Abstract 3956: Omomyc-based cell-penetrating peptides: From proof of concept to a clinically viable anti-Myc therapy
Bibliographic record
Abstract
Abstract Deregulation of the MYC oncoprotein drives tumorigenesis in most–if not all–cancers and generally correlates with poor prognosis, suggesting that its inhibition would be a useful therapeutic strategy. Indeed, we have shown that Myc inhibition displays extraordinary therapeutic benefit in various transgenic mouse models of cancer (i.e., skin, lung, pancreatic cancer and glioma), without eliciting resistance to therapy, and causes only mild, well-tolerated and reversible side effects in normal tissues. For these studies we employed a dominant negative inhibitor of Myc, called Omomyc, which is an effective inhibitor of Myc transactivation function both in vitro and in vivo. Omomyc has so far been utilized exclusively as a transgene, as a successful proof of principle whose application was believed to be solely limited to gene therapy. Here, though, we show that the purified Omomyc polypeptide itself spontaneously transduces into cancer cells and effectively interferes with MYC transcription, abrogating cell cycle and promoting apoptosis in different cancer cells, independently of their mutational profile. Efficacy of the Omomyc polypeptide in two complementary murine models of non-small cell lung cancer (NSCLC) establishes its therapeutic potential through both direct tissue delivery (intranasal) and systemic intravenous administration, providing for the first time evidence that the Omomyc polypeptide is an effective MYC inhibitor worthy of clinical development. Citation Format: Marie-Eve Beaulieu, Toni Jauset, Daniel Massó-Vallés, Sandra Martínez-Martín, Peter Rahl, Löika Maltais, Mariano F. Zacarias-Fluck, Sílvia Casacuberta-Serra, Erika Serrano del Pozo, Christopher Fiore, Laia Foradada, Virginia Castillo Cano, Matthew Guenther, Eduardo Romero Sanza, Marta Oteo, Cynthia Tremblay Tremblay, Génesis Martín, Danny Letourneau, Martin Montagne, Miguel Ángel Morcillo Alonso, Jonathan R. Whitfield, Pierre Lavigne, Laura Soucek. Omomyc-based cell-penetrating peptides: From proof of concept to a clinically viable anti-Myc therapy [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 3956.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".