Comprehensive Molecular Profiling of Sinonasal Teratocarcinosarcoma Highlights Recurrent SMARCA4 Inactivation and CTNNB1 Mutations
Bibliographic record
Abstract
Sinonasal teratocarcinosarcoma (TCS) is a rare tumor defined by intermixed neuroepithelial, mesenchymal, and epithelial elements. While its etiology was historically ambiguous, we recently reported frequent SMARCA4 loss by immunohistochemistry, suggesting that TCS might be related to SMARCA4-deficient sinonasal carcinomas. However, other molecular alterations including CTNNB1 mutation have been reported in TCS, and its full genetic underpinnings are unclear. Here, we performed the first comprehensive molecular analysis of sinonasal TCS to better understand its pathogenesis and classification. We collected 30 TCS including 22 cases from our initial study. Immunohistochemical loss of SMARCA4 was seen in 22 cases (73%), with total loss in 18 cases (60%). β-catenin showed nuclear localization in 14 cases (64%) of the subset tested. We selected 17 TCS for next-generation sequencing with enrichment for partial or intact SMARCA4 immunoexpression. We identified inactivating SMARCA4 mutations in 11 cases (65%) and activating CTNNB1 mutations in 6 cases (35%), including 5 cases with both. Of 5 cases that lacked SMARCA4 or CTNNB1 mutation, 2 harbored other SWI/SNF complex and Wnt pathway alterations, including 1 with SMARCB1 inactivation and 1 with concomitant APC and ARID1A mutations, and 3 had other findings, including DICER1 hotspot mutation. These findings confirm that SMARCA4 inactivation is the dominant genetic event in sinonasal TCS with frequent simultaneous CTNNB1 mutations. They further underscore a possible relationship between TCS and sinonasal carcinomas with neuroendocrine/neuroectodermal differentiation. However, while SMARCA4 and β-catenin immunohistochemistry may help confirm a challenging diagnosis, TCS should not be regarded as a molecularly defined entity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".