Soluble Immune Checkpoint Molecules as Predictors of Efficacy in Immuno-Oncology Combination Therapy in Advanced Renal Cell Carcinoma
Bibliographic record
Abstract
Immuno-oncology (IO) combination therapy is the first-line treatment for advanced renal cell carcinoma (RCC). However, biomarkers for predicting the response to IO combination therapy are lacking. Here, we investigated the association between the expression of soluble immune checkpoint molecules and the therapeutic efficacy of IO combination therapy in advanced RCC. The expression of soluble programmed cell death-1 (sPD-1), soluble programmed cell death ligand-1 (sPD-L1), soluble PD-L2 (sPD-L2), and lymphocyte activation gene-3 (sLAG-3) was assessed in plasma samples from 42 patients with advanced RCC who received first-line IO combination therapy. All IMDC risk classifications were represented among the patients, including 14.3, 57.1, and 28.6% with favorable, intermediate, and poor risk, respectively. Univariate analysis revealed that prior nephrectomy, sPD-L2 levels, and sLAG-3 levels were significant factors affecting progression-free survival (PFS), whereas multivariate analyses suggested that sPD-L2 and sLAG-3 levels were independent prognostic factors for PFS. In a univariate analysis of the overall survival, prior nephrectomy and sPD-L2 levels were significant factors; no significant differences were observed in the multivariate analysis. No significant correlation was observed between the sPD-L2 and sLAG-3 levels and PD-L2 and LAG-3 expression via immunohistochemistry. In conclusion, sPD-L2 and sLAG-3 expression may serve as a potential biomarker for predicting IO combination therapy efficacy.
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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) | 0.000 | 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".