The Predictive Value of Programmed Death Ligand 1 in Patients with Metastatic Renal Cell Carcinoma Treated with Immune-checkpoint Inhibitors: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
CONTEXT: Immune-checkpoint inhibitors (ICIs) are a mainstay treatment of metastatic renal cell carcinoma (mRCC). As not all patients benefit from ICIs, a biomarker-driven clinical decision-making strategy is desirable. OBJECTIVE: To assess the predictive value of programmed death ligand 1 (PD-L1) in mRCC patients treated with ICIs. EVIDENCE ACQUISITION: Multiple databases were searched for articles published up to April 2020 according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses statement. Studies comparing objective response rate (ORR), complete response rate (CRR), progressive disease rate (PDR), or progression-free survival (PFS) based on tumor PD-L1 status in mRCC patients were eligible. EVIDENCE SYNTHESIS: Six studies matched our eligibility criteria. Treatment with ICIs was associated with significantly higher ORRs and CRRs, and lower PDRs in patients with PD-L1-positive tumors than in those with PD-L1-negative status (odds ratio [OR] 1.84, 95% confidence interval [CI] 1.48-2.28; OR 3.11, 95% CI 2.04-4.75; and OR 0.43, 95% CI 0.31-0.60, respectively). ICI treatment was associated with significantly better PFS in PD-L1-positive patients than in sunitinib-treated patients (hazard ratio 0.65, 95% CI 0.57-0.74), whereas this was not found in patients with PD-L1-negative tumors. Compared with sunitinib, ICI combination therapy improved ORRs and PFS significantly in PD-L1-positive patients of all examined ICIs. Nivolumab plus ipilimumab had the highest likelihood of providing the highest ORR and longest PFS in PD-L1-positive patients. CONCLUSIONS: PD-L1 positivity of the tumor is associated with improved ORRs and prolonged PFS in mRCC patients receiving ICI treatment and thus helps identify mRCC patients most likely to benefit from ICI treatment. PATIENT SUMMARY: The use of an immune-checkpoint inhibitor for the treatment of metastatic renal cell carcinoma (mRCC) improved oncological outcomes, and the status of programmed death ligand 1 could contribute to guiding patients and clinicians when determining personalized treatment strategies for mRCC.
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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.032 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".