Pharmacogenomics in cancer treatment defining genetic bases for inter-individual differences in responses to chemotherapy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.000 | 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 it