Personalized Medicine for Kidney Transplantation
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Presentation at University of Antwerp - 13 June 2017
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Personalized Medicine for Kidney Transplantation
- 1. Gepersonaliseerde geneeskunde bij niertransplantatie Maarten Naesens University Hospitals Leuven, Belgium Antwerpen – 13 juni 2017
- 2. Personalized medicine builds on data and biomarkers rker Prognostic biomarker Predictive biomarker High disease probability se High risk / bad prognosis Low risk / good prognosis Predicted benefit from treatment X, not Y Predicted benefit from treatment Y, not X Risk/susceptibility biomarker High risk for diseaseLow risk for disease All patientsAll patients same treatment Biomarkers Traditional medicine Personalized medicine
- 3. Risk/susceptibility biomarker Risk/susceptibility biomarker Non-invasive diagnostic biomarker Non-invasive diagnostic biomarker Invasive diagnostic biomarker Prognostic biomarker Predictive biomarker High disease probabilityLow disease probability High risk for diseaseLow risk for disease No disease confirmation Disease confirmation High risk / bad prognosis Low risk / good prognosis Safety biomarker Predicted benefit from treatment X, not Y Predicted benefit from treatment Y, not X Start treatment X Start treatment Y Pharmacodynamic / response biomarker Monitoring biomarker - Disease activity - Pharmacokinetics (exposure) All patients Patients with confirmed disease Risk/susceptibility markers
- 4. Risk/susceptibility biomarker Risk/susceptibility biomarker Non-invasive diagnostic biomarker Non-invasive diagnostic biomarker Invasive diagnostic biomarker Prognostic biomarker Predictive biomarker High disease probabilityLow disease probability High risk for diseaseLow risk for disease No disease confirmation Disease confirmation High risk / bad prognosis Low risk / good prognosis Safety biomarker Predicted benefit from treatment X, not Y Predicted benefit from treatment Y, not X Start treatment X Start treatment Y Pharmacodynamic / response biomarker Monitoring biomarker - Disease activity - Pharmacokinetics (exposure) All patients Patients with confirmed disease Non-invasive diagnostic marker
- 5. Risk/susceptibility biomarker Risk/susceptibility biomarker Non-invasive diagnostic biomarker Non-invasive diagnostic biomarker Invasive diagnostic biomarker Prognostic biomarker Predictive biomarker High disease probabilityLow disease probability High risk for diseaseLow risk for disease No disease confirmation Disease confirmation High risk / bad prognosis Low risk / good prognosis Safety biomarker Predicted benefit from treatment X, not Y Predicted benefit from treatment Y, not X Start treatment X Start treatment Y Pharmacodynamic / response biomarker Monitoring biomarker - Disease activity - Pharmacokinetics (exposure) All patients Patients with confirmed disease Invasive diagnostic marker
- 6. Risk/susceptibility biomarker Risk/susceptibility biomarker Non-invasive diagnostic biomarker Non-invasive diagnostic biomarker Invasive diagnostic biomarker Prognostic biomarker Predictive biomarker High disease probabilityLow disease probability High risk for diseaseLow risk for disease No disease confirmation Disease confirmation High risk / bad prognosis Low risk / good prognosis Safety biomarker Predicted benefit from treatment X, not Y Predicted benefit from treatment Y, not X Start treatment X Start treatment Y Pharmacodynamic / response biomarker Monitoring biomarker - Disease activity - Pharmacokinetics (exposure) All patients Patients with confirmed disease Prognostic marker
