CD73 Promotes Resistance to HER2/ErbB2 Antibody Therapy
Bibliographic record
Abstract
Abstract Expression of the ectonucleotidase CD73 by tumor cells, stromal cells, and immune cells is associated in cancer with immune suppression. In this study, we investigated the role of CD73 on the activity of the anti-HER2/ErbB2 monoclonal antibody (mAb) trastuzumab. In a prospective, randomized phase III clinical trial evaluating the activity of trastuzumab, high levels of CD73 gene expression were associated significantly with poor clinical outcome. In contrast, high levels of PD-1 and PD-L1 were associated with improved clinical outcome. In immunocompetent mouse models of HER2/ErbB2–driven breast cancer, CD73 expression by tumor cells and host cells significantly suppressed immune-mediated responses mediated by anti-ErbB2 mAb. Furthermore, anti-CD73 mAb therapy enhanced the activity of anti-ErbB2 mAb to treat engrafted or spontaneous tumors as well as lung metastases. Gene ontology enrichment analysis from gene-expression data revealed a positive association of CD73 expression with extracellular matrix organization, TGFβ genes, epithelial-to-mesenchymal transition (EMT) transcription factors and hypoxia-inducible-factor (HIF)-1 gene signature. Human mammary cells treated with TGFβ or undergoing EMT upregulated CD73 cell-surface expression, confirming roles for these pathways. In conclusion, our findings establish CD73 in mediating resistance to trastuzumab and provide new insights into how CD73 is regulated in breast cancer. Cancer Res; 77(20); 5652–63. ©2017 AACR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".