Infiltration of CD8 <sup>+</sup> T cells into tumor cell clusters in triple-negative breast cancer
Bibliographic record
Abstract
Infiltration of [Formula: see text] T lymphocytes into solid tumors is associated with good prognosis in various types of cancer, including triple-negative breast cancer (TNBC). However, the mechanisms underlying different infiltration levels are largely unknown. Here, we have characterized the spatial profile of [Formula: see text] T cells around tumor cell clusters (tightly connected tumor cells) in the core and margin regions in TNBC patient samples. We found that in some patients, the [Formula: see text] T cell density first decreases when moving in from the boundary of the tumor cell clusters and then rises again when approaching the center. To explain various infiltration profiles, we modeled the dynamics of T cell density via partial differential equations. We spatially modulated the diffusion/chemotactic coefficients of T cells (to mimic physical barriers) or introduced the localized secretion of a diffusing T cell chemorepellent. Combining the spatial-profile analysis and the modeling led to support for the second idea; i.e., there exists a possible chemorepellent inside tumor cell clusters, which prevents [Formula: see text] T cells from infiltrating into tumor cell clusters. This conclusion was consistent with an investigation into the properties of collagen fibers which suggested that variations in desmoplastic elements does not limit infiltration of [Formula: see text] T lymphocytes, as we did not observe significant correlations between the level of T cell infiltration and fiber properties. Our work provides evidence that [Formula: see text] T cells can cross typical fibrotic barriers and thus their infiltration into tumor clusters is governed by other mechanisms possibly involving a local repellent.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".