LKB1 Loss Assessed by Immunohistochemistry as a Prognostic Marker to First-Line Therapy in Advanced Non-Small-Cell Lung Cancer
Bibliographic record
Abstract
(1) Background: Liver kinase B1 (LKB1) is a tumor suppressor gene involved in cell growth and metabolism. However, its alterations are not routinely assessed for guiding therapy in clinical practice. We assessed LKB1 expression by immunohistochemistry as a potential biomarker. (2) Methods: This bicentric retrospective cohort study analyzed data from patients with advanced NSCLC who initiated platinum-based chemotherapy or epidermal growth factor receptor- tyrosine kinase inhibitor (EGFR-TKI) between January 2016 and December 2020. Kaplan-Meier and Cox regression models were used for survival curves and multivariate analysis. (3) Results: 110 patients were evaluated, and the clinical stage IV predominated the lung adenocarcinoma histology. LKB1 loss was observed in 66.3% of cases. LKB1 loss was associated with non-smokers, the absence of wood smoke exposure and an EGFR wild-type status. The median progression-free survival (PFS) and overall survival (OS) in the population were 11.1 and 26.8 months, respectively, in the loss group, compared with cases exhibiting a positive expression. After an adjustment by age, smoking status, Eastern Cooperative Oncology Group Performance Score (ECOG-PS), EGFR status and type of administered therapy, LKB1 loss was significantly associated with worse PFS and OS. (4) Conclusion: Patients with an LKB1 loss had worse clinical outcomes. This study warrants prospective assessments to confirm the prognostic role of the LKB1 expression in advanced NSCLC.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".