Advertisement

Journal of Neuro-Oncology

, Volume 129, Issue 2, pp 289–300 | Cite as

Integrative analysis of diffusion-weighted MRI and genomic data to inform treatment of glioblastoma

  • Guido H. Jajamovich
  • Chandni R. Valiathan
  • Razvan Cristescu
  • Sangeetha Somayajula
Clinical Study

Abstract

Gene expression profiling from glioblastoma (GBM) patients enables characterization of cancer into subtypes that can be predictive of response to therapy. An integrative analysis of imaging and gene expression data can potentially be used to obtain novel biomarkers that are closely associated with the genetic subtype and gene signatures and thus provide a noninvasive approach to stratify GBM patients. In this retrospective study, we analyzed the expression of 12,042 genes for 558 patients from The Cancer Genome Atlas (TCGA). Among these patients, 50 patients had magnetic resonance imaging (MRI) studies including diffusion weighted (DW) MRI in The Cancer Imaging Archive (TCIA). We identified the contrast enhancing region of the tumors using the pre- and post-contrast T1-weighted MRI images and computed the apparent diffusion coefficient (ADC) histograms from the DW-MRI images. Using the gene expression data, we classified patients into four molecular subtypes, determined the number and composition of genes modules using the gap statistic, and computed gene signature scores. We used logistic regression to find significant predictors of GBM subtypes. We compared the predictors for different subtypes using Mann–Whitney U tests. We assessed detection power using area under the receiver operating characteristic (ROC) analysis. We computed Spearman correlations to determine the associations between ADC and each of the gene signatures. We performed gene enrichment analysis using Ingenuity Pathway Analysis (IPA). We adjusted all p values using the Benjamini and Hochberg method. The mean ADC was a significant predictor for the neural subtype. Neural tumors had a significantly lower mean ADC compared to non-neural tumors (\(p=0.005\)), with mean ADC of \(1.07\pm 0.16 \times 10^{-3}\) and \(1.23\pm 0.16\times 10^{-3}\; \mathrm{{mm^2/s}}\) for neural and non-neural tumors, respectively. Mean ADC showed an area under the ROC of 0.75 for detecting neural tumors. We found eight gene modules in the GBM cohort. The mean ADC was significantly correlated with the gene signature related with dendritic cell maturation (\(\rho =-0.51\), \(p=0.001\)). Mean ADC could be used as a biomarker of a gene signature associated with dendritic cell maturation and to assist in identifying patients with neural GBMs, known to be resistant to aggressive standard of care.

Keywords

Apparent diffusion coefficient Glioblastoma multiforme Imaging genomics Immunotherapy 

Notes

Acknowledgments

Data used in this research were obtained from The Cancer Imaging Archive (TCIA) sponsored by the Cancer Imaging Program, DCTD/NCI/NIH. The results published here are in part based upon data generated by the The Cancer Genome Atlas (TCGA) Research Network: http://cancergenome.nih.gov/.

Supplementary material

11060_2016_2174_MOESM1_ESM.pdf (906 kb)
(pdf 907 kb)

