Predictive Biomarkers for Checkpoint Blockade in Urothelial Cancer: A Systematic Review
Bibliographic record
Abstract
PURPOSE: Immune checkpoint inhibitors have had a major impact on the management of advanced urothelial cancer. Despite the impact only a minority of patients derive benefit. In this context predictive biomarkers which can assist in patient selection are needed. In this systematic review we surveyed the current biomarkers which have been investigated in clinical studies and their potential for patient selection. MATERIALS AND METHODS: We searched MEDLINE® and EMBASE®, and manually reviewed major meeting abstracts to find studies in humans of immune checkpoint inhibitors given for urothelial cancer that included biomarkers and clinical outcomes. Studies had to provide the correlation between biomarkers and outcomes to be included in analysis. Results published only in abstract form were included since several important biomarker studies have yet to be published. RESULTS: We retrieved 1,236 studies, of which 921 were unique and screened, including 144 which met criteria for full review and 25 were included in the analysis. The manual search yielded 1 additional entry not included in our systematic review for a total of 26 entries. The checkpoint inhibitors used in these studies included atezolizumab, avelumab, durvalumab, ipilimumab, nivolumab and pembrolizumab. The biomarkers tested included PD-L1 immunohistochemistry, molecular subtyping and immune gene expression analysis by RNA sequencing, targeted gene panels for mutations in DNA damage repair genes and estimation of the tumor mutational burden, genomic alterations and the total mutational burden by exome sequencing, analysis of tumor immune infiltrate by immunohistochemistry and T-cell receptor sequencing, and analysis of circulating immune cells and cytokines. CONCLUSIONS: No single biomarker has been able to accurately predict the response to immune checkpoint inhibitors. Most studies included only a treatment arm and without a comparator arm it is not possible to ascertain whether biomarkers are predictive or merely prognostic. While PD-L1 immunohistochemistry has been largely unsuccessful, other biomarkers reflecting the immunogenicity of the underlying tumor, the characteristics of the immune infiltrate and the properties of the patient immune system have shown promising data. However, all are in need of validation.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".