HuR Contributes to TRAIL Resistance by Restricting Death Receptor 4 Expression in Pancreatic Cancer Cells
Bibliographic record
Abstract
UNLABELLED: Pancreatic ductal adenocarcinoma (PDA) is one of the most lethal cancers, in part, due to resistance to both conventional and targeted therapeutics. TRAIL directly induces apoptosis through engagement of cell surface Death Receptors (DR4 and DR5), and has been explored as a molecular target for cancer treatment. Clinical trials with recombinant TRAIL and DR-targeting agents, however, have failed to show overall positive outcomes. Herein, we identify a novel TRAIL resistance mechanism governed by Hu antigen R (HuR, ELAV1), a stress-response protein abundant and functional in PDA cells. Exogenous HuR overexpression in TRAIL-sensitive PDA cell lines increases TRAIL resistance whereas silencing HuR in TRAIL-resistant PDA cells, by siRNA oligo-transfection, decreases TRAIL resistance. PDA cell exposure to soluble TRAIL induces HuR translocation from the nucleus to the cytoplasm. Furthermore, it is demonstrated that HuR interacts with the 3'-untranslated region (UTR) of DR4 mRNA. Pre-treatment of PDA cells with MS-444 (Novartis), an established small molecule inhibitor of HuR, substantially increased DR4 and DR5 cell surface levels and enhanced TRAIL sensitivity, further validating HuR's role in affecting TRAIL apoptotic resistance. NanoString analyses on the transcriptome of TRAIL-exposed PDA cells identified global HuR-mediated increases in antiapoptotic processes. Taken together, these data extend HuR's role as a key regulator of TRAIL-induced apoptosis. IMPLICATIONS: Discovery of an important new HuR-mediated TRAIL resistance mechanism suggests that tumor-targeted HuR inhibition increases sensitivity to TRAIL-based therapeutics and supports their re-evaluation as an effective treatment for PDA patients. Mol Cancer Res; 14(7); 599-611. ©2016 AACR.
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 | 0.001 | 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".