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Record W4414740450 · doi:10.1093/clinchem/hvaf086.639

B-251 Clinical Implementation and Outcome Assessment of DPYD Pharmacogenomic Testing to Guide Fluoropyrimidines Dosing for Cancer Patients in Saskatchewan

2025· article· en· W4414740450 on OpenAlexaffabout
Dan Zhang, Songfeng Lu, Pramath Kakodkar, Fergall Magee, Haji Chalchal, Vijayananda Kundapur, Yanwei Xi, Fang Wu

Bibliographic record

VenueClinical Chemistry · 2025
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Treatments and Studies
Canadian institutionsSaskatchewan Health AuthorityUniversity of Saskatchewan
Fundersnot available
KeywordsDPYDCapecitabineDihydropyrimidine dehydrogenasePharmacogenomicsPharmacogeneticsDosingGenotypingCYP2D6

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.472

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.110
GPT teacher head0.543
Teacher spread0.432 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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