Abstract P3-08-04: Germline Copy Number Polymorphisms Associated with Toxicity from Adjuvant Docetaxel
Bibliographic record
Abstract
Abstract Background: Chemotherapy-induced toxicities frequently limit the ability to administer full doses of cytotoxic drugs on schedule, and adversely affected quality of life. Although single nucleotide polymorphisms (SNPs) in cytochrome p-450 (CYP) and multi-drug resistance genes (MDR1) explain a proportion of interpatient variability in drug metabolism, there remains significant unexplained variability that may arise from genetic/heritable contributions; understanding these might enable more appropriate patient selection and individualized drug dosing. Copy number variations (CNVs) are structural variants (amplifications, deletions and insertions, etc.) in the genome and increasingly provide mechanistic explanations of gene dosage/disruption events and their clinical consequences. Materials and methods: We studied women (n=149) from Edmonton, Alberta, Canada who received docetaxel (Taxotere), doxorubicin (Adriamycin) and cyclophosphamide in the adjuvant setting. All subjects provided informed consent and the study was approved by the institutional research ethics board. Detailed toxicity profiles (grades 0-5) on these patients were documented and the population was genetically homogeneous (analysed by Helix Tree software using SNP markers). Our objective was to identify and analyse overall and docetaxel specific toxicities (characterized by hypersensitivity, fatigue, myalgia and neurotoxicity). We stratified patients as experiencing low toxicity (treated as controls) where the toxicity grade was between 0-2 (group 0; n=58) whereas patients experiencing grade ≥3 (cases) were classified as overall high-toxicity group (group 1; n=91). We further stratified group 1 into those experiencing docetaxel specific (group 2; n=36) and non-docetaxel related toxicities (group 3; n=54). We used Affymetrix SNP 6.0 high-throughput platform for copy number detection using germline DNA. Association analysis for CNV was carried out using Partek™ software and Fisher's exact test statistic. We compared groups 1, 2 and 3 with group 0 to detect associations with the chemotoxicity phenotype. Results: We identified an average of 110 CNVs per sample and a majority of these identified CNVs have been mapped to the database of genomic variants. We identified 350, 195 and 184 CNVs (group 1, 2 and 3 respectively) showing significant associations with the chemotoxicity in our study population and these CNVs harbor 67, 37 and 47 annotated genes, respectively. A number of genes from signal transduction pathways as well as oncogenes and transcription factors were found associated with chemotoxicity phenotypes. Analysis of CNV signatures for Gene Ontology term enrichment identified metallochaperone activity and biological adhesion pathways as dominant ones in molecular function and biological process categories, respectively. Conclusions: CNVs are increasingly associated with regulation of gene expression and the identified variants in this study require functional validation and independent replication as genetic determinants of docetaxel toxicity. To our knowledge, this is the first genome-wide CNV association study for chemotoxicity phenotypes. Citation Information: Cancer Res 2010;70(24 Suppl):Abstract nr P3-08-04.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".