Integrated molecular and multiparametric MRI mapping of high-grade glioma identifies regional biologic signatures.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
28 09 2023
28 09 2023
Historique:
received:
14
02
2023
accepted:
06
09
2023
medline:
2
10
2023
pubmed:
29
9
2023
entrez:
28
9
2023
Statut:
epublish
Résumé
Sampling restrictions have hindered the comprehensive study of invasive non-enhancing (NE) high-grade glioma (HGG) cell populations driving tumor progression. Here, we present an integrated multi-omic analysis of spatially matched molecular and multi-parametric magnetic resonance imaging (MRI) profiling across 313 multi-regional tumor biopsies, including 111 from the NE, across 68 HGG patients. Whole exome and RNA sequencing uncover unique genomic alterations to unresectable invasive NE tumor, including subclonal events, which inform genomic models predictive of geographic evolution. Infiltrative NE tumor is alternatively enriched with tumor cells exhibiting neuronal or glycolytic/plurimetabolic cellular states, two principal transcriptomic pathway-based glioma subtypes, which respectively demonstrate abundant private mutations or enrichment in immune cell signatures. These NE phenotypes are non-invasively identified through normalized K2 imaging signatures, which discern cell size heterogeneity on dynamic susceptibility contrast (DSC)-MRI. NE tumor populations predicted to display increased cellular proliferation by mean diffusivity (MD) MRI metrics are uniquely associated with EGFR amplification and CDKN2A homozygous deletion. The biophysical mapping of infiltrative HGG potentially enables the clinical recognition of tumor subpopulations with aggressive molecular signatures driving tumor progression, thereby informing precision medicine targeting.
Identifiants
pubmed: 37770427
doi: 10.1038/s41467-023-41559-1
pii: 10.1038/s41467-023-41559-1
pmc: PMC10539500
doi:
Substances chimiques
Biological Products
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
6066Informations de copyright
© 2023. Springer Nature Limited.
Références
Ostrom, Q. T. et al. CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2013-2017. Neuro Oncol. 22, iv1–iv96 (2020).
pubmed: 33123732
pmcid: 7596247
doi: 10.1093/neuonc/noaa200
Chang, P. D. et al. A multiparametric model for mapping cellularity in glioblastoma using radiographically localized biopsies. AJNR Am. J. Neuroradiol. 38, 890–898 (2017).
pubmed: 28255030
pmcid: 7960397
doi: 10.3174/ajnr.A5112
Weller, M. et al. EANO guidelines on the diagnosis and treatment of diffuse gliomas of adulthood. Nat. Rev. Clin. Oncol. 18, 170–186 (2021).
pubmed: 33293629
doi: 10.1038/s41571-020-00447-z
Louis, D. N. et al. The 2021 WHO classification of tumors of the central nervous system: a summary. Neuro Oncol. 23, 1231–1251 (2021).
pubmed: 34185076
pmcid: 8328013
doi: 10.1093/neuonc/noab106
Hernandez Martinez, A., Madurga, R., Garcia-Romero, N. & Ayuso-Sacido, A. Unravelling glioblastoma heterogeneity by means of single-cell RNA sequencing. Cancer Lett. 527, 66–79 (2022).
pubmed: 34902524
doi: 10.1016/j.canlet.2021.12.008
Barthel, F. P. et al. Longitudinal molecular trajectories of diffuse glioma in adults. Nature 576, 112–120 (2019).
pubmed: 31748746
pmcid: 6897368
doi: 10.1038/s41586-019-1775-1
Abou-El-Ardat, K. et al. Comprehensive molecular characterization of multifocal glioblastoma proves its monoclonal origin and reveals novel insights into clonal evolution and heterogeneity of glioblastomas. Neuro Oncol. 19, 546–557 (2017).
pubmed: 28201779
pmcid: 5464316
doi: 10.1093/neuonc/now231
Turajlic, S., Sottoriva, A., Graham, T. & Swanton, C. Resolving genetic heterogeneity in cancer. Nat. Rev. Genet. 20, 404–416 (2019).
