Advertisement

Quantitative Biology

, Volume 4, Issue 1, pp 1–12 | Cite as

“Radiotranscriptomics”: A synergy of imaging and transcriptomics in clinical assessment

  • Amal Katrib
  • William Hsu
  • Alex Bui
  • Yi Xing
Perspective

Abstract

Recent advances in quantitative imaging and “omics” technology have generated a wealth of mineable biological “big data”. With the push towards a P4 “predictive, preventive, personalized, and participatory” approach to medicine, researchers began integrating complementary tools to further tune existing diagnostic and therapeutic models. The field of radiogenomics has long pioneered such multidisciplinary investigations in neuroscience and oncology, correlating genotypic and phenotypic signatures to study structural and functional changes in relation to altered molecular behavior. Given the innate dynamic nature of complex disorders and the role of environmental and epigenetic factors in pathogenesis, the transcriptome can further elucidate serial modifications undetected at the genome level.We therefore propose “radiotranscriptomics” as a new member of the P4 medicine initiative, combining transcriptome information, including gene expression and isoform variation, and quantitative image annotations.

Keywords

quantitative imaging transcriptomics RNA-seq genomics image genomics radiogenomics systems biology precision medicine 

References

  1. 1.
    Loscalzo, J. and Barabasi, A. L. (2011) Systems biology and the future of medicine. Wiley Interdiscip. Rev. Syst. Biol. Med., 3, 619–627CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Trewavas, A. (2006) A Brief History of Systems Biology: "Every object that biology studies is a system of systems." Francois Jacob (1974). Plant Cell Online, 18, 2420–2430CrossRefGoogle Scholar
  3. 3.
    Jaffe, C. C. (2012) Imaging and genomics: is there a synergy? Radiology, 264, 329–331CrossRefPubMedGoogle Scholar
  4. 4.
    Lander, E. S. (1996) The new genomics: global views of biology. Science, 274, 536–539CrossRefPubMedGoogle Scholar
  5. 5.
    Pritchard, J. K. and Cox, N. J. (2002) The allelic architecture of human disease genes: common disease-common variantor not? Hum. Mol. Genet., 11, 2417–2423CrossRefPubMedGoogle Scholar
  6. 6.
    Strachan, T., Read, A. P. and Strachan, T. (2011) Human Molecular Genetics. 4th ed., New York: Garland ScienceGoogle Scholar
  7. 7.
    Bush, W. S. and Moore, J. H. (2012) Chapter 11: Genome-Wide Association Studies. PLoS Comput. Biol., 8, e1002822CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Moody, G. (2004) Digital code of life: how bioinformatics is revolutionizing science, medicine, and business. Hoboken: WileyGoogle Scholar
  9. 9.
    Visscher, P. M., Brown, M. A., McCarthy, M. I. and Yang, J. (2012) Five years of GWAS discovery. Am. J. Hum. Genet., 90, 7–24CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Li, J., Horstman, B. and Chen, Y. (2011) Detecting epistatic effects in association studies at a genomic level based on an ensemble approach. Bioinformatics, 27, i222–i229CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Iles, M. M. (2008) What can genome-wide association studies tell us about the genetics of common disease? PLoS Genet., 4, e33CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    George, A. L. Jr. (2008) Appraising the value of genomic association studies. J. Am. Soc. Nephrol., 19, 1840–1842CrossRefPubMedGoogle Scholar
  13. 13.
