Uncovering novel ShcA signaling networks and their impacts on breast cancer growth and therapeutic resistance
Bibliographic record
Abstract
Breast cancer is the most prevalent cancer in Canadian women, and although many advances have been made, all subtypes of breast cancer can develop therapeutic resistance. One such method of therapeutic resistance can be due to abnormal, or hyperactive receptor tyrosine kinase (RTK) signaling, as seen in HER2+ and basal breast cancers. ShcA is an adaptor protein which facilitates downstream RTK signaling cascades, including the MAPK and PI3K/AKT pathways. When ShcA Y313 cannot be phosphorylated, the resulting tumours demonstrated delayed tumour onset and delayed tumour outgrowth. To understand the mechanisms by which this occurs, BioID was performed on both wildtype and mutant ShcA cell populations. With numerous potential interactors identified, an shRNA screen was conducted to narrow down the most probable interactors. In this thesis, I identify potential novel interactors, including PPP6C and eIF4G2, which may be interacting with ShcA to facilitate these phenotypes. As recurrent disease becomes an ever-increasing concern, the identification of novel protein interactions may highlight new druggable targets to overcome these challenges and provide further insight into the complexities of intracellular signalling
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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) | 0.002 | 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".