Fit-For-Purpose PD-L1 Biomarker Testing For Patient Selection in Immuno-Oncology: Guidelines For Clinical Laboratories From the Canadian Association of Pathologists-Association Canadienne Des Pathologistes (CAP-ACP)
Bibliographic record
Abstract
Since 2014, programmed cell death protein 1 (PD-1)/programmed cell death ligand 1 (PD-L1) checkpoint inhibitors have been approved by various regulatory agencies for the treatment of multiple cancers including melanoma, lung cancer, urothelial carcinoma, renal cell carcinoma, head and neck cancer, classical Hodgkin lymphoma, colorectal cancer, gastroesophageal cancer, hepatocellular cancer, and other solid tumors. Of these approved drug/disease combinations, a subset also has regulatory agency-approved, commercially available companion/complementary diagnostic assays that were clinically validated using data from their corresponding clinical trials. The objective of this document is to provide evidence-based guidance to assist clinical laboratories in establishing fit-for-purpose PD-L1 biomarker assays that can accurately identify patients with specific tumor types who may respond to specific approved immuno-oncology therapies targeting the PD-1/PD-L1 checkpoint. These recommendations are issued as 38 Guideline Statements that address (i) assay development for surgical pathology and cytopathology specimens, (ii) reporting elements, and (iii) quality assurance (including validation/verification, internal quality assurance, and external quality assurance). The intent of this work is to provide recommendations that are relevant to any tumor type, are universally applicable and can be implemented by any clinical immunohistochemistry laboratory performing predictive PD-L1 immunohistochemistry testing.
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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 | 0.057 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.007 |
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".