Comparing NGS and NanoString platforms in peripheral blood mononuclear cell transcriptome profiling for advanced heart failure biomarker development
Main Article Content
Keywords
advanced heart failure, biomarker, NanoString, next generation sequencing, prediction test
Abstract
In preparation to create a clinical assay that predicts 1-year survival status of advanced heart failure (AdHF) patients before surgical/interventional therapies and to select the appropriate clinical assay platform for the future assay, we compared the properties of next generation sequencing (NGS) used in the gene discovery phase to the NanoString platform used in the clinical assay development phase. In 25 AdHF patients in a tertiary academic medical center from 2015 to 2016, PBMC samples were collected and aliquoted for NGS RNA whole transcriptome sequencing and compared to 770 genes represented on NanoString’s PanCancer IO 360 Gene Expression research panel. Prior to statistical analysis, NanoString and NGS expression values were log transformed. We computed Pearson correlation coefficients for each sample, comparing gene expression values between NanoString and NGS across the set of matched genes and for each of the matched genes across the set of samples. Genes were grouped by average NGS expression, and the NanoString-NGS correlation for each group was computed. Out of 770 genes from the NanoString panel, 734 overlapped between both platforms and showed high intrasample correlation. Within an individual sample, there was an expression-level dependent correlation between both platforms. The low- vs. intermediate/high-expression groups showed NGS average correlation 0.21 vs. 0.58–0.68, respectively, and NanoString average correlation 0.07–0.34 vs. 0.59–0.70, respectively. NanoString demonstrated high reproducibility (R2 > 0.99 for 100 ng input), sensitivity (probe counts between 100 and 500 detected and quantified), and robustness (similar gene signature scores across different RNA input concentrations, cartridges, and outcomes). Data from NGS and NanoString were highly correlated. These platforms play a meaningful, complementary role in the biomarker development process.
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References
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2. Group. BDW. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther. 2001;69(3):89-95.
3. Doehner W, Frenneaux M, Anker SD. Metabolic impairment in heart failure: the myocardial and systemic perspective. Journal of the American College of Cardiology. 2014;64(13):1388-400.
4. Yancy CW, Jessup M, Bozkurt B, Butler J, Casey DE, Jr., Colvin MM, et al. 2017 ACC/AHA/HFSA Focused Update of the 2013 ACCF/AHA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Failure Society of America. J Card Fail. 2017;23(8):628-51.
5. Yancy CW, Jessup M, Bozkurt B, Butler J, Casey DE, Jr., Drazner MH, et al. 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. Journal of the American College of Cardiology. 2013;62(16):e147-239.
6. Joseph SM, Rich MW. Targeting Frailty in Heart Failure. Current treatment options in cardiovascular medicine. 2017;19(4):31.
7. Levine B, Kalman J, Mayer L, Fillit HM, Packer M. Elevated circulating levels of tumor necrosis factor in severe chronic heart failure. The New England journal of medicine. 1990;323(4):236-41.
8. Deng MC, Dasch B, Erren M, Mollhoff T, Scheld HH. Impact of left ventricular dysfunction on cytokines, hemodynamics, and outcome in bypass grafting. The Annals of thoracic surgery. 1996;62(1):184-90.
9. Caruso R, Trunfio S, Milazzo F, Campolo J, De Maria R, Colombo T, et al. Early expression of pro- and anti-inflammatory cytokines in left ventricular assist device recipients with multiple organ failure syndrome. ASAIO journal. 2010;56(4):313-8.
10. Caruso R, Verde A, Cabiati M, Milazzo F, Boroni C, Del Ry S, et al. Association of pre-operative interleukin-6 levels with Interagency Registry for Mechanically Assisted Circulatory Support profiles and intensive care unit stay in left ventricular assist device patients. The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation. 2012;31(6):625-33.
11. Kaur K, Dhingra S, Slezak J, Sharma AK, Bajaj A, Singal PK. Biology of TNFalpha and IL-10, and their imbalance in heart failure. Heart failure reviews. 2009;14(2):113-23.
12. Soejima H, Irie A, Fukunaga T, Oe Y, Kojima S, Kaikita K, et al. Osteopontin expression of circulating T cells and plasma osteopontin levels are increased in relation to severity of heart failure. Circulation journal : official journal of the Japanese Circulation Society. 2007;71(12):1879-84.
13. Mann DL. Inflammatory mediators and the failing heart: past, present, and the foreseeable future. Circulation research. 2002;91(11):988-98.
14. Bondar G, Togashi R, Cadeiras M, Schaenman J, Cheng RK, Masukawa L, et al. Association between preoperative peripheral blood mononuclear cell gene expression profiles, early postoperative organ function recovery potential and long-term survival in advanced heart failure patients undergoing mechanical circulatory support. PloS one. 2017;12(12):e0189420.
15. Deng MC. Heart transplantation: the increasing challenges of evidence-based decision-making*. Journal of the American College of Cardiology. 2004;43(5):803-5.
16. Deng MC, De Meester JM, Smits JM, Heinecke J, Scheld HH. Effect of receiving a heart transplant: analysis of a national cohort entered on to a waiting list, stratified by heart failure severity. Comparative Outcome and Clinical Profiles in Transplantation (COCPIT) Study Group. Bmj. 2000;321(7260):540-5.
17. Deng MC. A peripheral blood transcriptome biomarker test to diagnose functional recovery potential in advanced heart failure. Biomark Med. 2018;12(6):619-35.
18. Tachibana C. Transcriptomics today: Microarrays, RNA-seq, and more. Science [Internet]. 2015.
19. Wallden B, Storhoff J, Nielsen T, Dowidar N, Schaper C, Ferree S, et al. Development and verification of the PAM50-based Prosigna breast cancer gene signature assay. BMC medical genomics. 2015;8:54.
20. Deng MC, Eisen HJ, Mehra MR, Billingham M, Marboe CC, Berry G, et al. Noninvasive discrimination of rejection in cardiac allograft recipients using gene expression profiling. American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons. 2006;6(1):150-60.
21. Adigopula S, Vivo RP, DePasquale EC, Nsair A, Deng MC. Management of ACCF/AHA Stage C heart failure. Cardiology clinics. 2014;32(1):73-93, viii.
22. Geiss GK, Bumgarner RE, Birditt B, Dahl T, Dowidar N, Dunaway DL, et al. Direct multiplexed measurement of gene expression with color-coded probe pairs. Nat Biotechnol. 2008;26(3):317-25.
23. Veldman-Jones MH, Brant R, Rooney C, Geh C, Emery H, Harbron CG, et al. Evaluating Robustness and Sensitivity of the NanoString Technologies nCounter Platform to Enable Multiplexed Gene Expression Analysis of Clinical Samples. Cancer Res. 2015;75(13):2587-93.
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Published
Jan 3, 2020
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Bondar G, Xu W, Elashoff D, Li X, Faure-Kumar E, Bao T-M, Grogan T, Moose J, Deng MC. Comparing NGS and NanoString platforms in peripheral blood mononuclear cell transcriptome profiling for advanced heart failure biomarker development. J Biol Methods [Internet]. 2020 Jan. 3 [cited 2024 Apr. 26];7(1):e123. Available from: https://jbmethods.org/index.php/jbm/article/view/300
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