Spatially distinct tumor immune microenvironments stratify triple-negative breast cancers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Understanding the tumor immune microenvironment (TIME) promises to be key for optimal cancer therapy, especially in triple-negative breast cancer (TNBC). Integrating spatial resolution of immune cells with laser capture microdissection gene expression profiles, we defined distinct TIME stratification in TNBC, with implications for current therapies including immune checkpoint blockade. TNBCs with an immunoreactive microenvironment exhibited tumoral infiltration of granzyme B+CD8+ T cells (GzmB+CD8+ T cells), a type 1 IFN signature, and elevated expression of multiple immune inhibitory molecules including indoleamine 2,3-dioxygenase (IDO) and programmed cell death ligand 1 (PD-L1), and resulted in good outcomes. An "immune-cold" microenvironment with an absence of tumoral CD8+ T cells was defined by elevated expression of the immunosuppressive marker B7-H4, signatures of fibrotic stroma, and poor outcomes. A distinct poor-outcome immunomodulatory microenvironment, hitherto poorly characterized, exhibited stromal restriction of CD8+ T cells, stromal expression of PD-L1, and enrichment for signatures of cholesterol biosynthesis. Metasignatures defining these TIME subtypes allowed us to stratify TNBCs, predict outcomes, and identify potential therapeutic targets for TNBC.
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.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it