Hypoxic-Core (HyCo) Spheroids Recapitulate Hallmarks of Clinical Hypoxia: A Simple Chip-Based Method for Translational Oncology
Bibliographic record
Abstract
Abstract Hypoxia influences the biology and response of cancers. No user-friendly device allows the study of hypoxia on highly-controlled clinically-relevant tumor models. Here, we describe how hypoxic-core (HyCo) spheroids generated using our unique non-perfused microfluidic device recapitulate key clinical hallmarks of hypoxia in vitro . Our PDMS-made system can generate up to 240 spheroids naturally exhibiting a diffusion-driven hypoxic core in only 4 days, here from two sarcoma cell lines. Compared to smaller normoxic spheroids from the same cell lines, known hypoxia-related genes are upregulated in HyCo spheroids. In addition, HyCo spheroids display hallmark hypoxia-induced resistance to radiotherapy and chemotherapy, along with increased invasiveness. Finally, to demonstrate applications of our HyCo spheroids and on-chip framework in drug development, we used our HyCo-derived gene expression dataset to select a drug candidate (diethyl-pythiDC) and confirmed its effect on spheroid invasiveness. Our results suggest that HyCo spheroids can be efficiently used as translational tools to integrate hypoxia in cancer research, without complex workflows or setups. Teaser On-chip spheroids with a natural hypoxic-core emulate clinical cancer hypoxia hallmarks essential for novel therapy development.
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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) | 0.002 | 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".