Plasma genotyping to identify novel resistance variants in advanced NSCLC(DISCOVER): Non-AGA report
Bibliographic record
Abstract
Results: A total of 807 LBs were performed in 791 patients (51.3% female; median age 63.5; ECOG 1 90.4%; stage IV 84.3%).Tumor types included thoracic (46%), gastrointestinal (19%), breast (10%), gynecologic (7%), and others.Overall, 67.3% of LBs were informative, with a median turnaround of 11 days.Informativeness was higher in metastatic vs non-metastatic disease (71% vs 47%, p<0.001) and in progressive vs non-progressive patients (70% vs 39%, p<0.001).ESCAT I/II alterations were found in 37%, including 30.8% of those progressing without prior molecular findings (16.4% ESCAT I).MTB recommended targeted therapy in 25.6% (50% trials, 34% standard, 16% compassionate use).CH variants were detected in 91.5% of patients, most frequently in DNMT3A (69.76%),TP53 (34.94%),CHEK2 (29.98%),ATM (25%), TET2 (16.60%), and ASXL1 (10%).After CH exclusion, 81.7% (646 patients) remained informative, with actionable findings remaining at 37%.Suspected germline variants were identified in 5.9% overall.Conclusions: PRECISO provides a comprehensive pan-tumor real-world dataset integrating LB with MTB recommendations and systematic CH filtering.LB was feasible and clinically informative, supporting therapy recommendations in onefourth of patients.Although CH was highly prevalent, it did not alter actionable ESCAT detection.To our knowledge, this is among the largest prospective pantumor series systematically applying CH filtering in an MTB setting.
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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 | 0.001 | 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 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".