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

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.

Predictive model for risk of severe gastrointestinal toxicity following chemotherapy using patient immune genetics and type of cancer: a pilot study.

PURPOSE: Severe chemotherapy-induced gastrointestinal toxicity (CIGT) is common and results in treatment delays, dose reductions, and potential premature treatment discontinuation. Currently, there is no diagnostic marker to predict CIGT. Proinflammatory cytokines, produced via toll-like receptor signaling, are key mediators of this toxicity. Hence, this pilot study investigated the association between immune genetic variability and severe CIGT risk. METHODS: Genomic DNA from 34 patients (10 with severe CIGT) who had received 5-fluoruracil-based chemotherapy regimens was analyzed for variants of IL-1B, IL-2, IL-6, IL-6R, IL-10, TNF-a, TGF-b, TLR2, TLR4, MD2, MYD88, BDNF, CRP, ICE, and OPRM1. Multivariate logistic regression created a prediction model of severe CIGT risk. RESULTS: There were no significant differences between patients with and without severe CIGT with regards to age, sex, type of cancer, or chemotherapy treatment regimens. The prediction model of severe CIGT risk included TLR2 and TNF-a genetic variability and cancer type (colorectal and gastric). This prediction model was both specific and sensitive, with a receiver operator characteristic area under the curve of 87.3 %. CONCLUSIONS: This is the first report of immune genetic variability, together with cancer type, being predictive of severe CIGT risk. These outcomes are being validated in a larger patient population.