Association between lung immune prognostic index, microbiome, and immunotherapy outcomes in non–small cell lung cancer.
Bibliographic record
Abstract
9050 Background: Host-related inflammatory biomarkers and gut microbiome are two major prognostic factors for non-small cell lung cancer (NSCLC) patients (pts) treated with immune checkpoint inhibitors (ICI). In this study, we aimed to assess an association between the Lung Immune Prognostic Index (LIPI) and the microbiome composition on ICI outcomes in two independent cohorts of NSCLC pts. Methods: We included 205 patients with advanced NSCLC treated with ICI (monotherapy or in combination with chemotherapy) from two independent cohorts. Metagenomics microbiome profiling was performed on the Canadian discovery cohort of 72 pts while 16S rRNA microbiome sequencing was used in the Japanese validation cohort of 133 pts. The LIPI score was calculated using the dNLR (neutrophils/[leucocytes-neutrophils]) and lactate deshydrogenase (LDH). Pts were classified as Good (G: 0 high factor), Intermediate (I: 1 high factor) and Poor (P: 2 high factors). Median overall survival (OS) was estimated using the Kaplan-Meier method. Microbiome diversity indexes and bacterial relative abundances were compared according to LIPI groups. Results: Among the 72 pts included in the discovery cohort, the median follow-up of 20.6 months (mos). The LIPI was distributed as follows: G (n = 31, 43.1%), I (31, 43.1%), P (10, 13.8%) and baseline characteristics were well balanced between the 3 groups. When segregating pts according to LIPI, the OS was 25.6 mo, 19.8 mo, and 5.7 mo in the G, I (HR: 1.71, 95%CI: 0.80-3.65) and P (HR: 3.97, 95%CI: 1.60-9.82) groups, respectively (p = 0.003). The microbiome alpha diversity was lower in the P group compared with the G group (p = 0.03), and there was a trend towards different microbiome composition in beta diversity (p = 0.055) between both groups. Pts in the G group had a favorable microbiome (enriched in Ruminococcus and Anaerostipes), while pts from the P group had an unfavorable microbiome (enriched in Enterobacteriaceae and Clostridium symbiosum and lavalense). Next, in the validation cohort of 133 pts, LIPI was distributed as follows: G (n = 62, 46.6%), I (51, 38.3%), P (20, 15%). Pts with G LIPI had not reached their median OS compared to pts in the I [15.7 mo HR: 1.60 (0.88-2.94)] and P [8.8 mo groups, HR: 2.02 (0.98-4.19)], p = 0.03. Similar to the discovery cohort, at the genus level, G LIPI group had enrichment of Ruminococcus as well as Anaerostipes compared with pts in the P and I groups with an overrepresentation of Hungatella. Conclusions: Host-related inflammatory biomarkers, represented by the LIPI, seemed to be associated with microbiome and ICI outcomes in pts treated with NSCLC. This observation was validated in an external validation cohort. This link could be in relation to the presence of proinflammatory bacteria in pts with poor LIPI.
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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) | 0.001 | 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".