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Advances in urinary proteome analysis and biomarker discovery

Advances in urinary proteome analysis and biomarker discovery. Joost Schanstra Inserm U858, I2MR Toulouse, France. B L O O D. U R I N E. - Serum versus plasma - Proteolysis. - Simple pre-analytic handling - Stable. Reduction of sample variability. Biomarkers in biofluids.

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Advances in urinary proteome analysis and biomarker discovery

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  1. Advances in urinary proteome analysis and biomarker discovery Joost Schanstra Inserm U858, I2MR Toulouse, France

  2. B L O O D U R I N E - Serum versus plasma - Proteolysis - Simple pre-analytic handling - Stable Reduction of sample variability Biomarkers in biofluids Petricoin et al., Use of proteomic patterns in serum to identify ovarian cancer. Lancet. 2002 • Irreproducible: - improper mass calibration, • technical flaws, • - improper execution of the experimental protocol

  3. ~30% Urine – pool for biomarkers of other diseases ? Urine – pool of biomarkers Sources of urinary proteins 1) Filtration and secretion of plasma proteins. 2) Secretion by various renal segments. 3) Proteolytic degradation products of extracellular matrix 4) Secretion by the urinary tract. 5) Dead cells. ~70% of the urinary proteins/peptides originate from the kidney and urinary tract ~70% Kidney “health” status

  4. Advances in urinary proteome analysis and biomarker discovery Advances in techniques

  5. 250 150 100 75 50 37 25 20 15 Reduce complexity A one-dimensional view of the urinary protein content • - Dynamic range • A few proteins make up the majority Courtesy: C. Lacroix, Toulouse

  6. Reduce complexity Fractionation Mass spectrometry Sample 2D-PAGE SELDI Capillary electrophoresis

  7. 250 150 100 75 50 37 25 20 15 Two-dimensional gel electrophoresis albumin Thongboonkerd, 2002 Mass (kDa) microglobulin Ig kappa chain acidic basic

  8. SELDI-MS (surface enhanced laser desorption/ionisation-mass spectrometry) 100 250 Mass (kDa) 150 100 75 50 37 25 20 15 3

  9. 250 150 100 75 50 37 25 20 15 Capillary electrophoresis coupled to mass-spectrometry Coon, in press. • - 5000 frequently • occurring peptides • collagen fragments !! Mass (kDa) Migration on CE (time)

  10. Three techniques suitable for the analysis of the urinary proteome SELDI 2D-PAGE CE-MS Medium throughput High throughput More amendable for use in the clinic

  11. Multiple biomarkers or “panels” of biomarkers proper statistics !! Advances in urinary proteome analysis and biomarker discovery Advances in concepts: panels rather than individual markers • Individual markers: • Lack of specificity (PSA, microalbuminuria…) • Sensitive to inter- and intra-individual variations

  12. Advances in urinary proteome analysis and biomarker discovery Advances in concepts: standards

  13. Biochemists 2. Select appropriate patient groups /clinical data/samples/proteomic analysis 3. Training population Statisticians/ bioinformatics extract biomarkers/ proper statistics!! Proteomics core platforms 5b. Identify 5a. Large scale validation pathophysiology clinic A standard flow scheme for clinical proteomics • Define a clear clinical question • - surgery or not ? • - graft rejection or not ? Clinicians 4. Validate in separate blinded population Mischak, Apweiler, Banks, Conaway, Coon, Dominiczak, Ehrich, Fliser, Hermjakob, Hochstrasser, Jankowski, Julian, Kolch, Massy, Neusuess, Novak, Peter, Rossing, Schanstra, Semmes, Theodorescu, Thongboonkerd, Weissinger, Van Eyk, Yamamoto Proteomics Clin. Appl. 1: 148 (2007).

  14. Advances in urinary proteome analysis and biomarker discovery Age matching Biomarkers selected for this age range old young ? ? old young

  15. Secretion of 325 out of 5000 urinary peptides is modified during aging Advances in urinary proteome analysis and biomarker discovery Age matching 324 individuals: 2-73 year Analysis of the urinary proteome by CE-MS of 324 individuals

  16. Age match !!!!! Renal aging = CKD ? Zurbig et al., submitted

  17. Outliers !! age 19-30 31-40 41-50 51-73 age markers y x ?? Zurbig et al., submitted

  18. Advances in urinary proteome analysis and biomarker discovery Advances: examples • Renal allograft rejection • Ureteropelvic junction obstruction • 3. Bladder Cancer (BCa)

  19. Classification: healthy stable transplants acute rejection Random forrest Acute rejection versus stable: - 4 markers (sens 90,5%, spec 83,3%) Prediction of acute rejection in renal allografts Year 2004 Training 20 healthy 22 stable transplants 23 acute rejection SELDI O’Riordan et al., 2004. JASN 15: 3240

  20. - -defensin-1 - -1-antichymotrypsin Validation of -defensin-1 in immunoassay AUC 0.749 Loss of sensitivity ! O’Riordan et al., 2007. Am J Transpl. 7:930 Prediction of acute rejection in renal allografts Year 2007 MS-based Immunoassay based Validation (n=45) Two markers AUC 0.912 Stable versus acute Sensitivity 91.3 % Specificity 77.3 %

  21. Obstructive nephropathy scintigraphies Spontaneous resolution Spontaneous resolution Intermediate obstruction ? pyeloplasty pyeloplasty Ureteropelvic junction (UPJ) obstruction birth 1 to 2 years Objective:identify early urinary biomarkers indicative for surgery

