Association of genetic variants with fatigue in patients with malignant glioma. Issue 2 (19th September 2017) - Armstrong, Terri S; Vera, Elizabeth; Zhou, Renke; Acquaye, Alvina A; Sullaway, Catherine M; Berger, Ann M; Breton, Ghislain; Mahajan, Anita; Wefel, Jeffrey S; Gilbert, Mark R; Bondy, Melissa; Scheurer, Michael E
Abstract: Background: Fatigue is a consistently reported, severe symptom among patients with gliomas throughout the disease trajectory. Genomic pathways associated with fatigue in glioma patients have yet to be identified. Methods: Clinical factors (performance status, tumor details, age, gender) were collected by chart review on glioma patients with fatigue ("I have lack of energy" on Functional Assessment of Cancer Therapy-Brain), as well as available genotyping data. Candidate genes in clock and inflammatory pathways were identified from a literature review, of which 50 single nucleotide polymorphisms (SNPs) in 7 genes were available. Clinical factors and SNPs identified by univariate analyses were included in a multivariate model for moderate-severe fatigue. Results: The study included 176 patients (median age = 47 years, 67% males). Moderate-severe fatigue was reported by 43%. Results from multivariate analysis revealed poor performance status and 2 SNPs were associated with fatigue severity. Moderate-severe fatigue was more common in patients with poor performance status (OR = 3.52, P < .01). For each additional copy of the minor allele in rs934945 ( PER2 ) the odds of fatigue decreased (OR = 0.51, P < .05). For each additional copy of the minor allele in rs922270 ( ARTNL2 ) the odds of fatigue increased (OR = 2.38, P < .01). Both of these genes are important in the circadian clock pathway, which has been implicated in diurnal preference, and duration and quality of sleep. No genes in the inflammatory pathway were associated with fatigue in the current study. Conclusions: Identifying patients at highest risk for fatigue during treatment allows for improved clinical monitoring and enrichment of patient selection for clinical trials.