- 7. Risk/susceptibility biomarker Risk/susceptibility biomarker Non-invasive diagnostic biomarker Non-invasive diagnostic biomarker Invasive diagnostic biomarker Prognostic biomarker Predictive biomarker High disease probabilityLow disease probability High risk for diseaseLow risk for disease No disease confirmation Disease confirmation High risk / bad prognosis Low risk / good prognosis Safety biomarker Predicted benefit from treatment X, not Y Predicted benefit from treatment Y, not X Start treatment X Start treatment Y Pharmacodynamic / response biomarker Monitoring biomarker - Disease activity - Pharmacokinetics (exposure) All patients Patients with confirmed disease Predictive marker Predictive marker
- 8. Risk/susceptibility biomarker Risk/susceptibility biomarker Non-invasive diagnostic biomarker Non-invasive diagnostic biomarker Invasive diagnostic biomarker Prognostic biomarker Predictive biomarker High disease probabilityLow disease probability High risk for diseaseLow risk for disease No disease confirmation Disease confirmation High risk / bad prognosis Low risk / good prognosis Safety biomarker Predicted benefit from treatment X, not Y Predicted benefit from treatment Y, not X Start treatment X Start treatment Y Pharmacodynamic / response biomarker Monitoring biomarker - Disease activity - Pharmacokinetics (exposure) All patients Patients with confirmed disease Predictive marker Safety markers Pharmacodynamic markers Monitoring markers
- 9. Risk/susceptibility biomarker Risk/susceptibility biomarker Non-invasive diagnostic biomarker Non-invasive diagnostic biomarker Invasive diagnostic biomarker Prognostic biomarker Predictive biomarker High disease probabilityLow disease probability High risk for diseaseLow risk for disease No disease confirmation Disease confirmation High risk / bad prognosis Low risk / good prognosis Safety biomarker Predicted benefit from treatment X, not Y Predicted benefit from treatment Y, not X Start treatment X Start treatment Y Pharmacodynamic / response biomarker Monitoring biomarker - Disease activity - Pharmacokinetics (exposure) All patients Patients with confirmed disease
- 10. Risk/susceptibility biomarker Risk/susceptibility biomarker Non-invasive diagnostic biomarker Non-invasive diagnostic biomarker Invasive diagnostic biomarker Prognostic biomarker Predictive biomarker High disease probabilityLow disease probability High risk for diseaseLow risk for disease No disease confirmation Disease confirmation High risk / bad prognosis Low risk / good prognosis Safety biomarker Predicted benefit from treatment X, not Y Predicted benefit from treatment Y, not X Start treatment X Start treatment Y Pharmacodynamic / response biomarker Monitoring biomarker - Disease activity - Pharmacokinetics (exposure) All patients Patients with confirmed disease PERSONALIZED MEDICINE Risk/susceptibility markers Non-invasive diagnostic markers Invasive diagnostic markers Prognostic markers Predictive markers Safety markers Pharmacodynamic markers Monitoring markers
- 11. PERSONALIZED MEDICINE Non-invasive diagnostic markers Invasive diagnostic markers Risk markers Predictive markersPrognostic markersDiagnostic markers
