Empirical chemosensitivity testing in a spheroid model of ovarian cancer using a microfluidics-based multiplex platform
Bibliographic record
Abstract
The use of biomarkers to infer drug response in patients is being actively pursued, yet significant challenges with this approach, including the complicated interconnection of pathways, have limited its application. Direct empirical testing of tumor sensitivity would arguably provide a more reliable predictive value, although it has garnered little attention largely due to the technical difficulties associated with this approach. We hypothesize that the application of recently developed microtechnologies, coupled to more complex 3-dimensional cell cultures, could provide a model to address some of these issues. As a proof of concept, we developed a microfluidic device where spheroids of the serous epithelial ovarian cancer cell line TOV112D are entrapped and assayed for their chemoresponse to carboplatin and paclitaxel, two therapeutic agents routinely used for the treatment of ovarian cancer. In order to index the chemoresponse, we analyzed the spatiotemporal evolution of the mortality fraction, as judged by vital dyes and confocal microscopy, within spheroids subjected to different drug concentrations and treatment durations inside the microfluidic device. To reflect microenvironment effects, we tested the effect of exogenous extracellular matrix and serum supplementation during spheroid formation on their chemotherapeutic response. Spheroids displayed augmented chemoresistance in comparison to monolayer culturing. This resistance was further increased by the simultaneous presence of both extracellular matrix and high serum concentration during spheroid formation. Following exposure to chemotherapeutics, cell death profiles were not uniform throughout the spheroid. The highest cell death fraction was found at the center of the spheroid and the lowest at the periphery. Collectively, the results demonstrate the validity of the approach, and provide the basis for further investigation of chemotherapeutic responses in ovarian cancer using microfluidics technology. In the future, such microdevices could provide the framework to assay drug sensitivity in a timeframe suitable for clinical decision making.
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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) | 0.000 | 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".