Sequencing and curation strategies for identifying candidate glioblastoma treatments.
Journal
BMC medical genomics
ISSN: 1755-8794
Titre abrégé: BMC Med Genomics
Pays: England
ID NLM: 101319628
Informations de publication
Date de publication:
25 04 2019
25 04 2019
Historique:
received:
11
09
2018
accepted:
28
03
2019
entrez:
27
4
2019
pubmed:
27
4
2019
medline:
31
12
2019
Statut:
epublish
Résumé
Prompted by the revolution in high-throughput sequencing and its potential impact for treating cancer patients, we initiated a clinical research study to compare the ability of different sequencing assays and analysis methods to analyze glioblastoma tumors and generate real-time potential treatment options for physicians. A consortium of seven institutions in New York City enrolled 30 patients with glioblastoma and performed tumor whole genome sequencing (WGS) and RNA sequencing (RNA-seq; collectively WGS/RNA-seq); 20 of these patients were also analyzed with independent targeted panel sequencing. We also compared results of expert manual annotations with those from an automated annotation system, Watson Genomic Analysis (WGA), to assess the reliability and time required to identify potentially relevant pharmacologic interventions. WGS/RNAseq identified more potentially actionable clinical results than targeted panels in 90% of cases, with an average of 16-fold more unique potentially actionable variants identified per individual; 84 clinically actionable calls were made using WGS/RNA-seq that were not identified by panels. Expert annotation and WGA had good agreement on identifying variants [mean sensitivity = 0.71, SD = 0.18 and positive predictive value (PPV) = 0.80, SD = 0.20] and drug targets when the same variants were called (mean sensitivity = 0.74, SD = 0.34 and PPV = 0.79, SD = 0.23) across patients. Clinicians used the information to modify their treatment plan 10% of the time. These results present the first comprehensive comparison of technical and machine augmented analysis of targeted panel and WGS/RNA-seq to identify potential cancer treatments.
Sections du résumé
BACKGROUND
Prompted by the revolution in high-throughput sequencing and its potential impact for treating cancer patients, we initiated a clinical research study to compare the ability of different sequencing assays and analysis methods to analyze glioblastoma tumors and generate real-time potential treatment options for physicians.
METHODS
A consortium of seven institutions in New York City enrolled 30 patients with glioblastoma and performed tumor whole genome sequencing (WGS) and RNA sequencing (RNA-seq; collectively WGS/RNA-seq); 20 of these patients were also analyzed with independent targeted panel sequencing. We also compared results of expert manual annotations with those from an automated annotation system, Watson Genomic Analysis (WGA), to assess the reliability and time required to identify potentially relevant pharmacologic interventions.
RESULTS
WGS/RNAseq identified more potentially actionable clinical results than targeted panels in 90% of cases, with an average of 16-fold more unique potentially actionable variants identified per individual; 84 clinically actionable calls were made using WGS/RNA-seq that were not identified by panels. Expert annotation and WGA had good agreement on identifying variants [mean sensitivity = 0.71, SD = 0.18 and positive predictive value (PPV) = 0.80, SD = 0.20] and drug targets when the same variants were called (mean sensitivity = 0.74, SD = 0.34 and PPV = 0.79, SD = 0.23) across patients. Clinicians used the information to modify their treatment plan 10% of the time.
CONCLUSION
These results present the first comprehensive comparison of technical and machine augmented analysis of targeted panel and WGS/RNA-seq to identify potential cancer treatments.
