ETV6-NTRK3 Is Expressed in a Subset of ALK-Negative Inflammatory Myofibroblastic Tumors
Bibliographic record
Abstract
Inflammatory myofibroblastic tumor (IMT) is a genetically heterogenous tumor of the viscera and soft tissues, with multiple molecular features having been demonstrated in this tumor type. About 50% of cases harbor an anaplastic lymphoma kinase (ALK) gene rearrangement, and recent studies have described novel fusions involving the ROS1 and PDGFRβ genes in a subset of ALK-negative cases. However, the molecular features of the remaining subset of cases are not yet defined. We report a case of a large, highly aggressive IMT of the lung in a 17-year-old girl. This case was molecularly characterized through whole-genome and transcriptome sequencing. Subsequently, we investigated a cohort of 15 ALK-negative IMTs of various anatomic sites. All cases were screened using fluorescence in situ hybridization (FISH) for rearrangement of the ETV6 locus and with reverse transcription polymerase chain reaction (RT-PCR) for the ETV6-NTRK3 fusion transcript. Whole-genome and transcriptome sequencing revealed an ETV6-NTRK3 fusion transcript in our index case. This was confirmed by FISH studies for ETV6 gene rearrangement, as well as by RT-PCR. In addition, 2 additional cases in our cohort demonstrated ETV6 rearrangement by FISH. The presence of ETV6-NTRK3 fusion transcript was demonstrated by RT-PCR in one of these additional cases. In summary, we demonstrate the expression of the ETV6-NTRK3 fusion oncogene in a small subset of IMTs, lending further support to the role of oncogenic tyrosine kinases in the pathophysiology of this tumor type. Our data also further expand the growing spectrum of tumor types expressing the ETV6-NTRK3 fusion.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".