Scale-Up of Breast Cancer Stem Cell Aggregate Cultures to Suspension Bioreactors
Bibliographic record
Abstract
It has been hypothesized that breast tumor formation results from the activity of a scarce population of cells known as Breast Cancer Stem Cells (BrCSCs) and that the development of effective breast cancer therapies may therefore ultimately rely upon the ability to effectively target these cells for eradication. The scarcity of BrCSCs in vivo severely compromises research on these populations, as analyses are restricted to those requiring small cell numbers, and has become a major impediment to the development of therapeutic strategies against breast cancer. Through the culture of murine tissue aggregates containing a population of BrCSCs, this study demonstrates the ability of propagating this scarce population in a controlled and reproducible manner, within suspension bioreactors. A rigorous theoretical framework has been developed in order to understand and characterize the implications of oxygen mass transfer within aggregates upon scale-up and thereby provide a foundation for the scale-up of aggregate cultures. A two-factor, two-level factorial experimental design was also performed in order to assess the effects of inoculation density and hydrodynamic shear upon cell yield. We discovered that the culture of the murine aggregates in a relatively low shear environment (tau(max) = 0.20 Pa) and inoculated at 3.50 x 10(4) cells/mL resulted in the best yields for the range of conditions investigated in suspension bioreactors. A detailed study on the oxygen uptake kinetics of the aggregates also revealed that the uptake rates were not significantly affected by mass transfer limitations, as uptake rates of aggregate cultures were found to be comparable to those observed in single cell cultures. Cells propagated in a process controlled 500 mL suspension bioreactor resulted in growth kinetics that were comparable to those observed in 125 mL bioreactors. Doubling times in the 500 mL vessel were found to be 23.9 h and attained a maximum cell density of 1.20 x 10(6) cells/mL. After enumerating the number of BrCSCs, this resulted in an approximately 20-fold increase in BrCSC numbers in batch suspension cultures. With greater attention being applied to BrCSCs, their propagation in suspension bioreactors makes available experimental avenues that are not currently accessible and may thereby enable the development of more effective therapeutic drugs for the treatment of breast cancer.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".