Sequencing and curation strategies for identifying candidate glioblastoma treatments Journal Article
| Authors: | Frank, M. O.; Koyama, T.; Rhrissorrakrai, K.; Robine, N.; Utro, F.; Emde, A. K.; Chen, B. J.; Arora, K.; Shah, M.; Geiger, H.; Felice, V.; Dikoglu, E.; Rahman, S.; Fang, X.; Vacic, V.; Bergmann, E. A.; Moore Vogel, J. L.; Reeves, C.; Khaira, D.; Calabro, A.; Kim, D.; Lamendola-Essel, M. F.; Esteves, C.; Agius, P.; Stolte, C.; Boockvar, J.; Demopoulos, A.; Placantonakis, D. G.; Golfinos, J. G.; Brennan, C.; Bruce, J.; Lassman, A. B.; Canoll, P.; Grommes, C.; Daras, M.; Diamond, E.; Omuro, A.; Pentsova, E.; Orange, D. E.; Harvey, S. J.; Posner, J. B.; Michelini, V. V.; Jobanputra, V.; Zody, M. C.; Kelly, J.; Parida, L.; Wrzeszczynski, K. O.; Royyuru, A. K.; Darnell, R. B. |
| Article Title: | Sequencing and curation strategies for identifying candidate glioblastoma treatments |
| Abstract: | 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. © 2019 The Author(s). |
| Journal Title: | BMC Medical Genomics |
| Volume: | 12 |
| ISSN: | 1755-8794 |
| Publisher: | Biomed Central Ltd |
| Date Published: | 2019-04-25 |
| Start Page: | 56 |
| Language: | English |
| DOI: | 10.1186/s12920-019-0500-0 |
| PUBMED: | 31023376 |
| PROVIDER: | scopus |
| PMCID: | PMC6485090 |
| DOI/URL: | |
| Notes: | Correction issued, see DOI: 10.1186/s12920-019-0563-y -- Article -- Source: Scopus |
Altmetric
Citation Impact
BMJ Impact Analytics
Related MSK Work