Abstract 3828: Understanding the molecular underpinnings of breast cancer brain metastases: implications for early detection and treatment
Bibliographic record
Abstract
Abstract Brain metastases (BrM) are a major cause of morbidity and mortality among patients with metastatic breast cancer. Women with metastatic HER2-positive (HER2+) and triple-negative breast cancer (TNBC) have a particularly high propensity to develop BrM, with up to half developing BrM during their lifetime. Currently, apart from assignment of risk by breast cancer subtype, risk factors for the development of BrM are lacking. Given that BrM are associated with a poor prognosis, particularly among patients with TNBC, there is a critical need for biomarkers to identify patients at high risk of BrM for whom future screening and/or prevention strategies can be employed. In this study we performed whole transcriptomic profiling of 53 brain metastases in triplicate with spatial resolution using Bruker’s Digital Spatial Profiler, Whole Transcriptome Assay. Patients with breast cancer BrM who were treated with surgery, whole brain radiotherapy (WBRT) and/or stereotactic radiosurgery (SRS) between 2008 and 2018 were selected, representative sections of tumor were cored and microarrayed prior to spatial transcriptomic profiling of the tumor and tumor microenvironment. Across both HER2+ and TNBC subtypes, spatial deconvolution revealed a high proportion of CD4+ and CD8+ naïve T-cells in the tumor compartments compared to the tumor microenvironment whereas macrophages were the most abundant immune cell type irrespective of subtype in the tumor microenvironment. Differential gene expression analysis demonstrated that higher expression levels of known HER2 mediated genes including MED1, STARD3 and GRB7 were present in the HER2+ brain subtypes. Markers for stem cell features were more prevalent within tumor compartments of TNBC BrM compared to HER2+ BrM. In conclusion, this study characterized the tumor and tumor microenvironment of breast cancer BrM using spatial transcriptomics, identifying molecular underpinnings that may be targeted for BrM prevention and/or treatment. Citation Format: Melanie Spears, Megan Hopkins, Rania Chehade, Italo Fernandes, Vida Talebian, Jane Bayani, Katarzyna Jerzak. Understanding the molecular underpinnings of breast cancer brain metastases: implications for early detection and treatment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3828.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".