PPAR and GST polymorphisms may predict changes in intellectual functioning in medulloblastoma survivors

  • Adeoye OyefiadeEmail author
  • Lauren Erdman
  • Anna Goldenberg
  • David Malkin
  • Eric Bouffet
  • Michael D. Taylor
  • Vijay Ramaswamy
  • Nadia Scantlebury
  • Nicole Law
  • Donald J. Mabbott
Laboratory Investigation



Advances in the treatment of pediatric medulloblastoma have led to improved survival rates, though treatment-related toxicity leaves children with significant long-term deficits. There is significant variability in the cognitive outcome of medulloblastoma survivors, and it has been suggested that this variability may be attributable to genetic factors. The aim of this study was to explore the contributions of single nucleotide polymorphisms (SNPs) in two genes, peroxisome proliferator activated receptor (PPAR) and glutathione-S-transferase (GST), to changes in general intellectual functioning in medulloblastoma survivors.


Patients (n = 44, meanage = 6.71 years, 61.3% males) were selected on the basis of available tissue samples and neurocognitive measures. Patients received surgical tumor resection, craniospinal radiation, radiation boost to the tumor site, and multiagent chemotherapy. Genotyping analyses were completed using the Illumina Human Omni2.5 BeadChip, and 41 single nucleotide polymorphisms (SNPs) were assessed across both genes. We used a machine learning algorithm to identify polymorphisms that were significantly associated with declines in general intellectual functioning following treatment for medulloblastoma.


We identified age at diagnosis, radiation therapy, chemotherapy, and eight SNPs associated with PPARs as predictors of general intellectual functioning. Of the eight SNPs identified, PPARα (rs6008197), PPARγ (rs13306747), and PPARδ (rs3734254) were most significantly associated with long-term changes in general intellectual functioning in medulloblastoma survivors.


PPAR polymorphisms may predict intellectual outcome changes in children treated for medulloblastoma. Importantly, emerging evidence suggests that PPAR agonists may provide an opportunity to minimize the effects of treatment-related cognitive sequelae in these children.


Medulloblastoma PPAR GST Random forest Intellectual functioning 



Funding for this work was provided by the Garron Family Cancer Center Small Grant Competition at The Hospital for Sick Children in Toronto. We thank Dr. Chao Lu for help with DNA microarray analysis, and Aziz Mezlini for help with our statistical approach.

Supplementary material

11060_2018_3083_MOESM1_ESM.tif (245 kb)
Supplementary material 1 Supplementary fig 1. Density plot of VIMP scores for variables with scores less than or equal to zero across all iterations of the prediction RFR model. Due to space considerations axes labels have been omitted. The dashed red line indicates a vimp score of zero. Values to the left of the dashed line indicate negative vimp scores. Supplementary fig 2. Boxplots showing the relationships between intellectual outcome and age at diagnosis, radiation therapy, and chemotherapy. Intellectual outcome was determined as the predicted change in FSIQ scores over time based on RFR model estimates. Older age at diagnosis predicted better intellectual outcome, while reduced craniospinal radiation with a tumor bed boost, as well as the SJMB03 chemotherapy protocol predicted better intellectual outcome. Radiation therapy: S-PF – Standard dose + posterior fossa boost, S-TB – Standard dose + tumor bed boost, R-PF – Reduced dose + posterior fossa boost, R-TB – Reduced dose + tumor bed boost. Chemotherapy protocols: CCCG 9961 (Vincristine, Lomustine, Cisplatin); POG 9631 (Etoposide, Cisplatin, Cyclophosphamide, Vincristine); SJMB03 (Vincristine, Cisplatin, Cyclophosphamide, Amifostine); 99703 (Vincristine, Cisplatin, Cyclophosphamide, Etoposide); ACNS 0331 (Lomustine, Cisplatin, Vincristine, Cyclophosphamide) (TIF 244 KB)
11060_2018_3083_MOESM2_ESM.tif (226 kb)
Supplementary material 2 (TIF 225 KB)
11060_2018_3083_MOESM3_ESM.docx (17 kb)
Supplementary material 3 (DOCX 17 KB)


