3D culture of pancreatic cancer cells <i>in vitro</i> recapitulates an aberrant mitochondrial oxidative phosphorylation genotype observed <i>in vivo</i>
Bibliographic record
Abstract
Abstract The treatment of many aggressive cancers remains a significant unmet medical need. An aberrant dependence of tumors on mitochondrial oxidative phosphorylation pathways has been well characterized in mediating the progression of pancreatic cancers, as well as some other malignancies. However, the discovery and development of new therapeutic strategies targeting or manipulating this pathway in pancreatic tumors has been relatively slow when compared with other cancers, limiting the current options for treating patients. One key technical challenge in discovering new therapies has been the limited ability of cancer cell lines when grown in 2D conditions to recapitulate the mitochondrial oxidative phosphorylation pathways in vitro that have been observed in vivo. In part, this is because 2D cultures fail to mimic the extracellular 3D tumoral environment. More generally, the translation of data based on 2D pancreatic cancer cell culture systems have also been constraining options for discovering new therapies. Conversely, 3D cultures can provide an improved technology platform in which pathologically relevant pathways in tumors can be recapitulated and analyzed in vitro. In this study, we have demonstrated in 3D cultures the reconstruction of the mitochondrial oxidative phosphorylation genotype in vitro, which more closely resembles that observed in vivo. Our study highlights the value of using transcriptomic readouts as a method to demonstrate the relevance and utility of 3D preclinical models in vitro , when grown in a more physiological extracellular environment. This key technological advance can now better enable the discovery and subsequent development of new therapeutic strategies targeting disease-relevant pathways found in pancreatic and other tumors.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".