pubmed: 30918367
doi: 10.1038/s41576-019-0114-6
Garofano, L. et al. Pathway-based classification of glioblastoma uncovers a mitochondrial subtype with therapeutic vulnerabilities. Nat. Cancer 2, 141–156 (2021).
pubmed: 33681822
pmcid: 7935068
doi: 10.1038/s43018-020-00159-4
Migliozzi, S. et al. Integrative multi-omics networks identify PKCdelta and DNA-PK as master kinases of glioblastoma subtypes and guide targeted cancer therapy. Nat. Cancer 4, 181–202 (2023).
pubmed: 36732634
pmcid: 9970878
doi: 10.1038/s43018-022-00510-x
Maia, A. C. Jr. et al. Stereotactic biopsy guidance in adults with supratentorial nonenhancing gliomas: role of perfusion-weighted magnetic resonance imaging. J. Neurosurg. 101, 970–976 (2004).
pubmed: 15597757
doi: 10.3171/jns.2004.101.6.0970
Donahue, K. M. et al. Utility of simultaneously acquired gradient-echo and spin-echo cerebral blood volume and morphology maps in brain tumor patients. Magn. Reson Med. 43, 845–853 (2000).
pubmed: 10861879
doi: 10.1002/1522-2594(200006)43:6<845::AID-MRM10>3.0.CO;2-J
Schmainda, K. M. et al. Characterization of a first-pass gradient-echo spin-echo method to predict brain tumor grade and angiogenesis. AJNR Am. J. Neuroradiol. 25, 1524–1532 (2004).
pubmed: 15502131
pmcid: 7976425
Hu, L. S. et al. Correlations between perfusion MR imaging cerebral blood volume, microvessel quantification, and clinical outcome using stereotactic analysis in recurrent high-grade glioma. AJNR Am. J. Neuroradiol. 33, 69–76 (2012).
pubmed: 22095961
pmcid: 7966183
doi: 10.3174/ajnr.A2743
Law, M. et al. Low-grade gliomas: dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging-prediction of patient clinical response. Radiology 238, 658–667 (2006).
pubmed: 16396838
doi: 10.1148/radiol.2382042180
Hu, L. S. et al. Reevaluating the imaging definition of tumor progression: perfusion MRI quantifies recurrent glioblastoma tumor fraction, pseudoprogression, and radiation necrosis to predict survival. Neuro Oncol. 14, 919–930 (2012).
pubmed: 22561797
pmcid: 3379799
doi: 10.1093/neuonc/nos112
Hu, L. S. et al. Relative cerebral blood volume values to differentiate high-grade glioma recurrence from posttreatment radiation effect: direct correlation between image-guided tissue histopathology and localized dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging measurements. AJNR Am. J. Neuroradiol. 30, 552–558 (2009).
pubmed: 19056837
pmcid: 7051449
doi: 10.3174/ajnr.A1377
Prah, M. A. et al. Spatial discrimination of glioblastoma and treatment effect with histologically-validated perfusion and diffusion magnetic resonance imaging metrics. J. Neurooncol. 136, 13–21 (2018).
pubmed: 28900832
doi: 10.1007/s11060-017-2617-3
Barajas, R. F. Jr. et al. Regional variation in histopathologic features of tumor specimens from treatment-naive glioblastoma correlates with anatomic and physiologic MR Imaging. Neuro Oncol. 14, 942–954 (2012).
pubmed: 22711606
pmcid: 3379808
doi: 10.1093/neuonc/nos128
Mohsen, L. A., Shi, V., Jena, R., Gillard, J. H. & Price, S. J. Diffusion tensor invasive phenotypes can predict progression-free survival in glioblastomas. Br. J. Neurosurg. 27, 436–441 (2013).
