Pharmacogenetic considerations for acute lymphoblastic leukemia therapies
Bibliographic record
Abstract
INTRODUCTION: Advances in our understanding of the pathobiology of childhood acute lymphoblastic leukemia (ALL) have led to risk-targeted treatment regimens and remarkable improvement in survival rates. Still, up to 20% of patients experience treatment failure due to drug resistance. Treatment-related toxicities are often life-threatening and are the primary cause of treatment interruption, while ALL survivors may develop complications due to exposure to chemotherapy and/or irradiation during a vulnerable period of development. Different factors may contribute to variable treatment outcomes including patient genetics that has been shown to play important role. AREAS COVERED: This review summarizes candidate gene and genome-wide association studies that identified common polymorphisms underlying variability in treatment responses including a few studies addressing late effects of the treatment. Genetic variants influencing antileukemic drug effects or leukemic cell biology have been identified, including for example variants in folate-dependent enzymes, influx and efflux transporters, metabolizing enzymes, drug receptor or apoptotic proteins. EXPERT OPINION: Many pharmacogenetic studies have been conducted in ALL and a variety of potential markers have been identified. Yet more comprehensive insight into genome variations influencing drug responses is needed. Whole exome/genome sequencing, careful study design, mechanistic explanation of association found and collaborative studies will ultimately lead to personalized treatment and improved therapeutic and health outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".