- 12. RISK markers
- 13. We have several widely used susceptibility/risk biomarkers in kidney transplantation • Number of HLA mismatches • Cross-matches • Pretransplant PRA% • Pretransplant DSA • De novo DSA occurrence Success story of HLA genotyping and antibody profiling TRANSPLANTATION MEDICINE = FRONTRUNNER IN PERSONALIZED MEDICINE
- 14. More personalization in allocation (AM program) leads to better outcome Heidt et al confidential
- 15. More personalization in allocation (AM program) leads to better outcome Heidt et al confidential
- 16. HLA epitope-based organ allocation
- 17. HLA epitope-based organ allocation instead of waiting time Low resolution Acceptable MM at the antigen level High/allelic resolution Acceptable MM at the epitope level
- 18. HLA epitope-based organ allocation instead of waiting time Third-Generation Sequencing (SMRT) Low resolution Acceptable MM at the antigen level High/allelic resolution Acceptable MM at the epitope level
- 19. New risk biomarkers in the pipeline for kidney transplantation • Epitope mismatch load1 • Genetic assessment for aHUS recurrence • Urinary or serum suPAR for FSGS recurrence2 • FSGS recurrence panel3 • PLA2R and THSD7A antibodies for recurrence of membranous glomerulopathy4,5 • Donor-reactive T-cell response6 • … Risk markers 1Wiebe et al Transplantation 2016; 2Franco Palacios et al Transplantation 2013; 3Delville et al Sci Transl Med 2014; 4Sprangers et al Transplant Rev 2013; 5Tomas et al J Clin Invest 2016; 6Crespo et al Clin Biochem 2016
- 21. We have several widely used diagnostic biomarkers in kidney transplantation Non-invasive: • Serum creatinine/eGFR • Proteinuria • DSAs • Renal ultrasound exam Invasive: • Histology of for-cause (indication) biopsies • Histology of protocol biopsies
- 22. Naesens et al. Am J Transplant 2013;13:86–99. Inflammation + ABMR Inflammation - ABMR Normal Chronic - inflammation Chronic + inflammation Transplant glomerulopathy gs cv mm ah ct ci cg ti i t ptc g v C4dglom C4dptc 0 max Individual lesion score Inflammation + ABMR Inflammation - ABMR Normal Chronic - inflammation Chronic + inflammation Transplant glomerulopathy gs cv mm ah ct ci cg ti i t ptc g v C4dglom C4dptc 0 max Individual lesion score
- 23. We are constantly refining the diagnostic Banff classification 1. Racusen LC et al. Kidney Int 1999;55:713–723; 2. Loupy A et al. Am J Transplant 2017;17(1):28–41. ABMR 2015 TCMR 1997–2015
- 24. Lesions are non-specific for the underlying etiology: TCMR and ABMR Lefaucheur C et al. Lancet 2013;381:313–319.
- 25. Kidney transplant histology is highly problematic as a diagnostic biomarker Naesens & Anglicheau – in press.
- 26. Loupy et al AJT 2017 BANFF 2015 consensus Invasive diagnostic markers in the pipeline for kidney transplantation
- 27. From MMDx website: www.molecular-microscope.com Invasive diagnostic markers in the pipeline for kidney transplantation
- 28. Non-invasive diagnostic markers in the pipeline for kidney transplantation • Urinary mRNA • Urinary miRNA • Urinary proteins/peptides • Blood mRNA • Blood miRNA • Blood proteins/peptides • ….
- 29. 0 25 50 75 100 0 25 50 75 100 Sensitivity for acute rejection (%) Specifictyforacuterejection(%) Perforin Granzyme B PI-9 CD103 FOXP3 CXCL10 NKG2D TIM3 Granulysin Multigene signature Urinary mRNA 0 25 50 75 100 0 Sensitivity for acute rejection (%) Multigene signature Non-invasive urinary mRNA markers in the pipeline for kidney transplantation lack accuracy Naesens and Anglicheau, in press mRNA
