Anaplastic lymphoma kinase 5A4 immunohistochemistry as a diagnostic assay in lung cancer: A Canadian reference testing center's results in population‐based reflex testing
Bibliographic record
Abstract
BACKGROUND: The presence of anaplastic lymphoma kinase (ALK) rearrangement predicts response to ALK tyrosine kinase inhibitor (TKI) therapy. Fluorescence in situ hybridization (FISH) was the initial reference standard to detect ALK rearrangement, but immunohistochemistry (IHC) using D5F3 has gained acceptance as an alternative diagnostic method. ALK IHC assays using other ALK antibodies have also been used as screening methods, but data supporting their utility as diagnostic tests have not been widely reported. METHODS: Data from reflexive clinical ALK IHC test using the 5A4 clone concurrent with epidermal growth factor receptor (EGFR) mutation testing were analyzed. ALK IHC results were reported as negative (-), equivocal, or positive (+), with equivocal or positive staining validated by FISH break-apart probe testing. Treatment outcomes were reviewed for ALK IHC+ patients. RESULTS: Between 2012 and 2015, 146 (2.5%) cases were reported as ALK IHC+, 188 (3.2%) were reported as equivocal, and 5624 (94.4%) were reported as ALK IHC-. Of the ALK IHC+ cases, 131/143(91.6%) were ALK FISH+. Excluding 6 cases in which FISH was inconclusive or not performed, the positive predictive value was 95.6%, and the negative predictive value was 100%. Most specimens (n = 5352 [89.6%]) were also successfully tested for EGFR. Clinical responses to ALK TKIs were noted in 49 ALK IHC+ patients, with a median progression-free survival of 9.9 months. CONCLUSIONS: ALK 5A4 IHC can serve as a robust diagnostic test for ALK-rearranged lung cancer and is associated with treatment response and survival. Optimized tissue allocation resulted in high success rates of combined reflex EGFR and ALK 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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".