Investigating the role of src in tissue organization during early breast cancer progression
Bibliographic record
Abstract
Breast cancer remains the most frequently diagnosed cancer amongst women, affecting 1 in 8 Canadian women.Precancerous breast lesions can be detected in the clinic; however, a major challenge is that there is no reliable way to identify the lesions that are most likely to progress to breast cancer.A better understanding of the early molecular events that drive transformation of normal tissues towards cancer will be essential to enable the prediction of progression, and for the development of targeted preventative therapies to prevent breast cancer.An emerging concept is that normal epithelial architecture plays a crucial role as physical barrier to cancer progression.Using genetically modified breast cancer mouse models and live-imaging of 3D organoids, I identified that asymmetric cell divisions are an early event in breast cancer progression that give rise to a population of de-polarized cells that proliferate to become the dominant population in tumors through asymmetric cell divisions.A challenge to understanding how tissue architecture functions to suppress tumorigenesis is that most oncogenes disrupt both proliferation/survival and tissue organization.In this project, I made use of a modified PyVmT model whereby hyper-proliferation was uncoupled from loss of tissue organization following oncogene induction and the mammary ducts organize similarly to normal tissue with regards to apical-basal cell polarity and cell adhesions.Remarkably, despite increased proliferation, these mice fail to form mammary tumors.For this thesis, I investigated the mechanisms that control cell division orientation in mouse CONTRIBUTIONS NMuMG cells were obtained courtesy of Dr. Alicia Viloria-Petit.PyV mT/Src KO mice were obtained courtesy of Dr. Harvey Smith and Dr.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".