NRG1 fusion-driven tumors: biology, detection, and the therapeutic role of afatinib and other ErbB-targeting agents
Bibliographic record
Abstract
Oncogenic gene fusions are hybrid genes that result from structural DNA rearrangements, leading to deregulated activity. Fusions involving the neuregulin-1 gene (NRG1) result in ErbB-mediated pathway activation and therefore present a rational candidate for targeted treatment. The most frequently reported NRG1 fusion is CD74-NRG1, which most commonly occurs in patients with invasive mucinous adenocarcinomas (IMAs) of the lung, although several other NRG1 fusion partners have been identified in patients with lung cancer, including ATP1B1, SDC4, and RBPMS. NRG1 fusions are also present in patients with other solid tumors, such as pancreatic ductal adenocarcinoma. In general, NRG1 fusions are rare across different types of cancer, with a reported incidence of <1%, with the notable exception of IMA, which represents ≈2%-10% of lung adenocarcinomas and has a reported incidence of ≈10%-30% for NRG1 fusions. A substantial proportion (≈20%) of NRG1 fusion-positive non-small-cell lung cancer cases are nonmucinous adenocarcinomas. ErbB-targeted treatments, such as afatinib, a pan-ErbB tyrosine kinase inhibitor, are potential therapeutic strategies to address unmet treatment needs in patients harboring NRG1 fusions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".