B-251 Clinical Implementation and Outcome Assessment of DPYD Pharmacogenomic Testing to Guide Fluoropyrimidines Dosing for Cancer Patients in Saskatchewan
Bibliographic record
Abstract
Abstract Background 5-Fluorouracil (5-FU) and its prodrug Capecitabine are widely used chemotherapy drugs for treating various solid tumours, including colorectal, breast, and gastrointestinal cancers). Annually, around two million patients receive treatment with these drugs. However, a significant challenge with 5-FU treatment is toxicity. Between 10-30% of patients experience severe side effects, and in about 0.5-1% of cases, these toxicities can become life-threatening. The primary cause of this toxicity is a deficiency in the enzyme Dihydropyrimidine Dehydrogenase (DPD), critical for metabolizing 5-FU. Variants in the DPYD gene, which encodes DPD, reduce or lose the enzyme activity of DPD. Patients with DPD enzyme deficiency are at great risk of severe toxicity. In this study, we validated and implemented the DPYD genotyping assay for cancer patients in Saskatchewan, Canada, to guide fluoropyrimidine dosing. We further assessed the clinical outcome post-implementation in Saskatchewan. Methods Six clinically relevant variants of the DPYD gene associated with DPD deficiency recommended by the 2017 Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline were included in the assay (transcript NM_000110.4), including *2A (rs3918290; c.1905+1G>A), *13 (rs55886062; c.1679T>G), c.2846A>T (rs67376798), and c.1129-5923C>G (rs75017182). The HapB3 haplotype was assessed by the c.1129-5923C>G (rs75017182) variant in combination with c.1236G>A (rs56038477) and c.483+18G>A (rs56276561). The Elucigene DPYD genotyping kit (Yourgene Health, UK) was used to detect these six semi-qualitatively. The validation process follows the technical standards for clinical pharmacogenomic testing and reporting established by the American College of Medical Genetics and Genomics. The assay*s sensitivity, specificity, accuracy, repeatability and reproducibility in detecting DPYD variants were included. Six months post-implementation of DPYD genotyping assays, patient outcomes were retrospectively evaluated. Patient demographics and clinical data were collected, including tumour types and staging, treatment regimen and dosage adjustment based on DPYD genotyping lab results, and toxicity incidence. This study adhered to institutional ethics guidelines. Results The DPYD pharmacogenomic assay demonstrated excellent performance with 100% sensitivity, specificity, accuracy, reproducibility, and repeatability. The detection limit was 1.25 ng/µL of DNA, ensuring high sensitivity. Over six months, 301 patient samples were tested, identifying 22 patients carrying at least one of the six DPYD variants. The most frequently observed allele was the HapB3 heterozygous genotype detected in 18 patients (5.9%). All detected variants exhibited reduced function or no function, with assigned DPD activity scores ranging from 1 to 1.5, indicating impaired fluoropyrimidines metabolism. Outcomes were evaluated for 21 enzyme-deficient patients, with 5-FU dose adjustments applied clinically. The majority of patients tolerate chemotherapy well without significant toxicity. Outcome evaluation for the 301 tested patients is ongoing. Conclusion DPYD testing allows for the early detection of DPD deficiencies, allowing personalized chemo drug dosing. These approaches improve patient outcomes and reduce the risk of severe side effects, highlighting the important roles in pharmacogenomics in personalized cancer 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".