- 30. 0 25 50 75 100 0 25 50 75 100 Sensitivity for acute rejection (%) Specifictyforacuterejection(%) Perforin Granzyme B PI-9 CD103 FOXP3 CXCL10 NKG2D TIM3 Granulysin Multigene signature Urinary mRNA 0 25 50 75 100 0 Sensitivity for acute rejection (%) Multigene signature Non-invasive urinary mRNA markers in the pipeline for kidney transplantation lack accuracy mRNA Naesens and Anglicheau, in press
- 31. 0 25 50 75 100 0 Sensitivity for acute rejection (%) Multigene signature 0 25 50 75 100 0 25 50 75 100 Sensitivity for acute rejection (%) Specifictyforacuterejection(%) CXCL9 CXCL10 Fractalkine Urinary proteins Non-invasive urinary protein markers in the pipeline for kidney transplantation lack accuracy Proteins Naesens and Anglicheau, in press
- 32. 0 25 50 75 100 0 25 50 75 100 Sensitivity for acute rejection (%) Specifictyforacuterejection(%) Blood mRNA Granzyme B Perforin FasL HLA-DRA Multigene signature Non-invasive blood mRNA markers in the pipeline for kidney transplantation seem promising Naesens and Anglicheau, in press mRNA
- 33. 0 25 50 75 100 0 25 50 75 100 Sensitivity for acute rejection (%) Specifictyforacuterejection(%) Blood mRNA Granzyme B Perforin FasL HLA-DRA Multigene signature Non-invasive blood mRNA markers in the pipeline for kidney transplantation seem promising Trugraf kSORT Naesens and Anglicheau, in press
- 34. The kSORT assay needs further validation in cross-sectional cohorts 17 peripheral blood mRNA gene-set Case-control setting -> PPV?? NPV?? Roedder et al PLOS Med 2014
- 35. The TruGraf assay needs further validation 200 peripheral blood mRNA geneset - early-access clinical programs started - large interventional trials ongoing (like the phase-3 trial with the p53 inhibitor QPI-1002) - prospective, randomized, multi-center clinical trial ongoing Kurian et al Am J Transplant 2014
- 36. AP-HP Paris CHU Limoges UZ Leuven MHH Hannover Clinical Centers AP-HP Paris INSERM Limoges KU Leuven Mosaiques Diagnostic GmbH Analytical Centers (-omics data) INSERM Toulouse CEA CNRS VITO Bio-informatics Center Acureomics UnivPDes Inserm-Transfert Coordination Cardinal Systems Urinary + plasma metabolomics Urinary proteomics + peptidomics Urinary miRNA Urinary mRNA Blood + biopsy miRNA Blood + biopsy mRNA Biopsy lipidomics, peptidomics, proteomics Urinary proteomics and peptidomics Blood + biopsy miRNA Urinary lipidomics Urinary proteomics + peptidomics www.biomargin.eu
- 37. PERSONALIZED MEDICINE Non-invasive diagnostic markers Invasive diagnostic markers Prognostic markers
- 38. Accuracy of a test determines its clinical value, not its p-value! Area under a ROC curve Interpretation 0.90 – 1.00 Excellent 0.80 – 0.90 Good 0.70 – 0.80 Fair 0.60 – 0.70 Poor 0.50 – 0.60 Fail False positive rate (1 – Specificity) Truepositiverate(Sensitivity) Perfect test AUC=1.00 Good test AUC=0.85 Failed test AUC=0.50 Positive predictive value (PPV) and negative predictive value (NPV) take disease prevalence into account
- 39. eGFR at 1 year is associated with graft outcome, and is a fair prognostic marker ROC for graft failure 5 year after biopsy according to 1 year MDRD eGFR 0 20 40 60 80 100 0 20 40 60 80 100 100% - Specificity% Sensitivity% AUC=0.77 p<0.0001 MRDR eGFR at 1 year and graft failure 1 5 10 15 0 20 40 60 80 100 Time after biopsy (years) Graftsurvival(%) >70 mL/min 60-70 mL/min 50-60 mL/min log-rank P<0.0001 40-50 mL/min 30-40 mL/min 20-30 mL/min <20 mL/min Speaker’s own unpublished data
