Clinical impact of <i>EGFR</i> mutation fraction and tumor cellularity in <i>EGFR</i> mutation positive NSCLC.
Bibliographic record
Abstract
e19077 Background: We investigated the impact of mutation fraction, tumour sample cellularity, and diagnostic specimen type on EGFR TKI response, time to treatment failure (TTF) and overall survival in advanced EGFR mutation positive NSCLC patients. Methods: From March 2010 to May 2012, EGFR testing in the province of Ontario (Canada) was conducted at a single centre, using fragment analysis for exon 19 deletion and Sau961 restriction enzyme digest for exon 21 mutation. Patients with EGFR mutation positive samples were identified, tumour cellularity, mutation fraction (percent tumour cells mutated) and clinical outcome data collected. Regression analysis was undertaken to assess the association between demographic variables, mutation fraction, tumour cellularity and sample type on clinical outcomes. Results: Of 173 patients identified to date, 153 received EGFR TKI and are included in this analysis, with median follow up of 10.6 months (range 0-44). Most are female (73%), never smokers (65%), have exon 19 deletions (55%; 45% exon21 L858R), and median age 64 years (range 34 to 95). Tumour specimens tested include resection (36%), cytology (32%), and core biopsy samples (32%). Median EGFR mutation fraction is 30% (range 1% to 96%); 18% had a low (<10%) mutation fraction. Responses (any reduction) were seen in 66%, mixed response or stable disease in 28%, and progression in 7%. Median TTF of EGFR TKI therapy is 13.8 months. Median OS from TKI start is 24.1 months, with 1-, 2- and 3-year rates of 75%, 51% and 42%. In multivariable analysis, increasing mutation fraction was associated with EGFR TKI response (OR 2.5, p<0.01), but not TTF or OS. Female sex was associated with TTF (HR 0.49, p=0.007), and testing of larger samples (lobectomy, wedge, excision) was associated with TTF and OS (HR 0.45, p=0.007; HR 0.26, p=0.0002). Exon 19 deletions are associated with a higher risk of death (HR 2.41, p=0.02) and a lower probability of response than L858R (OR 0.31, p=0.004). Conclusions: Pathologic features may be relevant to clinical outcomes in EGFR mutation positive NSCLC, including mutation fraction, sample cellularity, specimen tested and mutation type. Validation of the use of EGFR mutation fraction in clinical decision-making is ongoing.
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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 | 0.000 | 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) | 0.001 | 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".