Targeting CD73 and downstream adenosine receptor signaling in triple-negative breast cancer
Bibliographic record
Abstract
INTRODUCTION: Despite significant improvements in diagnosis and therapy over the past 20 years, breast cancer remains a worldwide public health issue. In particular, triple negative breast cancer (TNBC), a subset of very aggressive breast tumors, is associated with a poor prognosis and has very few efficient therapeutic options. The ectonucleotidase CD73 has recently emerged as a promising new target for TNBC in preclinical models. Pharmacological targeting of CD73 and downstream adenosine A2A/A2B receptor signaling is currently an active field of research that could lead to the development of new cancer therapeutics, including options against TNBC. AREAS COVERED: This article reviews the basic structural and molecular features of CD73 and its role in the development of cancer, with a particular focus on CD73's role in the biology of TNBC. EXPERT OPINION: It was recently demonstrated that CD73 expression in TNBC is associated with worse clinical outcomes and increased resistance to anthracycline chemotherapy. Targeted blockade of the CD73/A2A axis has been shown to impair various aspects of tumorigenesis and displays synergism with other anti-cancer treatments in preclinical studies. Hence, we strongly argue for the development of CD73 inhibitors and for the repositioning of A2A antagonists in cancer.
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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 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".