Oncological Response and Predictive Biomarkers for the Checkpoint Inhibitors in Castration-Resistant Metastatic Prostate Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Recently, checkpoint inhibitors have been investigated in metastatic prostate cancer, however their overall effect is unclear and needs to be further investigated. Objectives: The aim of this systematic review is to investigate the oncological response of metastatic castration-resistant prostate cancer patients to immune checkpoint inhibitors. Methods: Based on the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement, a systematic review of the literature was conducted through online electronic databases and the American Society of Clinical Oncology (ASCO) Meeting Library. Eligible publications were selected after a staged screening and selection process. RevMan 5.4 software was employed to run the quantitative analysis and forest plots. Risk of bias assessment was conducted using the Cochrane tool and Newcastle–Ottawa Scale for the randomized and non-randomized trials, respectively. Results: From the 831 results retrieved, 8 studies including 2768 patients were included. There was no significant effect on overall survival (OS) (overall response (OR) = 0.98; Z = 0.42; p = 0.67). Meanwhile, progression-free survival (PFS) was significantly better with immune checkpoint inhibitors administration (OR = 0.85; Z = 3.9; p < 0.0001). The subgroup analysis for oncological outcomes based on programmed death ligand 1 (PD-L1) positivity status displayed no significant effect, except on prostate-specific antigen response rate (PSA RR) (OR = 3.25; Z = 2.29; p = 0.02). Based on DNA damage repair (DDR), positive patients had a significantly better PFS and a trend towards better OS and overall response rate (ORR); the ORR was 40% in positive patients compared to 20% in the negative patients (OR = 2.46; Z = 1.3; p = 0.19), while PSA RR was 23.5% compared to 14.3% (OR = 1.88; Z = 0.88; p = 0.38). Better PFS was clearly associated with DDR positivity (OR = 0.70; Z = 2.48; p = 0.01) with a trend towards better OS in DDR positive patients (OR = 0.71; Z = 1.38; p = 0.17). Based on tumor mutation burden (TMB), ORR was 46.7% with high TMB versus 8.8% in patients with low TMB (OR = 11.88; Z = 3.0; p = 0.003). Conclusions: Checkpoint inhibitors provide modest oncological advantages in metastatic castration-resistant prostate cancer. There are currently no good predictive indicators that indicate a greater response in some patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".