<i>EGFR</i> -Mutant Adenocarcinomas That Transform to Small-Cell Lung Cancer and Other Neuroendocrine Carcinomas: Clinical Outcomes
Bibliographic record
Abstract
PURPOSE: Approximately 3% to 10% of EGFR (epidermal growth factor receptor) -mutant non-small cell lung cancers (NSCLCs) undergo transformation to small-cell lung cancer (SCLC), but their clinical course is poorly characterized. METHODS: We retrospectively identified patients with EGFR-mutant SCLC and other high-grade neuroendocrine carcinomas seen at our eight institutions. Demographics, disease features, and outcomes were analyzed. RESULTS: We included 67 patients-38 women and 29 men; EGFR mutations included exon 19 deletion (69%), L858R (25%), and other (6%). At the initial lung cancer diagnosis, 58 patients had NSCLC and nine had de novo SCLC or mixed histology. All but these nine patients received one or more EGFR tyrosine kinase inhibitor before SCLC transformation. Median time to transformation was 17.8 months (95% CI, 14.3 to 26.2 months). After transformation, both platinum-etoposide and taxanes yielded high response rates, but none of 17 patients who received immunotherapy experienced a response. Median overall survival since diagnosis was 31.5 months (95% CI, 24.8 to 41.3 months), whereas median survival since the time of SCLC transformation was 10.9 months (95% CI, 8.0 to 13.7 months). Fifty-nine patients had tissue genotyping at first evidence of SCLC. All maintained their founder EGFR mutation, and 15 of 19 with prior EGFR T790M positivity were T790 wild-type at transformation. Other recurrent mutations included TP53, Rb1, and PIK3CA. Re-emergence of NSCLC clones was identified in some cases. CNS metastases were frequent after transformation. CONCLUSION: There is a growing appreciation that EGFR-mutant NSCLCs can undergo SCLC transformation. We demonstrate that this occurs at an average of 17.8 months after diagnosis and cases are often characterized by Rb1, TP53, and PIK3CA mutations. Responses to platinum-etoposide and taxanes are frequent, but checkpoint inhibitors yielded no responses. Additional investigation is needed to better elucidate optimal strategies for this group.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".