Multiscale, multimodal analysis of tumor heterogeneity in IDH1 mutant vs wild-type diffuse gliomas
Autoři:
Michael E. Berens aff001; Anup Sood aff002; Jill S. Barnholtz-Sloan aff003; John F. Graf aff002; Sanghee Cho aff002; Seungchan Kim aff004; Jeffrey Kiefer aff001; Sara A. Byron aff001; Rebecca F. Halperin aff001; Sara Nasser aff001; Jonathan Adkins aff001; Lori Cuyugan aff001; Karen Devine aff003; Quinn Ostrom aff003; Marta Couce aff003; Leo Wolansky aff003; Elizabeth McDonough aff002; Shannon Schyberg aff002; Sean Dinn aff002; Andrew E. Sloan aff005; Michael Prados aff006; Joanna J. Phillips aff006; Sarah J. Nelson aff007; Winnie S. Liang aff001; Yousef Al-Kofahi aff002; Mirabela Rusu aff002; Maria I. Zavodszky aff002; Fiona Ginty aff002
Působiště autorů:
Translational Genomics Research Institute, Phoenix, AZ, United States of America
aff001; GE Research Center, Niskayuna, NY, United States of America
aff002; Department of Population and Quantitative Health Sciences and Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH, United States of America
aff003; Department of Electrical and Computer Engineering, Roy G. Perry College of Engineering, Prairie View A&M University, Prairie View, TX, United States of America
aff004; Department of Neurosurgery, University Hospitals-Seidman Cancer Center, Cleveland, OH, United States of America
aff005; Department of Neurological Surgery, Helen Diller Cancer Center, University of California San Francisco, San Francisco, CA, United States of America
aff006; Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, United States of America
aff007
Vyšlo v časopise:
PLoS ONE 14(12)
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pone.0219724
Souhrn
Glioma is recognized to be a highly heterogeneous CNS malignancy, whose diverse cellular composition and cellular interactions have not been well characterized. To gain new clinical- and biological-insights into the genetically-bifurcated IDH1 mutant (mt) vs wildtype (wt) forms of glioma, we integrated data from protein, genomic and MR imaging from 20 treatment-naïve glioma cases and 16 recurrent GBM cases. Multiplexed immunofluorescence (MxIF) was used to generate single cell data for 43 protein markers representing all cancer hallmarks, Genomic sequencing (exome and RNA (normal and tumor) and magnetic resonance imaging (MRI) quantitative features (protocols were T1-post, FLAIR and ADC) from whole tumor, peritumoral edema and enhancing core vs equivalent normal region were also collected from patients. Based on MxIF analysis, 85,767 cells (glioma cases) and 56,304 cells (GBM cases) were used to generate cell-level data for 24 biomarkers. K-means clustering was used to generate 7 distinct groups of cells with divergent biomarker profiles and deconvolution was used to assign RNA data into three classes. Spatial and molecular heterogeneity metrics were generated for the cell data. All features were compared between IDH mt and IDHwt patients and were finally combined to provide a holistic/integrated comparison. Protein expression by hallmark was generally lower in the IDHmt vs wt patients. Molecular and spatial heterogeneity scores for angiogenesis and cell invasion also differed between IDHmt and wt gliomas irrespective of prior treatment and tumor grade; these differences also persisted in the MR imaging features of peritumoral edema and contrast enhancement volumes. A coherent picture of enhanced angiogenesis in IDHwt tumors was derived from multiple platforms (genomic, proteomic and imaging) and scales from individual proteins to cell clusters and heterogeneity, as well as bulk tumor RNA and imaging features. Longer overall survival for IDH1mt glioma patients may reflect mutation-driven alterations in cellular, molecular, and spatial heterogeneity which manifest in discernable radiological manifestations.