pubmed: 23445331
doi: 10.3109/02688697.2013.771136
Lu, V. M. et al. The prognostic significance of CDKN2A homozygous deletion in IDH-mutant lower-grade glioma and glioblastoma: a systematic review of the contemporary literature. J. Neurooncol. 148, 221–229 (2020).
pubmed: 32385699
doi: 10.1007/s11060-020-03528-2
Yang, R. R. et al. IDH mutant lower grade (WHO Grades II/III) astrocytomas can be stratified for risk by CDKN2A, CDK4 and PDGFRA copy number alterations. Brain Pathol. 30, 541–553 (2020).
pubmed: 31733156
doi: 10.1111/bpa.12801
Shirahata, M. et al. Novel, improved grading system(s) for IDH-mutant astrocytic gliomas. Acta Neuropathol. 136, 153–166 (2018).
pubmed: 29687258
doi: 10.1007/s00401-018-1849-4
Brito, C. et al. Clinical insights gained by refining the 2016 WHO classification of diffuse gliomas with: EGFR amplification, TERT mutations, PTEN deletion and MGMT methylation. BMC Cancer 19, 968 (2019).
pubmed: 31623593
pmcid: 6798410
doi: 10.1186/s12885-019-6177-0
Umphlett, M. et al. IDH-mutant astrocytoma with EGFR amplification-Genomic profiling in four cases and review of literature. Neurooncol Adv. 4, vdac067 (2022).
pubmed: 35669011
pmcid: 9159664
Binder, Z. A. et al. Epidermal growth factor receptor extracellular domain mutations in glioblastoma present opportunities for clinical imaging and therapeutic development. Cancer Cell 34, 163–177.e167 (2018).
pubmed: 29990498
pmcid: 6424337
doi: 10.1016/j.ccell.2018.06.006
Patel, P. et al. MR perfusion-weighted imaging in the evaluation of high-grade gliomas after treatment: a systematic review and meta-analysis. Neuro Oncol. 19, 118–127 (2017).
pubmed: 27502247
doi: 10.1093/neuonc/now148
Kern, M., Auer, T. A., Picht, T., Misch, M. & Wiener, E. T2 mapping of molecular subtypes of WHO grade II/III gliomas. BMC Neurol. 20, 8 (2020).
pubmed: 31914945
pmcid: 6947951
doi: 10.1186/s12883-019-1590-1
Broen, M. P. G. et al. The T2-FLAIR mismatch sign as an imaging marker for non-enhancing IDH-mutant, 1p/19q-intact lower-grade glioma: a validation study. Neuro Oncol. 20, 1393–1399 (2018).
pubmed: 29590424
pmcid: 6120363
doi: 10.1093/neuonc/noy048
Matsui, Y. et al. phyC: clustering cancer evolutionary trees. PLoS Comput. Biol. 13, e1005509 (2017).
pubmed: 28459850
pmcid: 5432190
doi: 10.1371/journal.pcbi.1005509
Korber, V. et al. Evolutionary trajectories of IDH(WT) glioblastomas reveal a common path of early tumorigenesis instigated years ahead of initial diagnosis. Cancer Cell 35, 692–704.e612 (2019).
pubmed: 30905762
doi: 10.1016/j.ccell.2019.02.007
Brennan, C. W. et al. The somatic genomic landscape of glioblastoma. Cell 155, 462–477 (2013).
pubmed: 24120142
pmcid: 3910500
doi: 10.1016/j.cell.2013.09.034
Snuderl, M. et al. Mosaic amplification of multiple receptor tyrosine kinase genes in glioblastoma. Cancer Cell 20, 810–817 (2011).
pubmed: 22137795
doi: 10.1016/j.ccr.2011.11.005
Little, S. E. et al. Receptor tyrosine kinase genes amplified in glioblastoma exhibit a mutual exclusivity in variable proportions reflective of individual tumor heterogeneity. Cancer Res. 72, 1614–1620 (2012).
