Extending the information content of the MALDI analysis of biological fluids via multi-million shot analysis


Autoři: Maxim Tsypin aff001;  Senait Asmellash aff001;  Krista Meyer aff001;  Brandon Touchet aff001;  Heinrich Roder aff001
Působiště autorů: Biodesix Inc., Boulder, Colorado, United States of America aff001
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: 10.1371/journal.pone.0226012

Souhrn

Introduction

Reliable measurements of the protein content of biological fluids like serum or plasma can provide valuable input for the development of personalized medicine tests. Standard MALDI analysis typically only shows high abundance proteins, which limits its utility for test development. It also exhibits reproducibility issues with respect to quantitative measurements. In this paper we show how the sensitivity of MALDI profiling of intact proteins in unfractionated human serum can be substantially increased by exposing a sample to many more laser shots than are commonly used. Analytical reproducibility is also improved.

Methods

To assess what is theoretically achievable we utilized spectra from the same samples obtained over many years and combined them to generate MALDI spectral averages of up to 100,000,000 shots for a single sample, and up to 8,000,000 shots for a set of 40 different serum samples. Spectral attributes, such as number of peaks and spectral noise of such averaged spectra were investigated together with analytical reproducibility as a function of the number of shots. We confirmed that results were similar on MALDI instruments from different manufacturers.

Results

We observed an expected decrease of noise, roughly proportional to the square root of the number of shots, over the whole investigated range of the number of shots (5 orders of magnitude), resulting in an increase in the number of reliably detected peaks. The reproducibility of the amplitude of these peaks, measured by CV and concordance analysis also improves with very similar dependence on shot number, reaching median CVs below 2% for shot numbers > 4 million. Measures of analytical information content and association with biological processes increase with increasing number of shots.

Conclusions

We demonstrate that substantially increasing the number of laser shots in a MALDI-TOF analysis leads to more informative and reliable data on the protein content of unfractionated serum. This approach has already been used in the development of clinical tests in oncology.

Klíčová slova:

Immune response – Lasers – Mass spectra – Matrix-assisted laser desorption ionization time-of-flight mass spectrometry – Noise reduction – Oncology – Proteomes – Serum proteins


Zdroje

1. Karpova MA, Moshkovskii SA, Toropygin IY, Archakov AI. Cancer-specific MALDI-TOF profiles of blood serum and plasma: biological meaning and perspectives. J Proteomics 2010; 73(3): 537–551. doi: 10.1016/j.jprot.2009.09.011 19782778

2. Tiss A, Smith C, Menon U, Jacobs I, Timms JF, Cramer R, A well-characterised peak identification list of MALDI MS profile peaks for human blood serum. Proteomics 2010; 10(18):3388–3392. doi: 10.1002/pmic.201000100 20707003

3. Pietrowska M, Widłak P. MALDI-MS-Based Profiling of Serum Proteome: Detection of Changes Related to Progression of Cancer and Response to Anticancer Treatment. Int J Proteomics 2012; 2012: 926427. doi: 10.1155/2012/926427 22900176

4. duPont NC, Wang K, Wadhwa PD, Culhane JF, Nelson EL. Validation and comparison of luminex multiplex cytokine analysis kits with ELISA: Determinations of a panel of nine cytokines in clinical sample culture supernatants. J Reprod Immunol. 2005; 66(2): 175–191. doi: 10.1016/j.jri.2005.03.005 16029895

5. Elshal MF, McCoy JP. Multiplex bead array assays: performance evaluation and comparison of sensitivity to ELISA. Methods 2006; 38(4): 317–323. doi: 10.1016/j.ymeth.2005.11.010 16481199

6. Dossus L, Becker S, Achaintre D, Kaaks R, Rinaldi S. Validity of multiplex-based assays for cytokine measurements in serum and plasma from “non-diseased” subjects: Comparison with ELISA. J Immunol Methods 2009; 350(1–2): 125–132. doi: 10.1016/j.jim.2009.09.001 19748508

