Standard therapy‑resistant small cell lung cancer showing dynamic transition of neuroendocrine fate during the cancer trajectory: A case report
Bibliographic record
Abstract
While small cell lung cancer (SCLC) has been treated as a single disease historically, recent studies have suggested that SCLC can be classified into molecular subtypes based on the expression of lineage transcription factors such as achaete-scute homolog 1 (ASCL1), neurogenic differentiation factor 1 (NEUROD1), POU domain class 2 transcription factor 3 (POU2F3) and transcriptional coactivator YAP1 (YAP1). These transcription factor-based subtypes may be specifically targeted in therapy, and recent studies have suggested that the SCLC subtypes represent different stages of dynamic evolution of SCLC rather than independent diseases. Nevertheless, evidence of shift in neuroendocrine differentiation during SCLC evolution has been lacking in the clinical setting. In the present study, a 60-year-old male was diagnosed with extensive SCLC. The tumor responded not to the standard SCLC regimen of carboplatin, etoposide and atezolizumab, but to the non-SCLC regimen of carboplatin, nab-paclitaxel and pembrolizumab. The patient succumbed 5 months after the initial diagnosis and a pathological autopsy was performed. The tumor was originally negative for all four transcription factors, ASCL1, NEUROD1, POU2F3 and YAP1, in the biopsy specimens at diagnosis. Loss of synaptophysin expression and emergence of Myc proto-oncogene protein and YAP1 expression was recorded in the autopsy specimens, suggesting the transition to a decreased neuroendocrine fate during the disease trajectory. This case provides clinical evidence of dynamic transition of neuroendocrine fate during SCLC evolution. In light of SCLC heterogeneity and plasticity, development of precision medicine is required.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".