THE SPATIAL TRANSCRIPTOMIC LANDSCAPE OF BREAST CANCER BRAIN METASTASIS
Bibliographic record
Abstract
Abstract Brain metastases (BM) are responsible for the majority of cancer mortality. Breast cancer is one of the most common primary sites for brain metastasis. Metastatic cancer cells are known to diverge genetically and phenotypically from their primary counterpart. Together with the unique tumor brain microenvironment (TBME), this poses additional challenges in the treatment of BM. METHODS: We identified 30 cases of patient-paired, surgically resected brain metastasis with breast origin. Six cases of non-tumoral brain control were also included. Two tissue microarray (TMA) blocks were constructed to include all cases. Spatial RNAseq was performed while PanCK, CD45, and GFAP were used as morphology markers to annotate the regions of interest (ROIs). For each patient, five ROIs were analyzed: primary breast cancer (BC), primary breast cancer microenvironment (BCME), metastatic tumor cells (MTC), immediate TBME (iTBME), and distant TBME (dTBME). RESULTS: 1) Triple-negative breast cancers (TNBC) demonstrated distinct gene signatures in both primary and metastatic sites. 2) There were 9 BC shifted their genetic profiles and were reclassified into different molecular subtypes at their matched brain metastatic site. Functional enrichment analysis revealed enriched EMT, ECM-receptor interaction, and the complement system in these profile shifting-BC cases. In contrast, the BC cases that preserved their original profiles (non-shifting BC) shared upregulated pathways of ribosome biogenesis and cell cycle. 3) TBME underwent cellular and molecular plasticity characterized by elevated neutrophils, reactive astrocytes, and activated microglia. In the TBME homing TNBC, cancer-associated fibroblasts (CAF) represented a hub in the cellular interaction between MTCs and TBME.
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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 | 0.000 | 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 | 0.000 | 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".