Abstract 2167: Preclinical validation of an Omomyc cell-penetrating peptide as a viable anti-Myc therapy
Bibliographic record
Abstract
Abstract Deregulation of the MYC oncoprotein promotes tumorigenesis in most, if not all, cancers and is often associated with poor prognosis. However, targeting MYC has long been considered impossible based on the assumption that it would cause catastrophic side effects in normal tissues. Despite this general preconceived notion, we showed that MYC inhibition exerts extraordinary therapeutic impact in various genetic mouse models of cancer, and causes only mild, well-tolerated and reversible side effects. For these studies we employed the systemic and conditional expression of a dominant negative of MYC, called Omomyc, which we designed and validated, and that can inhibit MYC transactivation function both in vitro and in vivo. To date, Omomyc has only been considered a proof of principle, with any potential clinical application limited to gene therapy. Here we actually show that the 11 kDa Omomyc polypeptide spontaneously transduces into cancer cells, demonstrating unexpected cell-penetrating ability. Once inside the nuclei, the polypeptide effectively blocks MYC binding to its target DNA sites, interfering with MYC transcriptional regulation and halting cell proliferation. Moreover, intranasal (i.n.) administration of the Omomyc polypeptide in mice results in its rapid and persistent distribution to lungs, as well as to other organs (i.e. intestine, liver, kidneys and brain). Importantly, i.n. treatment of mice bearing either Non-Small-Cell-Lung-Cancer (NSCLC) or glioblastoma (GBM) with the Omomyc cell-penetrating peptide (OmomycCPP) significantly reduces tumor burden compared to their control counterparts. Notably, tumor regression is accompanied by significant reprogramming of the tumor microenvironment and tumor immune response. In summary, our data indicate that this novel generation of polypeptides represents a new opportunity to potentially inhibit MYC pharmacologically in a variety of malignant diseases. Citation Format: Marie-eve Beaulieu, Toni Jauset, Daniel Massó-Vallés, Peter Rahl, Sandra Martinez-Martin, Loika Maltais, Mariano F. Zacarias-Fluck, Silvia Casacuberta, Erika Serrano del Pozo, Christopher Fiore, Laia Foradada, Matthew Guenther, Eduardo Romero Sanz, Marta Oteo Vives, Cynthia Tremblay, Martin Montagne, Miguel Ángel Morcillo Alonso, Jonathan R. Whitfield, Pierre Lavigne, Laura Soucek. Preclinical validation of an Omomyc cell-penetrating peptide as a viable anti-Myc therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2167. doi:10.1158/1538-7445.AM2017-2167
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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) | 0.001 | 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 | 0.001 | 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".