pubmed: 22311673
doi: 10.1158/0008-5472.CAN-11-4069
Szerlip, N. J. et al. Intratumoral heterogeneity of receptor tyrosine kinases EGFR and PDGFRA amplification in glioblastoma defines subpopulations with distinct growth factor response. Proc. Natl Acad. Sci. USA 109, 3041–3046 (2012).
pubmed: 22323597
pmcid: 3286976
doi: 10.1073/pnas.1114033109
Verhaak, R. G. et al. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 17, 98–110 (2010).
pubmed: 20129251
pmcid: 2818769
doi: 10.1016/j.ccr.2009.12.020
Wang, J. et al. Clonal evolution of glioblastoma under therapy. Nat. Genet. 48, 768–776 (2016).
pubmed: 27270107
pmcid: 5627776
doi: 10.1038/ng.3590
Eskilsson, E. et al. EGFR heterogeneity and implications for therapeutic intervention in glioblastoma. Neuro Oncol. 20, 743–752 (2018).
pubmed: 29040782
doi: 10.1093/neuonc/nox191
Cancer Genome Atlas Research, N. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455, 1061–1068 (2008).
doi: 10.1038/nature07385
Sottoriva, A. et al. Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics. Proc. Natl Acad. Sci. USA 110, 4009–4014 (2013).
pubmed: 23412337
pmcid: 3593922
doi: 10.1073/pnas.1219747110
Munoz-Hidalgo, L. et al. Somatic copy number alterations are associated with EGFR amplification and shortened survival in patients with primary glioblastoma. Neoplasia 22, 10–21 (2020).
pubmed: 31751860
doi: 10.1016/j.neo.2019.09.001
Zhang, L. et al. Genomic analysis of primary and recurrent gliomas reveals clinical outcome related molecular features. Sci. Rep. 9, 16058 (2019).
pubmed: 31690770
pmcid: 6831607
doi: 10.1038/s41598-019-52515-9
Blomquist, M. R. et al. Temporospatial genomic profiling in glioblastoma identifies commonly altered core pathways underlying tumor progression. Neurooncol. Adv. 2, vdaa078 (2020).
pubmed: 32743548
pmcid: 7388612
Caravagna, G. et al. Detecting repeated cancer evolution from multi-region tumor sequencing data. Nat. Methods 15, 707–714 (2018).
pubmed: 30171232
pmcid: 6380470
doi: 10.1038/s41592-018-0108-x
Kim, H. et al. Whole-genome and multisector exome sequencing of primary and post-treatment glioblastoma reveals patterns of tumor evolution. Genome Res. 25, 316–327 (2015).
pubmed: 25650244
pmcid: 4352879
doi: 10.1101/gr.180612.114
Lee, J. K. et al. Spatiotemporal genomic architecture informs precision oncology in glioblastoma. Nat. Genet. 49, 594–599 (2017).
pubmed: 28263318
pmcid: 5627771
doi: 10.1038/ng.3806
Barkovich, A. J. Concepts of myelin and myelination in neuroradiology. AJNR Am. J. Neuroradiol. 21, 1099–1109 (2000).
pubmed: 10871022
pmcid: 7973874
Ostergaard, L. et al. High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part II: experimental comparison and preliminary results. Magn. Reson Med. 36, 726–736 (1996).
pubmed: 8916023
doi: 10.1002/mrm.1910360511
Calamante, F. et al. The physiological significance of the time-to-maximum (Tmax) parameter in perfusion MRI. Stroke 41, 1169–1174 (2010).
pubmed: 20413735
doi: 10.1161/STROKEAHA.110.580670
Calamante, F., Willats, L., Gadian, D. G. & Connelly, A. Bolus delay and dispersion in perfusion MRI: implications for tissue predictor models in stroke. Magn. Reson Med. 55, 1180–1185 (2006).
pubmed: 16598717
doi: 10.1002/mrm.20873
Bell, L. C. et al. Characterizing the influence of preload dosing on percent signal recovery (PSR) and cerebral blood volume (CBV) measurements in a patient population with high-grade glioma using dynamic susceptibility contrast MRI. Tomography 3, 89–95 (2017).