    Gibson, G. (2012) Rare and common variants: twenty arguments. Nat. Rev. Genet., 13, 135–145CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Bushberg, J. T. (2012) The essential physics of medical imaging. 3rd ed. Philadelphia: Wolters Kluwer Health/Lippincott Williams & WilkinsGoogle Scholar
  15. 15.
    Martí-Bonmatí, L., Sopena, R., Bartumeus, P. and Sopena, P. (2010) Multimodality imaging techniques. Contrast Media Mol. Imaging, 5, 180–189CrossRefPubMedGoogle Scholar
  16. 16.
    Padhani, A. R. and Miles, K. A. (2010) Multiparametric imaging of tumor response to therapy. Radiology, 256, 348–364CrossRefPubMedGoogle Scholar
  17. 17.
    Jenkinson, M., Bannister, P., Brady, M. and Smith, S. (2002) Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage, 17, 825–841CrossRefPubMedGoogle Scholar
  18. 18.
    Woods, R. P., Mazziotta, J. C. and Cherry, S. R. (1993) MRI-PET registration with automated algorithm. J. Comput. Assist. Tomogr., 17, 536–546CrossRefPubMedGoogle Scholar
  19. 19.
    Martin, K., Ibáñez, L., Avila, L., Barré, S. and Kaspersen, J. H. (2005) Integrating segmentation methods from the Insight Toolkit into a visualization application. Med. Image Anal., 9, 579–593CrossRefPubMedGoogle Scholar
  20. 20.
    Jenkinson, M., Beckmann, C. F., Behrens, T. E. J.,Woolrich, M.W. and Smith, S. M. (2012) Fsl. Neuroimage, 62, 782–790CrossRefPubMedGoogle Scholar
  21. 21.
    Fedorov, A., Beichel, R., Kalpathy-Cramer, J., Finet, J., Fillion-Robin, J.-C., Pujol, S., Bauer, C., Jennings, D., Fennessy, F., Sonka, M., et al. (2012) 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn. Reson. Imaging, 30, 1323–1341CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Rosset, A., Spadola, L. and Ratib, O. (2004) OsiriX: an open-source software for navigating in multidimensional DICOM images. J. Digit. Imaging, 17, 205–216CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Friston, K. J. (1995) Commentary and opinion: II. Statistical parametric mapping: ontology and current issues. J. Cereb. Blood Flow Metab., 15, 361–370PubMedGoogle Scholar
  24. 24.
    Filippi, M., Horsfield, M. A., Adèr, H. J., Barkhof, F., Bruzzi, P., Evans, A., Frank, J. A., Grossman, R. I., McFarland, H. F., Molyneux, P., et al. (1998) Guidelines for using quantitative measures of brain magnetic resonance imaging abnormalities in monitoring the treatment of multiple sclerosis. Ann. Neurol., 43, 499–506CrossRefPubMedGoogle Scholar
  25. 25.
    Netsch, T. and van Muiswinkel, A. (2004) Quantitative evaluation of image-based distortion correction in diffusion tensor imaging. IEEE Trans. Med. Imaging, 23, 789–798CrossRefPubMedGoogle Scholar
  26. 26.
    Hutton, C., Bork, A., Josephs, O., Deichmann, R., Ashburner, J. and Turner, R. (2002) Image distortion correction in fMRI: A quantitative evaluation. Neuroimage, 16, 217–240CrossRefPubMedGoogle Scholar
  27. 27.
    Gallardo-Estrella, L., Lynch, D.A., Prokop, M., Stinson, D., Zach, J., Judy, P.F., van Ginneken, B., van Rikxoort, E. M. (2015) Normalizing computed tomography data reconstructed with different filter kernels: effect on emphysema quantification. Eur. Radiol., 26, 478–486CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Fahey, F. H., Kinahan, P. E., Doot, R. K., Kocak, M., Thurston, H. and Poussaint, T. Y. (2010) Variability in PET quantitation within a multicenter consortium. Med. Phys., 37, 3660–3666CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Snook, L., Plewes, C. and Beaulieu, C. (2007) Voxel based versus region of interest analysis in diffusion tensor imaging of neurodevelopment. Neuroimage, 34, 243–252CrossRefPubMedGoogle Scholar
