Pharmacogenetic Predictors of Adverse Events and Response to Chemotherapy in Metastatic Colorectal Cancer: Results From North American Gastrointestinal Intergroup Trial N9741
Bibliographic record
Abstract
PURPOSE: With three available chemotherapy drugs for advanced colorectal cancer (CRC), response rate (RR) and survival outcomes have improved with associated morbidity, accentuating the need for tools to select optimal individualized treatment. Pharmacogenetics identifies the likelihood of adverse events or response based on variants in genes involved in drug transport, metabolism, and cellular targets. PATIENTS AND METHODS: Germline DNA was extracted from 520 patients on the North American Gastrointestinal Intergroup N9741 study. Three study arms were evaluated: IFL (fluorouracil [FU] + irinotecan [IRN]), FOLFOX (FU + oxaliplatin), and IROX (IRN + oxaliplatin). Information on adverse events, response, and disease-free survival was available. Thirty-four variants in 15 candidate genes for analysis based on previous associations with adverse events or outcome were assessed. Genotyping was performed using pyrosequencing. RESULTS: All variants were polymorphic. The homozygous UGT1A1*28 allele observed in 9% of patients was associated with risk of grade 4 neutropenia in patients on IROX (55% v 15%; P = .002). Deletion in GSTM1 was associated with grade 4 neutropenia after FOLFOX (28% v 16%; P = .02). Patients with a homozygous variant genotype for GSTP1 were more likely to discontinue FOLFOX because of neurotoxicity (24% v 10%; P = .01). The presence of a CYP3A5 variant was significantly associated with RR on IFL (29% v 60%; P = .0074). Most previously published genotype-toxicity or -efficacy relationships were not validated in this study. CONCLUSION: This study provides a platform to evaluate pharmacogenetic predictors of response or severe adverse events in advanced CRC. Pharmacogenetic studies can be conducted in multicenter trials, and our findings demonstrate that with continued research, clinical application is practical.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".