Neuroendocrine Transformation as a Mechanism of Resistance to Targeted Lung Cancer Therapies: Emerging Mechanisms and Their Therapeutic Implications
Bibliographic record
Abstract
Lung cancer is the leading cause of cancer-related deaths worldwide, highlighting a major clinical challenge. Lung cancer is broadly classified into two histologically distinct subtypes, termed small cell lung cancer (SCLC) or non-small cell lung cancer (NSCLC). Identification of various oncogenic drivers of NSCLC has facilitated the development of targeted therapies that have dramatically improved patient outcomes. However, acquired resistance to these targeted therapies is common, which ultimately results in patient relapse. Several on-target and off-target resistance mechanisms have been described for targeted therapies in NSCLC. One common off-target mechanism of resistance to these therapies is histological transformation of the initial NSCLC into SCLC, a highly aggressive form of lung cancer that exhibits neuroendocrine histology. This mechanism of resistance presents a significant clinical challenge, since there are very few treatments available for these relapsed patients. Although the phenomenon of NSCLC-to-SCLC transformation was described almost 20 years ago, only recently have we begun to understand the mechanisms underlying this therapy-driven response. These recent discoveries will be key to identifying novel biomarkers and therapeutic strategies to improve outcomes of patients that undergo NSCLC-to-SCLC transformation. Here, we highlight these recent advances and discuss the potential therapeutic strategies that they have uncovered to target this mechanism of resistance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".