7. Perkel JM. Multiplexed Protein Assays. 28 March 2011 [cited 20 March 2018] https://www.biocompare.com/Editorial-Articles/41806-Multiplexed-Protein-Assays/

8. Tighe PJ, Ryder RR, Todd I, Fairclough LC. ELISA in the multiplex era: Potentials and pitfalls. Proteomics Clin. Appl. 2015; 9(3–4):406–422. doi: 10.1002/prca.201400130 25644123

9. Ellington AD, Szostak JW. In vitro selection of RNA molecules that bind specific ligands. Nature 1990; 346(6287): 818–822. doi: 10.1038/346818a0 1697402

10. Tuerk C, Gold L. Systematic evolution of ligands by exponential enrichment: RNA ligands to bacteriophage T4 DNA polymerase. Science 1990; 249(4968): 505–510. doi: 10.1126/science.2200121 2200121

11. Gold L, Janjic N, Jarvis T, Schneider D, Walker JJ, Wilcox SK, Zichi D. Aptamers and the RNA world, past and present. Cold Spring Harb Perspect Biol. 2012; 4(3): a003582. doi: 10.1101/cshperspect.a003582 21441582

12. Gold L, Ayers D, Bertino J, Bock C, Bock A, Brody EN, et al. Aptamer-Based Multiplexed Proteomic Technology for Biomarker Discovery. PLoS One 2010; 5(12): e15004. doi: 10.1371/journal.pone.0015004 21165148

13. Candia J, Cheung F, Kotliarov Y, Fantoni G, Sellers B, Griesman T, et al. Assessment of Variability in the SOMAscan Assay. Sci Rep. 2017; 7(1): 14248. doi: 10.1038/s41598-017-14755-5 29079756

14. Nedelkov D, Kiernan UA, Niederkofler EE, Tubbs KA, Nelson RW. Investigating diversity in human plasma proteins. PNAS 2005; 102(31): 10852–10857. doi: 10.1073/pnas.0500426102 16043703

15. Trenchevska O, Nelson RW, Nedelkov D. Mass Spectrometric Immunoassays in Characterization of Clinically Significant Proteoforms. Proteomes 2016; 4(1): 13. doi: 10.3390/proteomes4010013 28248223

16. Trenchevska O, Nelson RW, Nedelkov D. Mass spectrometric immunoassays for discovery, screening and quantification of clinically relevant proteoforms. Bioanalysis 2016; 8(15) doi: 10.4155/bio-2016-0060 27396364

17. Nedelkov D. Human proteoforms as new targets for clinical mass spectrometry protein tests. Expert Review of Proteomics, 2017; 14(8): 691–699. doi: 10.1080/14789450.2017.1362337 28756725

18. Wu DC, Wang KY, Wang SSW, Huang CM, Lee YW, Chen MI, et al. Exploring the expression bar code of SAA variants for gastric cancer detection. Proteomics 2017; 17(11): 1600356. doi: 10.1002/pmic.201600356 28493537

19. Kiernan UA, Tubbs KA, Nedelkov D, Niederkofler EE, Nelson RW. Detection of novel truncated forms of human serum amyloid A protein in human plasma. FEBS Letters 2003; 537(1–3): 166–170. doi: 10.1016/s0014-5793(03)00097-8 12606051

20. Yassine HN, Trenchevska O, He H, Borges CR, Nedelkov D, Mack W, et al. Serum Amyloid A Truncations in Type 2 Diabetes Mellitus. PLoS ONE 2015; 10(1): e0115320. doi: 10.1371/journal.pone.0115320 25607823

21. Dakna M, Harris K, Kalousis A, Carpentier S, Kolch W, Schanstra JP, et al. Addressing the challenge of defining valid proteomic biomarkers and classifiers. BMC Bioinformatics 2010; 11: 594. doi: 10.1186/1471-2105-11-594 21208396

