggested to test the benefit of targeted treatment in molecular and/or clinical subgroups of patients. However, challenges remain in the implementation of such trials, including choice of assay, levels of evidence regarding gene variants, tumor heterogeneity, identifying resistance mechanisms, the necessity of screening large numbers of patients, infrastructure needs, and collaboration of investigators and industry. This article reviews current trial designs and discusses some of the considerations, advantages, and drawbacks of designing clinical trials that depend on particular molecular variants as eligibility criteria.
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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 | 0.025 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".