The role of atypical protein kinase C iota in cancer invasion
Bibliographic record
Abstract
Breast cancer is the most frequently diagnosed and the second leading cause of cancer deaths in Canadian women. The most morbid and deadly feature of the disease is the emergence of metastases. Breast cancer progression is a consequence of disruption of normal cell organization, polarity, and cell-cell adhesion, which allow cells to become more motile and invasive. Atypical protein kinase C iota (aPKCἰ) functions as an oncogene and is found to be elevated in lung adenocarcinoma, chronic myelogenous leukaemia, non-small cell lung cancer cell lines, ovarian, pancreatic, colon and breast cancers. In addition, several studies demonstrate an important role for aPKCι in promoting tumour cell invasion and the subsequent formation of metastases in a number of human cancers. Also, aPKCι was suggested to be involved in the extracellular matrix degradation in breast cancer. However, the exact mechanisms by which aPKCι promotes breast cancer cell invasion remain to be elucidated.The purpose of this thesis is to establish the role of aPKCἰ in breast cancer invasion and metastasis. To achieve this, a 3D collagen in vitro assay was chosen to examine the effect of aPKCἰ overexpression on breast cancer cell invasiveness, morphology, the formation of cellular protrusions. I also investigated the potential molecular mechanisms of aPKCι involvement in breast cancer cell invasion. The work in this thesis demonstrates that the aPKCἰ overexpressing cells are capable of invading further distances into the surrounding collagen matrix and possessed higher speed than the control cell line. Furthermore, these cells changed morphology, as characterized by the formation of multiple protrusions. Additionally, the number of cellular protrusions was found to correlate with the cell invasion velocity. Finally, a study of the the molecular pathway underlying the role of aPKCἰ in breast cancer cell invasion was initiated.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".