Klíčová slova:
Cancer treatment – Glioma cells – Biomarkers – Magnetic resonance imaging – Protein expression – Glioma – Angiogenesis
Zdroje
1. Ostrom QT, Gittleman H, Truitt G, Boscia A, Kruchko C, Barnholtz-Sloan JS. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2011–2015. Neuro Oncol. 2018;20(suppl_4):iv1–iv86. doi: 10.1093/neuonc/noy131 30445539
2. Brennan CW, Verhaak RG, McKenna A, Campos B, Noushmehr H, Salama SR, et al. The somatic genomic landscape of glioblastoma. Cell. 2013;155(2):462–77. doi: 10.1016/j.cell.2013.09.034 24120142
3. Cancer Genome Atlas Research N, Brat DJ, Verhaak RG, Aldape KD, Yung WK, Salama SR, et al. Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas. N Engl J Med. 2015;372(26):2481–98. doi: 10.1056/NEJMoa1402121 26061751
4. Ceccarelli M, Barthel FP, Malta TM, Sabedot TS, Salama SR, Murray BA, et al. Molecular Profiling Reveals Biologically Discrete Subsets and Pathways of Progression in Diffuse Glioma. Cell. 2016;164(3):550–63. doi: 10.1016/j.cell.2015.12.028 26824661
5. Consortium G. Glioma through the looking GLASS: molecular evolution of diffuse gliomas and the Glioma Longitudinal Analysis Consortium. Neuro Oncol. 2018;20(7):873–84. doi: 10.1093/neuonc/noy020 29432615
6. Cancer Genome Atlas Research N. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature. 2008;455(7216):1061–8. doi: 10.1038/nature07385 18772890
7. Phillips HS, Kharbanda S, Chen R, Forrest WF, Soriano RH, Wu TD, et al. Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis. Cancer Cell. 2006;9(3):157–73. doi: 10.1016/j.ccr.2006.02.019 16530701
8. Bhat KPL, Balasubramaniyan V, Vaillant B, Ezhilarasan R, Hummelink K, Hollingsworth F, et al. Mesenchymal differentiation mediated by NF-kappaB promotes radiation resistance in glioblastoma. Cancer Cell. 2013;24(3):331–46. doi: 10.1016/j.ccr.2013.08.001 23993863
9. Hegi ME, Diserens AC, Godard S, Dietrich PY, Regli L, Ostermann S, et al. Clinical trial substantiates the predictive value of O-6-methylguanine-DNA methyltransferase promoter methylation in glioblastoma patients treated with temozolomide. Clin Cancer Res. 2004;10(6):1871–4. doi: 10.1158/1078-0432.ccr-03-0384 15041700
10. Noushmehr H, Weisenberger DJ, Diefes K, Phillips HS, Pujara K, Berman BP, et al. Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma. Cancer Cell. 2010;17(5):510–22. doi: 10.1016/j.ccr.2010.03.017 20399149
11. Yan H, Parsons DW, Jin G, McLendon R, Rasheed BA, Yuan W, et al. IDH1 and IDH2 mutations in gliomas. N Engl J Med. 2009;360(8):765–73. doi: 10.1056/NEJMoa0808710 19228619
12. Cimino PJ, Zager M, McFerrin L, Wirsching HG, Bolouri H, Hentschel B, et al. Multidimensional scaling of diffuse gliomas: application to the 2016 World Health Organization classification system with prognostically relevant molecular subtype discovery. Acta Neuropathol Commun. 2017;5(1):39. doi: 10.1186/s40478-017-0443-7 28532485
13. Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, et al. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol. 2016;131(6):803–20. doi: 10.1007/s00401-016-1545-1 27157931
14. Aum DJ, Kim DH, Beaumont TL, Leuthardt EC, Dunn GP, Kim AH. Molecular and cellular heterogeneity: the hallmark of glioblastoma. Neurosurg Focus. 2014;37(6):E11. doi: 10.3171/2014.9.FOCUS14521 25434380
15. Sottoriva A, Spiteri I, Piccirillo SG, Touloumis A, Collins VP, Marioni JC, et al. Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics. Proc Natl Acad Sci U S A. 2013;110(10):4009–14. doi: 10.1073/pnas.1219747110 23412337
16. Kumar A, Boyle EA, Tokita M, Mikheev AM, Sanger MC, Girard E, et al. Deep sequencing of multiple regions of glial tumors reveals spatial heterogeneity for mutations in clinically relevant genes. Genome Biol. 2014;15(12):530. doi: 10.1186/s13059-014-0530-z 25608559
17. Patel AP, Tirosh I, Trombetta JJ, Shalek AK, Gillespie SM, Wakimoto H, et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science. 2014;344(6190):1396–401. doi: 10.1126/science.1254257 24925914
18. Furnari FB, Cloughesy TF, Cavenee WK, Mischel PS. Heterogeneity of epidermal growth factor receptor signalling networks in glioblastoma. Nat Rev Cancer. 2015;15(5):302–10. doi: 10.1038/nrc3918 25855404
19. Morokoff A, Ng W, Gogos A, Kaye AH. Molecular subtypes, stem cells and heterogeneity: Implications for personalised therapy in glioma. J Clin Neurosci. 2015;22(8):1219–26. doi: 10.1016/j.jocn.2015.02.008 25957782
20. Ryu YJ, Choi SH, Park SJ, Yun TJ, Kim JH, Sohn CH. Glioma: application of whole-tumor texture analysis of diffusion-weighted imaging for the evaluation of tumor heterogeneity. PLoS One. 2014;9(9):e108335. doi: 10.1371/journal.pone.0108335 25268588
21. Gerdes MJ, Sevinsky CJ, Sood A, Adak S, Bello MO, Bordwell A, et al. Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue. Proc Natl Acad Sci U S A. 2013;110(29):11982–7. doi: 10.1073/pnas.1300136110 23818604