- 40. Proteinuria is a risk factor for graft failure but a poor prognostic marker Naesens M et al J Am Soc Nephrol 20153 months 1 year 2 yearsD Time after transplantation (years) 572 119 40 430 68 16 163 23 7 532 102 33 No. at risk <0.3 g/24h 0.3-1.0 g/24h >1.0 g/24h Time after transplantation (yea 495 104 38 416 70 13 No. at risk <0.3 g/24h 0.3-1.0 g/24h >1.0 g/24h Biopsy time points (N=1335) 1 5 10 0 20 40 60 80 100 Time after biopsy (years) Percentsurvival >3.0 g/24h < 0.3 g/24h 0.3-1.0 g/24h 1.0-3.0 g/24h No. at risk <0.3 g/24h 0.3-1.0 g/24h 1.0-3.0 g/24h >3.0 g/24h 548 319 150 30 449 237 94 18 297 156 53 11 646 390 208 51 log-rank P <0.0001 Proteinuria B C 0 20 40 60 80 100 0 20 40 60 80 100 False Positive Fraction (%) TruePositiveFraction(%) AUC=0.66 (95% CI 0.63-0.69) P <0.0001 Biopsy time points (N=1335) 3 months (N=914) 100 ) 1 year (N=778) 100 ) 2 years (N=731) 100 ) ) D 572 119 40 430 68 16 163 23 7 532 102 33 No. at risk <0.3 g/24h 0.3-1.0 g/24h >1.0 g/24h 495 104 38 160 28 7 416 70 13 No. at risk <0.3 g/24h 0.3-1.0 g/24h >1.0 g/24h Biopsy time points (N=1335) 1 5 10 0 20 40 60 80 100 Time after biopsy (years) Percentsurvival >3.0 g/24h < 0.3 g/24h 0.3-1.0 g/24h 1.0-3.0 g/24h No. at risk <0.3 g/24h 0.3-1.0 g/24h 1.0-3.0 g/24h >3.0 g/24h 548 319 150 30 449 237 94 18 297 156 53 11 646 390 208 51 log-rank P <0.0001 Proteinuria B C 0 20 40 60 80 100 0 20 40 60 80 100 False Positive Fraction (%) TruePositiveFraction(%) AUC=0.66 (95% CI 0.63-0.69) P <0.0001 Biopsy time points (N=1335) 5 10 152 0 20 40 60 80 Time after transplantation (years) 0.3-1.0 g/24h > 1.0 g/24h 572 119 40 430 68 16 163 23 7 532 102 33 log-rank P <0.0001 at risk g/24h g/24h g/24h 5 10 0 20 40 60 80 Time after transplantation (y Percentsurviv 495 104 38 416 70 13 log-rank P <0.0001 No. at risk <0.3 g/24h 0.3-1.0 g/24h >1.0 g/24h Biopsy time points (N=1335) 60 80 100 >3.0 g/24h < 0.3 g/24h 0.3-1.0 g/24h 1.0-3.0 g/24h Proteinuria C 60 80 100 Fraction(%) Biopsy time points (N=1335)
- 41. Starzl TE et al Ann Surg 1974;180(4):606–614 ct ci ah cvi 64 cases transplanted between 1962–1964 in Colorado and Denver
- 42. The CADI score is an imperfect prognostic marker, despite the significant association with graft failure CADI score in indication biopsy 1 5 10 15 0 20 40 60 80 100 Time after biopsy (years) Graftsurvival(%) CADI 0 CADI 1 CADI 2-3 log-rank P<0.0001 CADI 4-5 CADI 6-7 CADI 8-9 CADI >9 ROC for graft failure 5 year after biopsy according to CADI score 0 20 40 60 80 100 0 20 40 60 80 100 100% - Specificity% Sensitivity% AUC=0.65 p<0.0001 CADI score in indication biopsy 1 5 10 15 0 20 40 60 80 100 Time after biopsy (years) Graftsurvival(%) CADI 0 CADI 1 CADI 2-3 log-rank P<0.0001 CADI 4-5 CADI 6-7 CADI 8-9 CADI >9 ROC for graft failure 5 year after biopsy according to CADI score 0 20 40 60 80 100 0 20 40 60 80 100 100% - Specificity% Sensitivity% AUC=0.65 p<0.0001 ROC for 5 year graft loss N=1335 indication biopsies Speaker’s own unpublished data
- 43. Prognostic models within a disease phenotype show which patients need treatment Loupy A et al. J Am Soc Nephrol 2015;26(7):1721–1731.
- 44. Prognostic models within a disease phenotype show which patients need treatment 50% Loupy A et al. J Am Soc Nephrol 2015;26(7):1721–1731.
