Isolation and Functional Assessment of Human Breast Cancer Stem Cells from Cell and Tissue Samples
Bibliographic record
Abstract
Breast cancer stem cells (BCSCs) are cancer cells with inherited or acquired stem cell-like characteristics. Despite their low frequency, they are major contributors to breast cancer initiation, relapse, metastasis and therapy resistance. It is imperative to understand the biology of breast cancer stem cells in order to identify novel therapeutic targets to treat breast cancer. Breast cancer stem cells are isolated and characterized based on expression of unique cell surface markers such as CD44, CD24 and enzymatic activity of aldehyde dehydrogenase (ALDH). These ALDHhighCD44+CD24- cells constitute the BCSC population and can be isolated by fluorescence-activated cell sorting (FACS) for downstream functional studies. Depending on the scientific question, different in vitro and in vivo methods can be used to assess the functional characteristics of BCSCs. Here, we provide a detailed experimental protocol for isolation of human BCSCs from both heterogenous populations of breast cancer cells as well as primary tumor tissue obtained from breast cancer patients. In addition, we highlight downstream in vitro and in vivo functional assays including colony forming assays, mammosphere assays, 3D culture models and tumor xenograft assays that can be used to assess BCSC function.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".