pubmed: 28825039
pmcid: 5557059
doi: 10.18383/j.tom.2017.00004
Semmineh, N. B. et al. Assessing tumor cytoarchitecture using multiecho DSC-MRI derived measures of the transverse relaxivity at tracer equilibrium (TRATE). Magn. Reson Med. 74, 772–784 (2015).
pubmed: 25227668
doi: 10.1002/mrm.25435
Boxerman, J. L., Schmainda, K. M. & Weisskoff, R. M. Relative cerebral blood volume maps corrected for contrast agent extravasation significantly correlate with glioma tumor grade, whereas uncorrected maps do not. AJNR Am. J. Neuroradiol. 27, 859–867 (2006).
pubmed: 16611779
pmcid: 8134002
Semmineh, N. B. et al. Optimization of acquisition and analysis methods for clinical dynamic susceptibility contrast MRI Using a population-based digital reference object. AJNR Am. J. Neuroradiol. 39, 1981–1988 (2018).
pubmed: 30309842
pmcid: 6239921
doi: 10.3174/ajnr.A5827
Stokes, A. M., Semmineh, N. B., Nespodzany, A., Bell, L. C. & Quarles, C. C. Systematic assessment of multi-echo dynamic susceptibility contrast MRI using a digital reference object. Magn. Reson Med. 83, 109–123 (2020).
pubmed: 31400035
doi: 10.1002/mrm.27914
Semmineh, N. B., Stokes, A. M., Bell, L. C., Boxerman, J. L. & Quarles, C. C. A population-based digital reference object (DRO) for optimizing dynamic susceptibility contrast (DSC)-MRI Methods for clinical trials. Tomography 3, 41–49 (2017).
pubmed: 28584878
pmcid: 5454781
doi: 10.18383/j.tom.2016.00286
Bell, L. C. et al. Evaluating the use of rCBV as a tumor grade and treatment response classifier across NCI quantitative imaging network sites: Part II of the DSC-MRI digital reference object (DRO) challenge. Tomography 6, 203–208 (2020).
pubmed: 32548297
pmcid: 7289259
doi: 10.18383/j.tom.2020.00012
Bell, L. C. et al. Evaluating multisite rCBV consistency from DSC-MRI imaging protocols and postprocessing software across the NCI quantitative imaging network sites using a digital reference object (DRO). Tomography 5, 110–117 (2019).
pubmed: 30854448
pmcid: 6403027
doi: 10.18383/j.tom.2018.00041
Molinaro, A. M. et al. Association of maximal extent of resection of contrast-enhanced and non-contrast-enhanced tumor with survival within molecular subgroups of patients with newly diagnosed glioblastoma. JAMA Oncol. 6, 495–503 (2020).
pubmed: 32027343
pmcid: 7042822
doi: 10.1001/jamaoncol.2019.6143
Milano, M. T. et al. Patterns and timing of recurrence after temozolomide-based chemoradiation for glioblastoma. Int. J. Radiat. Oncol. Biol. Phys. 78, 1147–1155 (2010).
pubmed: 20207495
doi: 10.1016/j.ijrobp.2009.09.018
Lasocki, A. & Gaillard, F. Non-contrast-enhancing tumor: a new frontier in glioblastoma research. AJNR Am. J. Neuroradiol. 40, 758–765 (2019).
pubmed: 30948373
pmcid: 7053910
doi: 10.3174/ajnr.A6025
Spiteri, I. et al. Evolutionary dynamics of residual disease in human glioblastoma. Ann. Oncol. 30, 456–463 (2019).
pubmed: 30452544
doi: 10.1093/annonc/mdy506
Barthel, F. P., Wesseling, P. & Verhaak, R. G. W. Reconstructing the molecular life history of gliomas. Acta Neuropathol. 135, 649–670 (2018).
