Interactions between normal and oncogenic cells during breast cancer initiation
Bibliographic record
Abstract
Breast cancer is the most commonly diagnosed cancer in females in Canada, accounting for 25% of all cancer cases.Currently, it is clinically challenging to assess the risk of pre-invasive breast lesions and to predict those that will progress to invasive cancer.Therefore, some woman may receive surgery unnecessarily, whereas others may delay needed surgery or treatment.Therefore, a deeper understanding of the molecular mechanisms underlying the initiation of breast cancer will provide a foundation to improve diagnosis, treatment decisions, and identify novel targets for more effective preventative treatment.An emerging concept is that during the initial stage of carcinogenesis, incipient transformed cells interact with the surrounding normal epithelial cells.In some systems, this interaction induces cell competition between these two types of cells, which may result in the elimination of transformed cells by a process named Epithelial Defense Against Cancer (EDAC).To study cell competition during breast cancer initiation, I developed and evaluated two complementary mosaic three-dimensional (3D) organotypic culture systems from primary mouse mammary epithelial cells.Both systems allow for the manipulation of the proportion of normal versus mutant (oncogene-expressing) cells and the desired temporal control of the oncogene expression.For this thesis, I investigated the effect of different proportions of normal cells on tumour progression.I demonstrated that normal cells delay tumour progression when present in an equal or greater proportion to mutant cells by maintaining the normal tissue architecture.Mechanistically, my work indicated that normal cells exhibited their effect by influencing mutant cells to undergo symmetric divisions that maintain tissue organization.By understanding how normal cells interact with mutant neighbours and influence tumour Contributions MTB/MIC mouse model was provided by Dr. William Muller.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".