Trolox enhances anti-leukemic effects of arsenic trioxide: the role of oxidative stress
Bibliographic record
Abstract
Arsenic trioxide (As2O3) has considerable efficacy in the treatment of acute promyelocytic leukemia (APL), inducing partial differentiation and promoting apoptosis of malignant promyelocytes. Although initial studies focused on the role of the characteristic APL fusion protein, PML-RARα, in mediating the response to As2O3, recent investigations indicate that its cytotoxic activities are mediated by mechanisms independent of this fusion protein. As2O3 affects numerous intracellular targets mainly through the accumulation of free radicals and consequent induction of oxidative stress and causes a wide range of alterations leading to apoptosis. The intracellular oxidative status has been shown to be important for As2O3 sensitivity. Hematologic cancers other than APL and, solid tumors are less responsive to As2O3 monotherapy in part because their increased redox buffering capacity. Thus, the use of As2O3 in other malignancies is limited by the toxicity of concentrations required to induce apoptosis. The primary goal of the work presented in this thesis was a search for agents that could enhance As2O3 efficacy in malignant cells, but not in normal cells. We demonstrated that trolox (6–hydroxy–2,5,7,8–tetramethylchroman–2–carboxylic acid), a widely known antioxidant, enhances As2O3-mediated apoptosis in APL, P388 murine lymphoma, myeloma and breast cancer cells through the potentiation of As2O3-induced oxidative stress. We performed in vivo experiments in P388 tumor-bearing mice, and show that As2O3 treatment prolonged survival, and the addition of trolox provided a significant further increase in life span and decreased the number of animal with visible macrometastasis. Importantly, trolox protected normal blood mononuclear cells and non-malignant hepatocytes from As2O3-mediated cytotoxicity in vitro and protected non-tumors and tumors-bearing animals from arsenic-induced hepatotoxicity. We next investigated the mechanisms responsible for the opposite eff
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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) | 0.001 | 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".