ABCC5 and ABCG1 polymorphisms predict irinotecan-induced severe toxicity in metastatic colorectal cancer patients
Bibliographic record
Abstract
OBJECTIVE: Irinotecan is a cytotoxic agent used widely for the treatment of solid tumors, particularly for metastatic colorectal cancers. Treatment with this drug frequently results in severe neutropenia and diarrhea that can markedly impact the course of treatment and patients' quality of life. Pharmacogenomic tailoring of irinotecan-based chemotherapy has been the subject of several investigations, but with limited data on ATP-binding cassette (ABC) and solute carrier (SLC) transporter genes. MATERIALS AND METHODS: In this study, we aimed to discover toxicity-associated markers in seven transporter genes participating in irinotecan pharmacokinetics involving the ABC transporter genes ABCB1, ABCC1, ABCC2, ABCC5, ABCG1, and ABCG2 and the solute carrier organic anion transporter gene SLCO1B1 and using a haplotype-tagging single-nucleotide polymorphisms (n=210 htSNPs) strategy. The profiles of 167 metastatic colorectal cancer Canadian patients treated with FOLFIRI-based regimens were examined and the findings were replicated in an independent cohort of 250 Italian patients. RESULTS: In combined cohorts, a two-marker ABCC5 rs3749438 and rs10937158 haplotype (T-C) predicted lower risk of severe diarrhea [odds ratio (OR) of 0.43; P=0.001]. The co-occurrence of ABCG1 rs225440T and ABCC5 rs2292997A predicted the risk of severe neutropenia (OR=5.93; P=0.0002), which was further improved when incorporating the well-known risk marker UGT1A1*28 rs8175347 (OR=7.68; P<0.0001). In contrast, carriers of one protective marker (UGT1 rs11563250G) but none of these risk alleles experienced significantly less severe neutropenia (8.2 vs. 34.0%; P<0.0001). CONCLUSION: This combination of predictive genetic markers could potentially lead to better risk assessment and may thus improve personalized treatment.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".