Capecitabine‐based treatment of a patient with a novel <i>DPYD</i> genotype and complete dihydropyrimidine dehydrogenase deficiency

Henricks, Linda M.; Siemerink, E.; Rosing, Hilde; Meijer, Judith; Goorden, Susan; Polstra, Abeltje M.; Zoetekouw, Lida; Cats, Annemieke; Schellens, Jan H.M.; Kuilenburg, André B. P.

doi:10.1002/ijc.31065

Cited by 21 publications

(13 citation statements)

References 23 publications

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“…Alterations affecting multiple genes that play a role in nucleotide transport and metabolism were identified in this case study and may have contributed to the response to therapy, including a novel somatic structural variant affecting DPYD. Discrete germline DPYD polymorphisms and deletions have been identified in cancer patients with severe 5-FU-associated toxicity (van Kuilenburg 2004;Etienne-Grimaldi et al 2017;van Kuilenburg et al 2017;Henricks et al 2018). Thus, testing for DPYD variants known to affect DPD enzyme activity is becoming more prominent in patients undergoing 5-FU chemotherapy (Deenen et al 2016).…”

Section: Discussionmentioning

confidence: 99%

“…Consequently, DPD activity moderates response to 5-FU, and DPD deficiency as a result of germline polymorphism is considered a major cause of 5-FU-associated toxicity (van Kuilenburg 2004). Deleterious variants in DPYD, the large gene encoding DPD, have been described as significantly impacting enzymatic activity (Etienne-Grimaldi et al 2017;van Kuilenburg et al 2017;Henricks et al 2018).…”

Section: Introductionmentioning

confidence: 99%

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Fluorouracil sensitivity in a head and neck squamous cell carcinoma with a somatic DPYD structural variant

Majounie¹,

Wee²,

Williamson³

et al. 2019

Cold Spring Harb Mol Case Stud

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Head and neck squamous cell carcinoma (HNSCC) is one of the most common cancers worldwide and represents a heterogeneous group of tumors, the majority of which are treated with a combination of surgery, radiation, and chemotherapy. Fluoropyrimidine (5-FU) and its oral prodrug, capecitabine, are commonly prescribed treatments for several solid tumor types including HNSCC. 5-FU-associated toxicity is observed in ∼30% of treated patients and is largely caused by germline polymorphisms in DPYD, which encodes dihydropyrimidine dehydrogenase, a key enzyme of 5-FU catabolism and deactivation. Although the association of germline DPYD alterations with toxicity is well-described, the potential contribution of somatic DPYD alterations to 5-FU sensitivity has not been explored. In a patient with metastatic HNSCC, in-depth genomic and transcriptomic integrative analysis on a biopsy from a metastatic neck lesion revealed alterations in genes that are associated with 5-FU uptake and metabolism. These included a novel somatic structural variant resulting in a partial deletion affecting DPYD, a variant of unknown significance affecting SLC29A1, and homozygous deletion of MTAP. There was no evidence of deleterious germline polymorphisms that have been associated with 5-FU toxicity, indicating a potential vulnerability of the tumor to 5-FU therapy. The discovery of the novel DPYD variant led to the initiation of 5-FU treatment that resulted in a rapid response lasting 17 wk, with subsequent relapse due to unknown resistance mechanisms. This suggests that somatic alterations present in this tumor may serve as markers for tumor sensitivity to 5-FU, aiding in the selection of personalized treatment strategies.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fluorouracil sensitivity in a head and neck squamous cell carcinoma with a somatic DPYD structural variant

Majounie¹,

Wee²,

Williamson³

et al. 2019

Cold Spring Harb Mol Case Stud

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“…This could be either prior to treatment or retrospectively after the occurrence of severe toxicity. DPD enzyme activity measurement in peripheral blood mononuclear cells (PBMCs) [24,25] was used as a reference to assess DPD activity, and has been used previously to determine dosages in DPYD variant-carrying patients [21,26]. A validated method [27] was used, containing radiolabeled thymine as a substrate and consisting of high-performance liquid chromatography (HPLC) with online radioisotope detection using liquid scintillation counting.…”

Section: Methodsmentioning

confidence: 99%

Diagnostic and Therapeutic Strategies for Fluoropyrimidine Treatment of Patients Carrying Multiple DPYD Variants

