Targeting CD73 with flavonoids inhibits cancer stem cells and increases lymphocyte infiltration in a triple-negative breast cancer mouse model
Bibliographic record
Abstract
Introduction: Chemotherapy remains the mainstay treatment for triple-negative breast cancer (TNBC) due to the lack of specific targets. Given a modest response of immune checkpoint inhibitors in TNBC patients, improving immunotherapy is an urgent and crucial task in this field. CD73 has emerged as a novel immunotherapeutic target, given its elevated expression on tumor, stromal, and specific immune cells, and its established role in inhibiting anti-cancer immunity. CD73-generated adenosine suppresses immunity by attenuating tumor-infiltrating T- and NK-cell activation, while amplifying regulatory T cell activation. Chemotherapy often leads to increased CD73 expression and activity, further suppressing anti-tumor immunity. While debulking the tumor mass, chemotherapy also enriches heterogenous cancer stem cells (CSC), potentially leading to tumor relapse. Therefore, drugs targeting both CD73, and CSCs hold promise for enhancing chemotherapy efficacy, overcoming treatment resistance, and improving clinical outcomes. However, safe and effective inhibitors of CD73 have not been developed as of now. Methods: We used in silico docking to screen compounds that may be repurposed for inhibiting CD73. The efficacy of these compounds was investigated through flow cytometry, RT-qPCR, CD73 activity, cell viability, tumorsphere formation, and other in vitro functional assays. For assessment of clinical translatability, TNBC patient-derived xenograft organotypic cultures were utilized. We also employed the ovalbumin-expressing AT3 TNBC mouse model to evaluate tumor-specific lymphocyte responses. Results: CSCs, and co-targeting CD73, YAP, and Wnt effectively suppressed the growth of human TNBC cell lines and patient-derived xenograft organotypic cultures. Furthermore, triple-drug combination inhibited paclitaxel-enriched CSCs and simultaneously improved lymphocyte infiltration in syngeneic TNBC mouse tumors. Discussion: Conclusively, our findings elucidate the significance of CSCs in impairing anti-tumor immunity. The high efficacy of our triple-drug regimen in clinically relevant platforms not only underscores the importance for further mechanistic investigations but also paves the way for potential development of new, safe, and cost-effective therapeutic strategies for TNBC.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".