  22. Extraction of group-specific markers Set of specific biomarkers separating different groups: Classification Support vector machines Training Patient groups: Individual CE-MS Profiles: Reference (compliled) Group profile: Healthy controls (n=13) Sp. Resolution (n=19) Pyeloplasty (n=19) 1 1 1 19 19 13

  23. 9 months: 34 out of 36 patients correctly predicted 15 months: 35 out of 36 patients correctly predicted Decramer et al., 2006. Nat Med. 12: 398. Small scale (n=36) prospective blinded validation Biomarkers obtained during « training » Intermediate Obstruction (n=36) CE-MS based prediction Spontaneous resolution prediction Clinical outcome pyeloplasty 1 month 1 to 2 years birth Large scale multicenter validation of markers started in 2008: 358 patients

  24. Reoccurrence • cystoscopy (invasive) • urine cytology (low-sensitivity) Urinary proteomics to search for biomarkers of BCa Bladder cancer Transitional cell carcinomas 80% superficial (Tis, Ta, T1)

  25. Transitional cell carcinoma detection: Training group Number of patients Training set Bladder cancer 46 controls 73 Confounding disease various Renal diseases 281 Prostate cancer 21 Renal cancer 24

  26. Training set Controls + confounding Bladder cancer 22 biomarkers

  27. Suitable biomarkers in the presence of confounding diseases Clinical setting Transitional cell carcinoma detection: Validation Controls + confounding diseases Bladder cancer (n=32) versus Controls (n=12) Various renal diseases (n=131) Nephrolithiasis (n=7) 32/32 Bladder cancer Sensitivity 100 % 12/12 Controls Specificity 100 % 124/131 Various renal diseases Specificity 95 % 6/7 Nephrolithiasis Specificity 86 % Theodorescu et al., Lancet Oncol. 7:230-240 (2006)

  28. Advances in urinary proteome analysis and biomarker discovery Advances: examples Urine as a source of biomarkers for non-urogenital diseases? Coronary artery disease

  29. Urinary proteomics for biomarkers of Coronary artery disease Study Setup Training set CAD (Glasgow) n = 30 Controls (Glasgow) n = 20 Additional controls: Ramipril (medication controls) n = 18 Hannover (center specific biais) n = 232 Validation set CAD n = 47 Controls n = 12

  30. upregulated in comparison Downregulated in comparison Training:15 biomarkers differentiate between CAD and non-CAD controls patients with CAD non-CAD controls

  31. Urinary markers for disease from more distant organs Low molecular weight proteins: glomerular filtration Validation of urinary CAD biomarkers 46/47 CAD Sensitivity 98% 10/12 controls Specificity 83% Zimmerli et al., 2008 Mol. Cell. Proteomics 7, 290-298

  32. 852.476 995.435 100 y9 b10 882.357 90 b9 80 tandem mass-spectrometry 1587.759 b16 70 60 1715.817 858.408 b18 50 b18 ++ Intensity 40 794.380 30 b16 ++ Sequence identification 1181.252 20 755.422 y12 y8 609.256 1318.578 1092.506 b13 b6 10 b11 0 600 800 1000 1200 1400 1600 m/z Advances in urinary proteome analysis and biomarker discovery From biomarkers to pathophysiology?

  33. Advances in urinary proteome analysis and biomarker discovery From biomarkers to pathophysiology? • Biomarkers: • Abundant plasma proteins and fragments. • (albumin, beta2-macroglobulin, alpha1-antitrypsin etc…). • Abundant (structural) kidney proteins and fragments. • (collagens, uromodullin). Not highly informative

  34. Advances in urinary proteome analysis and biomarker discovery Chasing low abundance proteins A few proteins and their fragments make up the major part of the urinary proteome Although we fractionate with proteomics these abundant proteins still “hide” the less abundant ones

  35. Advances in urinary proteome analysis and biomarker discovery Chasing low abundance proteins Immunodepletion of high abundance proteins Human plasma - Depletion of human serum albumin 815 other protein species + + 2091 other protein species - Depletion of IgGs Shen et al., 2005, Proteomics 5: 4034

  36. before after Advances in urinary proteome analysis and biomarker discovery Chasing low abundance proteins LARGE DYNAMIC RANGE REDUCED DYNAMIC RANGE + Ligand peptide library (millions of different ligands) Flow-Through: Highly abundant proteins Albumin IgGs

  37. Advances in urinary proteome analysis and biomarker discovery Conclusion * Urine has emerged as a stable «  reservoir » of biomarkers for both urogenital and non-urogenital diseases. * The variability in single biomarkers is counteracted by patterns that tolerate instability and inconsistency of individual polypeptides/biomarkers * First studies using adequate statistics and validation have been published. Large scale validation * Studies including confounding diseases are being published. One step closer to use in the clinic * The contribution of proteomics to the understanding of pathophysiology is still limited

  38. Thank you

  39. 22/24 Beyond urine Bilateral developmental nephropathy Post-natal renal function ? Presence of specific peptides/proteins in amniotic fluid Responsible for the modification of post-natal renal function Early renal lesions invisible by sonography Clinical question: can we define AF biomarkers predicting post-natal renal function?

  40. 842 non-redundant proteins 1000-1200 peptides/sample 2D-PAGE LC-MS CE-MS SELDI Identification of intra-amniotic inflammation (n=169, blinded, high spec/sens) Amniotic fluid is great source of biomarkers !! Buhimschi et al., 2007, PLoS Med 4(1): 84-94. Beyond urine Amniotic fluid !!

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