An Unbalanced Exon-Expression qPCR-based Assay for Detection of ALK Translocation (Fusion) in Lung Cancer
Bibliographic record
Abstract
Non-Small Cell Lung Cancer (NSCLC) constitutes 85-90% of all lung cancer.Accurate diagnosis and selection of targeted therapies in lung cancer depends on robust detection of the molecular events that underlie its pathogenesis.Since patients having a rearrangement in the Anaplastic Lymphoma Kinase (ALK) gene respond well to treatment with crizotinib, identification of such ALK mutations is necessary for the successful treatment of NSCLC.The most common rearrangement of the ALK gene in NSCLC involves fusion with echinoderm microtubule-associated protein-like 4 (EML 4) as the upstream partner.Current testing methods for this rearrangement (IHC and/or FISH) can be very subjective due to high operator variability.They require expert interpretation by a pathologist and have a long turnaround time.The FDAapproved Fluorescence In Situ Hybridization (FISH) test has been shown to lack sensitivity and is generally acknowledged to fail to detect rearrangements in up to 60% of patients.Here, we have adapted an approach described earlier and optimized it for use with degraded RNA obtained from Formalin-Fixed Paraffin-Embedded (FFPE) sections.This method is based on the unbalanced expression of 5'-and 3'-regions (exons) of the ALK gene.It is also applicable to the detection of other cancer-relevant gene rearrangements e.g.ROS 1 or RET that result in increased expression of the 3'-kinase domain.Patients with these rearrangements have been shown to respond to crizotinib and cabozantinib, respectively.Using NSCLC cell lines we demonstrate that our method is cost-effective, reproducible, sensitive, objective, and easy to use.Unlike FISH, it does not require interpretation by several scorers and it can be performed in any clinical laboratory with access to a qPCR instrument.Here we present the protocol for the method and validation with 197 clinical samples.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".