Prognostic ability of the Gustave Roussy Immune Score for patients with advanced pancreatic adenocarcinoma.
Bibliographic record
Abstract
469 Background: The Gustave Roussy Immune Score (GRIm-S) considers a composite of neutrophil to lymphocyte ratio (> 6 = 1), albumin (< 35 = 1) and LDH (> ULN = 1) and has been established as a prognostic score and may in aid in the selection of patients for phase 1 trials of immune checkpoint inhibitors. Methods: We explored the prognostic impact of the GRIm-S (high > 1) in patients enrolled on the COMPASS trial and correlated the score with genomic and clinical characteristics. Patients in this trial had biopsies for whole genomic and RNA sequencing prior to standard chemotherapy regimens in the advanced setting. Results: 252 patients were included in the analyses with a median follow-up time of 28 months. 16% of patients had a high GRIm-S with significantly shorter median overall survival (OS) of 4.1 months versus 10.0 months in those with a low score (HR 2.18, 95% CI 1.4-3.4, p < 0.0001). In the GRIm-S-high cohort, early progression with non-evaluable disease and disease progression were more common than in the GRIm-S low cohort (56% vs 31%, p = 0.003). In a multivariable analysis, a high GRIm-S was poorly prognostic (HR 1.6 95% CI 1.3-1.9, p < 0.001), whereas the classical RNA subtype (vs. basal-like) (HR 0.41, 95% CI 0.3-0.6, p < 0.001) and a high HRDetect score (HR 0.47 95% CI 0.3-0.7, p < 0.001) associated with superior OS. The GRIm-S did not correlate with RNA subtypes or with specific KRAS mutations. There were no differences in structural variant load or tumour mutational burden between groups. However those with a high GRIm-S did have a higher total target lesion diameter at baseline (p < 0.001). Conclusions: The GRIm-S identifies a subset of patients who have aggressive pancreas cancer and short life expectancy. This information may help clinicians in treatment decision making and selection for clinical trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".