References

  1. 1.
    Barajas RF Jr, Hodgson JG, Chang JS, Vandenberg SR, Yeh RF, Parsa AT, McDermott MW, Berger MS, Dillon WP, Cha S (2010) Glioblastoma multiforme regional genetic and cellular expression patterns: influence on anatomic and physiologic mr imaging 1. Radiology 254(2):564–576CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Bedard PL, Hansen AR, Ratain MJ, Siu LL (2013) Tumour heterogeneity in the clinic. Nature 501(7467):355–364CrossRefPubMedGoogle Scholar
  3. 3.
    Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B 57:289–300Google Scholar
  4. 4.
    Bleeker FE, Molenaar RJ, Leenstra S (2012) Recent advances in the molecular understanding of glioblastoma. J Neuro-oncol 108(1):11–27CrossRefGoogle Scholar
  5. 5.
    Carrillo J, Lai A, Nghiemphu P, Kim H, Phillips H, Kharbanda S, Moftakhar P, Lalaezari S, Yong W, Ellingson B et al (2012) Relationship between tumor enhancement, edema, idh1 mutational status, mgmt promoter methylation, and survival in glioblastoma. Am J Neuroradiol 33(7):1349–1355CrossRefPubMedGoogle Scholar
  6. 6.
    Chang H, Han J, Borowsky A, Loss L, Gray JW, Spellman PT, Parvin B (2013) Invariant delineation of nuclear architecture in glioblastoma multiforme for clinical and molecular association. IEEE Trans Med Imaging 32(4):670–682CrossRefPubMedGoogle Scholar
  7. 7.
    Chenevert TL, Ross BD (2009) Diffusion imaging for therapy response assessment of brain tumor. Neuroimaging Clin N Am 19(4):559–571CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Chenevert TL, McKeever PE, Ross BD (1997) Monitoring early response of experimental brain tumors to therapy using diffusion magnetic resonance imaging. Clin Cancer Res 3(9):1457–1466PubMedGoogle Scholar
  9. 9.
    Chenevert TL, Stegman LD, Taylor JM, Robertson PL, Greenberg HS, Rehemtulla A, Ross BD (2000) Diffusion magnetic resonance imaging: an early surrogate marker of therapeutic efficacy in brain tumors. J Natl Cancer Inst 92(24):2029–2036CrossRefPubMedGoogle Scholar
  10. 10.
    Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M et al (2013) The cancer imaging archive (tcia): maintaining and operating a public information repository. J Digital Imaging 26(6):1045–1057CrossRefGoogle Scholar
  11. 11.
    Colen RR, Vangel M, Wang J, Gutman DA, Hwang SN, Wintermark M, Jain R, Jilwan-Nicolas M, Chen JY, Raghavan P et al (2014) Imaging genomic mapping of an invasive mri phenotype predicts patient outcome and metabolic dysfunction: a tcga glioma phenotype research group project. BMC Med Genomics 7(1):30CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Crawford FW, Khayal IS, McGue C, Saraswathy S, Pirzkall A, Cha S, Lamborn KR, Chang SM, Berger MS, Nelson SJ (2009) Relationship of pre-surgery metabolic and physiological mr imaging parameters to survival for patients with untreated gbm. J Neuro-oncol 91(3):337–351CrossRefGoogle Scholar
  13. 13.
    Dabney AR (2005) Classification of microarrays to nearest centroids. Bioinformatics 21(22):4148–4154CrossRefPubMedGoogle Scholar
  14. 14.
    Dabney AR, Storey JD (2007) Optimality driven nearest centroid classification from genomic data. PLoS One 2(10):e1002CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Di Costanzo A, Trojsi F, Giannatempo G, Vuolo L, Popolizio T, Catapano D, Bonavita S, d’Angelo V, Tedeschi G, Scarabino T (2006) Spectroscopic, diffusion and perfusion magnetic resonance imaging at 3.0 tesla in the delineation of glioblastomas: preliminary results. J Exp Clin Cancer Res 25(3):383–390PubMedGoogle Scholar