  30. 30.
    Poldrack, R. A. (2007) Region of interest analysis for fMRI. Soc. Cogn. Affect. Neurosci., 2, 67–70CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Park, H. J., Kubicki, M., Shenton, M. E., Guimond, A., McCarley, R. W., Maier, S. E., Kikinis, R., Jolesz, F. A., Westin, C.-F. (2003) Spatial normalization of diffusion tensor MRI using multiple channels. Neuroimage, 20, 1995–2009CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Smith, S. M., Jenkinson, M., Johansen-Berg, H., Rueckert, D., Nichols, T. E., Mackay, C. E., Watkins, K. E., Ciccarelli, O., Cader, M. Z., Matthews, P. M., et al. (2006) Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage, 31, 1487–1505CrossRefPubMedGoogle Scholar
  33. 33.
    Buckler, A. J., Bresolin, L., Dunnick, N. R. and Sullivan, D. C. (2011) A collaborative enterprise for multi-stakeholder participation in the advancement of quantitative imaging. Radiology, 258, 906–914CrossRefPubMedGoogle Scholar
  34. 34.
    Kumar, V., Gu, Y., Basu, S., Berglund, A., Eschrich, S. A., Schabath, M. B., Forster, K., Aerts, H. J. W. L., Dekker, A., Fenstermacher, D., et al. (2012) Radiomics: the process and the challenges. Magn. Reson. Imaging, 30, 1234–1248CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Aerts, H. J., Velazquez, E.R., Leijenaar, R.T., Parmar, C., Grossmann, P., Carvalho, S., Bussink, J., Monshouwer, R., Haibe-Kains, B., Rietveld, D. (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun., 5, 4006PubMedPubMedCentralGoogle Scholar
  36. 36.
    Lambin, P., Rios-Velazquez, E., Leijenaar, R., Carvalho, S., van Stiphout, R. G. P. M., Granton, P., Zegers, C. M. L., Gillies, R., Boellard, R., Dekker, A., et al. (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur. J. Cancer, 48, 441–446CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Medland, S. E., Jahanshad, N., Neale, B. M. and Thompson, P. M. (2014) Whole-genome analyses of whole-brain data: working within an expanded search space. Nat. Neurosci., 17, 791–800CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Colen, R., Foster, I., Gatenby, R., Giger, M. E., Gillies, R.,Gutman, D., Heller, M., Jain, R., Madabhushi, A., Madhavan, S., et al. (2014) NCI Workshop Report: Clinical and computational requirements for correlating imaging phenotypes with genomics signatures. Transl. Oncol., 7, 556–569CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Thompson, P. M., Stein, J. L., Medland, S. E., Hibar, D. P., Vasquez. A. A., Renteria, M. E., Toro, R., Jahanshad, N., Schumann, G., Franke, B. et al. (2014) large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging Behav., 8, 153–182PubMedPubMedCentralGoogle Scholar
  40. 40.
    Jack, C. R. Jr., Bernstein, M. A., Fox, N. C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P. J., L Whitwell, J.,Ward, C., et al. (2008) The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods. J. Magn. Reson. Imaging, 27, 685–691CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Van Essen, D. C., Ugurbil, K., Auerbach, E., Barch, D., Behrens, T. E. J., Bucholz, R., Chang, A., Chen, L., Corbetta, M., Curtiss, S.W., et al. (2012) The Human Connectome Project: a data acquisition perspective. Neuroimage, 62, 2222–2231CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    van Erp, T. G. M., Cannon, T. D., Tran, H. L., Wobbekind, A. D., Huttunen, M., Lonnqvist, J., Kaprio, J., Salonen, O., Valanne, L., Poutanen, V. -P. (2004) Genetic influences on human brain morphology.In Biomedical Imaging: Nano to Macro, IEEE International Symposium, 583–586Google Scholar