22. Kiefer J, Sara N, Graf JL, Kodira C, Ginty F, Newberg L, et al. Hallmarks of Cancer Gene Set Annotation2017.
23. Bai HX, Lee AM, Yang L, Zhang P, Davatzikos C, Maris JM, et al. Imaging genomics in cancer research: limitations and promises. Br J Radiol. 2016;89(1061):20151030. doi: 10.1259/bjr.20151030 26864054
24. Ellingson BM. Radiogenomics and imaging phenotypes in glioblastoma: novel observations and correlation with molecular characteristics. Curr Neurol Neurosci Rep. 2015;15(1):506. doi: 10.1007/s11910-014-0506-0 25410316
25. Pinker K, Shitano F, Sala E, Do RK, Young RJ, Wibmer AG, et al. Background, current role, and potential applications of radiogenomics. J Magn Reson Imaging. 2018;47(3):604–20. doi: 10.1002/jmri.25870 29095543
26. Sala E, Mema E, Himoto Y, Veeraraghavan H, Brenton JD, Snyder A, et al. Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging. Clin Radiol. 2017;72(1):3–10. doi: 10.1016/j.crad.2016.09.013 27742105
27. Carrillo JA, Lai A, Nghiemphu PL, Kim HJ, Phillips HS, Kharbanda S, et al. Relationship between tumor enhancement, edema, IDH1 mutational status, MGMT promoter methylation, and survival in glioblastoma. AJNR Am J Neuroradiol. 2012;33(7):1349–55. doi: 10.3174/ajnr.A2950 22322613
28. Park YW, Han K, Ahn SS, Bae S, Choi YS, Chang JH, et al. Prediction of IDH1-Mutation and 1p/19q-Codeletion Status Using Preoperative MR Imaging Phenotypes in Lower Grade Gliomas. AJNR Am J Neuroradiol. 2018;39(1):37–42. doi: 10.3174/ajnr.A5421 29122763
29. Su CQ, Lu SS, Zhou MD, Shen H, Shi HB, Hong XN. Combined texture analysis of diffusion-weighted imaging with conventional MRI for non-invasive assessment of IDH1 mutation in anaplastic gliomas. Clin Radiol. 2018.
