Genetic variants and cognitive functions in patients with brain tumors

Abstract

Patients with brain tumors treated with radiotherapy (RT) and chemotherapy (CT) often experience cognitive dysfunction. We reported that single nucleotide polymorphisms (SNPs) in the APOE, COMT, and BDNF genes may influence cognition in brain tumor patients. In this study, we assessed whether genes associated with late-onset Alzheimer’s disease (LOAD), inflammation, cholesterol transport, dopamine and myelin regulation, and DNA repair may influence cognitive outcome in this population. One hundred and fifty brain tumor patients treated with RT ± CT or CT alone completed a neurocognitive assessment and provided a blood sample for genotyping. We genotyped genes/SNPs in these pathways, (i) LOAD risk/inflammation/cholesterol transport, (ii) dopamine regulation, (iii) myelin regulation, (iv) DNA repair, (v) blood–brain barrier disruption, (vi) cell cycle regulation, and (vii) response to oxidative stress. White matter (WM) abnormalities were rated on brain MRIs. Multivariable linear regression analysis with Bayesian shrinkage estimation of SNP effects, adjusting for relevant demographic, disease, and treatment variables, indicated strong associations (posterior association summary [PAS] > 0.95) among tests of attention, executive functions, and memory and 33 SNPs in genes involved in LOAD/inflammation/cholesterol transport (eg, PDE7A, IL-6), dopamine regulation (eg, DRD1, COMT), myelin repair (eg, TCF4), DNA repair (eg, RAD51), cell cycle regulation (eg, SESN1), and response to oxidative stress (eg, GSTP1). The SNPs were not significantly associated with WM abnormalities. This novel study suggests that polymorphisms in genes involved in aging and inflammation, dopamine, myelin and cell cycle regulation, and DNA repair and response to oxidative stress may be associated with cognitive outcome in patients with brain tumors.

Publication
Neuro-Oncology, Volume 21 (2019)
Axel S. Martin
Axel S. Martin
Research Biostatistician

My research interests include high-dimensional time-to-event data prediction and stratification in cancer genomics, lately I have also developed an interest in causal inference and individualized treatment rules.

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