  43. 43.
    McIntosh, A., Deary, I. and Porteous, D. J. (2014) Two-back makes step forward in brain imaging genomics. Neuron, 81, 959–961CrossRefPubMedGoogle Scholar
  44. 44.
    Saykin, A. J., Shen, L., Foroud, T. M., Potkin, S. G., Swaminathan, S., Kim, S., Risacher, S. L., Nho, K., Huentelman, M. J., Craig, D. W., et al. (2010) Alzheimer's Disease Neuroimaging Initiative biomarkers as quantitative phenotypes: Genetics core aims, progress, and plans. Alzheimers Dement., 6, 265–273CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Stein, J. L., Medland, S. E., Vasquez, A. A., Hibar, D. P., Senstad, R. E., Winkler, A. M., Toro, R., Appel, K., Bartecek, R., Bergmann, Ø., et al. (2012) Identification of common variants associated with human hippocampal and intracranial volumes. Nat. Genet., 44, 552–561CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Kochunov, P., Glahn, D. C., Nichols, T. E.,Winkler, A. M., Hong, E. L., Holcomb, H. H., Stein, J. L., Thompson, P. M., Curran, J. E., Carless, M. A., et al. (2011) Genetic analysis of cortical thickness and fractional anisotropy of water diffusion in the brain. Front. Neurosci., 5, 120CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Hibar, D. P., Stein, J. L., Renteria, M. E., Arias-Vasquez, A., Desrivières, S., Jahanshad, N., Toro, R., Wittfeld, K., Abramovic, L., Andersson, M., et al. (2015) Common genetic variants influence human subcortical brain structures. Nature, 520, 224–229CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    Sprooten, E., Fleming, K. M., Thomson, P. A., Bastin, M. E., Whalley, H. C., Hall, J., Sussmann, J. E., McKirdy, J., Blackwood, D., Lawrie, S. M., et al. (2013) White matter integrity as an intermediate phenotype: Exploratory genome-wide association analysis in individuals at high risk of bipolar disorder. Psychiatry Res., 206, 223–231CrossRefPubMedGoogle Scholar
  49. 49.
    Hariri, A. R. andWeinberger, D. R. (2003) Imaging genomics. Br. Med. Bull., 65, 259–270CrossRefPubMedGoogle Scholar
  50. 50.
    van den Heuvel, M. P., van Soelen, I. L. C., Stam, C. J., Kahn, R. S., Boomsma, D. I. and Hulshoff Pol, H. E. (2013) Genetic control of functional brain network efficiency in children. Eur. Neuropsychopharmacol., 23, 19–23CrossRefPubMedGoogle Scholar
  51. 51.
    Kochunov, P., Jahanshad, N., Marcus, D., Winkler, A., Sprooten, E., Nichols, T. E., Wright, S. N., Hong, L. E., Patel, B., Behrens, T., et al. (2015) Heritability of fractional anisotropy in human white matter: a comparison of Human Connectome Project and ENIGMA-DTI data. Neuroimage, 111, 300–311CrossRefPubMedGoogle Scholar
  52. 52.
    Ramanan, V. K., Risacher, S. L., Nho, K., Kim, S., Shen, L., McDonald, B. C., Yoder, K. K., Hutchins, G. D., West, J. D., Tallman, E. F., et al. (2015) GWAS of longitudinal amyloid accumulation on 18F-florbetapir PET in Alzheimer’s disease implicates microglial activation gene IL1RAP. Brain, 138, 3076–3088CrossRefPubMedGoogle Scholar
  53. 53.
    Jahanshad, N., Kohannim, O., Toga, A. W., McMahon, K. L., de Zubicaray, G. I., Hansell, N. K., Montgomery, G.W., Martin, N.G., Wright, M. J.,Thompson, P.M. (2012) Diffusion Imaging Protocol Effects on Genetic Associations. IN Proc. IEEE Int. Symp. Biomed Imaging, 944–947Google Scholar
  54. 54.
