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 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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".