Terri S Armstrong1, Elizabeth Vera1, Renke Zhou2,3, Alvina A Acquaye1, Catherine M Sullaway4, Ann M Berger5, Ghislain Breton6, Anita Mahajan7, Jeffrey S Wefel4, Mark R Gilbert1, Melissa Bondy8,3, Michael E Scheurer2,3. 1. Neuro-Oncology Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland. 2. Department of Pediatrics, Baylor College of Medicine, Houston, Texas. 3. Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, Texas. 4. Department of Neuro-Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas. 5. University of Nebraska Medical Center, Omaha, Nebraska. 6. McGovern Medical School, Houston, Texas. 7. Mayo Clinic, Rochester, MN. 8. Department of Medicine, Baylor College of Medicine, Houston, Texas.
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.
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.
Authors: Paul D Brown; Karla V Ballman; Teresa A Rummans; Matthew J Maurer; Jeff A Sloan; Bradley F Boeve; Lalit Gupta; David F Tang-Wai; Robert M Arusell; Matthew M Clark; Jan C Buckner Journal: J Neurooncol Date: 2006-02 Impact factor: 4.130
Authors: T S Armstrong; T Mendoza; I Gning; I Gring; C Coco; M Z Cohen; L Eriksen; Ming-Ann Hsu; M R Gilbert; C Cleeland Journal: J Neurooncol Date: 2006-04-06 Impact factor: 4.130
Authors: J B Hogenesch; Y Z Gu; S M Moran; K Shimomura; L A Radcliffe; J S Takahashi; C A Bradfield Journal: J Neurosci Date: 2000-07-01 Impact factor: 6.167
Authors: Bang-Ning Lee; Robert Dantzer; Keith E Langley; Gary J Bennett; Patrick M Dougherty; Adrian J Dunn; Christina A Meyers; Andrew H Miller; Richard Payne; James M Reuben; Xin Shelley Wang; Charles S Cleeland Journal: Neuroimmunomodulation Date: 2004 Impact factor: 2.492
Authors: Charles S Cleeland; Gary J Bennett; Robert Dantzer; Patrick M Dougherty; Adrian J Dunn; Christina A Meyers; Andrew H Miller; Richard Payne; James M Reuben; Xin Shelley Wang; Bang-Ning Lee Journal: Cancer Date: 2003-06-01 Impact factor: 6.860
Authors: Dorela D Shuboni-Mulligan; Demarrius Young; Julianie De La Cruz Minyety; Nicole Briceno; Orieta Celiku; Amanda L King; Jeeva Munasinghe; Herui Wang; Kendra A Adegbesan; Mark R Gilbert; DeeDee K Smart; Terri S Armstrong Journal: Sci Rep Date: 2022-07-01 Impact factor: 4.996