Crosstalk between Immune Checkpoint Modulators, Metabolic Reprogramming and Cellular Plasticity in Triple-Negative Breast Cancer
Bibliographic record
Abstract
Breast cancer is one of the major causes of mortality in women worldwide. Accounting for 15-20% of all breast cancer diagnoses, the triple-negative breast cancer (TNBC) subtype presents with an aggressive clinical course, heightened metastatic potential and the poorest short-term prognosis. TNBC does not respond to hormonal therapy, only partially responds to radio- and chemotherapy, and has limited targeted therapy options, thus underlining the critical need for better therapeutic treatments. Although immunotherapy based on immune checkpoint inhibition is emerging as a promising treatment option for TNBC patients, activation of cellular plasticity programs such as metabolic reprogramming (MR) and epithelial-to-mesenchymal transition (EMT) causes immunotherapy to fail. In this report, we review the role of MR and EMT in immune checkpoint dysregulation in TNBCs and specifically shed light on development of novel combination treatment modalities for this challenging disease. We highlight the clinical relevance of crosstalk between MR, EMT, and immune checkpoints in TNBCs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".