Developing ALK Immunohistochemistry and In Situ Hybridization Proficiency Testing for Non–Small Cell Lung Cancer in Canada
Bibliographic record
Abstract
Intrachromosomal rearrangements involving the ALK gene are found in 3% to 5% of non-small cell lung cancers. Crizotinib is a tyrosine kinase inhibitor that has been shown to prolong progression-free survival in patients with advanced non-small cell lung cancer harboring ALK gene rearrangements. In Canada, ALK immunohistochemistry (IHC) is used as a screening test before confirmation by fluorescence in situ hybridization (FISH). Canadian Immunohistochemistry Quality Control (CIQC) provides ALK (Lung Cancer) proficiency testing (PT) for Canadian IHC laboratories. Samples included 32 previously characterized cases (IHC and FISH) either from the Canadian ALK (CALK) project or from CIQC reference laboratories. The same design was used for both runs. A total of 20 laboratories participated in Run 1 and 22 in Run 2. Some laboratories participated in the anticipation of future need and used the PT exercise as a part of test development and validation. Results of the IHC testing were first self-reported using the CIQC TMA Scorer and then evaluated by expert assessment. FISH results were self-reported only. Participants also reported details about IHC and FISH protocols. The κ-values were calculated, for which values >0.80 were used as acceptable results, respectively. The pass rate between the 2 runs and between different primary antibodies were compared. Six of the 22 protocols (27%) in Run 1 and 15 of the 22 (68%) protocols in Run 2 passed the CIQC PT criteria for IHC testing. The increase in the pass rate for Run 2 was significant (P=0.03, Wilcoxon signed-rank test). All reported FISH results were correct. CALK laboratories had significantly higher κ-values than non-CALK laboratories (P=0.002, t test). PT for IHC for rare diseases such as ALK-positive lung cancer is feasible, but challenging. The academic nature of the CIQC program and collaboration on a national level facilitated the development of appropriate PT samples. Participating laboratories made use of the PT exercise either to confirm that their testing was properly calibrated or to improve their protocols, which was confirmed by the achievement of significantly better results in Run 2. They also used CIQC's PT program for new test development and optimization.
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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 | 0.023 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.003 |
| 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".