Programmed Death Ligand-1 Immunohistochemistry— A New Challenge for Pathologists: A Perspective From Members of the Pulmonary Pathology Society
Bibliographic record
Abstract
The binding of programmed death ligand-1 and ligand-2 (PD-L1 and PD-L2) to PD-1 blocks T-cell-mediated immune response to tumor. Antibodies that target programmed death receptor-1 (PD-1) will block the ligand-receptor interface, thereby allowing T cells to attack the tumor and increase antitumor immune response. In clinical trials, PD-1 inhibitors have been associated with an approximately 20% overall response rate in unselected patients with non-small cell lung cancer, with sustained tumor response in a subset of patients treated by these immune checkpoint inhibitors. Facing a proliferation of PD-L1 immunohistochemistry clones, staining platforms, and scoring criteria, the pathologist must decide on the feasibility of introducing a newly approved companion diagnostic assay that may require purchase not only of a specific antibody kit but of a particular staining platform. Given the likely reality that clinical practice may, in the near future, demand access to 4 different PD-L1 antibodies coupled with different immunohistochemistry platforms, laboratories will be challenged with deciding among this variety of testing methods, each with its own potential benefits. Another immediate challenge to PD-L1 testing in lung cancer patients is that of access to adequate tumor tissue, given that non-small cell lung cancer samples are often extremely limited in size. With PD-L1 testing it has become clear that the historically used US regulatory approach of one assay-one drug will not be sustainable. One evolving concept is that of complementary diagnostics, a novel regulatory pathway initiated by the US Food and Drug Administration, which is distinct from companion diagnostics in that it may present additional flexibility. Although pathologists need to face the practical reality that oncologists will be asking regularly for the PD-L1 immunohistochemistry status of their patients' tumors, we should also keep in mind that there may be room for improvement of biomarkers for immunotherapy response. The field is rich with opportunities for investigation into biomarkers of immunotherapy response, particularly in the form of collaborative, multidisciplinary studies that incorporate oncologists, pathologists, and basic scientists. Pathologists must take the lead in the rational incorporation of these biomarkers into clinical practice.
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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 | 0.033 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.018 | 0.029 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".