Time-dependent chromatin maturation during 3D spheroid culture improves preclinical modeling of non-small cell lung cancer
Bibliographic record
Abstract
ABSTRACT Non-small cell lung cancer (NSCLC) is the deadliest cancer worldwide. Therapeutic progress stagnate, highlighting the complexity to replicate NSCLC in preclinical models. Drug discovery studies rely mostly on cancer cells in two-dimension (2D), which poorly predict drug efficacy in patients. There is a growing interest in three-dimensional (3D) preclinical models, such as 3D spheroids, to better model tumor phenotype and improve therapeutic prediction. However, a comprehensive view of 3D culture methods impact on transcriptomes, epigenomes and pharmacological responses and their correlations to NSCLC tumors is still missing. Here, we demonstrate that NSCLC spheroids undergo time-dependent transcriptomic and epigenomic changes, which peak after 3 weeks of culture. While DNA methylome remained stable, chromatin methylation and acetylation marks gained features of advanced NSCLC in a time-dependent manner. Single-cell transcriptomic profiling of spheroids demonstrated that time of 3D culture improved the correlation to NSCLC tumors. Moreover, long-term culture of 3D spheroids increased drug screening predictability, by showing resistance to drugs that failed in NSCLC patients (such as HDAC inhibitors) while demonstrating novel pharmacological vulnerabilities and synergistic interactions (such as combination of PRMT and HDAC inhibitors). Strikingly, reverting 3D spheroids back to 2D culture rapidly reversed transcriptomic, epigenetic and pharmacological signatures acquired after 3 weeks of 3D culture, highlighting the critical impact of cell culture conditions on NSCLC phenotype. Collectively, our findings demonstrate that implementing a time-dependent maturation process into 3D spheroid culture induces chromatin and transcriptomic changes that enhance NSCLC preclinical modeling.
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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) | 0.001 | 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".