Abstract PO-057: Systemic delivery of miR-27a* using ultrasound-targeted microbubble cavitation causes tumor regression
Bibliographic record
Abstract
Abstract Benefits from treatment with antibodies targeting the overexpression of epidermal growth factor receptor (EGFR) in head and neck squamous cell carcinoma (HNSCC) are observed primarily in combination with radiation therapy; anti-EGFR monotherapy results in less than 10% disease response. Despite extensive biological justification for EGFR inhibitors, marginal clinical benefit has been observed in broad populations. MicroRNAs (miRs) are short, endogenous, noncoding RNA molecules (19-22 nucleotides) that bind to target mRNAs, causing post-transcriptional RNA interference. Unlike siRNAs, which target a single gene, miRs have evolved to simultaneously target multiple proteins, making them attractive therapeutic candidates. Our previous studies showed that miR-27a* is repressed in HNSCC, and miR-27a* levels correlate with survival. We identified multiple miR-27a* targets in the PI3K-AKT1-mTOR pathway, the most commonly overexpressed mitogenic pathway in HNSCC, including EGFR, AKT1, and mTOR. Ectopic expression of miR-27a* inhibits tumor growth in both in vitro and in vivo HNSCC models, suggesting miR-27a* as a potential therapeutic intervention. However, substantial challenges exist in delivery of miRs to tumors. In the current study, we used an image-guided theranostic platform to systemically deliver miR-27a* to tumors. SCCVII-derived xenografts in immunocompetent C3H/HeJ mice were treated with miR-27a* or control miR mimics (miR-C). Therapeutic ultrasound was delivered using a single-element immersion transducer oriented directly over the tumor site, following continuous infusion of microbubbles loaded with 10 µg miR-27a* or miR-C into the jugular vein. Successful destruction and replenishment of microbubbles in the tumor area was confirmed by simultaneous contrast-specific ultrasound perfusion imaging with a clinical ultrasound imaging system. Using miRNAScope, an in situ hybridization-based method, we were able to visualize ultrasound-targeted microbubble cavitation (UTMC)-mediated delivery and spatial distribution of miR-27a* to the tumor cells. We found that UTMC delivery of miR-27a* lowered protein expression of miR-27a* targets (EGFR, AKT1, mTOR, NUP62, and ΔNP63α) compared with UTMC-delivered miR-C or intravenous miR-27a* treated mice at 48 hours after treatment. We also found that UTMC delivery of miR-27a* resulted in a significant reduction in tumor volume compared with both UTMC-delivered miR-C and intravenously delivered miR-27a* after 2 weeks (n=6 all groups). The potential therapeutic effects of miR-27a* were confirmed by its observed effect in lowering miR-27a* target levels and a significant inhibition of tumor growth. In conclusion, these observations clearly show that i) UTMC is an effective systemic miR delivery platform with the ability to concentrate miR delivery to the tumor site, and ii) miR-27a* is a potentially effective treatment for HNSCC. Citation Format: Nikhil S. Chari, Cheng Chen, Thiruganesh Ramasamy, Xucai Chen, Wei Lu, Khaja B. Khan, Lorena I. Gomez, Luisa M. Solis, Flordeliza S. Villanueva, Stephen Y. Lai. Systemic delivery of miR-27a* using ultrasound-targeted microbubble cavitation causes tumor regression [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Innovating through Basic, Clinical, and Translational Research; 2023 Jul 7-8; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2023;29(18_Suppl):Abstract nr PO-057.
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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) | 0.002 | 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".