Abstract
Introduction
Multidisciplinary studies for glial tumors has produced an enormous amount of information including imaging, histology, and a large cohort of molecular data (i.e. genomics, epigenomics, metabolomics, proteomics, etc.). The big data resources are made possible through open access that offers great potential for new biomarker or therapeutic intervention via deep-learning and/or machine learning for integrated multi-omics analysis. An equally important effort to define the hallmarks of glial tumors will also advance precision neuro-oncology and inform patient-specific therapeutics. This review summarizes past studies regarding tumor classification, hallmarks of cancer, and hypothetical mechanisms. Leveraging on advanced big data approaches and ongoing cross-disciplinary endeavors, this review also discusses how to integrate multiple layers of big data toward the goal of precision medicine.
Results
In addition to basic research of cancer biology, the results from integrated multi-omics analysis will highlight biological processes and potential candidates as biomarkers or therapeutic targets. Ultimately, these collective resources built upon an armamentarium of accessible data can re-form clinical and molecular data to stratify patient-tailored therapy.
Conclusion
We envision that a comprehensive understanding of the link between molecular signatures, tumor locations, and patients’ history will identify a molecular taxonomy of glial tumors to advance the improvements in early diagnosis, prevention, and treatment.
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References
Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell 144(5):646–674
Negrini S, Gorgoulis VG, Halazonetis TD (2010) Genomic instability–an evolving hallmark of cancer. Nat Rev 11(3):220–228
Adams JM, Cory S (2007) The Bcl-2 apoptotic switch in cancer development and therapy. Oncogene 26(9):1324–1337
Levine B, Kroemer G (2008) Autophagy in the pathogenesis of disease. Cell 132(1):27–42
Levine B, Kroemer G (2008) SnapShot: macroautophagy. Cell 132(1):162 e1
Mizushima N (2007) Autophagy: process and function. Genes Dev 21(22):2861–2873
Hanahan D, Folkman J (1996) Patterns and emerging mechanisms of the angiogenic switch during tumorigenesis. Cell 86(3):353–364
Talmadge JE, Fidler IJ (2010) AACR centennial series: the biology of cancer metastasis: historical perspective. Cancer Res 70(14):5649–5669
Dvorak HF (1986) Tumors: wounds that do not heal. Similarities between tumor stroma generation and wound healing. New Engl J Med 315(26):1650–1659
Warburg O (1956) On respiratory impairment in cancer cells. Science 124(3215):269–270
Warburg O (1956) On the origin of cancer cells. Science 123(3191):309–314
Vajdic CM, van Leeuwen MT (2009) Cancer incidence and risk factors after solid organ transplantation. Int J Cancer 125(8):1747–1754
Yang L, Pang Y, Moses HL (2010) TGF-beta and immune cells: an important regulatory axis in the tumor microenvironment and progression. Trends Immunol 31(6):220–227
Shields JD, Kourtis IC, Tomei AA, Roberts JM, Swartz MA (2010) Induction of lymphoidlike stroma and immune escape by tumors that express the chemokine CCL21. Science 328(5979):749–752
Li B, Severson E, Pignon JC et al (2016) Comprehensive analyses of tumor immunity: implications for cancer immunotherapy. Genome Biol 17(1):174
Quail DF, Joyce JA (2017) The microenvironmental landscape of brain tumors. Cancer Cell 31(3):326–341
Wang Q, Hu B, Hu X et al (2017) Tumor evolution of glioma-intrinsic gene expression subtypes associates with immunological changes in the microenvironment. Cancer Cell 32(1):42