pubmed: 29616301
pmcid: 5904231
doi: 10.1007/s00401-018-1842-y
Phillips, H. S. et al. Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis. Cancer Cell 9, 157–173 (2006).
pubmed: 16530701
doi: 10.1016/j.ccr.2006.02.019
Wang, Q. et al. Tumor evolution of glioma-intrinsic gene expression subtypes associates with immunological changes in the microenvironment. Cancer Cell 32, 42–56.e46 (2017).
pubmed: 28697342
pmcid: 5599156
doi: 10.1016/j.ccell.2017.06.003
An, Z. et al. EGFR cooperates with EGFRvIII to Recruit Macrophages in Glioblastoma. Cancer Res. 78, 6785–6794 (2018).
pubmed: 30401716
pmcid: 6295222
doi: 10.1158/0008-5472.CAN-17-3551
Saleem, H. et al. The TICking clock of EGFR therapy resistance in glioblastoma: target Independence or target Compensation. Drug Resist. Updat. 43, 29–37 (2019).
pubmed: 31054489
doi: 10.1016/j.drup.2019.04.002
Kim, J. et al. Spatiotemporal evolution of the primary glioblastoma genome. Cancer Cell 28, 318–328 (2015).
pubmed: 26373279
doi: 10.1016/j.ccell.2015.07.013
Varn, F. S. et al. Glioma progression is shaped by genetic evolution and microenvironment interactions. Cell 185, 2184–2199.e2116 (2022).
pubmed: 35649412
pmcid: 9189056
doi: 10.1016/j.cell.2022.04.038
Venkataramani, V. et al. Glioblastoma hijacks neuronal mechanisms for brain invasion. Cell 185, 2899–2917.e2831 (2022).
pubmed: 35914528
doi: 10.1016/j.cell.2022.06.054
Hu, L. S. et al. Radiogenomics to characterize regional genetic heterogeneity in glioblastoma. Neuro Oncol. 19, 128–137 (2017).
pubmed: 27502248
doi: 10.1093/neuonc/now135
Hu, L. S. et al. Multi-parametric MRI and TExture Analysis to Visualize Spatial Histologic Heterogeneity and Tumor Extent in Glioblastoma. PLoS ONE 10, e0141506 (2015).
pubmed: 26599106
pmcid: 4658019
doi: 10.1371/journal.pone.0141506
Hu, L. S. et al. Uncertainty quantification in the radiogenomics modeling of EGFR amplification in glioblastoma. Sci. Rep. 11, 3932 (2021).
pubmed: 33594116
pmcid: 7886858
doi: 10.1038/s41598-021-83141-z
Price, S. J. et al. Improved delineation of glioma margins and regions of infiltration with the use of diffusion tensor imaging: an image-guided biopsy study. AJNR Am. J. Neuroradiol. 27, 1969–1974 (2006).
pubmed: 17032877
pmcid: 7977915
Barajas, R. F. et al. Consensus recommendations for MRI and PET imaging of primary central nervous system lymphoma: guideline statement from the International Primary CNS Lymphoma Collaborative Group (IPCG). Neuro Oncol. 23, 1056–1071 (2021).
pubmed: 33560416
pmcid: 8248856
doi: 10.1093/neuonc/noab020
Hu, L. S. et al. Optimized preload leakage-correction methods to improve the diagnostic accuracy of dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging in posttreatment gliomas. AJNR Am. J. Neuroradiol. 31, 40–48 (2010).
pubmed: 19749223
pmcid: 4323177
doi: 10.3174/ajnr.A1787
Hoxworth, J. M. et al. Performance of standardized relative CBV for quantifying regional histologic tumor burden in recurrent high-grade glioma: comparison against normalized relative CBV using image-localized stereotactic biopsies. AJNR Am. J. Neuroradiol. 41, 408–415 (2020).
pubmed: 32165359
pmcid: 7077911
doi: 10.3174/ajnr.A6486
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
pubmed: 19451168
pmcid: 2705234
doi: 10.1093/bioinformatics/btp324
DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).
