The synergistic role of IL-7/IL-7R and ZEB2 in breast cancer
Bibliographic record
Abstract
Breast cancer remains a significant health challenge in Canada, necessitating the continued exploration of novel therapeutic strategies. This thesis investigates the role of two key players, Zinc Finger E-box Binding Homeobox 2 (ZEB2 transcription factor) and Interleukin-7 Receptor (IL-7R), in breast cancer progression and therapeutic response. We sought to unravel the intricate interplay between these proteins and their roles in breast cancer biology. Utilizing the DEPMAP (Dependency Map) database, a comprehensive resource that provides information on gene essentiality and drug responses across various cancer cell lines, we identified ZEB2 and IL-7R as potential therapeutic targets in breast cancer. Subsequent validation experiments confirmed their overexpression in breast cancer cell lines, highlighting their potential as prognostic indicators and therapeutic targets. Immunofluorescent imaging and enzyme-linked immunosorbent assays (ELISA) revealed intriguing insights into the intracellular localization of IL-7R and the autocrine/intracrine signaling potential in breast cancer cells. Functional assays demonstrated the pro-proliferative and pro-migratory effects of IL-7 stimulation in breast cancer cells. Additionally, our investigation into downstream signaling pathways unveiled the activation of the Phosphoinositide 3-kinase/Protein Kinase B (PI3K/AKT) pathway upon IL-7 stimulation, suggesting potential therapeutic avenues for targeting this pathway in breast cancer treatment. In vitro drug combination studies revealed promising synergistic effects between Lysine- Specific Demethylase 1 (LSD1) and AKT inhibitors in ZEB2High IL-7RHigh breast cancer cells. While our findings present promising avenues for breast cancer treatment, further research is warranted. Ultimately, this thesis contributes to the growing body of knowledge aimed at advancing precision medicine approaches for breast cancer treatment.
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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 | 0.000 | 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".