22. Kolch W, Neususs C, Pelzing M, Mischak H. Capillary electrophoresis-mass spectrometry as a powerful tool in clinical diagnosis and biomarker discovery. Mass Spectrom Rev 2005; 24(6): 959–977. doi: 10.1002/mas.20051 15747373

23. Listgarten J, Emili A. Statistical and computational methods for comparative proteomic profiling using liquid chromatography-tandem mass spectrometry. Mol Cell Proteomics. 2005; 4(4): 419–34. doi: 10.1074/mcp.R500005-MCP200 15741312

24. Swan AL, Mobasheri A, Allaway D, Liddell S, Bacardit J. Application of machine learning to proteomics data: classification and biomarker identification in postgenomics biology. OMICS. 2013; 17(12): 595–610. doi: 10.1089/omi.2013.0017 24116388

25. Robotti E, Manfredi M, Marengo E. Biomarkers Discovery through Multivariate Statistical Methods: A Review of Recently Developed Methods and Applications in Proteomics. J Proteomics Bioinform 2014; S3: 003. doi: 10.4172/jpb.S3-003

26. Fan Z, Kong F, Zhou Y, Chen Y, Dai Y. Intelligence Algorithms for Protein Classification by Mass Spectrometry. Biomed Res Int. 2018; 2018: 2862458. doi: 10.1155/2018/2862458 30534555

27. Grapov D, Fahrmann J, Wanichthanarak K, Khoomrung S. Rise of Deep Learning for Genomic, Proteomic, and Metabolomic Data Integration in Precision Medicine. OMICS. 2018; 22(10): 630–636. doi: 10.1089/omi.2018.0097 30124358

28. Roder J, Oliveira C, Net L, Tsypin M, Linstid B, Roder H. A dropout-regularized classifier development approach optimized for precision medicine test discovery from omics data. BMC Bioinformatics. 2019; 20(1): 325. doi: 10.1186/s12859-019-2922-2 31196002

29. Roder H, Oliveira C, Net L, Linstid B, Tsypin M, Roder J. Robust identification of molecular phenotypes using semi-supervised learning. BMC Bioinformatics. 2019; 20(1): 273. doi: 10.1186/s12859-019-2885-3 31138112

30. Anderson NL, Anderson NG. The human plasma proteome: History, character, and diagnostic prospects. Mol. Cell. Proteomics 2002; 1(11): 845–867. doi: 10.1074/mcp.r200007-mcp200 12488461

31. Service RF. Proteomics ponders prime time. Science 2008; 321: 1758–1761. doi: 10.1126/science.321.5897.1758 18818332

32. Schwenk JM, Omenn GS, Sun Z, Campbell DS, Baker MS, Overall CM, et al. The Human Plasma Proteome Draft of 2017: Building on the Human Plasma PeptideAtlas from Mass Spectrometry and Complementary Assays. J Proteome Res 2017; 16(12): 4299–4310. doi: 10.1021/acs.jproteome.7b00467 28938075

33. Hortin GL. The MALDI-TOF mass spectrometric view of the plasma proteome and peptidome. Clin Chem. 2006; 52(7): 1223–37. doi: 10.1373/clinchem.2006.069252 16644871

34. Greco V, Piras C, Pieroni L, Ronci M, Putignani L, Roncada P, et al. Applications of MALDI-TOF mass spectrometry in clinical proteomics. Expert Rev Proteomics. 2018; 15(8): 683–696. doi: 10.1080/14789450.2018.1505510 30058389

35. Krutchinsky AN, Chait BT. On the nature of the chemical noise in MALDI mass spectra. J Am Soc Mass Spectrom. 2002; 13(2): 129–34. doi: 10.1016/s1044-0305(01)00336-1 11838016

36. Knochenmuss R, Karbach V, Wiesli U, Breuker K, Zenobi R. The Matrix Suppression Effect in Matrix Assisted Laser Desorption/Ionization: Application to Negative Ions and Further Characteristics. Rapid Commun. Mass Spectrom. 1998; 12(9): 529–534.

