Fetal Type Morphologies Suggest the Presence of DICER1 Hotspot Mutations in Non–small Cell Lung Cancer
Bibliographic record
Abstract
Germline and somatic pathogenic variants (PVs) in DICER1 , encoding a miRNA biogenesis protein, are associated with a wide variety of highly specific pathologic entities. The lung tumors pleuropulmonary blastoma, pulmonary blastoma (PB), and well-differentiated fetal lung adenocarcinoma (WDFLAC) are all known to harbor DICER1 biallelic variants (loss of function and/or somatic hotspot missense mutations), and all share pathologic features reminiscent of the immature lung. However, the role of DICER1 PVs in non-small cell lung cancer (NSCLC) is relatively unknown. Here, we aimed to establish the spectrum of lung pathologies associated with DICER1 hotspot PVs and to compare the mutational landscape of DICER1 -mutated NSCLC with and without hotspots. We queried DNA sequencing data from 12,146 NSCLCs featuring somatic DICER1 variants. 235 (1.9%) cases harboring ≥ 1 DICER1 PV were found and 9/235 (3.8%) were DICER1 hotspot-positive cases. Histologic review of DICER1 hotspot-positive cases showed that all but one tumor were classified as within the histologic spectrum of PB/WDFLAC, whereas all the DICER1 non-hotspot double variants were classified as lung adenocarcinomas, not otherwise specified. Comparison between the mutational landscape of DICER1 hotspot-positive and hotspot-negative cases revealed a higher frequency of CTNNB1 mutations in the hotspot-positive cases (5/9 vs. 2/225; P <0.00001). We conclude that DICER1 somatic hotspots are not implicated in the most common forms of NSCLC but rather select for morphologic features of lung tumor types such as PB and WDFLAC. As a corollary, cases showing this tumor morphology should undergo testing for DICER1 variants, and if positive, genetic counseling should be considered.
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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 | 0.002 | 0.001 |
| 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) | 0.003 | 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".