Molecular Biomarkers of Response to PD-1/ PD-L1 Immune Checkpoint Blockade in Advanced Bladder Cancer
Bibliographic record
Abstract
Background: The activity of PD-1/PD-L1 inhibitors in the treatment of advanced bladder cancer (BC) is promising for many patients. However, a subset of patients do not benefit from treatment, thus leading to an effort to better identify predictive molecular biomarkers of response. Objective: To conduct a systematic review of the literature on predictive molecular biomarkers associated with response to PD-1 and PD-L1 inhibitors in advanced bladder cancer, defined as locally-advanced, unresectable, or metastatic (mBC) disease. Methods: A search of the literature was performed using Embase (1947 -January 2019), Medline (1946 -January 2019), and EBM Reviews for Cochrane Central Register of Controlled Trials (as of December 2018). Studies examining the association of molecular biomarkers with clinical outcome in BC treated with PD-1 or PD-L1 monotherapy were included. Outcomes of interest were overall survival (OS), cancer-specific survival (CSS), progression-free survival (PFS), duration of response, and objective response rate (ORR). Results: Using the study search criteria, 899 unique abstract citations were found, of which 834 did not meet the eligibility criteria. Full text of the remaining 65 citations were screened, and 50 studies excluded, including 18 review articles. Eight additional studies from the bibliography of the review papers were included, making a total of 23 studies. Five PD-1 / PD-L1 antibodies have been tested in BC immunohistochemistry (IHC). These studies used different expression scoring criteria and generally had poor ability to discriminate likelihood for response. Overall, the data suggests CD8 + T cell infiltration is necessary to mediate an antitumor immune response, but other immune cell populations, such as neutrophils may suppress T cell-mediated immunity and efficacy of PD-1/PD-L1 blockade. An IFN signature is a promising predictor, but there needs to be consensus on the optimal gene panel composition, and prospective validation. Tumor mutation burden (TMB) is a promising predictor in six studies reporting on 1200 patients, but there is not a consensus on the optimal definition of "high TMB". Detection of T cell receptor (TCR) clonal expansion has only been conducted in small studies and so its predictive value remains inconclusive. Epithelial-mesenchymal transformation (EMT) and transforming growth factor (TGF) are associated with poor prognosis and possibly intrinsic resistance to PD-1/PD-L1 checkpoint blockade, but more work needs to be done to build upon and confirm the initial findings.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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; both teacher heads agree on what is shown here.
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".