A multi-omics analysis of glioma chemoresistance using a hybrid microphysiological model of glioblastoma
Bibliographic record
Abstract
Abstract Chemoresistance is a major clinical challenge in the management of glioblastoma (GBM) Temozolomide (TMZ) is the chemotherapeutic drug of choice for GBM; however, the therapeutic effect of TMZ is limited due to the development of resistance. Recapitulating GBM chemoresistance in a controlled environment is thus essential in understanding the mechanism of chemoresistance. Herein, we present a hybrid microphysiological model of chemoresistant GBM-on-a-chip (HGoC) by directly co-culturing TMZ-resistant GBM spheroids with healthy neurons to mimic the microenvironment of both the tumor and the surrounding healthy tissue. We characterized the model with proteomics, lipidomics, and secretome assays. The results showed that our artificial model recapitulated the molecular signatures of recurrent GBM in humans. Both showed alterations in vesicular transport and cholesterol pathways, mitotic quiescence, and a switch in metabolism to oxidative phosphorylation associated with a transition from mesenchymal to amoeboid. This is the first report to unravel the interplay of all these molecular changes as a mechanism of chemoresistance in glioblastoma. Moreover, we have shown that the acquisition of resistance increases invasiveness and the presence of neurons decreases this property.
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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 | 0.000 |
| 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".