37. Burkitt WI, Giannakopulos AE, Sideridou F, Bashir S, Derrick PJ. Discrimination effects in MALDI-MS of mixtures of peptides—Analysis of the Proteome. Aust J Chem 2003; 56 (5): 369–377

38. Luxembourg SL, McDonnell LA, Duursma MC, Guo X, Heeren RMA. Effect of Local Matrix Crystal Variations in Matrix-Assisted Ionization Techniques for Mass Spectrometry. Anal. Chem. 2003; 75(10): 2333–2341. doi: 10.1021/ac026434p 12918974

39. Jones EA, Lockyer NP, Kordys J, Vickerman JC. Suppression and Enhancement of Secondary Ion Formation Due to the Chemical Environment in Static-Secondary Ion Mass Spectrometry. J. Am. Soc. Mass Spectrom. 2007; 18(8): 1559–1567. doi: 10.1016/j.jasms.2007.05.014 17604641

40. Aresta A, Calvano CD, Palmisano F, Zambonin CG, Monaco A, Tommasi S, et al. Impact of sample preparation in peptide/protein profiling in human serum by MALDI-TOF mass spectrometry. J Pharm Biomed Anal. 2008; 46(1): 157–164. https://doi.org/10.1016/j.jpba.2007.10.015 18035512

41. Weidmann S, Mikutis G, Barylyuk K, Zenobi R. Mass discrimination in high-mass MALDI-MS. J Am Soc Mass Spectrom. 2013; 24(9): 1396–404. doi: 10.1007/s13361-013-0686-x 23836380

42. Albrethsen J. Reproducibility in protein profiling by MALDI-TOF mass spectrometry. Clin Chem. 2007; 53(5): 852–8. doi: 10.1373/clinchem.2006.082644 17395711

43. Rose K, Bougueleret L, Baussant T, Bohm G, Botti P, Colinge J, et al. Industrial scale proteomics: from liters of plasma to chemically synthesized proteins. Proteomics 2004; 4(7): 2125–2150. doi: 10.1002/pmic.200300718 15221774

44. Metz TO, Jacobs JM, Gritsenko MA, Fontès G, Qian WJ, Camp DG, et al. Advances and challenges in liquid chromatography-mass spectrometry-based proteomics profiling for clinical applications. Mol Cell Proteomics 2006; 5(10): 1727–1744. doi: 10.1074/mcp.M600162-MCP200 16887931

45. Dayon L, Kussmann M. Proteomics of human plasma: A critical comparison of analytical workflows in terms of effort, throughput and outcome. EuPA Open Proteomics 2013; 1: 8–16. doi: 10.1016/j.euprot.2013.08.001

46. Li XJ, Lee LW, Hayward C, Brusniak MY, Fong PY, McLean M, et al. An integrated quantification method to increase the precision, robustness, and resolution of protein measurement in human plasma samples. Clin Proteomics. 2015; 12(1): 3. doi: 10.1186/1559-0275-12-3 25838814

47. Cominetti O, Núñez Galindo A, Corthésy J, Oller Moreno S, Irincheeva I, Valsesia A, et al. Proteomic Biomarker Discovery in 1000 Human Plasma Samples with Mass Spectrometry. J Proteome Res. 2016 Feb 5;15(2):389–99. doi: 10.1021/acs.jproteome.5b00901 26620284

48. Keshishian H, Burgess MW, Specht H, Wallace L, Clauser KR, Gillette MA, et al. Quantitative, multiplexed workflow for deep analysis of human blood plasma and biomarker discovery by mass spectrometry. Nat Protoc. 2017; 12(8): 1683–1701. doi: 10.1038/nprot.2017.054 28749931

49. Dayon L, Núñez Galindo A, Cominetti O, Corthésy J, Kussmann M. A Highly Automated Shotgun Proteomic Workflow: Clinical Scale and Robustness for Biomarker Discovery in Blood. Methods Mol Biol. 2017; 1619: 433–449. doi: 10.1007/978-1-4939-7057-5_30 28674902