  1. 1.
    Packer RJ, Cogen P, Vezina G, Rorke LB (1999) Medulloblastoma: clinical and biologic aspects. Neuro Oncol 1:232–250. CrossRefGoogle Scholar
  2. 2.
    Mulhern RK, Kepner JL, Thomas PR et al (1998) Neuropsychologic functioning of survivors of childhood medulloblastoma randomized to receive conventional or reduced-dose craniospinal irradiation: a Pediatric Oncology Group study. J Clin Oncol 16:1723–1728. CrossRefGoogle Scholar
  3. 3.
    Walter AW, Mulhern RK, Gajjar A et al (1999) Survival and neurodevelopmental outcome of young children with medulloblastoma at St Jude Children’s Research Hospital. J Clin Oncol 17:3720–3728. CrossRefGoogle Scholar
  4. 4.
    Palmer SL, Gajjar A, Reddick WE et al (2003) Predicting intellectual outcome among children treated with 35–40 Gy Craniospinal irradiation for medulloblastoma. Neuropsychology 17:548–555. CrossRefGoogle Scholar
  5. 5.
    Mabbott DJ (2006) Diffusion tensor imaging of white matter after cranial radiation in children for medulloblastoma: correlation with IQ. Neuro Oncol 8:244–252. CrossRefGoogle Scholar
  6. 6.
    Moxon-Emre I, Bouffet E, Taylor MD et al (2014) Impact of craniospinal dose, boost volume, and neurologic complications on intellectual outcome in patients with medulloblastoma. J Clin Oncol 32:1760–1768. CrossRefGoogle Scholar
  7. 7.
    Barahmani N, Carpentieri S, Li XN et al (2009) Glutathione S-transferase M1 and T1 polymorphisms may predict adverse effects after therapy in children with medulloblastoma. Neuro Oncol 11:292–300. CrossRefGoogle Scholar
  8. 8.
    Brackett J, Krull KR, Scheurer ME et al (2012) Antioxidant enzyme polymorphisms and neuropsychological outcomes in medulloblastoma survivors: a report from the Childhood Cancer Survivor Study. Neuro Oncol 14:1018–1025. CrossRefGoogle Scholar
  9. 9.
    Wefel JS, Noll KR, Scheurer ME (2016) Neurocognitive functioning and genetic variation in patients with primary brain tumours. Lancet Oncol 17:e97–e108CrossRefGoogle Scholar
  10. 10.
    Williams JG, Kubelik AR, Livak KJ et al (1990) DNA polymorphisms amplified by arbitrary primers are useful as genetic markers. Nucl Acids Res 18:6531–6535. CrossRefGoogle Scholar
  11. 11.
    Monje ML, Palmer T (2003) Radiation injury and neurogenesis. Curr Opin Neurol 16:129–134CrossRefGoogle Scholar
  12. 12.
    Gao HM, Hong JS (2008) Why neurodegenerative diseases are progressive: uncontrolled inflammation drives disease progression. Trends Immunol 29:357–365. CrossRefGoogle Scholar
  13. 13.
    Hayes JD, Pulford DJ (1995) The Glutathione-S-Transferase supergene family—regulation of Gst and the Contribution of the isoenzymes to cancer chemoprotection and drug-resistance. Crit Rev Biochem Mol Biol 30:445–600. CrossRefGoogle Scholar
  14. 14.
    Berger J, Moller DE, Moller D et al (2002) The Mechanisms of action of PPARs. Annu Rev Med 53:409–435. CrossRefGoogle Scholar
  15. 15.
    Warden A, Truitt J, Merriman M et al (2016) Localization of PPAR isotypes in the adult mouse and human brain. Sci Rep 6:27618. CrossRefGoogle Scholar
  16. 16.
    Kersten S, Desvergne B, Wahli W (2000) Roles of PPARS in health and disease. Nature 405:421–424. CrossRefGoogle Scholar
  17. 17.
    Ramanan S, Zhao W, Riddle DR, Robbins ME (2010) Review article: role of PPARs in radiation-induced brain injury. PPAR ResearchGoogle Scholar