  16. 16.
    Doucette T, Rao G, Rao A, Shen L, Aldape K, Wei J, Dziurzynski K, Gilbert M, Heimberger AB (2013) Immune heterogeneity of glioblastoma subtypes: extrapolation from the cancer genome atlas. Cancer Immunol Res 1(2):112–122CrossRefPubMedGoogle Scholar
  17. 17.
    Egger J, Kapur T, Fedorov A, Pieper S, Miller JV, Veeraraghavan H, Freisleben B, Golby AJ, Nimsky C, Kikinis R (2013) Gbm volumetry using the 3d slicer medical image computing platform. Sci Rep 3:1364CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    ElBanan MG, Amer AM, Zinn PO, Colen RR (2015) Imaging genomics of glioblastoma: state of the art bridge between genomics and neuroradiology. Neuroimaging Clin N Am 25(1):141–153CrossRefPubMedGoogle Scholar
  19. 19.
    Ellingson B, Sahebjam S, Kim H, Pope W, Harris R, Woodworth D, Lai A, Nghiemphu P, Mason W, Cloughesy T (2014) Pretreatment adc histogram analysis is a predictive imaging biomarker for bevacizumab treatment but not chemotherapy in recurrent glioblastoma. Am J Neuroradiol 35(4):673–679CrossRefPubMedGoogle Scholar
  20. 20.
    Ellingson BM (2015) Radiogenomics and imaging phenotypes in glioblastoma: novel observations and correlation with molecular characteristics. Curr Neurol Neurosci Rep 15(1):1–12CrossRefGoogle Scholar
  21. 21.
    Fridman WH, Galon J, Pagès F, Tartour E, Sautès-Fridman C, Kroemer G (2011) Prognostic and predictive impact of intra-and peritumoral immune infiltrates. Cancer Res 71(17):5601–5605CrossRefPubMedGoogle Scholar
  22. 22.
    Galons JP, Altbach MI, Paine-Murrieta GD, Taylor CW, Gillies RJ (1999) Early increases in breast tumor xenograft water mobility in response to paclitaxel therapy detected by non-invasive diffusion magnetic resonance imaging. Neoplasia 1(2):113–117CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Guo, A.C., Cummings, T.J., Dash, R.C., Provenzale, J.M.: Lymphomas and high-grade astrocytomas: comparison of water diffusibility and histologic characteristics 1. Radiology 224(1), 177–183 (2002)CrossRefPubMedGoogle Scholar
  24. 24.
    Gupta RK, Cloughesy TF, Sinha U, Garakian J, Lazareff J, Rubino G, Rubino L, Becker DP, Vinters HV, Alger JR (2000) Relationships between choline magnetic resonance spectroscopy, apparent diffusion coefficient and quantitative histopathology in human glioma. J Neuro-oncol 50(3):215–226CrossRefGoogle Scholar
  25. 25.
    Gutman DA, Cooper LA, Hwang SN, Holder CA, Gao J, Aurora TD, Dunn WD Jr, Scarpace L, Mikkelsen T, Jain R et al (2013) Mr imaging predictors of molecular profile and survival: multi-institutional study of the tcga glioblastoma data set. Radiology 267(2):560–569CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Gutman DA, Dunn WD Jr, Grossmann P, Cooper LA, Holder CA, Ligon KL, Alexander BM, Aerts HJ (2015) Somatic mutations associated with mri-derived volumetric features in glioblastoma. Neuroradiology 57(12):1227–1237CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Hall DE, Moffat BA, Stojanovska J, Johnson TD, Li Z, Hamstra DA, Rehemtulla A, Chenevert TL, Carter J, Pietronigro D et al (2004) Therapeutic efficacy of dti-015 using diffusion magnetic resonance imaging as an early surrogate marker. Clin Cancer Res 10(23):7852–7859CrossRefPubMedGoogle Scholar
