Molecular Testing Guideline for Selection of Lung Cancer Patients for EGFR and ALK Tyrosine Kinase Inhibitors
Bibliographic record
Abstract
OBJECTIVE: To establish evidence-based recommendations for the molecular analysis of lung cancers that are required to guide EGFR- and ALK-directed therapies, addressing which patients and samples should be tested, and when and how testing should be performed. PARTICIPANTS: Three cochairs without conflicts of interest were selected, one from each of the 3 sponsoring professional societies: College of American Pathologists, International Association for the Study of Lung Cancer, and Association for Molecular Pathology. Writing and advisory panels were constituted from additional experts from these societies. EVIDENCE: Three unbiased literature searches of electronic databases were performed to capture published articles from January 2004 through February 2012, yielding 1533 articles whose abstracts were screened to identify 521 pertinent articles that were then reviewed in detail for their relevance to the recommendations. EVIDENCE was formally graded for each recommendation. CONSENSUS PROCESS: Initial recommendations were formulated by the cochairs and panel members at a public meeting. Each guideline section was assigned to at least 2 panelists. Drafts were circulated to the writing panel (version 1), advisory panel (version 2), and the public (version 3) before submission (version 4). CONCLUSIONS: The 37 guideline items address 14 subjects, including 15 recommendations (evidence grade A/B). The major recommendations are to use testing for EGFR mutations and ALK fusions to guide patient selection for therapy with an epidermal growth factor receptor (EGFR) or anaplastic lymphoma kinase (ALK) inhibitor, respectively, in all patients with advanced-stage adenocarcinoma, regardless of sex, race, smoking history, or other clinical risk factors, and to prioritize EGFR and ALK testing over other molecular predictive tests. As scientific discoveries and clinical practice outpace the completion of randomized clinical trials, evidence-based guidelines developed by expert practitioners are vital for communicating emerging clinical standards. Already, new treatments targeting genetic alterations in other, less common driver oncogenes are being evaluated in lung cancer, and testing for these may be addressed in future versions of these guidelines.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".