- 45. We lack good prognostic biomarkers in kidney transplantation • eGFR • Proteinuria • Histology have on itself insufficient prognostic capacity In addition, and even more importantly, these markers reflect primarily past injury, and not future/ongoing injury We do not identify those the patients that need treatment
- 46. Adapted from Naesens et al. J Am Soc Nephrol 2016;27(1):281–292. 0.3-1.0 vs. <0.3 g/24h 1.0-3.0 vs. <0.3 g/24h >3.0 vs. <0.3 g/24h 30-45 vs. >45 mL/min/m2 15-30 vs. >45 mL/min/m2 <15 vs. >45 mL/min/m2 g+ptc ≥2 vs. <2 Banff grade 1 vs. 0 Banff grade 2-3 vs. 0 Banff grade 1 vs. 0 Banff grade 2-3 vs. 0 Present vs. absent Present vs. absent 0.1 1 10 100 Proteinuria eGFR IFTA Transplant glomerulopathy GNF PVAN microcirc. inflammation Hazard ratio (95% CI) for kidney graft loss Prognostic model? Several prognostic markers are independent risk factors for graft failure Decide who to treat Surrogate endpoint
- 47. iBox provides a prognostic nomogram, but is not (yet) disease-specific Loupy, Aubert, Orandi, Naesens et al submitted
- 48. iBox provides a prognostic nomogram, but is not (yet) disease-specific ROC-AUC = 0.81-0.84 Loupy, Aubert, Orandi, Naesens et al submitted
- 49. Prognostic markers in the pipeline for kidney transplantation • Edmonton classifier for graft loss1 • Edmonton “ABMR molecular score”2 • GOCAR 13-geneset3 1Einecke et al J Clin Invest 2010; 2Loupy et al JASN 2013; 3O’Connell Lancet 2016
- 50. Molecular “Risk score” predicts graft outcome better than histology or proteinuria Low risk score High risk score Time after biopsy Survivalprobability Einecke et al J Clin Invest 2010 AUC=0.83 Risk score for graft loss: Early biopsies: Sensitivity = 100% Specificity = 41% PPV = 5% NPV = 100% Late biopsies: Sensitivity = 83% Specificity = 63% PPV = 47% NPV = 90%
- 51. INTERCOM STUDY (multicenter) ABMR Score - Histology - ABMR Score - Histology + ABMR Score + Histology + ABMR Score + Histology - Halloran et al Am J Transplant 2013 ABMR score for graft loss: Sensitivity = 75% Specificity = 81% PPV = 48% NPV = 93% ROC AUC=0.81 Molecular “ABMR score” predicts graft outcome better than histology of ABMR
- 52. “GoCAR 13-gene score” predicts CADI better than clinical and pathological parameters O’Connell, Zhang et al Lancet 2016
- 53. GoCAR score for graft loss: PPV = ??? NPV = ??? ROC AUC=0.84 “GoCAR 13-gene score” predicts graft failure better than clinical and pathological parameters O’Connell, Zhang et al Lancet 2016
- 54. PERSONALIZED MEDICINE Non-invasive diagnostic markers Invasive diagnostic markers Predictive markers
- 55. Prognostic test = risk for graft failure Predictive test = success of therapy Yazdani & Naesens Transplantation 2017
- 56. The history of novel immunosuppressants gives us a useful and broad armamentarium Kahan B Nat Rev Nephrol 2003;3:831–838.
