Data‐Driven Assessment of the Association of Polymorphisms in 5‐Fluorouracil Metabolism Genes with Outcome in Adjuvant Treatment of Colorectal Cancer

Abstract

A major challenge in the assessment of medicines, treatment options, etc., is to establish a framework for the comparison of risks and benefits of many different types and magnitudes, a framework that at the same time allows a clear distinction between the roles played by the statistical analyses of data and by judgements based on personal experience and expertise. The purpose of this study was to demonstrate how clinical data can be weighted, scored and presented by the use of an eight-step data-driven benefit-risk assessment method, where two genetic profiles are compared. Our aim was to present a comprehensive approach that is simple to apply, allows direct comparison of different types of risks and benefits, quantifies the clinical relevance of data and is tailored for the comparison of different options. We analysed a cohort of 302 patients with colorectal cancer treated with 5-Fluorouracil (5-FU). Endpoints were cure rate, survival rate, time-to-death (TTD), time-to-relapse (TTR) and main adverse drug reactions. Multifactor dimensionality reduction (MDR) was used to identify genetic interaction profiles associated with outcome. We have been able to demonstrate that a specific MDR-derived combination (the MDR-1 group) of dihydropyrimidine dehydrogenase and thymidylate synthase polymorphisms is associated with increased and clinically significant difference for cure and survival rates, TTD and probably also for TTR, which are seen as the most important endpoints. An inferior profile was observed for severe myocardial ischaemia. A probably inferior profile was seen for severe arthralgia/myalgia and severe infections. A clear superior profile was seen for severe mucositis/stomatitis. The proposed approach offers comprehensive, data-driven assessment that can facilitate decision processes, for example, in a clinical setting. It employs descriptive statistical methods to highlight the clinically relevant differences between options.

Examining SNP-SNP interactions and risk of clinical outcomes in colorectal cancer using multifactor dimensionality reduction based methods

“…In this regard, there has been little work done on studying multi-loci interactions in colorectal cancer with respect to survival outcomes, especially using a large number of variants. For example, limited MDR-based interaction analyses were conducted ( Iglesias et al, 2009 ; Afzal et al, 2011a , 2011b ; Pander et al, 2011 ; Sarac et al, 2012 ; Hu et al, 2018 ), investigating the interactions among a small number of polymorphisms ( n = 5–17). These studies identified interacting polymorphisms that are associated with treatment response and/or survival outcomes.…”

“…Frontiers in Genetics frontiersin.org et al, 2011a, 2011bPander et al, 2011;Sarac et al, 2012;Hu et al, 2018), investigating the interactions among a small number of polymorphisms (n = 5-17). These studies identified interacting polymorphisms that are associated with treatment response and/or survival outcomes.…”

“…Studies that have so far considered the interactions of genetic variants in colorectal cancer outcomes using MDR are quite limited ( Iglesias et al, 2009 ; Afzal et al, 2011a ; Pander et al, 2011 ; Sarac et al, 2012 ; Hu et al, 2018 ; Jung et al, 2020 ). As a result, potential SNP interactions that may be associated with patient outcomes largely remain unknown.…”

Multifactor dimensionality reduction method identifies novel SNP interactions in the WNT protein interaction networks that are associated with recurrence risk in colorectal cancer

“…Methodologies (such as, Multifactor Dimensionality Reduction [MDR]) that address this challenge and tools that utilize these methods (such as GMDR 0.9) have been developed to examine interactions in a relatively feasible way (16)(17)(18). We and others have previously shown the utility of MDR and examining interactions among genetic variables in colorectal cancer (19)(20)(21)(22)(23). As these studies showed, examining interactions is a promising research area and can reveal new biomarkers that can predict patient outcomes.…”