Pharmacogenomics in cancer treatment defining genetic bases for inter-individual differences in responses to chemotherapy
Bibliographic record
Abstract
PURPOSE OF REVIEW: Pharmacogenomics is evolving rapidly due to the expansion of genomics and proteomics, the emerging technologies, knowledge of the molecular basis of neoplasms and of drug pathways. This article will give an update on the genetic basis of variable therapeutic responses to anticancer agents in children. RECENT FINDINGS: The majority of recent findings concern the pharmacogenetics of key components of acute lymphoblastic leukemia treatment, 6-mercaptopurine and methotrexate. This is not surprising given that leukemia is the most common cancer affecting children, accounting for 25-35% of childhood malignancies worldwide with acute lymphoblastic leukemia comprising 80% of leukemia cases. In certain patients treatment fails due to drug resistance, rendering acute lymphoblastic leukemia the leading cause of cancer-related death in children. Most of the studies use a candidate gene approach adding a new body of evidence to existing knowledge. Recent findings relating to other childhood tumors and the potential to optimize treatment of these malignancies are briefly discussed. SUMMARY: Interindividual differences in drug responses are an important cause of resistance to treatment and adverse drug reactions. Pharmacogenetics tends to identify the genetic basis of these suboptimal responses allowing traditional treatment to be complemented by genotype-based drug dose adjustment.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".