  28. 28.
    Hamstra DA, Chenevert TL, Moffat BA, Johnson TD, Meyer CR, Mukherji SK, Quint DJ, Gebarski SS, Fan X, Tsien CI et al (2005) Evaluation of the functional diffusion map as an early biomarker of time-to-progression and overall survival in high-grade glioma. Proc Natl Acad Sci USA 102(46):16759–16764CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Hamstra DA, Galbán CJ, Meyer CR, Johnson TD, Sundgren PC, Tsien C, Lawrence TS, Junck L, Ross DJ, Rehemtulla A et al (2008) Functional diffusion map as an early imaging biomarker for high-grade glioma: correlation with conventional radiologic response and overall survival. J Clin Oncol 26(20):3387–3394CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Hayashida Y, Hirai T, Morishita S, Kitajima M, Murakami R, Korogi Y, Makino K, Nakamura H, Ikushima I, Yamura M et al (2006) Diffusion-weighted imaging of metastatic brain tumors: comparison with histologic type and tumor cellularity. Am J Neuroradiol 27(7):1419–1425PubMedGoogle Scholar
  31. 31.
    Henson JW, Gaviani P, Gonzalez RG (2005) Mri in treatment of adult gliomas. Lancet Oncol 6(3):167–175CrossRefPubMedGoogle Scholar
  32. 32.
    Jackson RJ, Fuller GN, Abi-Said D, Lang FF, Gokaslan ZL, Shi WM, Wildrick DM, Sawaya R (2001) Limitations of stereotactic biopsy in the initial management of gliomas. Neuro-oncology 3(3):193–200PubMedPubMedCentralGoogle Scholar
  33. 33.
    Jolliffe I (2002) Principal component analysis. Wiley Online Library, HobokenGoogle Scholar
  34. 34.
    Kono K, Inoue Y, Nakayama K, Shakudo M, Morino M, Ohata K, Wakasa K, Yamada R (2001) The role of diffusion-weighted imaging in patients with brain tumors. Am J Neuroradiol 22(6):1081–1088PubMedGoogle Scholar
  35. 35.
    Lazovic J, Jensen MC, Ferkassian E, Aguilar B, Raubitschek A, Jacobs RE (2008) Imaging immune response in vivo: cytolytic action of genetically altered t cells directed to glioblastoma multiforme. Clin Cancer Res 14(12):3832–3839CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Le Bihan D, Douek P, Argyropoulou M, Turner R, Patronas N, Fulham M (1993) Diffusion and perfusion magnetic resonance imaging in brain tumors. Top Magn Reson Imaging 5(2):25–31CrossRefPubMedGoogle Scholar
  37. 37.
    Lee KC, Moffat BA, Schott AF, Layman R, Ellingworth S, Juliar R, Khan AP, Helvie M, Meyer CR, Chenevert TL et al (2007) Prospective early response imaging biomarker for neoadjuvant breast cancer chemotherapy. Clin Cancer Res 13(2):443–450CrossRefPubMedGoogle Scholar
  38. 38.
    Mardor Y, Roth Y, Lidar Z, Jonas T, Pfeffer R, Maier SE, Faibel M, Nass D, Hadani M, Orenstein A et al (2001) Monitoring response to convection-enhanced taxol delivery in brain tumor patients using diffusion-weighted magnetic resonance imaging. Cancer Res 61(13):4971–4973PubMedGoogle Scholar
  39. 39.
    Mardor Y, Roth Y, Ocherashvilli A, Spiegelmann R, Tichler T, Daniels D, Maier SE, Nissim O, Ram Z, Baram J et al (2004) Pretreatment prediction of brain tumors response to radiation therapy using high b-value diffusion-weighted mri. Neoplasia 6(2):136–142CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    McConville P, Hambardzumyan D, Moody JB, Leopold WR, Kreger AR, Woolliscroft MJ, Rehemtulla A, Ross BD, Holland EC (2007) Magnetic resonance imaging determination of tumor grade and early response to temozolomide in a genetically engineered mouse model of glioma. Clin Cancer Res 13(10):2897–2904CrossRefPubMedGoogle Scholar
