Erb-b2 Receptor Tyrosine Kinase 2 (ERBB2) Promotes ATG12-Dependent Autophagy Contributing to Treatment Resistance of Breast Cancer Cells
Bibliographic record
Abstract
The epidermal growth factor receptor (EGFR) family member erb-b2 receptor tyrosine kinase 2 (ERBB2) is overexpressed in many types of cancers leading to (radio- and chemotherapy) treatment resistance, whereas the underlying mechanisms are still unclear. Autophagy is known to contribute to cancer treatment resistance. In this study, we demonstrate that ERBB2 increases the expression of different autophagy genes including ATG12 (autophagy-related 12) and promotes ATG12-dependent autophagy. We clarify that lapatinib, a dual inhibitor for EGFR and ERBB2, promoted autophagy in cells expressing only EGFR but inhibited autophagy in cells expressing only ERBB2. Furthermore, breast cancer database analysis of 35 genes in the canonical autophagy pathway shows that the upregulation of ATG12 and MAP1LC3B is associated with a low relapse-free survival probability of patients with ERBB2-positive breast tumors following treatments. Downregulation of ERBB2 or ATG12 increased cell death induced by chemotherapy drugs in ERBB2-positive breast cancer cells, whereas upregulation of ERBB2 or ATG12 decreased the cell death in ERBB2-negative breast cancer cells. Finally, ERBB2 antibody treatment led to reduced expression of ATG12 and autophagy inhibition increasing drug or starvation-induced cell death in ERBB2-positive breast cancer cells. Taken together, this study provides a novel approach for the treatment of ERBB2-positive breast cancer by targeting ATG12-dependent autophagy.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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 teacher head, 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".