50. Bhosale SD, Moulder R, Kouvonen P, Lahesmaa R, Goodlett DR Mass Spectrometry-Based Serum Proteomics for Biomarker Discovery and Validation. Methods Mol Biol. 2017; 1619: 451–466. doi: 10.1007/978-1-4939-7057-5_31 28674903

51. Bruderer R, Muntel J, Müller S, Bernhardt OM, Gandhi T, Cominetti O, et al. Analysis of 1508 Plasma Samples by Capillary-Flow Data-Independent Acquisition Profiles Proteomics of Weight Loss and Maintenance. Mol Cell Proteomics. 2019; 18(6): 1242–1254. doi: 10.1074/mcp.RA118.001288 30948622

52. Pernemalm M, Sandberg A, Zhu Y, Boekel J, Tamburro D, Schwenk JM, et al. In-depth human plasma proteome analysis captures tissue proteins and transfer of protein variants across the placenta. Elife. 2019; 8: e41608. doi: 10.7554/eLife.41608 30958262

53. Anderson L, Hunter CL. Quantitative mass spectrometric multiple reaction monitoring assays for major plasma proteins. Mol Cell Proteomics 2006; 5(4): 573–588. doi: 10.1074/mcp.M500331-MCP200 16332733

54. Addona TA, Abbatiello SE, Schilling B, Skates SJ, Mani DR, Bunk DM, et al. Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma. Nat Biotechnol 2009; 27(7): 633–641. doi: 10.1038/nbt.1546 19561596

55. Chambers AG, Percy AJ, Yang J, Borchers CH. Multiple Reaction Monitoring Enables Precise Quantification of 97 Proteins in Dried Blood Spots. Mol Cell Proteomics 2015; 14(11): 3094–3104. doi: 10.1074/mcp.O115.049957 26342038

56. Ozcan S, Cooper JD, Lago SG, Kenny D, Rustogi N, Stocki P, Bahn S. Towards reproducible MRM based biomarker discovery using dried blood spots. Sci Rep 2017; 7: 45178. doi: 10.1038/srep45178 28345601

57. Lehmann S, Picas A, Tiers L, Vialaret J, Hirtz C. Clinical perspectives of dried blood spot protein quantification using mass spectrometry methods. Crit Rev Clin Lab Sci. 2017; 54(3): 173–184. doi: 10.1080/10408363.2017.1297358 28393579

58. Li H, Han J, Pan J, Liu T, Parker CE, Borchers CH. Current trends in quantitative proteomics—an update. J Mass Spectrom 2017; 52(5): 319–341. doi: 10.1002/jms.3932 28418607

59. Kearney P, Hunsucker SW, Li XJ, Porter A, Springmeyer S, Mazzone P. An integrated risk predictor for pulmonary nodules. PLoS One 2017; 12(5): e0177635. doi: 10.1371/journal.pone.0177635 28545097

60. Silvestri GA, Tanner NT, Kearney P, Vachani A, Massion PP, Porter A, et al. Assessment of Plasma Proteomics Biomarker’s Ability to Distinguish Benign From Malignant Lung Nodules: Results of the PANOPTIC (Pulmonary Nodule Plasma Proteomic Classifier) Trial. Chest 2018; 154(3): 491–500. doi: 10.1016/j.chest.2018.02.012 29496499

61. Römpp A, Dekker L, Taban I, Jenster G, Boogerd W, Bonfrer H, et al. Identification of leptomeningeal metastasis-related proteins in cerebrospinal fluid of patients with breast cancer by a combination of MALDI-TOF, MALDI-FTICR and nanoLC-FTICR MS. Proteomics 2007; 7(3): 474–81. doi: 10.1002/pmic.200600719 17274072

