Cytokines performance during nivolumab treatment: A subgroup analysis of NIVACTOR study.
Bibliographic record
Abstract
e18008 Background: Nivolumab is approved for 2nd line R/M SCCHN, but only ̃20% of patients will benefit. Therefore, there is an unmet need for predictive markers. Nivactor is a prospective real-life study of nivolumab in SCC-HN. We report an ancillary study on circulating cytokines in a subgroup of patients from Nivactor. Methods: We analysed changes of circulating cytokines (IL-2/4/5/6/8/10/12/13/15, CCL-2/4/22, CXCL-10, IFN-γ, TGF-β, TNF-α, VEGF) during nivolumab therapy at baseline (T0), cycle 3 (T1), cycle 7 (T2) and at disease progression (TPD). ROC analysis identifies cytokines that correlate best with PFS and OS and calculate cut-off points. Cox analysis was performed to assess HR. Hierarchical Clustering on Principal Components (HCPC) was performed to cluster pts and then compared clusters for good or poor PFS and OS. Correction for multiplicity was applied. Results: 19 pts were analysed. Using ROC analysis, we were able to identify cut-off values correlated with PFS at T0 only for IL-6 and IL-10. Similarly, we identified IL-6, IL-10, TGF-β and VEGF cut-off for OS. Then we performed Cox analysis dividing the population based on the identified cut-offs and we found that pts with IL-6 or IL-10 lower than cut off had a significant better PFS and OS (p = 0.049 and p = 0.003 for PFS, respectively; p = 0.029 and p = 0.006 for OS, respectively). TGF-β lower than cut-off correlates with better OS (p = 0.02). Interestingly, pts with all the 3 cytokines below the cut-off show the best OS. Finally, we performed a multivariate analysis using HCPC to identify 4 clusters of pts. Cluster 2 has the best PFS and OS than all other cluster considered together (p = 0.001 and p = 0.004 respectively). Patients in cluster 2 show at T0 lower IL-6/10/15 and at T1 lower IL-5/10 and higher IFN-γ compared to the others. Longitudinal analysis among the 3 time points shows that the increase of IFN-γ, IL-5 and IL-10 between T0 and T1 correlates with better PFS (p = 0.01, p = 0.03, p = 0.02, respectively); only IFN-γ and IL-5 correlates with better OS (p = 0.04; p = 0.02, respectively). Conclusions: ROC analysis can identify cytokines whose basal level may predict patients that could benefit from nivolumab treatment: low IL-6 and IL-10 positively influence both PFS and OS. Low TGF-β does not correlate with PFS but only with better OS and can be considered a pure prognostic marker. Longitudinal analysis offers information on the effect of treatment on circulating cytokines, and show that the subgroup of pts with better PFS exhibits an increase of inflammatory cytokines values.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".