Abstract P5-04-24: Molecular stratification of ER+/HER2- breast cancer cell lines to predict sensitivity to targeted agents
Bibliographic record
Abstract
Abstract Background: Approximately 70% of all breast cancers are estrogen receptor positive (ER+) at diagnosis and are dependent on estrogen signaling for tumour growth and proliferation. Some ER+ breast cancers can be effectively treated with adjuvant endocrine therapies including tamoxifen, but despite favorable improvements in overall survival, resistance to endocrine therapy is common and has been associated with dysregulation of several signaling pathways. These pathways can be targeted with specific inhibitors, many of which are currently under clinical investigation. However currently there is a lack of predictive biomarkers to identify which patients should receive treatment with targeted therapy. The goal of this study was to determine whether alterations in specific signaling pathways can be identified and used to stratify breast cancer cell lines to the most effective experimental treatments. Methods/Results: Fifteen ER+/HER2- cell lines were characterized using a NanoString PAM50-like assay as well as next generation sequencing and were then stratified according to alterations in three key signaling pathways: CCND/CDK, PI3K/AKT/mTOR and FGFR. High-throughput small-molecule screenings were performed to identify the IC50 values of 24 inhibitors across the strata. Variation in inhibitor sensitivity was observed between cell lines based on molecular alterations. Cell lines with a PIK3CA mutation in combination with a CDK-pathway alteration were more sensitive to CDK inhibitors (50 to 120nM) than cell lines with alterations in the CDK-pathway alone or PIK3CA mutations alone (170nM to >5000nM). In addition, cell lines with the dual alterations demonstrated stronger synergy between CDK and PI3K-pathway inhibitors compared to either alteration alone. Conclusions: The results suggest that stratification according to molecular alterations in specific signaling pathways may predict sensitivity to targeted inhibitors in a panel of ER+/HER2- luminal breast cancer cell lines. Work is ongoing to identify the optimal synergistic inhibitor combinations for each strata. The ultimate goal is to translate this work into a novel personalized medicine approach, using molecular stratification based on a combination of molecular events in a functional pathway as opposed to single genes. Citation Format: Bathurst L, Liao L, Crozier C, Lyttle N, Marcellus R, Bayani J, Al-awar R, Bartlett J, Spears M. Molecular stratification of ER+/HER2- breast cancer cell lines to predict sensitivity to targeted agents [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P5-04-24.
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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 | 0.001 |
| 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) | 0.002 | 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".