Spontaneous Epithelial-Mesenchymal Transition and Resistance to HER-2-Targeted Therapies in HER-2-Positive Luminal Breast Cancer
Bibliographic record
Abstract
Resistance to trastuzumab, a rationally designed HER-2-targeting antibody, remains a major hurdle in the management of HER-2-positive breast cancer. Preclinical studies suggest the mechanisms of trastuzumab resistance are numerous. Unfortunately, the majority of these studies are based around HER-2-positive (HER-2+) luminal cell lines. The role of epithelial to mesenchymal transition (EMT), a genetic program that confers a basal phenotype, may represent a novel mechanism of escape for HER-2+ luminal cells from trastuzumab treatment. Here we investigated this possibility using a model of clonal selection in HER-2+ luminal breast cancer cells. Following a random isolation and expansion of "colony clusters" from SKBR-3 cell lines, several colony clusters underwent a spontaneous EMT in-vitro. In addition to expression of conventional EMT markers, all mesenchymal colony clusters displayed a predominant CD44+/CD24- phenotype with decreased HER-2 expression and elevated levels of a β1-integrin isoform with a high degree of N-glycosylation. Treatment with a β1-integrin function-blocking antibody, AIIB2, preferentially decreased the N-glycosylated form of β1-integrin, impaired mammosphere formation and restored epithelial phenotype in mesenchymal colony clusters. Using this model we provide the first clear evidence that resistance to trastuzumab (and lapatinib) can occur spontaneously as HER-2+ cells shift from a luminal to a basal/mesenchymal phenotype following EMT. While the major determinant of trastuzumab resistance in mesenchymal colony clusters is likely the down regulation of the HER-2 protein, our evidence suggests that multiple factors may contribute, including expression of N-glycosylated β1-integrin.
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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".