Development of Resistance to Targeted Therapies Transforms the Clinically Associated Molecular Profile Subtype of Breast Tumor Xenografts
Bibliographic record
Abstract
The effectiveness of therapies targeting specific pathways in breast cancer, such as the estrogen receptor or HER2, is limited because many tumors manifest resistance, either de novo or acquired, during the course of treatment. To investigate molecular mechanisms of resistance, we used two xenograft models of estrogen receptor-positive (ER+) breast cancer, one with and one without HER2 overexpression (MCF7/HER2-18 and MCF7 wt, respectively). Mice with established tumors were assigned to the following treatment groups: estrogen supplementation (E2), estrogen deprivation (ED), ED plus tamoxifen (Tam), all with or without the epidermal growth factor receptor tyrosine kinase inhibitor gefitinib (G). Another group received ED plus the antiestrogen fulvestrant (MCF7 wt only). Tumors with acquired or de novo resistance to these endocrine therapies were profiled for gene expression and compared with tumors in the E2 control group. One class of genes underexpressed in endocrine-resistant tumors (relative to E2-treated tumors) were estrogen inducible in vitro and associated with ER+ human breast cancers (luminal subtype). Another class of genes overexpressed in tumors with acquired resistance in both models represented transcriptional targets of HER2 signaling and was associated with ER-/HER2+ human cancers (ERBB2+ subtype). A third class of genes overexpressed in MCF7/HER2-18 tumors exhibiting de novo resistance to tamoxifen was associated with ER+ human cancers but not with estrogen-regulated genes. Thus, in response to various endocrine therapy regimens, these xenograft breast tumors shut down classic estrogen signaling and activate alternative pathways such as HER2 that contribute to treatment resistance. Over time, the molecular phenotype of breast cancer can change.
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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 | 0.001 | 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) | 0.000 | 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 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".