Lung cancer in never-smokers from the Princess Margaret Cancer Centre.
Bibliographic record
Abstract
8099 Background: Lung cancer in never smokers accounts for ~15-20% of cases, and globally is a growing clinical problem. Methods: We identified 570 never-smokers with lung cancer diagnosed from 1988-2013 at the Princess Margaret Cancer Centre. Clinical and demographic data were retrieved from the electronic patient record with the aim of characterizing the epidemiology, demographics, pathology, molecular profile, treatment, and survival in these patients. Results: There were 409 females (72%) and 161 males (28%), median age 62.1 years (18– 94), 46% Caucasian, 34% Asian, 6% Black, 4% South Asian, 4 % Filipino, 6% Other/Unknown. Environmental tobacco exposure was identified in only 17%. A history of prior malignancy was present in 89 (14.2%) patients (1 cancer 81; multiple cancers 8 patients). Most patients (87.3%) had adenocarcinoma and most presented in stage IV - 54.9%, followed by I - 26%, III - 12.8% and II - 6.3%. In stage IV patients, 88% were ECOG PS 0-1. Among 310 patients with molecular results to date, the mutation rate was 71% (EGFR mutation [mut] 76%, ALK 10%, KRAS 4%, HER2 1.4% BRAF 0.5%, other 1.8%, multiple mut 6.3%. Brain metastases at presentation occurred in 18.4% stage IV patients (77/419): 67% (52/77) with EGFR mut vs 22% (17/77) with EGFR WT vs 10% (8/77) with other mut (P=0.0023). Median overall survival (OS) of EGFR mut patients vs EGFR WT with brain metastases at presentation was 57 vs 16 mo (P=0.031). Stage IV patients with EGFR and ALK mut received targeted treatment in 88% (121/138) and 89% (16/18) cases, respectively, with 65% (92/138, 66% for EGFR and 14/18, 82% for ALK) staying on TKI >6 months. Overall, 68% (285/419) of patients received 1-3 lines of systemic therapy (range 0-8 lines). Median OS was 47 mos. Median OS for patients with known vs unknown mutation status was 59 mo vs 34 (P<0.001). Early stage (P<0.0001), PS 0-1 (P<0.0001), presence of EGFR (P=0.003) or ALK mut (P=0.035) were associated with longer survival, but not Asian ethnicity (P=0.6) or female sex (P=0.1). Conclusions: Lung cancer in never-smokers represents a distinct clinical and molecular entity characterized by a high incidence of targetable mutations and long survival. Updated molecular profiling results will be presented for the entire cohort.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".