Bibliographic record
Abstract
Autophagy, a lysosome-mediated degradation and recycling process that functions to promote stress adaptation and cell survival, is a promising novel target for anticancer therapy. Breast cancer, the most prevalent cancer and the 2nd leading cause of cancer deaths in Canadian women, consists of many diseases (intrinsic subtypes), which are all treated differently. Several studies indicate that some breast cancer subtypes might be more sensitive to autophagy inhibition than others. However, our knowledge of prognostic and predictive values of different autophagy-related biomarkers, as well as the effectiveness of autophagy inhibitors, in various breast cancer subtypes, essential for effective patient treatment selection and potentially better outcomes, is extremely limited. Using a tissue microarray from a large population-based cohort of breast cancer patients, we evaluated prognostic values of two autophagy proteins, the microtubule-associated protein 1 light chain 3B (MAP1LC3B, or LC3B), and a cysteine protease ATG4B, across different breast cancer subtypes. We found that LC3B expression was highest in triple-negative, in particular - basal-like, breast cancers, a group of malignancies characterized by an extremely aggressive course of disease, inferior survival, and limited treatment options. High LC3B expression was associated with adverse outcomes across all breast cancer subtypes. A potential drug target ATG4B demonstrated a poor prognostic value in HER2 positive breast cancers, but a favorable prognostic value in Luminal A breast cancers. Using in vitro and in vivo breast cancer models, we studied the roles of autophagy, in general, and ATG4B, in particular, in different breast cancer subtypes. We tested the possibility of sensitization of breast cancer cells to chemotherapy or targeted therapy by modulating autophagy. We showed that treatment with HCQ is a useful strategy to sensitize triple-negative breast cancer cells to chemotherapy and that ATG4B inhibition is a promising approach in combinational treatment of HER2 positive breast cancers. In conclusion, using a context-dependent approach to autophagy evaluation and modulation in breast cancers, we discovered novel associations with prognosis and sensitivity to treatment across various breast cancer subtypes. These findings should be taken into consideration when planning future pre-clinical and clinical studies with autophagy inhibitors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".