Clinical outcomes after whole genome sequencing in patients with metastatic non-small cell lung cancer.
Bibliographic record
Abstract
e20563 Background: The Personalized OncoGenomics (POG) program at the BC Cancer Agency integrates whole genome (DNA) and RNA sequencing into practice for metastatic malignancies. We examine patients with metastatic NSCLC and report the prevalence of actionable targets, treatments, and outcomes. Methods: Between 2012-2016, 217 patients had a tumor biopsy and blood sample with comprehensive DNA (80X; 40X normal) and RNA sequencing followed by in-depth bioinformatics to identify potential cancer “drivers” and/or actionable/treatable targets. In NSCLC cases, we compared the progression-free survival (PFS) of “POG-informed therapies” with the PFS of the last regimen prior to POG (PFS ratio). Results: In 29 NSCLC cases, median age was 60.2 years (range 39.4-72.6), 11 were male (38%), and histologies were: adenocarcinoma (93%); squamous cell carcinoma (7%). Potential molecular targets (i.e. cancer “drivers”) were identified in 26 (90%), and 21 (72%) had actionable targets. 13 received POG-informed therapies, of which 3 had no therapy before POG. Of 10 patients with POG-informed therapy, median PFS ratio was 0.94 (IQR 0.2-3.4). 3 (30%) had a PFS ratio ≥1.3, and 3 (30%) had a PFS ratio ≥0.8 and <1.3. Conclusions: In this NSCLC cohort, 30% demonstrated longer PFS with POG-informed therapies. Larger studies will help clarify the role of whole genome analysis in clinical practice. [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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".