Bibliographic record
Abstract
Cancer cells rapidly evolve a multitude of defense mechanisms to evade the effects of the oncologist's drug arsenal. Unfortunately, clinical strategies to overcome these lag far behind. This mismatch likely underlies our inability to implement new durable treatment strategies. Here, a new form of multidrug resistance, inducible drug glucuronidation, is discussed. This form was discovered while developing means to target a specific oncogene, the eukaryotic translation initiation factor 4E (eIF4E), with its inhibitor ribavirin. In two clinical studies, ribavirin treatment led to substantial clinical responses, but all responding patients eventually relapsed. In most cases, this was due to the overexpression of the sonic hedgehog transcription factor Gli1, which elevated the UDP glucuronsyltransferase UGT1A enzymes. UGT1As add glucuronic acid to many drugs. Indeed, these cells are resistant to not only ribavirin, but also Ara-C, and likely other drugs. Inhibition of Gli1 reduced UGT1As, eliminated drug glucuronides, and renewed sensitivity to ribavirin and Ara-C. These studies highlight that cancer cells and their resistant counterparts metabolize drugs differently from each other as well as from normal cells. Likely, these inducible modifications go beyond glucuronidation. Understanding the extent of inducible drug modifications and the pathways that drive expression of the corresponding enzymatic machinery will better position us to finally make resistance futile.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".