Development of CDK4/6 Inhibitor Resistant ER-positive HER2-negative Breast Cancer Cell Lines to Elucidate Novel Mechanisms of Resistance
Bibliographic record
Abstract
Treatment with CDK4/6 inhibitors (CDK4/6i) in combination with hormone therapy is now the standard of care for patients with advanced or metastatic ER-positive/HER2-negative (ER+/HER2-) breast cancer. However, many patients experience disease progression due to intrinsic or acquired resistance. Predictive biomarkers to identify patients most likely to respond to initial therapy as well as therapeutic targets to overcome or delay onset of resistance, are needed to improve patient outcomes. To achieve these outcomes will require a better understanding of the molecular mechanisms responsible for CDK4/6i sensitivity and resistance. In this study, I established in-vitro models of acquired resistance to two CDK4/6i, palbociclib and abemaciclib, using MCF7 and T47D cell lines. Genomic, transcriptomic, and proteomic analyses were performed in parental cells and resistant derivatives. Strikingly, acquired resistance across all models was associated with an upregulated interferon (IFN) response as well as loss of ER/PR expression and signaling. Importantly, expression of an IFN-based gene signature (IFN-sig) derived from these models was also upregulated in early-stage breast tumours intrinsically resistant to CDK4/6i as well as to endocrine therapy, and thus warrants further clinical evaluation as a novel predictive biomarker. While additional signaling pathways altered in resistant cell lines included cyclin D-CDK4/6-RB, EGFR/HER, and AKT/mTORC1, therapeutic targeting of these pathways could not re-sensitize all models to CDK4/6 inhibition. However, EGFR/HER or AKT/mTOR inhibitors in combination with CDK4/6i synergistically reduced cell viability in parental cell lines, representing potential treatment strategies to delay onset of resistance. In summary, I have developed and characterized CDK4/6i resistant ER+/HER2- breast cancer cell lines and demonstrated that mechanisms of resistance are diverse. Importantly, I have identified the IFN-sig as a potential novel predictive biomarker of CDK4/6i response, and potential treatment strategies to overcome or prevent the development of resistance, for further evaluation.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".