Is There a Role for Programmed Death Ligand-1 Testing and Immunotherapy in Colorectal Cancer With Microsatellite Instability? Part II—The Challenge of Programmed Death Ligand-1 Testing and Its Role in Microsatellite Instability-High Colorectal Cancer
Bibliographic record
Abstract
CONTEXT: - The world of oncology has changed dramatically in the past few years with the introduction of checkpoint inhibitors and immunotherapy. The promising findings of a small, phase 2 clinical trial that led to the US Food and Drug Administration breakthrough designation and approval of the anti-programmed death receptor-1 (PD-1) drug pembrolizumab (Keytruda, Merck, Kenilworth, New Jersey) to treat metastatic/refractory microsatellite instability-high colorectal cancer (CRC) has significantly boosted interest in immunomodulatory therapies in microsatellite instability-high CRC. OBJECTIVES: - To review the immune response to cancer and the role of immune checkpoints, focusing on the technical and interpretation challenges of PD-1/programmed death ligand-1 (PD-L1) testing by pathologists and the clinical implications of the test and the therapeutic potential of treating CRC with checkpoint inhibitors. DATA SOURCES: - A PubMed review was performed of articles pertaining to CRC, microsatellite instability and mismatch repair systems, molecular classification, immune response, PD-1/PD-L1, and immunotherapy. CONCLUSIONS: - Exciting success with anti-PD-1/PD-L1 and anticytotoxic T-lymphocyte-associated protein 4 (CTLA4) checkpoint inhibitors has already been reported in melanoma and in lung and renal carcinomas. Recently, microsatellite instability-high CRCs, expressing PD-L1 by immunohistochemistry, regardless of the level of that PD-L1 expression, appeared to respond to checkpoint blockades with anti-PD-1 or anti-PD-L1 agents, whereas microsatellite-stable tumors were much less responsive. With microsatellite instability routinely tested by most centers, studies that include larger cohorts are required to study the predictive role of PD-1/PD-L1 expression in microsatellite instability-high CRC, to assess which immunohistochemistry antibodies to use, to refine the scoring criteria, and to critically analyze the interpretation pitfalls.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".