62. Stoop MP, Dekker LJ, Titulaer MK, Lamers RJ, Burgers PC, Sillevis Smitt PA, et al. Quantitative matrix-assisted laser desorption ionization-fourier transform ion cyclotron resonance (MALDI-FT-ICR) peptide profiling and identification of multiple-sclerosis-related proteins. J Proteome Res. 2009; 8(3): 1404–14. doi: 10.1021/pr8010155 19159215

63. Nicolardi S, Palmblad M, Hensbergen PJ, Tollenaar RA, Deelder AM, van der Burgt YE. Precision profiling and identification of human serum peptides using Fourier transform ion cyclotron resonance mass spectrometry. Rapid Commun Mass Spectrom. 2011; 25(23): 3457–63. doi: 10.1002/rcm.5246 22095492

64. Nicolardi S, van der Burgt YE, Wuhrer M, Deelder AM. Mapping O-glycosylation of apolipoprotein C-III in MALDI-FT-ICR protein profiles. Proteomics. 2013; 13(6): 992–1001. doi: 10.1002/pmic.201200293 23335445

65. Nicolardi S. Development of ultrahigh resolution FTICR mass spectrometry methods for clinical proteomics. Doctoral Dissertation, Leiden University. 2014. ISBN: 978-94-6182-435-6. http://hdl.handle.net/1887/25784

66. Nicolardi S, Bogdanov B, Deelder AM, Palmblad M, van der Burgt YE. Developments in FTICR-MS and Its Potential for Body Fluid Signatures. Int J Mol Sci. 2015; 16(11): 27133–44. doi: 10.3390/ijms161126012 26580595

67. Yergey AL, Coorssen JR, Backlund PS Jr, Blank PS, Humphrey GA, Zimmerberg J, et al. De novo sequencing of peptides using MALDI/TOF-TOF. J Am Soc Mass Spectrom 2002; 13(7): 784–791. doi: 10.1016/S1044-0305(02)00393-8 12148803

68. Vestal ML, Campbell JM. Tandem time-of-flight mass spectrometry. Methods Enzymol. 2005; 402: 79–108. doi: 10.1016/S0076-6879(05)02003-3 16401507

69. Vestal ML. Modern MALDI time-of-flight mass spectrometry. J Mass Spectrom. 2009; 44(3): 303–17. doi: 10.1002/jms.1537 19142962

70. Vestal ML. The future of biological mass spectrometry. J Am Soc Mass Spectrom 2011; 22(6): 953–9. doi: 10.1007/s13361-011-0108-x 21953036

71. Standing KG, Vestal ML. Time-of-flight mass spectrometry (TOFMS): From niche to mainstream. International Journal of Mass Spectrometry 2015; 377: 295–308. doi: 10.1016/j.ijms.2014.09.002

72. Mitchell M, Mali S, King CC, Bark SJ. Enhancing MALDI Time-Of-Flight Mass Spectrometer Performance through Spectrum Averaging. PLoS ONE 2015; 10(3): e0120932. doi: 10.1371/journal.pone.0120932 25798583

73. Jansen BC, Bondt A, Reiding KR, Lonardi E, De Jong CJ, Falck D, et al. Pregnancy-associated serum N-glycome changes studied by high-throughput MALDI-TOF-MS. Sci Rep. 2016; 6: 23296 doi: 10.1038/srep23296 27075729

74. Reiding KR, Blank D, Kuijper DM, Deelder AM, Wuhrer M. High-throughput profiling of protein N-glycosylation by MALDI-TOF-MS employing linkage-specific sialic acid esterification. Anal Chem. 2014; 86(12): 5784–93. doi: 10.1021/ac500335t 24831253

75. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005; 102(43): 15545–50. doi: 10.1073/pnas.0506580102 16199517

76. Andrew AM. Another efficient algorithm for convex hulls in two dimensions. Information Processing Letters 1979; 9(5):216–219. doi: 10.1016/0020-0190(79)90072-3

77. Algorithm Implementation/Geometry/Convex hull/Monotone chain. [cited 28 August 2017]. https://en.wikibooks.org/wiki/Algorithm_Implementation/Geometry/Convex_hull/Monotone_chain

