2O TILs and PD-L1 early dynamics in the randomized Neo-CheckRay phase II trial evaluating neo-adjuvant immuno-radiation and adenosine pathway blockade for early-stage, high risk ER+/HER2- breast cancer (BC)
Bibliographic record
Abstract
Background: Immunotherapy has transformed treatment for advanced non-small cell lung cancer (NSCLC).However, reliable biomarkers for treatment selection remain scarce.Gut microbiota (GM) has emerged as a potential biomarker, but its role in chemo-immunotherapy for NSCLC is unclear.Methods: The phase III trial (JCOG2007, NIPPON) compared pembrolizumab plus platinum doublet chemotherapy (PC) and nivolumab-ipilimumab plus platinum doublet chemotherapy (NIC) in treatment-naïve advanced NSCLC patients without driver gene alterations.As an ancillary biomarker study, 270 patients with baseline fecal samples were analyzed for GM composition out of 295 patients enrolled in JCOG2007.16S rDNA sequencing was performed for the subsequent GM diversity and differential abundance analysis.Results: The beta diversity analysis of the overall cohort (n¼270) revealed distinct microbial structures between responders and non-responders, categorized by overall survival (OS) at 12 or 18 months.Subsequent LEfSe analysis identified specific bacterial genera that differed between the groups, with Fusicatenibacter, Butyricicoccus, and Blautia being enriched in patients with longer OS.Regarding adverse events (AEs), lower microbial alpha diversity and the presence of certain taxa were linked to a higher risk of severe AEs ( grade 4).Additionally, favorable genera, including Fusicatenibacter and Butyricicoccus, were associated with a lower risk of severe AEs.Lastly, regimen-specific analysis showed that higher abundance of Fusicatenibacter and Butyricicoccus in the NIC arm were linked to better OS compared to the CP arm (Hazard Ratio (HR) for OS ¼ 0.56 and 0.52, respectively).Conversely, the higher abundance of Prevotellaceae NK3B31 was associated with higher mortality risk in the NIC arm (HR for OS ¼ 2.33).Conclusions: GM may serve as a biomarker for chemo-immunotherapy in advanced NSCLC.Differences in microbial diversity and specific bacterial genera were associated with prognosis and severe AEs, with potential regimen-specific effects.These findings support integrating GM profiling into clinical practice to optimize first-line treatment strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".