Abstract B23: Pushing Myc inhibition towards the clinic by direct delivery of cell-penetrating peptides
Bibliographic record
Abstract
Abstract Inhibiting Myc has long been regarded as a promising cancer treatment. However, clinical Myc inhibition was considered unfeasible due to its central role in normal proliferation and the difficulties of targeting a nuclear transcription factor. The expression of Omomyc (a Myc inhibitor derived from the dimerization and DNA-binding domain of Myc) in the KRasG12D non-small cell lung cancer (NSCLC) mouse model challenged these assumptions, as it resulted in dramatic tumor clearance with only limited and well tolerated side effects in normal tissues (Soucek et al., 2008 and 2013). Omomyc expression proved equally potent in several other mouse models of cancer, revealing the huge potential of this inhibitory approach against multiple cancer types including papilloma, pancreas and glioma (Soucek et al., 2004; Sodir et al., 2011; Annibali et al., 2014). Recently, Max*, a b-HLH-LZ peptide derived from Myc's obligate protein partner Max, was shown to spontaneously enter cells (Montagne et al., 2012). As Omomyc and Max* display high structural homology, we hypothesized that Omomyc could also behave as a cell-penetrating peptide and thus recapitulate the effects of its transgenic counterpart. Our preliminary results show that the Omomyc peptide is well folded in solution; it transduces into cancer cells and effectively stops their proliferation in a dose-dependent manner.In vivo, nasal instillation of fluorescently-labeled Omomyc peptide leads to its rapid distribution to lungs and brain, as well as to other organs (G.I. tract, liver), as observed by IVIS® imaging and immunohistochemistry. Finally, a short treatment with the Omomyc peptide reduces the tumor size and number of Ki67 positive cells in the KRasG12D-induced NSCLC mouse model. In summary, the Omomyc cell penetrating peptide represents a new opportunity to pharmacologically inhibit Myc in a variety of malignant diseases. Citation Format: Marie-Eve Beaulieu, Toni Jauset, Daniel Massó-Valles, Jonathan R. Whitfield, Erika Serrano del Pozo, Cynthia Tremblay, Loïka Maltais, Martin Montagne, Pierre Lavigne, Laura Soucek. Pushing Myc inhibition towards the clinic by direct delivery of cell-penetrating peptides. [abstract]. In: Proceedings of the AACR Special Conference on Myc: From Biology to Therapy; Jan 7-10, 2015; La Jolla, CA. Philadelphia (PA): AACR; Mol Cancer Res 2015;13(10 Suppl):Abstract nr B23.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".