    Nair, V. S., Gevaert, O., Davidzon, G., Napel, S., Graves, E. E., Hoang, C. D., Shrager, J. B., Quon, A., Rubin, D. L. and Plevritis, S. K. (2012) Prognostic PET 18F-FDG uptake imaging features are associated with major oncogenomic alterations in patients with resected non-small cell lung cancer. Cancer Res., 72, 3725–3734CrossRefPubMedPubMedCentralGoogle Scholar
  55. 55.
    Jahanshad, N., Kochunov, P. V., Sprooten, E., Mandl, R. C., Nichols, T. E., Almasy, L., Blangero, J., Brouwer, R. M., Curran, J. E., de Zubicaray, G. I., et al. (2013) Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: a pilot project of the ENIGMA-DTI working group. Neuroimage, 81, 455–469CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    Thomason, M. E. and Thompson, P. M. (2011) Diffusion imaging, white matter, and psychopathology. Annu. Rev. Clin. Psychol., 7, 63–85CrossRefPubMedGoogle Scholar
  57. 57.
    Greaves, M. and Maley, C. C. (2012) Clonal evolution in cancer. Nature, 481, 306–313CrossRefPubMedPubMedCentralGoogle Scholar
  58. 58.
    Chowdhury, R., Ganeshan, B., Irshad, S., Lawler, K., Eisenblätter, M., Milewicz, H., Rodriguez-Justo, M., Miles, K., Ellis, P., Groves, A., et al. (2014) The use of molecular imaging combined with genomic techniques to understand the heterogeneity in cancer metastasis. Br. J. Radiol., 87, 20140065CrossRefPubMedPubMedCentralGoogle Scholar
  59. 59.
    Yamamoto, S., Maki, D. D., Korn, R. L. and Kuo, M. D. (2012) Radiogenomic analysis of breast cancer using MRI: a preliminary study to define the landscape. AJR Am. J. Roentgenol., 199, 654–663CrossRefPubMedGoogle Scholar
  60. 60.
    Zinn, P. O., Majadan, B., Sathyan, P., Singh, S. K., Majumder, S., Jolesz, F. A. and Colen, R. R. (2011) Radiogenomic mapping of edema/ cellular invasion MRI-phenotypes in glioblastoma multiforme. PLoS One, 6, e25451CrossRefPubMedPubMedCentralGoogle Scholar
  61. 61.
    Karlo, C. A., Di Paolo, P. L., Chaim, J., Hakimi, A. A., Ostrovnaya, I., Russo, P., Hricak, H., Motzer, R., Hsieh, J. J. and Akin, O. (2014) Radiogenomics of clear cell renal cell carcinoma: associations between CT imaging features and mutations. Radiology, 270, 464–471CrossRefPubMedPubMedCentralGoogle Scholar
  62. 62.
    Wang, Z., Gerstein, M. and Snyder, M. (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet., 10, 57–63CrossRefPubMedPubMedCentralGoogle Scholar
  63. 63.
    Dove, A. (1999) Proteomics: translating genomics into products? Nat. Biotechnol., 17, 233–236CrossRefPubMedGoogle Scholar
  64. 64.
    Smaczniak, C., Li, N., Boeren, S., America, T., van Dongen, W., Goerdayal, S. S., de Vries, S., Angenent, G. C. and Kaufmann, K. (2012) Proteomics-based identification of low-abundance signaling and regulatory protein complexes in native plant tissues. Nat. Protoc., 7, 2144–2158CrossRefPubMedGoogle Scholar
  65. 65.
    Fiehn, O. (2002) Metabolomics—the link between genotypes and phenotypes. Plant Mol. Biol., 48, 155–171CrossRefPubMedGoogle Scholar
  66. 66.
    Hitzemann, R., Bottomly, D., Darakjian, P., Walter, N., Iancu, O., Searles, R., Wilmot, B. and McWeeney, S. (2013) Genes, behavior and next-generation RNA sequencing. Genes Brain Behav., 12, 1–12CrossRefPubMedGoogle Scholar
  67. 67.
    Steger, D., Berry, D., Haider, S., Horn, M.,Wagner, M., Stocker, R. and Loy, A. (2011) Systematic spatial bias in DNA microarray hybridization is caused by probe spot position-dependent variability in lateral diffusion. PLoS One, 6, e23727CrossRefPubMedPubMedCentralGoogle Scholar
  68. 68.
    Hartl, D. L. and Ruvolo, M. (2012) Genetics: analysis of genes and genomes. 8th ed. Burlington: Jones & Bartlett LearningGoogle Scholar
