Importance of <i>DICER1 </i>pathogenic variants in non-small cell lung cancer.
Bibliographic record
Abstract
e20602 Background: DICER1 encodes a miRNA processing enzyme involved in regulation of cell proliferation and differentiation. The role of DICER1 pathogenic variants (PVs) in non-small cell lung cancer (NSCLC) is relatively unknown. Germline and/or somatic PVs have been reported in pleuropulmonary blastoma, pulmonary blastoma (PB), and low-grade/well-differentiated fetal adenocarcinoma (WDFLAC), with clustering of somatic PVs at specific metal ion binding residues referred to as hotspot alterations. PVs in CTNNB1, resulting in constitutive activation of the WNT signaling pathway, have also been implicated in PB and WDFLAC. This study seeks to establish the frequency of somatic DICER1 hotspot PVs that occur in all NSCLCs, their co-occurrence with other PVs in DICER1 and with alterations of CTNNB1/WNT signaling, and their histologic correlates. Methods: Molecular sequencing data collected from 12,146 NSCLCs at Caris Life Sciences were queried for cases featuring somatic DICER1 variants. For those tumors harboring hotspot PVs, sequencing for CTNNB1 and APC mutations was performed, as well as histological review, including examination of beta-catenin immunohistochemical (IHC) staining. Results: Of the 12,146 NSCLCs analyzed, 235 (1.9%) were found to have one or more DICER1 PVs. Of these 235, 225 cases demonstrated one variant, while 10 tumors featured two. Gene analysis revealed that 8 of the observed PVs cases contained hotspot alterations: 2 in the single variant cases and 6 in the double variant cases. The remaining 4 double variant cases harbored a combination of truncating, non-hotspot missense, or splice variants. No significant differences were observed for age or smoking status between DICER1 hotspot-positive and hotspot-negative groups. All but one tumor were considered to be in histologic spectrum of PB/WDFLAC. Of the 8 DICER1 hotspot-positive cases, 1 had a PV in APC, 4 had a CTNNB1 PV, 1 had a PV in both APC and CTNNB1, and 2 had neither. Beta-catenin IHC showed nuclear positivity in 5 tumors that had a PV in either CTNNB1 and/or APC.. Conclusions: This study demonstrates that DICER1 somatic hotspots select for morphologic features of tumor types such as PB and WDFLAC, but are not implicated in the most common forms of NSCLC. Furthermore, DICER1 hotspot-positive NSCLCs frequently harbor CTNNB1 and/or APC alterations, with many cases exhibiting nuclear staining by beta-catenin IHC, suggesting a possible synergistic relationship between DICER1 hotspot PVs and activation of the WNT signaling pathway in the formation of pulmonary tumors. These findings emphasize the importance of molecular testing in clinical practice such that detecting somatic DICER1 hotspot alterations aids in the diagnosis of rare lung cancers, which ultimately require special consideration with respect to disease severity and treatment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".