Soeda A, Hara A, Kunisada T, Yoshimura S, Iwama T, Park DM (2015) The evidence of glioblastoma heterogeneity. Sci Rep 5:7979
Lin CA, Rhodes CT, Lin C, Phillips JJ, Berger MS (2017) Comparative analyses identify molecular signature of MRI-classified SVZ-associated glioblastoma. Cell Cycle 16(8):765–775
Wesseling P, Jacques TS (2018) Taxonomy of CNS tumours; a series of three short reviews on the WHO 2016 classification and beyond. Neuropathol Appl Neurobiol 44(2):137–138
Wesseling P, Capper D (2018) WHO 2016 classification of gliomas. Neuropathol Appl Neurobiol 44(2):139–150
Lee E, Yong RL, Paddison P, Zhu J (2018) Comparison of glioblastoma (GBM) molecular classification methods. Semin Cancer Biol 53:201–211
Stichel D, Ebrahimi A, Reuss D et al (2018) Distribution of EGFR amplification, combined chromosome 7 gain and chromosome 10 loss, and TERT promoter mutation in brain tumors and their potential for the reclassification of IDHwt astrocytoma to glioblastoma. Acta Neuropathol 136(5):793–803
Shirahata M, Ono T, Stichel D et al (2018) Novel, improved grading system(s) for IDH-mutant astrocytic gliomas. Acta Neuropathol 136(1):153–166
Louis DN, Perry A, Reifenberger G et al (2016) The 2016 World health organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 131(6):803–820
Brat DJ, Aldape K, Colman H et al (2018) cIMPACT-NOW update 3: recommended diagnostic criteria for “Diffuse astrocytic glioma, IDH-wildtype, with molecular features of glioblastoma, WHO grade IV”. Acta Neuropathol 136(5):805–810
Verhaak RG, Hoadley KA, Purdom E et al (2010) Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 17(1):98–110
Patel AP, Tirosh I, Trombetta JJ et al (2014) Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344(6190):1396–1401
Haskins WE, Zablotsky BL, Foret MR et al (2013) Molecular characteristics in MRI-classified group 1 glioblastoma multiforme. Front Oncol 3:182
Gollapalli K, Ghantasala S, Kumar S et al (2017) Subventricular zone involvement in Glioblastoma—A proteomic evaluation and clinicoradiological correlation. Sci Rep 7(1):1449
Cole RH, Tang SY, Siltanen CA et al (2017) Printed droplet microfluidics for on demand dispensing of picoliter droplets and cells. Proc Natl Acad Sci USA 114(33):8728–8733
Shahi P, Kim SC, Haliburton JR, Gartner ZJ, Abate AR (2017) Abseq: ultrahigh-throughput single cell protein profiling with droplet microfluidic barcoding. Sci Rep 7:44447
Mistry AM, Wooten DJ, Davis LT, Mobley BC, Quaranta V, Ihrie RA (2019) Ventricular-subventricular zone contact by glioblastoma is not associated with molecular signatures in bulk tumor data. Sci Rep 9(1):1842
Tandel GS, Biswas M, Kakde OG et al (2019) A review on a deep learning perspective in brain cancer classification. Cancers. 11(1):111
Gerstein M (2012) Genomics: ENCODE leads the way on big data. Nature 489(7415):208
Maher B (2012) ENCODE: the human encyclopaedia. Nature 489(7414):46–48
Xiong HY, Alipanahi B, Lee LJ et al (2015) RNA splicing. The human splicing code reveals new insights into the genetic determinants of disease. Science 347(6218):1254806
Nie D, Lu J, Zhang H et al (2019) Multi-channel 3D deep feature learning for survival time prediction of brain tumor patients using multi-modal neuroimages. Sci Rep 9(1):1103
Gilbertson RJ, Gutmann DH (2007) Tumorigenesis in the brain: location, location, location. Cancer Res 67(12):5579–5582
Zong H, Verhaak RG, Canoll P (2012) The cellular origin for malignant glioma and prospects for clinical advancements. Expert Rev Mol Diagn 12(4):383–394
