BRCA Mutation Negatively Impacts TPT1/TCTP-Regulated Autophagic Response in Breast Cancer
Bibliographic record
Abstract
Autophagy plays a crucial role in both cancer prevention and cancer progression. The multi-faceted process allows for the degradation of cellular components as well as their recycling for energy. TPT1 (tumor protein, translationally controlled 1) overexpression is associated with cancer progression in several tumor types, and it has been identified as a regulator of autophagic expression through its control of various upstream pathways, including mTOR and p53. Through the inhibition of p53 and the indirect promotion of mTOR, TPT1 effectively impedes the autophagic response. Commonly mutated in breast cancer tumor cells, BRCA is a tumor suppressor protein in which a mutation may lead to tumorigenesis. A search in the TCGA database revealed that TPT1 overexpression is associated with increased survivability in breast cancer patients with BRCA mutations; however, previous studies iterated the opposite, indicating TPT1 overexpression led to poor prognosis in breast cancer. In this study, we describe the influence of BRCA mutations on TPT1-mediated autophagic pathways. In breast cancer wild-type cells, TPT1 knockdown leads to increased autophagic expression and autophagosome formation while in BRCA mutant cells, the effect is negated. Furthermore, we demonstrate that BRCA mutations block TPT1-mediated upregulation of mTOR and its downstream signaling molecules. We further demonstrate that BRCA mutations do not impact AMPK phosphorylation by p53 or the repression of p53 through TPT1. Overall, these findings suggest that BRCA mutations can antagonize TPT1-mediated autophagy via the regulation of p53-independent mTOR signaling. Further understanding of the interaction between these pathways may contribute to new therapeutic approaches to treating breast cancer patients.
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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".