Abstract 2010: Charactering the interactomes of the Myc family of oncogenes
Bibliographic record
Abstract
Abstract The Myc family of transcription factors, c-Myc, N-Myc and L-Myc, are known to be deregulated in a large variety of cancers. Mechanisms responsible for the deregulation of the activity of Myc family members in cancer are not well understood. A number of protein-protein interactions and post-translational modifications have been suggested to promote the oncogenic activity of Myc. Traditional biochemical approaches have not been successful at mapping the interactors of Myc family members due to their tight association with chromatin and the labile nature of Myc proteins. Mapping the protein-protein interactions that support the aberrant activity of Myc family of oncoproteins in cancer cells is of high interest, particularly in a more natural context in xenograft models in vivo. A new mass spectrometry-based technique, BioID-MS, which relies on proximity-based biotin labeling, has recently emerged as a key advance for the characterization of hard-to-detect protein-protein interactions in living cells. Herein, we are reporting a new application of the BioID-MS technique for the characterization of c-Myc interactors in human cell line in vivo, in mouse tumor xenografts. Using the in vivo BioID assay, we were able to identify more than 30 known and validated c-Myc interactors, some of which include the components of the STAGA complex and SWI/SNF chromatin remodelling complex. We were further able to identify more than 100 novel high-confidence c-Myc interactors, which include components of the DNA repair and replication machinery, general transcription and elongation factors, and the co-regulator of transcription-like DNA helicase protein chromodomain 8 (CHD8). Using ENCODE ChIP-seq datasets we were able to map some of the high-confidence interactors to coincident binding sites with c-Myc throughout the genome. This provided further credibility that the newly identified putative interactors could co-occupy sites on chromatin with c-Myc and could be functionally important for activity. Furthermore, we validated the Myc-CHD8 interaction using a number of approaches, including yeast two hybrid and proximity-based ligation assays. These findings suggest that the BioID-MS technique can be used to extend the mapping of the Myc interactome and contribute to a greater understanding of Myc regulation by protein-protein interactions. Furthermore, we are currently in the process of employing this technique to map the interactomes of two other Myc family members, N-Myc and L-Myc. We are interested in identifying common interactors of the Myc family members that contribute to their oncogenic activity, validate them, and explore whether these interactors could be potential therapeutic targets in cancers with deregulated Myc activity. Citation Format: Diana Resetca, Dharmesh Dingar, Manpreet Kalkat, Brian Raught, Linda Z. Penn. Charactering the interactomes of the Myc family of oncogenes. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2010.
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 | 0.000 | 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) | 0.000 | 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".