30. Li ZC, Bai H, Sun Q, Zhao Y, Lv Y, Zhou J, et al. Multiregional radiomics profiling from multiparametric MRI: Identifying an imaging predictor of IDH1 mutation status in glioblastoma. Cancer Med. 2018;7(12):5999–6009. doi: 10.1002/cam4.1863 30426720
31. Chang K, Bai HX, Zhou H, Su C, Bi WL, Agbodza E, et al. Residual Convolutional Neural Network for the Determination of IDH Status in Low- and High-Grade Gliomas from MR Imaging. Clin Cancer Res. 2018;24(5):1073–81. doi: 10.1158/1078-0432.CCR-17-2236 29167275
32. Kickingereder P, Sahm F, Radbruch A, Wick W, Heiland S, Deimling A, et al. IDH mutation status is associated with a distinct hypoxia/angiogenesis transcriptome signature which is non-invasively predictable with rCBV imaging in human glioma. Sci Rep. 2015;5:16238. doi: 10.1038/srep16238 26538165
33. Byron SA, Tran NL, Halperin RF, Phillips JJ, Kuhn JG, de Groot JF, et al. Prospective Feasibility Trial for Genomics-Informed Treatment in Recurrent and Progressive Glioblastoma. Clin Cancer Res. 2018;24(2):295–305. doi: 10.1158/1078-0432.CCR-17-0963 29074604
34. Hangauer MJ, Viswanathan VS, Ryan MJ, Bole D, Eaton JK, Matov A, et al. Drug-tolerant persister cancer cells are vulnerable to GPX4 inhibition. Nature. 2017;551(7679):247–50. doi: 10.1038/nature24297 29088702
35. Viswanathan VS, Ryan MJ, Dhruv HD, Gill S, Eichhoff OM, Seashore-Ludlow B, et al. Dependency of a therapy-resistant state of cancer cells on a lipid peroxidase pathway. Nature. 2017;547(7664):453–7. doi: 10.1038/nature23007 28678785
36. Xie Y, Hou W, Song X, Yu Y, Huang J, Sun X, et al. Ferroptosis: process and function. Cell Death Differ. 2016;23(3):369–79. doi: 10.1038/cdd.2015.158 26794443
37. Padfield D, Rittscher J, Roysam B. Coupled minimum-cost flow cell tracking for high-throughput quantitative analysis. Med Image Anal. 2011;15(4):650–68. doi: 10.1016/j.media.2010.07.006 20864383
38. Halperin RF, Carpten JD, Manojlovic Z, Aldrich J, Keats J, Byron S, et al. A method to reduce ancestry related germline false positives in tumor only somatic variant calling. BMC Med Genomics. 2017;10(1):61. doi: 10.1186/s12920-017-0296-8 29052513
39. Aran D, Butte AJ. Digitally deconvolving the tumor microenvironment. Genome Biol. 2016;17(1):175. doi: 10.1186/s13059-016-1036-7 27549319
40. Teschendorff AE, Breeze CE, Zheng SC, Beck S. A comparison of reference-based algorithms for correcting cell-type heterogeneity in Epigenome-Wide Association Studies. BMC Bioinformatics. 2017;18(1):105. doi: 10.1186/s12859-017-1511-5 28193155
41. Newberg LA, Chen X, Kodira CD, Zavodszky MI. Computational de novo discovery of distinguishing genes for biological processes and cell types in complex tissues. PLoS One. 2018;13(3):e0193067. doi: 10.1371/journal.pone.0193067 29494600
42. Gaujoux R, Seoighe C. CellMix: a comprehensive toolbox for gene expression deconvolution. Bioinformatics. 2013;29(17):2211–2. doi: 10.1093/bioinformatics/btt351 23825367
43. Graf JF, Zavodszky MI. Characterizing the heterogeneity of tumor tissues from spatially resolved molecular measures. PLoS One. 2017;12(11):e0188878. doi: 10.1371/journal.pone.0188878 29190747
44. Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, et al. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Trans Med Imaging. 2015;34(10):1993–2024. doi: 10.1109/TMI.2014.2377694 25494501
45. Team RC. R: A language and environment for statistical computing 2014 [Available from: http://www.r-project.org/.
46. Alcantara Llaguno SR, Wang Z, Sun D, Chen J, Xu J, Kim E, et al. Adult Lineage-Restricted CNS Progenitors Specify Distinct Glioblastoma Subtypes. Cancer Cell. 2015;28(4):429–40. doi: 10.1016/j.ccell.2015.09.007 26461091
47. Alcantara Llaguno SR, Xie X, Parada LF. Cell of Origin and Cancer Stem Cells in Tumor Suppressor Mouse Models of Glioblastoma. Cold Spring Harb Symp Quant Biol. 2016;81:31–6. doi: 10.1101/sqb.2016.81.030973 27815542
48. Kosti I, Jain N, Aran D, Butte AJ, Sirota M. Cross-tissue Analysis of Gene and Protein Expression in Normal and Cancer Tissues. Sci Rep. 2016;6:24799. doi: 10.1038/srep24799 27142790