Safa AR, Saadatzadeh MR, Cohen-Gadol AA, Pollok KE, Bijangi-Vishehsaraei K (2015) Glioblastoma stem cells (GSCs) epigenetic plasticity and interconversion between differentiated non-GSCs and GSCs. Genes Dis 2(2):152–163
Altmann C, Keller S, Schmidt MHH (2019) The role of SVZ stem cells in glioblastoma. Cancers 11(4):e448
Claus EB, Walsh KM, Wiencke JK et al (2015) Survival and low-grade glioma: the emergence of genetic information. Neurosurg Focus 38(1):E6
Ghinda CD, Duffau H (2017) Network plasticity and intraoperative mapping for personalized multimodal management of diffuse low-grade gliomas. Front Surg 4:3
Southwell DG, Hervey-Jumper SL, Perry DW, Berger MS (2016) Intraoperative mapping during repeat awake craniotomy reveals the functional plasticity of adult cortex. J Neurosurg 124(5):1460–1469
Hervey-Jumper SL, Berger MS (2016) Maximizing safe resection of low- and high-grade glioma. J Neuro-oncol 130(2):269–282
Herbet G, Maheu M, Costi E, Lafargue G, Duffau H (2016) Mapping neuroplastic potential in brain-damaged patients. Brain 139(Pt 3):829–844
Wrensch M, Minn Y, Chew T, Bondy M, Berger MS (2002) Epidemiology of primary brain tumors: current concepts and review of the literature. Neuro-oncology. 4(4):278–299
Choi HS, Shim YK, Kaye WE, Ryan PB (2006) Potential residential exposure to toxics release inventory chemicals during pregnancy and childhood brain cancer. Environ Health Perspect 114(7):1113–1118
McKean-Cowdin R, Calle EE, Peters JM et al (2009) Ambient air pollution and brain cancer mortality. Cancer Causes Control 20(9):1645–1651
Searles Nielsen S, McKean-Cowdin R, Farin FM, Holly EA, Preston-Martin S, Mueller BA (2010) Childhood brain tumors, residential insecticide exposure, and pesticide metabolism genes. Environ Health Perspect 118(1):144–149
Nordsborg RB, Meliker JR, Ersboll AK, Jacquez GM, Poulsen AH, Raaschou-Nielsen O (2014) Space-time clusters of breast cancer using residential histories: a Danish case-control study. BMC Cancer 14:255
Clarke MA, Joshu CE (2017) Early life exposures and adult cancer risk. Epidemiol Rev 39(1):11–27
White MC, Peipins LA, Holman DM (2016) Labeling cancer risk factors as lifestyle limits prevention activities across the life span. Pediatrics 138(Suppl 1):S95–S97
Holman DM, Ports KA, Buchanan ND et al (2016) The association between adverse childhood experiences and risk of cancer in adulthood: a systematic review of the literature. Pediatrics 138(Suppl 1):S81–S91
Holman DM, Buchanan ND (2016) Opportunities during early life for cancer prevention: highlights from a series of virtual meetings with experts. Pediatrics 138(Suppl 1):S3–S14
White MC, Holman DM, Massetti GM (2016) Foreword: cancer prevention can start early and last a lifetime. Pediatrics 138(Suppl 1):S1–S2
Acknowledgements
We acknowledge the members of UCSF, Brain Tumor Center and Lin’s laboratory for their comments and suggestions. Authors are grateful to Drs. Adam Abate at UCSF and Chenwei Lin at the Fred Hutchinson Cancer Research Center for their insightful discussion in multi-omics microfluidics and big data approach, respectively.
Funding
Funding supported by the SPORE grant P50CA097257 to MSB and the SCORE grant SC3GM112543 to CAL from the National Institutes of Health.
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Lin, CH.A., Berger, M.S. Advancing neuro-oncology of glial tumors from big data and multidisciplinary studies. J Neurooncol 146, 1–7 (2020). https://doi.org/10.1007/s11060-019-03369-8
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DOI: https://doi.org/10.1007/s11060-019-03369-8