LyP-1-Modified Oncolytic Adenoviruses Targeting Transforming Growth Factor β Inhibit Tumor Growth and Metastases and Augment Immune Checkpoint Inhibitor Therapy in Breast Cancer Mouse Models
Bibliographic record
Abstract
We report here the development of oncolytic adenoviruses (Ads) that have reduced toxicity, enhanced tumor tropism, produce strong antitumor response, and can overcome resistance to immune checkpoint inhibitor therapy in breast cancer. We have shown that LyP-1 receptor (p32) is highly expressed on the surface of breast cancer cells and tumors from cancer patients, and that increased stromal expression of transforming growth factor β-1 (TGFβ-1) is associated with triple-negative breast cancer. Therefore, we constructed oncolytic Ads, AdLyp.sT and mHAdLyp.sT, in which the p32-binding LyP-1 peptide was genetically inserted into the adenoviral fiber protein. Both AdLyp.sT and mHAdLyp.sT express sTGFβRIIFc, a TGFβ decoy that can inhibit TGFβ pathways. mHAdLyp.sT is an Ad5/48 chimeric hexon virus in which hypervariable regions (HVRs 1-7) of Ad5 are replaced with the corresponding Ad48 HVRs. AdLyp.sT and mHAdLyp.sT exhibited better binding, replication, and produced higher sTGFβRIIFc protein levels in breast cancer cell lines compared with Ad.sT or mHAd.sT control viruses without LyP-1 peptide modification. Systemic delivery of mHAdLyp.sT in mice resulted in reduced hepatic/systemic toxicity compared with Ad.sT and AdLyp.sT. Intravenous delivery of AdLyp.sT and mHAdLyp.sT elicited a strong antitumor response in a human MDA-MB-231 bone metastasis model in mice, as indicated by bioluminescence imaging, radiographic tumor burden, serum TRACP 5b and calcium, and body weight analyses. Furthermore, intratumoral delivery of AdLyp.sT in 4T1 model in immunocompetent mice inhibited tumor growth and metastases, and augmented anti-PD-1 and anti-CTLA-4 therapy. Based on these studies, we believe that AdLyp.sT and mHAdLyp.sT can be developed as potential targeted immunotherapy agents for the treatment of breast 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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".