Gustave Roussy Immune Score and Royal Marsden Hospital Prognostic Score Are Biomarkers of Immune-Checkpoint Inhibitor for Non-Small Cell Lung Cancer
Bibliographic record
Abstract
BACKGROUND: The Gustave Roussy Immune Score (GRIm-Score) and the Royal Marsden Hospital prognostic score (RMH score) were recently developed in order to improve a better participant selection for phase I trials. The GRIm-Score is formed by combination of lactate dehydrogenase (LDH), serum albumin concentration, and neutrophil-to-lymphocyte ratio (NLR). The RMH score is calculated by LDH, albumin, and number of metastases. These two scores have been validated only in phase I trials. The purpose of this study was to assess whether these scores are useful for practical treatment of immune-checkpoint inhibitor (ICI) monotherapy in pretreated non-small cell lung cancer (NSCLC). METHODS: This was a retrospective and single-centered study of 76 NSCLC patients treated with ICI monotherapy between December 2015 and October 2018 at our hospital. We divided 76 patients into high and low GRIm-Score and RMH score groups. Comparison of overall survival (OS) and progression free survival (PFS) was performed by Kaplan-Meier curves and log-rank tests. Independent prognostic factors of OS and PFS were analyzed by multivariate Cox proportional hazard analyses. RESULTS: The OS of the high GRIm-Score group was significantly shorter than that of the low score group (low vs. high; median 19.9 vs. 3.2 months, P < 0.01), while no significant difference was observed in PFS (2.6 vs. 2.1 months, P = 0.13). The PFS of the high RMH score was significantly shorter than that of the low score group (low vs. high; 2.6 vs. 1.8 months, P = 0.01), while there was no significant difference in OS (16.0 vs. 10.4, P = 0.24). Multivariate analyses detected high GRIm-Score (hazard ratio (HR) 3.93, 95% confidence interval (CI) 2.04 - 7.58, P < 0.01), and high RMH score (HR 1.76, 95% CI 1.03 - 3.02, P = 0.04) as poor prognostic factors of OS and PFS, respectively. CONCLUSIONS: Baseline GRIm-Score and RMH score were independent prognostic factors of OS and PFS of ICI monotherapy for pretreated NSCLC patients, respectively. These two scores are not only selection biomarkers for patients in experimental trials, but also useful prognostic biomarkers for NSCLC patients practically treated with ICI therapy.
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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 | 0.001 |
| 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) | 0.002 | 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".