Assessing ER Positive Breast Cancer Heterogeneity and Identification of Tumour Microenvironment Molecular Markers in Response to Treatment Via Multi-omic Approach
Bibliographic record
Abstract
Breast cancer (BC) remains the most prevalent malignancy among women. It is a heterogeneous disease and this in part, explains why most current therapeutics work best when multiple agents are combined. In this study, patients from the neoadjuvant trial of pre-operative exemestane or letrozole +/-celecoxib in the treatment of ER positive postmenopausal early BC (NEO-EXCEL) were profiled by targeted sequencing and spatial proteomic analysis to measure and assess the role of heterogeneity on treatment outcome. In matched samples, genes most frequently mutated included PIK3CA, NQO1, and MAP3K1. Frequent copy number changes in FGF3, FGF4, and FGF19 were identified. No significant differences were noted between pre- and post-treatment samples. Spatial profiling identified high-expressing proteins fibronectin, SMA, CD127, and CD44 in both tumour and TME compartments. Within the non-responders, lower levels of T-cells and macrophages were present. Uncovering the drivers of heterogeneity through a multi-omic study will help understand its effects on tumour progression.
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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) | 0.001 | 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) | 0.000 | 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".