- 57. Evidence-based medicine in transplantation is NOT personalized medicine
- 58. y Non-invasive diagnostic biomarker stic Invasive diagnostic biomarker Prognostic biomarker Predictive biomarker High disease probabilitylity High risk for disease High risk / bad prognosis Low risk / good prognosis Predicted benefit from treatment X, not Y Predicted benefit from treatment Y, not X Start treatment X Start treatment Y Patients with confirmed disease Prognostic biomarker Predictive biomarker
- 59. Prognostic biomarker Predictive biomarker y Non-invasive diagnostic biomarker stic Invasive diagnostic biomarker Prognostic biomarker Predictive biomarker High disease probabilitylity High risk for disease High risk / bad prognosis Low risk / good prognosis Predicted benefit from treatment X, not Y Predicted benefit from treatment Y, not X Start treatment X Start treatment Y Patients with confirmed disease
- 60. Prognostic biomarker Predictive biomarker y Non-invasive diagnostic biomarker stic Invasive diagnostic biomarker Prognostic biomarker Predictive biomarker High disease probabilitylity High risk for disease High risk / bad prognosis Low risk / good prognosis Predicted benefit from treatment X, not Y Predicted benefit from treatment Y, not X Start treatment X Start treatment Y Patients with confirmed disease
- 61. Prognostic biomarker Predictive biomarker y Non-invasive diagnostic biomarker stic Invasive diagnostic biomarker Prognostic biomarker Predictive biomarker High disease probabilitylity High risk for disease High risk / bad prognosis Low risk / good prognosis Predicted benefit from treatment X, not Y Predicted benefit from treatment Y, not X Start treatment X Start treatment Y Patients with confirmed disease ?
- 62. We urgently need new therapies for the high-risk patients, through awareness and renewed investment 1 2 3 40 5 0 50 100 Years after transplantation Survivalprobability(%) (death-censored) Transplanted in UZ Leuven 01/01/2011 - 01/09/2016 No HLAAbs (N=508) Non-DSA HLAAbs (N=110) DSA (N=58) Log-rank P = 1.9E-05 1 2 3 40 5 0 50 100 Years after transplantation Survivalprobability(%) (overallgraftsurvival) Transplanted in UZ Leuven 01/01/2011 - 01/09/2016 No HLAAbs (N=508) Non-DSA HLAAbs (N=110) DSA (N=58) Log-rank P = 3.9E-03 Naesens et al - unpublished
- 63. PERSONALIZED MEDICINE Non-invasive diagnostic markers Invasive diagnostic markers Risk markers Prognostic markersDiagnostic markers
- 64. PERSONALIZED MEDICINE Non-invasive diagnostic markers Invasive diagnostic markers Risk markers Predictive markersPrognostic markersDiagnostic markers
Hinweis der Redaktion
- This is a 30 min presentation
- Dear colleagues, our profession is changing rapidly. I am a nephrologist, taking care of kidney transplant patients, and together with all of you, I am witnessing one of the major paradigm shift in the history of medicine. While in traditional medicine, we have treated our patients according to what we think is best for the patient population as a whole, we are now moving towards personalized medicine, where we provide specific treatment X to individual patients, and treatment Y to other individuals. This personalized or individualized treatment builds on data, often big data, and biomarkers. This is what this presentation will be about. I will guide you through how I see kidney transplant pathology has a place in this transition of traditional to personalized. When we discuss biomarkers, we should be very careful. There are many different types of biomarkers, and each of them has a specific place in personalization of medicine. Why personalized medicine? This buzz-word is used all the time, but clearly illustrates a crucial aspect of medicine, and the changing paradigms in our profession. In more traditional medicine, think of e.g. transplantation, we treat all the patients the same way, and hope that the treatment will be beneficial for the group of patients. In personalized medicine, we are trying to pick those patients from the population that benefit from the treatment, and also identify which patients need which treatment to get better outcome. And for that, we need patient data, often big data, and biomarkers. When I say biomarkers, it is becoming obvious that there are many different types of biomarkers, and that we need to be clear on these different biomarker types.
- It is important to clearly identify the different types of biomarkers, as this is crucial in understanding how we can use the available biomarkers for real clinical benefit. Risk markers are very important, as they provide us tools to know which patients need extra attention, to be maximally efficient, and not waste time and money to patients who have no risk for disease.
- In high-risk patients, you need to know the timing when disease processes start, preferably at the subclinical level. This search for early disease manifestations typically needs repeated assessment, and should thus be done with non-invasive markers,; eg in blood samples, urine samples, ultrasound examinations etc. Invasive markers cannot be repeated too often, because of their invasiveness and thus risk of side effects of the monitoring on itself. Something we of course need to avoid at all times. Non-invasive markers provide you the probability of active or beginning disease.
