Neuroendocrine differentiation distinguishes basaloid variant of lung squamous cell carcinoma
Bibliographic record
Abstract
BACKGROUND: Neuroendocrine (NE) differentiation is widely studied in non-small cell lung carcinomas (NSCLC) however, its significance remains unclear in basaloid squamous cell carcinomas (B-SqCC). This study aims to assess the extent of NE differentiation in B-SqCC and characterize the underlying molecular process. METHODS: This study evaluated resected B-SqCC, small cell lung cancer (SCLC) and poorly differentiated SqCC (PD-SqCC) from 2005 to 2020 at the Ottawa Hospital. Samples were subject to pathological review, immunohistochemistry (IHC) and survival analysis. Gene expression analysis was performed on B-SqCC samples exhibiting NE+ and NE- regions (paired samples) to identify differentially expressed genes (DEGs). These DEGs were subsequently validated in unpaired B-SqCC and TCGA samples. RESULTS: B-SqCC cases were more likely to exhibit nuclear molding, resetting and peripheral palisading than PD-SqCC. B-SqCC were also more likely to demonstrate NE differentiation compared to PD-SqCC (p = 0.006). Pure basaloid squamous cell carcinoma (PB-SqCC) experienced poorer disease-free survival (HR = 3.12, p = 0.043) adjusted for stage. Molecular characterization of paired B-SqCC samples demonstrated DEGs implicated in NOTCH signaling, SCLC and pulmonary neuroendocrine differentiation. Hierarchical clustering using discovered DEGs in unpaired B-SqCC samples distinguished tumors based on NE status (p = 0.048). Likewise, clustering The Cancer Genome Atlas (TCGA) samples with DEGs distinguished B-SqCC from SqCC samples (p = 0.0094). CONCLUSION: This study provides IHC and molecular evidence of significant NE-differentiation in B-SqCC and demonstrates their aggressive clinical behavior. These findings suggest that B-SqCC are biologically distinct from SqCC and share characteristics with SCLC.
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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 | 0.000 |
| 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".