Molecular Pathways: GLI1-Induced Drug Glucuronidation in Resistant Cancer Cells
Bibliographic record
Abstract
Drug resistance remains a major impediment in the development of durable cancer therapies. Studies in acute myelogenous leukemia (AML) patients revealed a new form of multidrug resistance. Here, increased glioma-associated protein GLI1 leads to elevation of the UDP-glucuronosyl transferase (UGT) enzymes. UGTs add glucuronic acid to xenobiotics and metabolites. Traditionally, the loss of these enzymes is thought to contribute to cancer as a result of impaired clearance of environmental carcinogens. However, we demonstrate that overexpression of UGTs can contribute to oncogenesis by promoting drug resistance. Indeed, UGT levels in AML patients treated with ribavirin and/or cytarabine were elevated at relapse relative to diagnosis. This was reversed by GLI1 inhibition, suggesting a clinically relevant strategy to overcome drug resistance. Further, overexpression of UGTs can also lead to drug resistance in other cancers, such as certain Hsp90 inhibitors and vorinostat in colorectal and chronic lymphoblastic leukemia, respectively. Not all drugs are targets of glucuronidation, suggesting that UGT status could be relevant to treatment choice. Here, we describe several facets of UGT biology and how these could be exploited clinically. These studies demonstrate how drugs in cancer cells can be metabolized differentially than their normal counterparts. In summary, we describe a new form of drug resistance relevant to a variety of cancer contexts.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".