Canadian Multicenter Project on Standardization of Programmed Death-Ligand 1 Immunohistochemistry 22C3 Laboratory-Developed Tests for Pembrolizumab Therapy in NSCLC
Bibliographic record
Abstract
INTRODUCTION: The programmed death-ligand 1 (PD-L1) immunohistochemistry (IHC) assay is used to select patients for first or second-line pembrolizumab monotherapy in NSCLC. The PD-L1 IHC 22C3 pharmDx assay requires an Autostainer Link 48 instrument. Laboratories without this stainer have the option to develop a highly accurate 22C3 IHC laboratory-developed test (LDT) on other instruments. The Canadian 22C3 IHC LDT validation project was initiated to harmonize the quality of PD-L1 22C3 IHC LDT protocols across 20 Canadian pathology laboratories. METHODS: Centrally optimized 22C3 LDT protocols were distributed to participating laboratories. The LDT results were assessed against results using reference PD-L1 IHC 22C3 pharmDx. Analytical sensitivity and specificity were assessed using cell lines with varying PD-L1 expression levels (phase 1) and IHC critical assay performance controls (phase 2B). Diagnostic sensitivity and specificity were assessed using whole sections of 50 NSCLC cases (phase 2A) and tissue microarrays with an additional 50 NSCLC cases (phase 2C). RESULTS: In phase 1, 80% of participants reached acceptance criteria for analytical performance in the first attempt with disseminated protocols. However, in phase 2A, only 40% of participants reached the desired diagnostic accuracy for both 1% and 50% tumor proportion score cutoff. In phase 2B, further protocol modifications were conducted, which increased the number of successful laboratories to 75% in phase 2C. CONCLUSIONS: It is possible to harmonize highly accurate 22C3 LDTs for both 1% and 50% tumor proportion score in NSCLC across many laboratories with different platforms. However, despite a centralized approach, diagnostic validation of predictive IHC LDTs can be challenging and not always successful.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.065 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".