  18. 18.
    Heneka MT, Sastre M, Dumitrescu-Ozimek L et al (2005) Acute treatment with the PPARγ agonist pioglitazone and ibuprofen reduces glial inflammation and Aβ1-42 levels in APPV717I transgenic mice. Brain 128:1442–1453. CrossRefGoogle Scholar
  19. 19.
    Watson GS, Cholerton BA, Reger MA et al (2005) Preserved cognition in patients with early Alzheimer disease and amnestic mild cognitive impairment during treatment with rosiglitazone: a preliminary study. Am J Geriatr Psychiatry 13:950–958. Google Scholar
  20. 20.
    Ramanan S, Kooshki M, Zhao W et al (2009) The PPARα agonist fenofibrate preserves hippocampal neurogenesis and inhibits microglial activation after whole-brain irradiation. Int J Radiat Oncol Biol Phys 75:870–877. CrossRefGoogle Scholar
  21. 21.
    Zhao W, Payne V, Tommasi E et al (2007) Administration of the peroxisomal proliferator-activated receptor γ agonist pioglitazone during fractionated brain irradiation prevents radiation-induced cognitive impairment. Int J Radiat Oncol Biol Phys 67:6–9. CrossRefGoogle Scholar
  22. 22.
    Daynes RA, Jones DC (2002) Emerging roles of PPARs in inflammation and immunity. Nat Rev Immunol 2:748–759CrossRefGoogle Scholar
  23. 23.
    Ricote M, Glass CK (2007) PPARs and molecular mechanisms of transrepression. Biochim Biophys Acta 1771:926–935CrossRefGoogle Scholar
  24. 24.
    McKinney BA, Reif DM, Ritchie MD, Moore JH (2006) Machine learning for detecting gene-gene interactions: a review. Appl Bioinform 5:77–88CrossRefGoogle Scholar
  25. 25.
    Breiman L (2001) Random Forests. Mach Learn 45:5–32. CrossRefGoogle Scholar
  26. 26.
    Chen X, Ishwaran H (2012) Random forests for genomic data analysis. Genomics 99:323–329CrossRefGoogle Scholar
  27. 27.
    Lunetta KL, Hayward LB, Segal J, van Eerdewegh P (2004) Screening large-scale association study data: exploiting interactions using random forests. BMC Genet. Google Scholar
  28. 28.
    Wechsler D (2002) WPPSI-III administration and scoring manual. Psychol Corp. Google Scholar
  29. 29.
    Wechsler D (2004) The Wechsler intelligence scale for children—fourth editionGoogle Scholar
  30. 30.
    Liu YR, Hu TM, Lan TH et al (2014) Association of the PPAR-γ gene with altered glucose levels and psychosis profile in schizophrenia patients exposed to antipsychotics. Psychiatry Investig 11:179–185. CrossRefGoogle Scholar
  31. 31.
    Glas J, Seiderer J, Markus C et al (2011) Role of PPARG gene variants in inflammatory bowel disease. Inflamm Bowel Dis 17:1057–1058CrossRefGoogle Scholar
  32. 32.
    Combarros O, Rodríguez-Rodríguez E, Mateo I et al (2011) APOE dependent-association of PPAR-γ genetic variants with Alzheimer’s disease risk. Neurobiol Aging. Google Scholar
  33. 33.
    Wang H, Taverna D, Stram DO et al (2013) Genetic variation in the inflammation and innate immunity pathways and colorectal cancer risk. Cancer Epidemiol Biomarkers Prev 22:2094–2101. CrossRefGoogle Scholar
  34. 34.
    Costa M, Squassina A, Congiu D et al (2013) Investigation of endocannabinoid system genes suggests association between peroxisome proliferator activator receptor-α gene (PPARA) and schizophrenia. Eur Neuropsychopharmacol 23:749–759. CrossRefGoogle Scholar
  35. 35.