  41. 41.
    McLendon R, Friedman A, Bigner D, Van Meir EG, Brat DJ, Mastrogianakis GM, Olson JJ, Mikkelsen T, Lehman N, Aldape K et al (2008) Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455(7216):1061–1068CrossRefGoogle Scholar
  42. 42.
    Mischel PS, Shai R, Shi T, Horvath S, Lu KV, Choe G, Seligson D, Kremen TJ, Palotie A, Liau LM et al (2003) Identification of molecular subtypes of glioblastoma by gene expression profiling. Oncogene 22(15):2361–2373CrossRefPubMedGoogle Scholar
  43. 43.
    Moton S, Elbanan M, Zinn PO, Colen RR (2015) Imaging genomics of glioblastoma: biology, biomarkers, and breakthroughs. Top Magn Reson Imaging 24(3):155–163CrossRefPubMedGoogle Scholar
  44. 44.
    Naeini, K.M., Pope, W.B., Cloughesy, T.F., Harris, R.J., Lai, A., Eskin, A., Chowdhury, R., Phillips, H.S., Nghiemphu, P.L., Behbahanian, Y., et al.: Identifying the mesenchymal molecular subtype of glioblastoma using quantitative volumetric analysis of anatomic magnetic resonance images. Neuro-oncology 15, 626–634 (2013)CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Nicholas S, Mathios D, Ruzevick J, Jackson C, Yang I, Lim M (2013) Current trends in glioblastoma multiforme treatment: radiation therapy and immune checkpoint inhibitors. Brain Tumor Res Treat 1(1):2–8CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Paldino M, Desjardins A, Friedman H, Vredenburgh J, Barboriak D (2014) A change in the apparent diffusion coefficient after treatment with bevacizumab is associated with decreased survival in patients with recurrent glioblastoma multiforme. Br J Radiol 85(1012):382–389CrossRefGoogle Scholar
  47. 47.
    Palucka K, Banchereau J (2012) Cancer immunotherapy via dendritic cells. Nat Rev Cancer 12(4):265–277CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    Pardoll DM (2012) The blockade of immune checkpoints in cancer immunotherapy. Nat Rev Cancer 12(4):252–264CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Patel AP, Tirosh I, Trombetta JJ, Shalek AK, Gillespie SM, Wakimoto H, Cahill DP, Nahed BV, Curry WT, Martuza RL et al (2014) Single-cell rna-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344(6190):1396–1401CrossRefPubMedPubMedCentralGoogle Scholar
  50. 50.
    Pope W, Mirsadraei L, Lai A, Eskin A, Qiao J, Kim H, Ellingson B, Nghiemphu P, Kharbanda S, Soriano R et al (2012) Differential gene expression in glioblastoma defined by adc histogram analysis: relationship to extracellular matrix molecules and survival. Am J Neuroradiol 33(6):1059–1064CrossRefPubMedGoogle Scholar
  51. 51.
    Pope WB (2015) Genomics of brain tumor imaging. Neuroimaging Clin N Am 25(1):105–119CrossRefPubMedGoogle Scholar
  52. 52.
    Pope WB, Kim HJ, Huo J, Alger J, Brown MS, Gjertson D, Sai V, Young JR, Tekchandani L, Cloughesy T et al (2009) Recurrent glioblastoma multiforme: Adc histogram analysis predicts response to bevacizumab treatment 1. Radiology 252(1):182–189CrossRefPubMedGoogle Scholar
  53. 53.
    Pope WB, Qiao XJ, Kim HJ, Lai A, Nghiemphu P, Xue X, Ellingson BM, Schiff D, Aregawi D, Cha S et al (2012) Apparent diffusion coefficient histogram analysis stratifies progression-free and overall survival in patients with recurrent gbm treated with bevacizumab: a multi-center study. J Neuro-oncol 108(3):491–498CrossRefGoogle Scholar
  54. 54.
