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.
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 | 0.055 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".