Advanced statistics identification of participant and treatment predictors associated with severe adverse effects induced by fluoropyrimidine-based chemotherapy

Samantha K Korver; Joanne M Bowen; Rachel J Gibson; Imogen A Ball; Kate R Secombe; Taylor J Wain; Richard M Logan; Jonathan Tuke; Kelly R Mead; Alison M Richards; Christos S Karapetis; Dorothy M Keefe; Janet K Coller

doi:10.1007/s00280-023-04538-3

Advanced statistics identification of participant and treatment predictors associated with severe adverse effects induced by fluoropyrimidine-based chemotherapy

Cancer Chemother Pharmacol. 2023 Jun;91(6):507-521. doi: 10.1007/s00280-023-04538-3. Epub 2023 May 10.

Authors

Affiliations

¹ Discipline of Pharmacology, School of Biomedicine, The University of Adelaide, L2 Helen Mayo South, Adelaide, SA, 5000, Australia.
² Discipline of Physiology, School of Biomedicine, The University of Adelaide, Adelaide, Australia.
³ School of Allied Health Science and Practice, The University of Adelaide, Adelaide, Australia.
⁴ Adelaide Dental School, The University of Adelaide, Adelaide, Australia.
⁵ School of Mathematical Sciences, The University of Adelaide, Adelaide, Australia.
⁶ Flinders Medical Centre, Bedford Park, Australia.
⁷ Flinders University, Bedford Park, Australia.
⁸ Discipline of Medicine, The University of Adelaide, Adelaide, Australia.
⁹ Discipline of Pharmacology, School of Biomedicine, The University of Adelaide, L2 Helen Mayo South, Adelaide, SA, 5000, Australia. janet.coller@adelaide.edu.au.

Abstract

Purpose: Adverse effects following fluoropyrimidine-based chemotherapy regimens are common. However, there are no current accepted diagnostic markers for prediction prior to treatment, and the underlying mechanisms remain unclear. This study aimed to determine genetic and non-genetic predictors of adverse effects.

Methods: Genomic DNA was analyzed for 25 single nucleotide polymorphisms (SNPs). Demographics, comorbidities, cancer and fluoropyrimidine-based chemotherapy regimen types, and adverse effect data were obtained from clinical records for 155 Australian White participants. Associations were determined by bivariate analysis, logistic regression modeling and Bayesian network analysis.

Results: Twelve different adverse effects were observed in the participants, the most common severe adverse effect was diarrhea (12.9%). Bivariate analysis revealed associations between all adverse effects except neutropenia, between genetic and non-genetic predictors, and between 8 genetic and 12 non-genetic predictors with more than 1 adverse effect. Logistic regression modeling of adverse effects revealed a greater/sole role for six genetic predictors in overall gastrointestinal toxicity, nausea and/or vomiting, constipation, and neutropenia, and for nine non-genetic predictors in diarrhea, mucositis, neuropathy, generalized pain, hand-foot syndrome, skin toxicity, cardiotoxicity and fatigue. The Bayesian network analysis revealed less directly associated predictors (one genetic and six non-genetic) with adverse effects and confirmed associations between six adverse effects, eight genetic predictors and nine non-genetic predictors.

Conclusion: This study is the first to link both genetic and non-genetic predictors with adverse effects following fluoropyrimidine-based chemotherapy. Collectively, we report a wealth of information that warrants further investigation to elucidate the clinical significance, especially associations with genetic predictors and adverse effects.

Keywords: Adverse effects; Bayesian network analysis; Fluoropyrimidine-based chemotherapy; Logistic modeling; Risk.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Antimetabolites
Antineoplastic Combined Chemotherapy Protocols / adverse effects
Australia
Bayes Theorem
Diarrhea / chemically induced
Drug-Related Side Effects and Adverse Reactions* / etiology
Drug-Related Side Effects and Adverse Reactions* / genetics
Fluorouracil
Humans
Neutropenia* / chemically induced
Neutropenia* / epidemiology

Substances

Fluorouracil
Antimetabolites