Clinical outcomes after whole-genome sequencing in patients with metastatic non-small-cell lung cancer
Why this work is in the frame
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Bibliographic record
Abstract
The Personalized Onco-Genomics (POG) program at BC Cancer integrates whole-genome (DNA) and RNA sequencing into practice for metastatic malignancies. We examined the subgroup of patients with metastatic non-small-cell lung cancer (NSCLC) and report the prevalence of actionable targets, treatments, and outcomes. We identified patients who were enrolled in the POG program between 2012 and 2016 who had a tumor biopsy and blood samples with comprehensive DNA (80×, 40× normal) and RNA sequencing followed by in-depth bioinformatics to identify potential cancer drivers and actionable 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). In 29 NSCLC cases, 11 were male (38%), the median age was 60.2 yr (range: 39.4–72.6), and histologies included were adenocarcinoma (93%) and squamous cell carcinoma (7%). Potential molecular targets (i.e., cancer drivers including TP53 mutations) were identified in 26 (90%), and 21 (72%) had actionable targets. Therapies based on standard-of-care mutation analysis, such as EGFR mutations, were not considered POG-informed therapies. Thirteen received POG-informed therapies, of which three had no therapy before POG; therefore a comparator PFS could not be obtained. Of 10 patients with POG-informed therapy, median PFS ratio was 0.94 (IQR 0.2–3.4). Three (30%) had a PFS ratio ≥1.3, and three (30%) had a PFS ratio ≥0.8 and <1.3. In this small cohort of NSCLC, 30% demonstrated longer PFS with POG-informed therapies. Larger studies will help clarify the role of whole-genome analysis in clinical practice.
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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 | 0.000 |
| 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 it