  69. 69.
    Koltai, H. and Weingarten-Baror, C. (2008) Specificity of DNA microarray hybridization: characterization, effectors and approaches for data correction. Nucleic Acids Res., 36, 2395–2405CrossRefPubMedPubMedCentralGoogle Scholar
  70. 70.
    Barry,W. T., Kernagis, D. N., Dressman, H. K., Griffis, R. J., Hunter, J. D., Olson, J. A., Marks, J. R., Ginsburg, G. S., Marcom, P. K., Nevins, J. R., et al. (2010) Intratumor heterogeneity and precision of microarray-based predictors of breast cancer biology and clinical outcome. J. Clin. Oncol., 28, 2198–2206CrossRefPubMedPubMedCentralGoogle Scholar
  71. 71.
    Sugano, S. (2009) Introduction: next-generation DNA sequencing and bioinformatics. Tanpakushitsu kakusan koso. Protein, nucleic acid, enzyme, 54, 1233–1237PubMedGoogle Scholar
  72. 72.
    Coppola, G. (2014) The OMICs: applications in neuroscience. New York: Oxford University PressGoogle Scholar
  73. 73.
    Rapaport, F., Khanin, R.,Liang, Y., Pirun, M., Krek, A., Zumbo, P., Mason, C. E., Socci, N. D. and Betel, D. (2013) Comprehensive evaluation of differential gene expression analysis methods for RNAseq data. Genome Biol., 14, R95CrossRefPubMedPubMedCentralGoogle Scholar
  74. 74.
    Lu, Z. X., Jiang, P. and Xing, Y. (2012) Genetic variation of pre-mRNA alternative splicing in human populations. Wiley Interdiscip. Rev. RNA, 3, 581–592CrossRefPubMedPubMedCentralGoogle Scholar
  75. 75.
    Liu, S. L. and Cheng, C. H. (2013) Alternative RNA splicing and cancer. Wiley Interdiscip. Rev. RNA, 4, 547–566CrossRefPubMedPubMedCentralGoogle Scholar
  76. 76.
    Damodaran, S., Berger, M. F. and Roychowdhury, S. (2015) Clinical tumor sequencing: opportunities and challenges for precision cancer medicine. Am. Soc. Clin. Oncol. Educ. Book, 35, e175–e182CrossRefGoogle Scholar
  77. 77.
    Van Keuren-Jensen, K., Keats, J. J. and Craig, D. W. (2014) Bringing RNA-seq closer to the clinic. Nat. Biotechnol., 32, 884–885CrossRefPubMedGoogle Scholar
  78. 78.
    Gonzalez-Angulo, A. M., Hennessy, B. T. and Mills, G. B. (2010) Future of personalized medicine in oncology: a systems biology approach. J. Clin. Oncol., 28, 2777–2783CrossRefPubMedPubMedCentralGoogle Scholar
  79. 79.
    Prior, F. W., Clark, K., Commean, P., Freymann, J., Jaffe, C., Kirby, J., Moore, S., Smith, K., Tarbox, L., Vendt, B. et al. (2013) TCIA: An information resource to enable open science. Conf. Proc. IEEE Eng. Med. Biol. Soc., 2013, 1282–1285PubMedPubMedCentralGoogle Scholar
  80. 80.
    Gutman, D. A., Cooper, L. A. D., Hwang, S. N., Holder, C. A., Gao, J. J., Aurora, T. D., Dunn,W. D., 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, 560–569CrossRefPubMedPubMedCentralGoogle Scholar
  81. 81.
    Zhang, B. and Horvath, S. (2005) Ageneral framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol., 4, 10.2202/1544-6115.1128Google Scholar
  82. 82.
    Patel, A. P., Tirosh, I., Trombetta, J. J., Shalek, A. K., Gillespie, S. M., Wakimoto, H., Cahill, D. P., Nahed, B. V., Curry,W. T., Martuza, R. L., et al. (2014) Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science, 344, 1396–1401CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH 2016

Authors and Affiliations

  1. 1.Department of Microbiology, Immunology, and Molecular GeneticsUniversity of California, Los AngelesLos AngelesUSA
  2. 2.Department of Radiological SciencesUniversity of California, Los AngelesLos AngelesUSA

Personalised recommendations