Pretreatment Lung Immune Prognostic Index Is a Prognostic Marker of Chemotherapy and Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitor
Bibliographic record
Abstract
BACKGROUND: Lung immune prognostic index (LIPI) was recently developed on the basis of the combination of baseline derived neutrophil to lymphocyte ratio (dNLR) and lactate dehydrogenase (LDH). This index was demonstrated as a specific biomarker of immune checkpoint inhibitors for non-small cell lung cancer (NSCLC). We aimed to show that LIPI may be a useful biomarker of cytotoxic chemotherapy and epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) for NSCLC. METHODS: We retrospectively collected 175 wild-type EGFR adenocarcinomas, 131 NSCLCs harboring mutant EGFR and 110 squamous cell carcinomas. All patients initiated first-line cytotoxic chemotherapy or EGFR-TKI monotherapy between July 2007 and August 2017 at our hospital. These patients were divided into good, intermediate and poor LIPI groups. We compared their overall survival (OS) and progression-free survival (PFS). Multivariate analyses detected prognostic and predictive factors of OS and PFS. RESULTS: The good LIPI group survived longer than the intermediate and poor LIPI groups in wild-type EGFR adenocarcinoma (good, intermediate and poor LIPI groups: median 19.6, 11.5 and 3.3 months, P < 0.01, respectively) and mutant EGFR NSCLC (45.4, 25.6 and 15.7 months, P < 0.01). The PFS of good LIPI group was significantly longer that those of the other two groups in mutant EGFR NSCLC (16.6, 12.6 and 8.3 months, P < 0.01). The intermediate group (hazard ratio (HR) 1.49, 95% confidential interval (CI) 1.03 - 2.15, P = 0.04) of wild-type EGFR adenocarcinoma, intermediate (HR 2.30, 95% CI 1.33 - 3.99, P < 0.01) and poor (HR 2.76, 95% CI 1.03 - 7.42, P = 0.04) groups of mutant EGFR NSCLC were independent prognostic factors of poor OS. The intermediate (HR 1.57, 95% CI 1.01 - 2.44, P = 0.04) and poor (HR 2.63, 95% CI 1.14 - 6.07, P = 0.02) groups were significant prognostic factors of PFS of mutant EGFR NSCLC. CONCLUSIONS: LIPI was an independent prognostic factor of chemotherapy for adenocarcinoma with wild-type EGFR and of EGFR-TKI for NSCLC harboring mutant EGFR. Thus, LIPI was not a specific biomarker for ICI therapy, but a useful biomarker for chemotherapy and EGFR-TKI therapy in specific subsets of 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 | 0.000 | 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) | 0.002 | 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".