Abstract IA10: Molecular testing to personalized EGFR and ALK inhibitor therapies in lung cancer.
Bibliographic record
Abstract
Abstract The concept of “oncogene addiction” led to the realization that not all patients respond equally to each targeted therapy, and best therapeutic results are obtained when the therapy is given to patients whose tumor harbors the “driver mutation” that is sensitive to the drug. In lung cancer, two currently approved new therapies require patient selection based on the presence of sensitizing epidermal growth factor (EGFR) tyrosine kinase domain (TKD) mutation or the anaplastic lymphoma kinase (ALK) gene rearrangement. EGFR mutations occur almost always in lung cancer of adenocarcinoma histology, and uncommonly in other histologies except when they also contain adenocarcinoma component. They are more common in East Asian (~45%) compared to Caucasian (~15-25%) patients, in never/light smokers and in female patients. Ninety percent of EGFR sensitizing mutations involve the 3-5 amino acid deletions on exon 19 and the L858R mutation on exon 21. Although ALK rearrangement also occurs mainly in lung adenocarcinoma and never/light smokers, it is not associated with sex or ethnicity. A great majority of the ALK gene rearrangements that occur in lung cancer involve a fusion between the C-terminal of ALK containing the kinase domain and the N-terminal of echinoderm microtubule-associated protein-like 4 (EML4), which results from an inversion on the short arm of chromosome 2. While EGFR mutations are detected by mainly by sequencing or PCR-based techniques, ALK rearrangement is best detected by the fluorescent in situ hybridization (FISH). However, recent data suggest that the oncogenic ALK fusion proteins can be detected with close to 100% sensitivity and specificity in lung cancer by immunohistochemistry using commercially available ALK specific antibodies, while the native ALK protein is not expressed in normal lung cells. Based on a systematic review of the published literatures, experts representing the International Association for the Study of Lung Cancer (IASLC), in collaboration with the College of American Pathologists (CAP) and the Association of Molecular Pathologists (AMP) recently issued a guideline on Molecular Testing of EGFR and ALK in Lung Cancer. The testing algorithm reported in this guideline will be discussed. Citation Format: Ming Sound Tsao. Molecular testing to personalized EGFR and ALK inhibitor therapies in lung cancer. [abstract]. In: Proceedings of the AACR-IASLC Joint Conference on Molecular Origins of Lung Cancer; 2014 Jan 6-9; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2014;20(2Suppl):Abstract nr IA10.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".