Capture‐based next‐generation sequencing reveals multiple actionable mutations in cancer patients failed in traditional testing
Bibliographic record
Abstract
BACKGROUND: Targeted therapies including monoclonal antibodies and small molecule inhibitors have dramatically changed the treatment of cancer over past 10 years. Their therapeutic advantages are more tumor specific and with less side effects. For precisely tailoring available targeted therapies to each individual or a subset of cancer patients, next-generation sequencing (NGS) has been utilized as a promising diagnosis tool with its advantages of accuracy, sensitivity, and high throughput. METHODS: We developed and validated a NGS-based cancer genomic diagnosis targeting 115 prognosis and therapeutics relevant genes on multiple specimen including blood, tumor tissue, and body fluid from 10 patients with different cancer types. The sequencing data was then analyzed by the clinical-applicable analytical pipelines developed in house. RESULTS: We have assessed analytical sensitivity, specificity, and accuracy of the NGS-based molecular diagnosis. Also, our developed analytical pipelines were capable of detecting base substitutions, indels, and gene copy number variations (CNVs). For instance, several actionable mutations of EGFR,PIK3CA,TP53, and KRAS have been detected for indicating drug susceptibility and resistance in the cases of lung cancer. CONCLUSION: Our study has shown that NGS-based molecular diagnosis is more sensitive and comprehensive to detect genomic alterations in cancer, and supports a direct clinical use for guiding targeted therapy.
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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 | 0.000 | 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 | 0.000 |
| 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".