49. Maier T, Guell M, Serrano L. Correlation of mRNA and protein in complex biological samples. FEBS Lett. 2009;583(24):3966–73. doi: 10.1016/j.febslet.2009.10.036 19850042
50. Akan P, Alexeyenko A, Costea PI, Hedberg L, Solnestam BW, Lundin S, et al. Comprehensive analysis of the genome transcriptome and proteome landscapes of three tumor cell lines. Genome Med. 2012;4(11):86. doi: 10.1186/gm387 23158748
51. Wasserman JK, Nicholas G, Yaworski R, Wasserman AM, Woulfe JM, Jansen GH, et al. Radiological and pathological features associated with IDH1-R132H mutation status and early mortality in newly diagnosed anaplastic astrocytic tumours. PLoS One. 2015;10(4):e0123890. doi: 10.1371/journal.pone.0123890 25849605
52. Semov A, Moreno MJ, Onichtchenko A, Abulrob A, Ball M, Ekiel I, et al. Metastasis-associated protein S100A4 induces angiogenesis through interaction with Annexin II and accelerated plasmin formation. J Biol Chem. 2005;280(21):20833–41. doi: 10.1074/jbc.M412653200 15788416
53. Basagiannis D, Zografou S, Murphy C, Fotsis T, Morbidelli L, Ziche M, et al. VEGF induces signalling and angiogenesis by directing VEGFR2 internalisation through macropinocytosis. J Cell Sci. 2016;129(21):4091–104. doi: 10.1242/jcs.188219 27656109
54. Wu CX, Lin GS, Lin ZX, Zhang JD, Liu SY, Zhou CF. Peritumoral edema shown by MRI predicts poor clinical outcome in glioblastoma. World J Surg Oncol. 2015;13:97. doi: 10.1186/s12957-015-0496-7 25886608
55. Tong L, Yi L, Liu P, Abeysekera IR, Hai L, Li T, et al. Tumour cell dormancy as a contributor to the reduced survival of GBM patients who received standard therapy. Oncol Rep. 2018;40(1):463–71. doi: 10.3892/or.2018.6425 29749548
56. Wu T, Li Y, Liu B, Zhang S, Wu L, Zhu X, et al. Expression of Ferritin Light Chain (FTL) Is Elevated in Glioblastoma, and FTL Silencing Inhibits Glioblastoma Cell Proliferation via the GADD45/JNK Pathway. PLoS One. 2016;11(2):e0149361. doi: 10.1371/journal.pone.0149361 26871431
57. Friedmann-Morvinski D. Glioblastoma heterogeneity and cancer cell plasticity. Crit Rev Oncog. 2014;19(5):327–36. doi: 10.1615/critrevoncog.2014011777 25404148
58. Meyer M, Reimand J, Lan X, Head R, Zhu X, Kushida M, et al. Single cell-derived clonal analysis of human glioblastoma links functional and genomic heterogeneity. Proc Natl Acad Sci U S A. 2015;112(3):851–6. doi: 10.1073/pnas.1320611111 25561528
59. Chen KH, Boettiger AN, Moffitt JR, Wang S, Zhuang X. RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells. Science. 2015;348(6233):aaa6090. doi: 10.1126/science.aaa6090 25858977
60. Shah S, Lubeck E, Zhou W, Cai L. In Situ Transcription Profiling of Single Cells Reveals Spatial Organization of Cells in the Mouse Hippocampus. Neuron. 2016;92(2):342–57. doi: 10.1016/j.neuron.2016.10.001 27764670
61. Stahl PL, Salmen F, Vickovic S, Lundmark A, Navarro JF, Magnusson J, et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science. 2016;353(6294):78–82. doi: 10.1126/science.aaf2403 27365449
62. Chen Z, Hambardzumyan D. Immune Microenvironment in Glioblastoma Subtypes. Front Immunol. 2018;9:1004. doi: 10.3389/fimmu.2018.01004 29867979
63. Dey MF, H.; Heimberger A.B. The Role of Glioma Microenvironment in Immune Modulation: Potential Targets for Intervention. Letters in Drug Design & Discovery. 2006;3(7):11.
64. Inda MM, Bonavia R, Mukasa A, Narita Y, Sah DW, Vandenberg S, et al. Tumor heterogeneity is an active process maintained by a mutant EGFR-induced cytokine circuit in glioblastoma. Genes Dev. 2010;24(16):1731–45. doi: 10.1101/gad.1890510 20713517
65. Inda MM, Bonavia R, Seoane J. Glioblastoma multiforme: a look inside its heterogeneous nature. Cancers (Basel). 2014;6(1):226–39.
66. Kohanbash G, Carrera DA, Shrivastav S, Ahn BJ, Jahan N, Mazor T, et al. Isocitrate dehydrogenase mutations suppress STAT1 and CD8+ T cell accumulation in gliomas. J Clin Invest. 2017;127(4):1425–37. doi: 10.1172/JCI90644 28319047
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