    Prins RM, Soto H, Konkankit V, Odesa SK, Eskin A, Yong WH, Nelson SF, Liau LM (2011) Gene expression profile correlates with t-cell infiltration and relative survival in glioblastoma patients vaccinated with dendritic cell immunotherapy. Clin Cancer Res 17(6):1603–1615CrossRefPubMedGoogle Scholar
  55. 55.
    Rao A, Rao G, Gutman DA, Flanders AE, Hwang SN, Rubin DL, Colen RR, Zinn PO, Jain R, Wintermark M et al (2015) A combinatorial radiographic phenotype may stratify patient survival and be associated with invasion and proliferation characteristics in glioblastoma. J Neurosurg 67:1–10Google Scholar
  56. 56.
    Ringnér M (2008) What is principal component analysis? Nat Biotechnol 26(3):303–304CrossRefPubMedGoogle Scholar
  57. 57.
    Rolle CE, Sengupta S, Lesniak MS (2010) Challenges in clinical design of immunotherapy trials for malignant glioma. Neurosurg Clin N Am 21(1):201–214CrossRefPubMedPubMedCentralGoogle Scholar
  58. 58.
    Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53–65CrossRefGoogle Scholar
  59. 59.
    Ryken TC, Aygun N, Morris J, Schweizer M, Nair R, Spracklen C, Kalkanis SN, Olson JJ (2014) The role of imaging in the management of progressive glioblastoma. J Neuro-oncol 118(3):435–460CrossRefGoogle Scholar
  60. 60.
    Sadeghi N, D’Haene N, Decaestecker C, Levivier M, Metens T, Maris C, Wikler D, Balériaux D, Salmon I, Goldman S (2008) Apparent diffusion coefficient and cerebral blood volume in brain gliomas: relation to tumor cell density and tumor microvessel density based on stereotactic biopsies. Am J Neuroradiol 29(3):476–482CrossRefPubMedGoogle Scholar
  61. 61.
    Saraswathy S, Crawford FW, Lamborn KR, Pirzkall A, Chang S, Cha S, Nelson SJ (2009) Evaluation of mr markers that predict survival in patients with newly diagnosed gbm prior to adjuvant therapy. J Neuro-oncol 91(1):69–81CrossRefGoogle Scholar
  62. 62.
    Schaefer PW, Grant PE, Gonzalez RG (2000) Diffusion-weighted mr imaging of the brain 1. Radiology 217(2):331–345CrossRefPubMedGoogle Scholar
  63. 63.
    Schag CC, Heinrich RL, Ganz P (1984) Karnofsky performance status revisited: reliability, validity, and guidelines. J Clin Oncol 2(3):187–193PubMedGoogle Scholar
  64. 64.
    Sims JS, Ung TH, Neira JA, Canoll P, Bruce JN (2015) Biomarkers for glioma immunotherapy: the next generation. J Neuro-oncol 123:359–372CrossRefGoogle Scholar
  65. 65.
    Stadnik TW, Chaskis C, Michotte A, Shabana WM, van Rompaey K, Luypaert R, Budinsky L, Jellus V, Osteaux M (2001) Diffusion-weighted mr imaging of intracerebral masses: comparison with conventional mr imaging and histologic findings. Am J Neuroradiol 22(5):969–976PubMedGoogle Scholar
  66. 66.
    Stupp R, Hegi ME, Mason WP, van den Bent MJ, Taphoorn MJ, Janzer RC, Ludwin SK, Allgeier A, Fisher B, Belanger K et al (2009) Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase iii study: 5-year analysis of the eortc-ncic trial. Lancet Oncol 10(5):459–466CrossRefPubMedGoogle Scholar
  67. 67.
    Sunwoo L, Choi SH, Park CK, Kim JW, Yi KS, Lee WJ, Yoon TJ, Song SW, Kim JE, Kim JY et al (2013) Correlation of apparent diffusion coefficient values measured by diffusion mri and mgmt promoter methylation semiquantitatively analyzed with ms-mlpa in patients with glioblastoma multiforme. J Magn Reson Imaging 37(2):351–358CrossRefPubMedGoogle Scholar
  68. 68.
