Real-time clinical application of next-generation sequencing (NGS): Results from a multicenter program.
Bibliographic record
Abstract
11016 Background: NGS techniques enable the identification of actionable mutations in clinical tumor samples. The objective of this study is to assess feasibility and explore the impact of real-time targeted NGS on therapeutic decision-making. Methods: Patients (pts) with advanced solid tumors underwent a biopsy of a metastatic lesion. The first phase was performed with Sequenom MassARRAY somatic genotyping and Pacific Biosciences RS-targeted NGS. The second phase broadened genomic coverage in both Sequenom and Illumina MiSeq. All pts had a molecular profiling (mp) report issued after identified actionable mutations were verified by Sanger sequencing in a CLIA-lab and reviewed by an expert panel. “Actionability” was defined as having prognostic, predictive or diagnostic implications on patient management. Details of clinical outcomes and subsequent matched therapy, if applicable, were captured. Referring physicians were surveyed on the impact of mutation results on their treatment recommendations. Results: These are summarized in the Table. Conclusions: Broader mp platforms resulted in more identified actionable mutations which required a longer time for verification prior to reporting, but may yield a greater impact on clinical decision-making. However, the matching of pts to drugs based on their molecular profiles depends highly on drug access. For mp to be clinically relevant, it must be coupled with access to approved drugs or to investigational agents on clinical trials. Clinical trial information: NCT01345513. [Table: see text]
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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 | 0.019 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".