Synergistic RAS-MAPK and AKT Activation in MYC-Driven Tumors via Adjacent PVT1 Rearrangements
Bibliographic record
Abstract
ABSTRACT MYC-driven (MYC+) cancers are aggressive and often fatal. MYC dysregulation is a key event in these cancers, but overexpression of MYC alone is not always enough to cause cancer. Plasmocytoma Variant Translocation 1 ( PVT1 ), a long non-coding RNA (lncRNA) adjacent to MYC on chromosome 8 is a rearrangement hotspot in many MYC+ cancers. In addition to being co-amplified with MYC, the genomic rearrangement at PVT1 involves translocation, which has had obscure functional consequences. We report that translocation at the PVT1 locus cause asymmetric enrichment of 5’-PVT1 and loss of 3’-PVT1. Despite being classified as a non-coding RNA, the retained 5’ region of PVT1 generates a circular RNA (CircPVT1) that codes for the novel peptide we call Firefox (FFX). FFX augments AKT signaling and synergistically activates MYC and mTORC1 in these cells. Further, the 3’ end of PVT1, which is lost during the translocation, codes for a tumor-suppressing micropeptide we named as Honeybadger (HNB). We demonstrate that HNB interacts with KRAS and disrupts the activation of KRAS effectors. Loss of HNB leads to activation of RAS/MAPK signaling pathway, and enhances MYC stability by promoting phosphorylation of MYC at Ser 62 . These findings identify PVT1 as a critical node that synchronizes MYC, AKT, and RAS-MAPK activities in cancer. Our study thus identifies a key mechanism by which rearrangements at the PVT1 locus activate additional oncogenic pathways that synergize with MYC to exacerbate the aggressiveness of MYC+ cancers. This newfound understanding explains the poor prognosis associated with MYC+ cancers and offers potential therapeutic targets that could be leveraged in treatment strategies for these cancers.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".