A novel immunotherapy targeting MMP-14 limits hypoxia, immune suppression and metastasis in triple-negative breast cancer models
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Bibliographic record
Abstract
// Binbing Ling 1 , Kathleen Watt 1 , Sunandan Banerjee 2 , Daniel Newsted 1 , Peter Truesdell 1 , Jarrett Adams 2 , Sachdev S. Sidhu 2 and Andrew W.B. Craig 1 1 Department of Biomedical and Molecular Sciences, Queen’s University, Cancer Biology and Genetics Division, Queen’s Cancer Research Institute, Kingston, ON, Canada 2 The Donnelly Centre, University of Toronto, Toronto, ON, Canada Correspondence to: Andrew W.B. Craig, email: andrew.craig@queensu.ca Keywords: MMP-14, inhibitory antibody, breast cancer, hypoxia, metastasis Received: November 04, 2016 Accepted: April 22, 2017 Published: May 09, 2017 ABSTRACT Matrix metalloproteinase-14 (MMP-14) is a clinically relevant target in metastatic cancers due to its role in tumor progression and metastasis. Since active MMP-14 is localized on the cell surface, it is amenable to antibody-mediated blockade in cancer, and here we describe our efforts to develop novel inhibitory anti-MMP-14 antibodies. A phage-displayed synthetic humanized Fab library was screened against the extracellular domain of MMP-14 and a panel of MMP14-specific Fabs were identified. A lead antibody that inhibits the catalytic domain of MMP-14 (Fab 3369) was identified and treatment of MDA-MB-231 breast cancer cells with Fab 3369 led to significant loss of extracellular matrix degradation and cell invasion abilities. In mammary orthotopic tumor xenograft assays, MMP-14 blockade by IgG 3369 limited tumor growth and metastasis. Analysis of tumor tissue sections revealed that MMP-14 blockade limited tumor neoangiogenesis and hypoxia. Similar effects of MMP-14 blockade in syngeneic 4T1 mammary tumors were observed, along with increased detection of cytotoxic immune cell markers. In conclusion, we show that immunotherapies targeting MMP-14 can limit immune suppression, tumor progression, and metastasis in triple-negative breast cancer.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it