pubmed: 21478889
pmcid: 3083463
doi: 10.1038/ng.806
Lee, S. et al. NGSCheckMate: software for validating sample identity in next-generation sequencing studies within and across data types. Nucleic Acids Res. 45, e103 (2017).
pubmed: 28369524
pmcid: 5499645
doi: 10.1093/nar/gkx193
Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013).
pubmed: 23396013
pmcid: 3833702
doi: 10.1038/nbt.2514
Kendig, K. I. et al. Sentieon DNASeq variant calling workflow demonstrates strong computational performance and accuracy. Front. Genet. 10, 736 (2019).
pubmed: 31481971
pmcid: 6710408
doi: 10.3389/fgene.2019.00736
Koboldt, D. C. et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22, 568–576 (2012).
pubmed: 22300766
pmcid: 3290792
doi: 10.1101/gr.129684.111
Hiltemann, S., Jenster, G., Trapman, J., van der Spek, P. & Stubbs, A. Discriminating somatic and germline mutations in tumor DNA samples without matching normals. Genome Res. 25, 1382–1390 (2015).
pubmed: 26209359
pmcid: 4561496
doi: 10.1101/gr.183053.114
Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).
pubmed: 20601685
pmcid: 2938201
doi: 10.1093/nar/gkq603
D’Angelo, F. et al. The molecular landscape of glioma in patients with Neurofibromatosis 1. Nat. Med. 25, 176–187 (2019).
pubmed: 30531922
doi: 10.1038/s41591-018-0263-8
Riester, M. et al. PureCN: copy number calling and SNV classification using targeted short read sequencing. Source Code Biol. Med. 11, 13 (2016).
pubmed: 27999612
pmcid: 5157099
doi: 10.1186/s13029-016-0060-z
Mermel, C. H. et al. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. 12, R41 (2011).
pubmed: 21527027
pmcid: 3218867
doi: 10.1186/gb-2011-12-4-r41
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
pubmed: 23104886
doi: 10.1093/bioinformatics/bts635
Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).
pubmed: 24227677
doi: 10.1093/bioinformatics/btt656
Risso, D., Schwartz, K., Sherlock, G. & Dudoit, S. GC-content normalization for RNA-Seq data. BMC Bioinform. 12, 480 (2011).
doi: 10.1186/1471-2105-12-480
Zhang, Y., Parmigiani, G. & Johnson, W. E. ComBat-seq: batch effect adjustment for RNA-seq count data. NAR Genom. Bioinform. 2, lqaa078 (2020).
pubmed: 33015620
pmcid: 7518324
doi: 10.1093/nargab/lqaa078
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
pubmed: 19910308
doi: 10.1093/bioinformatics/btp616
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
pubmed: 16199517
pmcid: 1239896
doi: 10.1073/pnas.0506580102
Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).
pubmed: 22455463
pmcid: 3339379
doi: 10.1089/omi.2011.0118
Frattini, V. et al. A metabolic function of FGFR3-TACC3 gene fusions in cancer. Nature 553, 222–227 (2018).
pubmed: 29323298
pmcid: 5771419
doi: 10.1038/nature25171
Caruso, F. P. et al. A map of tumor-host interactions in glioma at single-cell resolution. Gigascience 9, giaa109 (2020).
Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).
pubmed: 21546393
pmcid: 3106198
doi: 10.1093/bioinformatics/btr260
Newman, A. M. et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat. Biotechnol. 37, 773–782 (2019).
pubmed: 31061481
pmcid: 6610714
doi: 10.1038/s41587-019-0114-2
Darmanis, S. et al. Single-cell RNA-Seq analysis of infiltrating neoplastic cells at the migrating front of human glioblastoma. Cell Rep. 21, 1399–1410 (2017).
pubmed: 29091775
pmcid: 5810554
doi: 10.1016/j.celrep.2017.10.030