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.

Associations of Immune Genetic Variability with Gulf War Illness in 1990-1991 Gulf War Veterans from the Gulf War Illness Consortium (GWIC) Multisite Case-Control Study.

Gulf War illness (GWI) encompasses a constellation of persistent debilitating symptoms associated with significant changes in central nervous system (CNS) and immune functioning. Currently, there is no validated biomarker for GWI risk susceptibility. Given the impact of immune responses linked to GWI symptomology, genetic variability that causes persistent inflammatory/immune alterations may be key. This Boston University-based Gulf War Illness Consortium (GWIC) study investigated the impact of single nucleotide polymorphisms (SNPs) in variants of immune and pain genetic markers IL1B, IL2, IL6, IL6R, IL10, TNF, TGF, TLR2, TLR4, MD2, MYD88, BDNF, CRP, ICE, COMT and OPRM1 on GWI occurrence in a Caucasian subset of Gulf War (GW) veterans with (cases, n = 170) and without (controls, n = 34) GWI. Logistic regression modeling created a prediction model of GWI risk that associated genetic variability in TGF (rs1800469, p = 0.009), IL6R (rs8192284, p = 0.004) and TLR4 (rs4986791, p = 0.013) with GWI occurrence. This prediction model was specific and sensitive, with a receiver operator characteristic area under the curve of 71.4%. This is the first report of immune genetic variability being predictive of GWI and warrants validation in larger independent cohorts. Future reports will present interactions of these genetic risk factors with other characteristics of GW service.