Abstract 4401: Integrated genomic and functional microRNA analysis identifies miR-30-5p as a tumor suppressor and novel therapeutic nanomedicine in head and neck cancer
Bibliographic record
Abstract
Abstract We have developed an integrative approach to elucidate the functional role of deregulated microRNAs (miRs) and identify candidates for miR replacement therapy in cancer, applied here to head and neck squamous cell carcinomas (HNSCC). Our method involves integration of results from TCGA and other validation databases of genetic and expression data, including: miR expression, mRNA expression, copy number variation, DNA methylation. This data is then intersected with functional in vitro genome-wide miR screening for anti-proliferative miR mimics. We report that the miR-30-5p family is commonly repressed and correlates with overexpression of a broad network of proliferative and metastasis-related oncogenic mRNAs, including the growth receptors EGFR, MET and IGF1R. We show that re-expression of miR-30a-5p repressed this gene program, downstream signaling, proliferation, migration, and invasion in vitro. We have developed a chemically modified mimic of miR-30-5p with 50x improved stability in human serum, and 5X improvement in IC50 in vitro by XTT assay. We have formulated our novel miR-30a-5p mimic into Transferrin Receptor targeted nanoparticles which strongly inhibited HNSCC xenograft tumor growth, and regulated miR-30-5p family targets in vivo. Together with data linking decreased miR-30 family expression with DNA copy loss and promoter hypermethylation, and clinical disease-specific survival, for the first time, we have reported a more global picture of the function of this important tumor suppressor and identified a subset of patients that may benefit from miR replacement therapy with our novel miR-30-5p based nanomedicine. This research was supported by NIDCD intramural projects ZIA-DC-000073, 74, and NCI grant U43CA22156701. Citation Format: Anthony D. Saleh, Robert F. Place, Hui Cheng, Rita Das, Thomas M. Harris, Geoffrey Childs, Gordon A. Robertson, Kathleen F. Pirollo, Esther H. Chang, Zhong Chen, Carter Van Waes. Integrated genomic and functional microRNA analysis identifies miR-30-5p as a tumor suppressor and novel therapeutic nanomedicine in head and neck cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4401.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".