MYC drives platinum resistant SCLC that is overcome by the dual PI3K-HDAC inhibitor fimepinostat
Bibliographic record
Abstract
Abstract Background Small cell lung cancer (SCLC) is an aggressive neuroendocrine cancer with an appalling overall survival of less than 5% (Zimmerman et al. J Thor Oncol 14:768-83, 2019). Patients typically respond to front line platinum-based doublet chemotherapy, but almost universally relapse with drug resistant disease. Elevated MYC expression is common in SCLC and has been associated with platinum resistance. This study evaluates the capacity of MYC to drive platinum resistance and through screening identifies a drug capable of reducing MYC expression and overcoming resistance. Methods Elevated MYC expression following the acquisition of platinum resistance in vitro and in vivo was assessed. Moreover, the capacity of enforced MYC expression to drive platinum resistance was defined in SCLC cell lines and in a genetically engineered mouse model that expresses MYC specifically in lung tumors. High throughput drug screening was used to identify drugs able to kill MYC-expressing, platinum resistant cell lines. The capacity of this drug to treat SCLC was defined in vivo in both transplant models using cell lines and patient derived xenografts and in combination with platinum and etoposide chemotherapy in an autochthonous mouse model of platinum resistant SCLC. Results MYC expression is elevated following the acquisition of platinum resistance and constitutively high MYC expression drives platinum resistance in vitro and in vivo. We show that fimepinostat decreases MYC expression and that it is an effective single agent treatment for SCLC in vitro and in vivo. Indeed, fimepinostat is as effective as platinum-etoposide treatment in vivo. Importantly, when combined with platinum and etoposide, fimepinostat achieves a significant increase in survival. Conclusions MYC is a potent driver of platinum resistance in SCLC that is effectively treated with fimepinostat.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".