Identifiants
pubmed: 31023376
doi: 10.1186/s12920-019-0500-0
pii: 10.1186/s12920-019-0500-0
pmc: PMC6485090
doi:
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
56Subventions
Organisme : NCATS NIH HHS
ID : UL1 TR000043
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR001863
Pays : United States
Organisme : Howard Hughes Medical Institute
Pays : United States
Organisme : NCI NIH HHS
ID : P30 CA008748
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS102665
Pays : United States
Organisme : NINDS NIH HHS
ID : R21 NS088775
Pays : United States
Organisme : NINDS NIH HHS
ID : R21 NS087241
Pays : United States
Organisme : NINDS NIH HHS
ID : R03 NS087349
Pays : United States
Commentaires et corrections
Type : ErratumIn
Références
Oncotarget. 2016 Apr 19;7(16):21556-69
pubmed: 26933808
Nat Genet. 2015 Mar;47(3):257-62
pubmed: 25642631
Cancer Cell. 2017 Jul 10;32(1):42-56.e6
pubmed: 28697342
J Neurooncol. 2012 Apr;107(2):359-64
pubmed: 22045118
Genet Med. 2018 Oct;20(10):1196-1205
pubmed: 29388947
Sci Rep. 2016 Sep 13;6:32992
pubmed: 27623107
Sci Transl Med. 2017 Apr 19;9(386):
pubmed: 28424332
Drug Discov Today. 2015 Dec;20(12):1422-8
pubmed: 26320725
Clin Cancer Res. 2009 Jul 15;15(14):4622-9
pubmed: 19584161
Nat Commun. 2013;4:2185
pubmed: 23887589
Nature. 2013 Aug 22;500(7463):415-21
pubmed: 23945592
Neuro Oncol. 2015 Aug;17(8):1043-5
pubmed: 25964311
Bioinformatics. 2012 Sep 15;28(18):i333-i339
pubmed: 22962449
Cancer Res. 1997 Oct 1;57(19):4183-6
pubmed: 9331071
PLoS One. 2012;7(3):e33684
pubmed: 22479427
Annu Rev Pathol. 2009;4:127-50
pubmed: 18767981
Cancer Cell. 2015 Jan 12;27(1):15-26
pubmed: 25584892
Nature. 2012 Jul 26;487(7408):500-4
pubmed: 22763439
Cancer Discov. 2017 Aug;7(8):818-831
pubmed: 28572459
Oncologist. 2018 Feb;23(2):179-185
pubmed: 29158372
Mol Cancer Ther. 2012 Feb;11(2):317-28
pubmed: 22188813
Cancer Cell. 2010 Jan 19;17(1):98-110
pubmed: 20129251
Nucleic Acids Res. 2016 Jul 27;44(13):6274-86
pubmed: 27260798
N Engl J Med. 2015 Jun 25;372(26):2509-20
pubmed: 26028255
Nat Genet. 2016 Jul;48(7):768-76
pubmed: 27270107
Neurol Genet. 2017 Jul 11;3(4):e164
pubmed: 28740869
Cancer. 2014 Jan 1;120(1):77-85
pubmed: 24108668
Nat Med. 2014 Nov;20(11):1220-1
pubmed: 25375911
Nat Med. 2017 Jun;23(6):703-713
pubmed: 28481359
Mol Cell Biol. 2014 Oct;34(20):3843-54
pubmed: 25113560
Brain Tumor Pathol. 2014 Jul;31(3):172-6
pubmed: 24894018
Clin Cancer Res. 2012 Jun 15;18(12):3218-22
pubmed: 22679179
Nature. 2008 Nov 27;456(7221):470-6
pubmed: 18978772
Sci Transl Med. 2015 Apr 15;7(283):283ra53
pubmed: 25877891
Cancer Discov. 2016 Sep;6(9):963-71
pubmed: 27325282
Acta Neuropathol Commun. 2016 Aug 08;4(1):79
pubmed: 27503138
Science. 2014 Feb 28;343(6174):1010-4
pubmed: 24578576
Oncol Rep. 2013 Nov;30(5):2020-6
pubmed: 23970376
Cell. 2016 Jan 28;164(3):550-63
pubmed: 26824661
Front Oncol. 2015 Nov 30;5:259
pubmed: 26649278
Neuro Oncol. 2015 Aug;17(8):1051-63
pubmed: 25934816
Am J Hum Genet. 2016 Jan 7;98(1):58-74
pubmed: 26749308
J Natl Cancer Inst. 2001 Aug 15;93(16):1246-56
pubmed: 11504770
Cancer Res. 2006 Apr 15;66(8):3987-91
pubmed: 16618716
Proc Natl Acad Sci U S A. 2010 Jun 22;107(25):11501-6
pubmed: 20534551
J Mol Diagn. 2018 Nov;20(6):822-835
pubmed: 30138725
Int J Cancer. 2016 Jul 15;139(2):414-23
pubmed: 26914704
Nature. 2014 Apr 3;508(7494):118-22
pubmed: 24670642
Cancer Discov. 2015 Feb;5(2):143-53
pubmed: 25472943
Trends Cancer. 2016 Aug;2(8):392-395
pubmed: 28741491
Proc Natl Acad Sci U S A. 2015 Apr 28;112(17):5473-8
pubmed: 25827230
Clin Cancer Res. 2007 Apr 1;13(7):2038-45
pubmed: 17404084
Methods Mol Biol. 2018;1741:1-29
pubmed: 29392687
J Clin Pharmacol. 2016 Dec;56(12):1484-1499
pubmed: 27197880
Science. 2014 Jan 10;343(6167):189-193
pubmed: 24336570
Neuro Oncol. 2017 Mar 1;19(3):394-404
pubmed: 27571882
J Mol Diagn. 2015 May;17(3):251-64
pubmed: 25801821
BMC Mol Biol. 2006 Jan 31;7:3
pubmed: 16448564
Cell Syst. 2015 Sep 23;1(3):210-223
pubmed: 26645048