Clinical significance of CD73 in triple-negative breast cancer: multiplex analysis of a phase III clinical trial
Bibliographic record
Abstract
Background: CD73 is an ecto-enzyme that promotes tumor immune escape through the production of immunosuppressive extracellular adenosine in the tumor microenvironment. Several CD73 inhibitors and adenosine receptor antagonists are being evaluated in phase I clinical trials. Patients and methods: Full-face sections from formalin-fixed paraffin-embedded primary breast tumors from 122 samples of triple-negative breast cancer (TNBC) from the BIG 02-98 adjuvant phase III clinical trial were included in our analysis. Using multiplex immunofluorescence and image analysis, we assessed CD73 protein expression on tumor cells, tumor-infiltrating leukocytes and stromal cells. We investigated the associations between CD73 protein expression with disease-free survival (DFS), overall survival (OS) and the extent of tumor immune infiltration. Results: Our results demonstrated that high levels of CD73 expression on epithelial tumor cells were significantly associated with reduced DFS, OS and negatively correlated with tumor immune infiltration (Spearman's R= -0.50, P < 0.0001). Patients with high levels of CD73 and low levels of tumor-infiltrating leukocytes had the worse clinical outcome. Conclusions: Taken together, our study provides further support that CD73 expression is associated with a poor prognosis and reduced anti-tumor immunity in human TNBC and that targeting CD73 could be a promising strategy to reprogram the tumor microenvironment in this BC subtype.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".