    Thomas AA, Fisher JL, Rahme GJ, Hampton TH, Baron U, Olek S, Schwachula T, Rhodes CH, Gui J, Tafe LJ et al (2015) Regulatory T cells are not a strong predictor of survival for patients with glioblastoma. Neuro-oncology 17(6):801–809Google Scholar
  69. 69.
    Thomas G, Wang J, Mahmood Z, ElBanan MG, Zinn PO, Colen RR (2015) Diffusion imaging genomic mapping identifies genomic targets involved in invasion and poor prognosis. In: American Society of Newuroradiology 52th annual meetingGoogle Scholar
  70. 70.
    Tibshirani R, Walther G, Hastie T (2001) Estimating the number of clusters in a data set via the gap statistic. J R Stat Soc 63(2):411–423CrossRefGoogle Scholar
  71. 71.
    Van Elsas A, Hurwitz AA, Allison JP (1999) Combination immunotherapy of b16 melanoma using anti-cytotoxic t lymphocyte-associated antigen 4 (ctla-4) and granulocyte/macrophage colony-stimulating factor (gm-csf)-producing vaccines induces rejection of subcutaneous and metastatic tumors accompanied by autoimmune depigmentation. J Exp Med 190(3):355–366CrossRefPubMedPubMedCentralGoogle Scholar
  72. 72.
    Vauléon E, Tony A, Hamlat A, Etcheverry A, Chiforeanu DC, Menei P, Mosser J, Quillien V, Aubry M (2012) Immune genes are associated with human glioblastoma pathology and patient survival. BMC Med Genomics 5(1):41CrossRefPubMedPubMedCentralGoogle Scholar
  73. 73.
    Verhaak, R.G., Hoadley, K.A., Purdom, E., Wang, V., Qi, Y., Wilkerson, M.D., Miller, C.R., Ding, L., Golub, T., Mesirov, J.P., et al.: Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in pdgfra, idh1, egfr, and nf1. Cancer Cell 17(1), 98–110 (2010)CrossRefPubMedPubMedCentralGoogle Scholar
  74. 74.
    Vezhnevets V, Konouchine V (2005) Growcut: interactive multi-label nd image segmentation by cellular automata. In: Proceedings of graphicon, citeseer, pp 150–156Google Scholar
  75. 75.
    Vrabec M, Van Cauter S, Himmelreich U, Van Gool SW, Sunaert S, De Vleeschouwer S, Šuput D, Demaerel P (2011) Mr perfusion and diffusion imaging in the follow-up of recurrent glioblastoma treated with dendritic cell immunotherapy: a pilot study. Neuroradiology 53(10):721–731CrossRefPubMedGoogle Scholar
  76. 76.
    Weller M, Cloughesy T, Perry JR, Wick W (2013) Standards of care for treatment of recurrent glioblastoma are we there yet? Neuro-oncology 15(1):4–27CrossRefPubMedGoogle Scholar
  77. 77.
    Wen Q, Jalilian L, Lupo JM, Molinaro AM, Chang SM, Clarke J, Prados M, Nelson SJ (2015) Comparison of adc metrics and their association with outcome for patients with newly diagnosed glioblastoma being treated with radiation therapy, temozolomide, erlotinib and bevacizumab. J Neuro-oncol 121(2):331–339CrossRefGoogle Scholar
  78. 78.
    Zhang Z, Jiang H, Chen X, Bai J, Cui Y, Ren X, Chen X, Wang J, Zeng W, Lin S (2014) Identifying the survival subtypes of glioblastoma by quantitative volumetric analysis of mri. J Neuro-oncol 119(1):207–214CrossRefGoogle Scholar
  79. 79.
    Zinn PO, Hatami M, Colen RR (2015) Diffusion mri adc mapping of glioblastoma edema/tumor invasion and associated gene signatures. Neurosurgery 62:210CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Guido H. Jajamovich
    • 1
  • Chandni R. Valiathan
    • 1
  • Razvan Cristescu
    • 2
  • Sangeetha Somayajula
    • 1
  1. 1.Scientific InformaticsMerck Research LaboratoriesBostonUSA
  2. 2.Department of Genetics and PharmacogenomicsMerck Research LaboratoriesBostonUSA

Personalised recommendations