78. Gibb S, Strimmer K. Mass Spectrometry Analysis Using MALDIquant. In: Datta S, Mertens BJA, editors. Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry. Frontiers in Probability and the Statistical Sciences, Springer International Publishing Switzerland 2017, pp.101–124. doi: 10.1007/978-3-319-45809-0_6

79. Savitzky A, Golay MJE. Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Anal Chem. 1964; 36(8): 1627–1639. doi: 10.1021/ac60214a047

80. Inverse Erf. [cited 01 October 2019]. http://mathworld.wolfram.com/InverseErf.html

81. Gene Ontology Consortium: http://www.geneontology.org/

82. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000; 25(1): 25–29. doi: 10.1038/75556 10802651

83. Carbon S, Ireland A, Mungall CJ, Shu S, Marshall B, Lewis S, et al. AmiGO: online access to ontology and annotation data. Bioinformatics. 2009; 25(2): 288–289. doi: 10.1093/bioinformatics/btn615 19033274

84. http://amigo.geneontology.org/amigo

85. https://www.ebi.ac.uk/QuickGO/

86. Guide to GO evidence codes. http://geneontology.org/docs/guide-go-evidence-codes/

87. Grigorieva J, Asmellash S, Oliveira C, Roder H, Net L, Roder J. Application of protein set enrichment analysis to correlation of protein functional sets with mass spectral features and multivariate proteomic tests. Clinical Mass Spectrometry 2019; (Forthcoming) doi: 10.1016/j.clinms.2019.09.001

88. Roder J, Linstid B, Oliveira C. Improving the power of gene set enrichment analyses. BMC Bioinformatics. 2019; 20(1): 257. doi: 10.1186/s12859-019-2850-1 31101008

89. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society. Series B (Methodological) 1995; 57(1): 289–300.

90. Weber JS, Sznol M, Sullivan RJ, Blackmon S, Boland G, Kluger HM, et al. A Serum Protein Signature Associated with Outcome after Anti-PD-1 Therapy in Metastatic Melanoma. Cancer Immunol Res. 2018; 6(1): 79–86. doi: 10.1158/2326-6066.CIR-17-0412 29208646

91. Smit EF, Aerts JG, Muller M, Niemeijer AN, Roder H, Oliveira C, et al. Prediction of primary resistance to anti-PD1 therapy (APD1) in second-line NSCLC. In: 43rd ESMO Congress (ESMO 2018) 19–23 October 2018, Munich, Germany. Annals of Oncology 2018; 29(suppl_8): mdy269.068.

92. Aerts J, Smit E, Muller M, Niemeijer A, Oliveira C, Roder H, et al. Detection of Primary Immunotherapy Resistance to PD-1 Checkpoint Inhibitors (PD1CI) in 2nd Line NSCLC. In: IASLC 19th World Conference on Lung Cancer, 23–26 September 2018, Toronto, Canada. J Thorac Oncol. 2018; 13(10): S424.

93. Kowanetz M, Leng N, Roder J, Oliveira C, Asmellash S, Meyer K, et al. Evaluation of Immune–related Markers in circulating proteome and their association with atezolizumab efficacy in patients with 2L+ NSCLC. In: 33rd Annual Meeting of the Society for Immunotherapy of Cancer (SITC 2018), 8–11 November 2018, Washington DC, USA. J Immunother Cancer. 2018; 6(Suppl 1): 114 doi: 10.1186/s40425-018-0422-y 30400835

94. Ascierto PA, Capone M, Grimaldi AM, Mallardo D, Simeone E, Madonna G, et al. Proteomic test for anti-PD-1 checkpoint blockade treatment of metastatic melanoma with and without BRAF mutations. J Immunother Cancer. 2019; 7(1): 91. doi: 10.1186/s40425-019-0569-1 30925943


Článek vyšel v časopise

PLOS One


2019 Číslo 12