Lunenburg

Henricks

Kuilenburg

et al. 2018

Genes

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DPYD genotyping prior to fluoropyrimidine treatment is increasingly implemented in clinical care. Without phasing information (i.e., allelic location of variants), current genotype-based dosing guidelines cannot be applied to patients carrying multiple DPYD variants. The primary aim of this study is to examine diagnostic and therapeutic strategies for fluoropyrimidine treatment of patients carrying multiple DPYD variants. A case series of patients carrying multiple DPYD variants is presented. Different genotyping techniques were used to determine phasing information. Phenotyping was performed by dihydropyrimidine dehydrogenase (DPD) enzyme activity measurements. Publicly available databases were queried to explore the frequency and phasing of variants of patients carrying multiple DPYD variants. Four out of seven patients carrying multiple DPYD variants received a full dose of fluoropyrimidines and experienced severe toxicity. Phasing information could be retrieved for four patients. In three patients, variants were located on two different alleles, i.e., in trans. Recommended dose reductions based on the phased genotype differed from the phenotype-derived dose reductions in three out of four cases. Data from publicly available databases show that the frequency of patients carrying multiple DPYD variants is low (< 0.2%), but higher than the frequency of the commonly tested DPYD*13 variant (0.1%). Patients carrying multiple DPYD variants are at high risk of developing severe toxicity. Additional analyses are required to determine the correct dose of fluoropyrimidine treatment. In patients carrying multiple DPYD variants, we recommend that a DPD phenotyping assay be carried out to determine a safe starting dose.

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“…To exemplify how we calculated the PDS (Equation (1)), the following drug regimen was considered in the pancreatic adenocarcinoma cohort consisting of 129 patients: capecitabine (cytotoxic chemotherapy agent) + ruxolitinib (targeted agent). For this regimen, the predictive biomarkers were MBD4, TYMP, TYMS-, DPYD-(for capecitabine) [36][37][38][39] and JAK1, JAK2, TYK2 (for ruxolitinib). 40 Entering this gene set (which includes all selected biomarkers for all drugs in the regimen) into cBioPortal, 41 Figure S1 for a visual depiction of this example).…”

Section: ) Molecular Profiles 2) Clinical Trials With Outcomes 3) Bimentioning

confidence: 99%

Machine learning model to predict oncologic outcomes for drugs in randomized clinical trials

Schperberg

Boichard

Tsigelny

et al. 2020

Intl Journal of Cancer

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Predicting oncologic outcome is challenging due to the diversity of cancer histologies and the complex network of underlying biological factors. In this study, we determine whether machine learning (ML) can extract meaningful associations between oncologic outcome and clinical trial, drug‐related biomarker and molecular profile information. We analyzed therapeutic clinical trials corresponding to 1102 oncologic outcomes from 104 758 cancer patients with advanced colorectal adenocarcinoma, pancreatic adenocarcinoma, melanoma and nonsmall‐cell lung cancer. For each intervention arm, a dataset with the following attributes was curated: line of treatment, the number of cytotoxic chemotherapies, small‐molecule inhibitors, or monoclonal antibody agents, drug class, molecular alteration status of the clinical arm's population, cancer type, probability of drug sensitivity (PDS) (integrating the status of genomic, transcriptomic and proteomic biomarkers in the population of interest) and outcome. A total of 467 progression‐free survival (PFS) and 369 overall survival (OS) data points were used as training sets to build our ML (random forest) model. Cross‐validation sets were used for PFS and OS, obtaining correlation coefficients (r) of 0.82 and 0.70, respectively (outcome vs model's parameters). A total of 156 PFS and 110 OS data points were used as test sets. The Spearman correlation (rs) between predicted and actual outcomes was statistically significant (PFS: rs = 0.879, OS: rs = 0.878, P < .0001). The better outcome arm was predicted in 81% (PFS: N = 59/73, z = 5.24, P < .0001) and 71% (OS: N = 37/52, z = 2.91, P = .004) of randomized trials. The success of our algorithm to predict clinical outcome may be exploitable as a model to optimize clinical trial design with pharmaceutical agents.

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Capecitabine‐based treatment of a patient with a novel DPYD genotype and complete dihydropyrimidine dehydrogenase deficiency

Cited by 21 publications

References 23 publications

Fluorouracil sensitivity in a head and neck squamous cell carcinoma with a somatic DPYD structural variant

Fluorouracil sensitivity in a head and neck squamous cell carcinoma with a somatic DPYD structural variant

Diagnostic and Therapeutic Strategies for Fluoropyrimidine Treatment of Patients Carrying Multiple DPYD Variants

Machine learning model to predict oncologic outcomes for drugs in randomized clinical trials

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