- Very often, the disease is then confirmed and the exact phenotype is often determined based on invasive markers, like biopsies. Disease confirmation, but also phenotypic classification, diagnostic fine-tuning
- Once the disease/diagnosis is confirmed, you cannot yet start treatment. Ideally, first it is necessary to know which patients will cure even without treatment, and which patients have bad prognosis if not treated. We need to know for which patients treatment is necessary, and in which patients treatment and treatment-associated side efffects and costs can be avoided. This is done with prognostic biomarkers.
- And it is only the integration of all these different markers that brings us the potential of true personalized medicine. .
- Only with this integration of many different types of biomarkers, we can come to true personalized medicine.
- Urinary 3-gene mRNA expression signature, and wide range of other suggested molecules 3, 44 Wide range of urinary target proteins like CXCL10 and CXCL9 3 Blood 17 gene mRNA expression “kSORT™” 47 Blood 200-gene mRNA expression (“Trugraf”) 46 Several blood and urine miRNAs 3 Molecular microscope for allograft pathology
- The problem is to define how we can use this to improve on the gold standard, which is histology.
- Urinary 3-gene mRNA expression signature, and wide range of other suggested molecules 3, 44 Wide range of urinary target proteins like CXCL10 and CXCL9 3 Blood 17 gene mRNA expression “kSORT™” 47 Blood 200-gene mRNA expression (“Trugraf”) 46 Several blood and urine miRNAs 3 Molecular microscope for allograft pathology
- Once a diagnosis is made, it is clear that we then need to identify which patients or diseases need treatment, and which not. No all rejections are deleterious for outcome, not all histological changes need treatment.
- And this brings us to a crucial issue in biomarker research. IT IS NOT THE P-VALUES THAT COUNT, BUT THE ACCURACY OF THE MARKER.
- The fact that histology is an ideal instrument for evaluating kidney allograft prognosis was already evident for Tom Starzl and his team, in the publication they made of the first 64 cases of succesful kidney transplants wordlwide. They performed protocol-specified biopsies at 2 years after transplantation, and discovered that several biopsies had chronic injury, as is illustrated in this slide.
- You cannot use on-of phenomena (like ABMR present vs. absent) to calculate a prognostic value.
- The accuracy of the diagnosis of ABMR for prediction of survival after 8 years is very low: 50% chance to still have your graft, 50 % chance of graft loss.
- This risk score was calculated on only the late biopsies! NB. The predictive performance was explicitly mentioned in the manuscript
- Predictive performance (apart from ROC AUC) was NOT mentioned in the manuscript, but could be deducted ABMR score for graft loss: Sensitivity = 24/32 = 75% Specificity = 33/293 = 81% PPV = 24/50 = 48% NPV = 108/116 = 93% ROC AUC=0.81 This study is on late ABMR, and perhaps not representative of early ABMR? (the prevalence of early ABMR in these low-risk cohorts is less than 2% of biopsies with molecular assessment). In addition, these indications biopsies had lots of chronic injury, concomitant diseases etc. As in cancer, homogenous and well phenotyped cohorts are needed to ascertain whether the molecularmicroscope strategy could be helpful and add to the conventional assessments. Interesting : both histology of TCMR and TCMR molecular microscope score were not associated with graft survival.
- This risk score was calculated on only the late biopsies! NB. The predictive performance was explicitly mentioned in the manuscript
- This risk score was calculated on only the late biopsies! NB. The predictive performance was explicitly mentioned in the manuscript
- Problem: very few novel drugs developed in transplantatation. Predictive marker development for prediction of novel treatment success therefore severely hampered.
- Problem: very few novel drugs developed in transplantatation. Predictive marker development for prediction of novel treatment success therefore severely hampered.