A robust protocol for the systematic collection and expansion of cells from ER <sup>+</sup> breast cancer tumors and their matching tumor-adjacent tissues
Bibliographic record
Abstract
Abstract Therapy resistance and tumor recurrence are major challenges in the clinical management of breast cancer. Current data indicates that the breast tumor microenvironment (TME) and the tumor immune microenvironment (TIME) are important modulators of breast cancer cell response to chemotherapies and the development of therapy resistance. To this end, the ability to recreate the tumor microenvironment in the laboratory using autologous primary cells that make up the breast TME has become an indispensable tool for cancer researchers as it allows the study of tumor immunobiology in the context of therapy resistance. Moreover, the clinical relevance of data obtained from single cell transcriptomics and proteomics platforms would be greatly improved if primary autologous tumor cells were used. In this article, we report a robust and efficient workflow to obtain autologous cancer cells, cancer-associated fibroblasts, and tumor-infiltrating immune cells from primary human breast cancer tumors obtained from mastectomy procedures. As well, we show that this protocol can be used to obtain normal-like epithelial cells, fibroblasts, and immune cells from the matching tumor-adjacent breast tissue samples. Also, a robust methodology to expand each of these primary cell types in vitro is presented that allows the maintenance of the primary tumor cell phenotype. The availability of a large number of autologous primary human breast tumor cells and their matching tumor-adjacent tissues will facilitate the study of differential and cancer cell-specific gene expression patterns that will further our understanding of how the TME and TIME influence therapy resistance in the breast tumor context.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.010 |
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".