    Zandi PP, Belmonte PL, Willour VL et al (2008) Association study of Wnt signaling pathway genes in bipolar disorder. Arch Gen Psychiatry 65:785–793. CrossRefGoogle Scholar
  36. 36.
    Johnson DC, Corthals SL, Walker B et al (2011) Genetic factors underlying the risk of thalidomide-related neuropathy in patients with multiple myeloma. J Clin Oncol 29:797–804. CrossRefGoogle Scholar
  37. 37.
    Holzapfel J, Heun R, Lütjohann D et al (2006) PPARD haplotype influences cholesterol metabolism but is no risk factor of Alzheimer’s disease. Neurosci Lett 408:57–61. CrossRefGoogle Scholar
  38. 38.
    Rednam S, Scheurer ME, Adesina A et al (2013) Glutathione S-transferase P1 single nucleotide polymorphism predicts permanent ototoxicity in children with medulloblastoma. Pediatr Blood Cancer 60:593–598. CrossRefGoogle Scholar
  39. 39.
    Harris SE, Fox H, Wright AF et al (2007) A genetic association analysis of cognitive ability and cognitive ageing using 325 markers for 109 genes associated with oxidative stress or cognition. BMC Genet 8:43. CrossRefGoogle Scholar
  40. 40.
    Pyper SR, Viswakarma N, Yu S, Reddy JK (2010) PPARα: energy combustion, hypolipidemia, inflammation and cancer. Nucl Recept Signal. Google Scholar
  41. 41.
    Altshuler DM, Durbin RM, Abecasis GR et al (2012) An integrated map of genetic variation from 1,092 human genomes. Nature 491:56–65. CrossRefGoogle Scholar
  42. 42.
    Price AL, Patterson NJ, Plenge RM et al (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38:904–909. CrossRefGoogle Scholar
  43. 43.
    Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS (2008) Random survival forests. Ann Appl Stat 2:841–860. CrossRefGoogle Scholar
  44. 44.
    Aleshin S, Strokin M, Sergeeva M, Reiser G (2013) Peroxisome proliferator-activated receptor (PPAR)beta/delta, a possible nexus of PPARalpha- and PPARgamma-dependent molecular pathways in neurodegenerative diseases: Review and novel hypotheses. Neurochem Int 63:322–330CrossRefGoogle Scholar
  45. 45.
    Aleshin S, Reiser G (2013) Role of the peroxisome proliferator-activated receptors (PPAR)-α, β/δ and γ triad in regulation of reactive oxygen species signaling in brain. Biol Chem 394:1553–1570CrossRefGoogle Scholar
  46. 46.
    Cole PD, Finkelstein Y, Stevenson KE et al (2015) Polymorphisms in genes related to oxidative stress are associated with inferior cognitive function after therapy for childhood acute lymphoblastic leukemia. J Clin Oncol 33:2205–2211. CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Adeoye Oyefiade
    • 1
    • 6
    Email author
  • Lauren Erdman
    • 2
  • Anna Goldenberg
    • 2
  • David Malkin
    • 3
    • 4
  • Eric Bouffet
    • 3
    • 4
  • Michael D. Taylor
    • 3
    • 5
  • Vijay Ramaswamy
    • 3
  • Nadia Scantlebury
    • 1
  • Nicole Law
    • 1
  • Donald J. Mabbott
    • 1
    • 3
    • 6
  1. 1.Neurosciences and Mental HealthThe Hospital for Sick ChildrenTorontoCanada
  2. 2.Genetics and Genome Biology ProgramThe Hospital for Sick ChildrenTorontoCanada
  3. 3.Division of Hematology/OncologyThe Hospital for Sick ChildrenTorontoCanada
  4. 4.Department of PediatricsUniversity of TorontoTorontoCanada
  5. 5.Division of NeurosurgeryThe Hospital for Sick ChildrenTorontoCanada
  6. 6.Department of PsychologyUniversity of TorontoTorontoCanada

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