Clinico-pathologic and molecular characteristics of patients with resected lung adenocarcinoma.
Bibliographic record
Abstract
e23134 Background: Lung adenocarcinoma (LA) is the most common lung malignancy. Recently, a novel histologic classification was recognized as a stage-independent predictor of survival. Furthermore, important molecular discoveries in the lung cancer genome have identified targetable driver mutations that led to new therapeutic interventions. The purpose of this study was to assess clinico-pathologic and molecular characteristics of patients with resected LA at our centre. Methods: Through pathology records we identified patients with resected LA between January 2005 - December 2008. We collected demographic, treatment, pathologic tumor characteristics and disease outcomes. Molecular analysis was undertaken to investigate genomic profile by next generation sequencing assays (NGS). Data were analyzed to determine associations between clinico-pathologic and molecular characteristics and disease outcomes (chi-square tests, independent samples t-tests and Mann-Whitney U). Results: We identified 121 eligible patients. Mean age: 65; 58% female; 7% nonsmokers; 81% lobectomy. Stage distribution: I – 64%, II – 20%, III – 16%. Histologic subtype: mucinous 9%; lepidic 11%; acinar 46%; solid 27%; micro/papillary 7%. Neo/adjuvant therapy – 35%. Of these patients we obtained DNA and RNA from 36 tumors and molecular analysis was successful on all samples. Genomic alterations results: KRAS – 55%; EGFR – 11%; p53 – 51%; MET 8%; RET – 6%; HER 2 – 3% BRAF – 3%; ALK and ROS – 0%. Five-year overall survival (OS) was 69%; by stage: I – 79%; II – 37%; III – 65%; by neo/adjuvant therapy (yes vs no): stage I – 85 vs 78%; II – 41 vs 29%; III – 50 vs 87%; by histologic subtype: mucinous – 82%; lepidic – 92%; acinar – 70%; solid/micro/papillary – 56%; by KRAS status: wild-type 75%, mutated 74%. Conclusions: In this predominantly smoking population with resected LA the most common histologic subtype was acinar; the proportion of KRAS-mutations was higher than the national average, and the incidence of RET rearrangements was higher than expected. In this small study significant OS benefit with neo/adjuvant therapy was seen in stage I and II LA but not stage III; no significant associations were found between histological subtypes and KRAS mutation status.
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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 | 0.001 |
| 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) | 0.003 | 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".