The tumor microenvironment of 14,837 breast cancers is associated with clinical outcome independently of genomic subtypes
Bibliographic record
Abstract
The tumor microenvironment (TME) contributes to breast cancer heterogeneity and outcome but is rarely considered in clinical decision-making. We address this gap by systematically characterizing the TME's cellular composition to establish its independent clinical utility across intrinsic and genomic subtypes. We first compare 15 TME profiling methods in 693 samples and then apply the deconvolution algorithm InstaPrism to a meta-dataset of 14,837 expression profiles. We identify seven distinct TME patterns that associate with disease-free survival independently of intrinsic subtype. We also identify TME features that modulate chemotherapy response, relapse, and metastatic risk, with divergent patterns observed across estrogen receptor subtypes. Notably, long-term recurrence was regulated by vascular stromal cells and the innate immune response. Furthermore, the depletion of B cell lineage derivatives in metastatic lesions suggests an opportunity for therapeutic intervention. These results provide evidence for using TME characterization as a prognostic and predictive biomarker and identify potential targets for TME-based intervention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".