Transplant Webinar Series: Ep. 9 Biomarkers for Post-Transplant Immune Injury

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Immune monitoring after kidney transplantation Maarten Naesens Maarten Naesens, MD PhD 52 nd ERA-EDTA meeting University of Leuven, Belgium London, May 2015

Scoping Paper Work Programme 2018-2020: Personalised Medicine is one of the five research areas within Priority 1

The paradigm of personalized medicine PREDICT DIAGNOSE WAIT PREVENT TREAT

The paradigm of personalized medicine PREDICT DIAGNOSE PREVENT TREAT

Personalized medicine builds on data and s Traditional medicine All patients same treatment Personalized medicine Predictive Data / Biomarkers Predicted benefit from treatment X, not Y Predicted benefit from treatment Y, not X

All patients Patients with confirmed disease Risk/susceptibility Risk/susceptibility markers Prognostic Low risk for disease High risk for disease Low risk / good prognosis High risk / bad prognosis Risk/susceptibility Non-invasive diagnostic Predictive Low disease probability High disease probability Predicted benefit from treatment X, not Y Predicted benefit from treatment Y, not X Non-invasive diagnostic Invasive diagnostic Start treatment X Start treatment Y No disease confirmation Disease confirmation Safety Pharmacodynamic / response Monitoring - Disease activity - Pharmacokinetics (exposure) Naesens & Anglicheau J Am Soc Nephrol 2018

All patients Patients with confirmed disease Risk/susceptibility Prognostic Low risk for disease High risk for disease Low risk / good prognosis High risk / bad prognosis Risk/susceptibility Non-invasive diagnostic Non-invasive diagnostic markers Predictive Low disease probability High disease probability Predicted benefit from treatment X, not Y Predicted benefit from treatment Y, not X Non-invasive diagnostic Invasive diagnostic Start treatment X Start treatment Y No disease confirmation Disease confirmation Safety Pharmacodynamic / response Monitoring - Disease activity - Pharmacokinetics (exposure) Naesens & Anglicheau J Am Soc Nephrol 2018

All patients Patients with confirmed disease Risk/susceptibility Prognostic Low risk for disease High risk for disease Low risk / good prognosis High risk / bad prognosis Risk/susceptibility Non-invasive diagnostic Predictive Low disease probability High disease probability Predicted benefit from treatment X, not Y Predicted benefit from treatment Y, not X Non-invasive diagnostic Invasive diagnostic Invasive diagnostic Start markers treatment X Start treatment Y No disease confirmation Disease confirmation Safety Pharmacodynamic / response Monitoring - Disease activity - Pharmacokinetics (exposure) Naesens & Anglicheau J Am Soc Nephrol 2018

All patients Patients with confirmed disease Risk/susceptibility Prognostic Prognostic markers Low risk for disease High risk for disease Low risk / good prognosis High risk / bad prognosis Risk/susceptibility Non-invasive diagnostic Predictive Low disease probability High disease probability Predicted benefit from treatment X, not Y Predicted benefit from treatment Y, not X Non-invasive diagnostic Invasive diagnostic Start treatment X Start treatment Y No disease confirmation Disease confirmation Safety Pharmacodynamic / response Monitoring - Disease activity - Pharmacokinetics (exposure) Naesens & Anglicheau J Am Soc Nephrol 2018

All patients Patients with confirmed disease Risk/susceptibility Prognostic Low risk for disease High risk for disease Low risk / good prognosis High risk / bad prognosis Risk/susceptibility Non-invasive diagnostic Predictive markers Predictive Low disease probability High disease probability Predicted benefit from treatment X, not Y Predicted benefit from treatment Y, not X Non-invasive diagnostic Invasive diagnostic Start treatment X Start treatment Y No disease confirmation Disease confirmation Safety Pharmacodynamic / response Monitoring - Disease activity - Pharmacokinetics (exposure) Naesens & Anglicheau J Am Soc Nephrol 2018

All patients Patients with confirmed disease Risk/susceptibility Prognostic Low risk for disease High risk for disease Low risk / good prognosis High risk / bad prognosis Risk/susceptibility Non-invasive diagnostic Predictive Low disease probability High disease probability Predicted benefit from treatment X, not Y Predicted benefit from treatment Y, not X Non-invasive diagnostic Invasive diagnostic Start treatment X Start treatment Y No disease confirmation Disease confirmation Safety, pharmacodynamic, monitoring markers Safety Pharmacodynamic / response Monitoring - Disease activity - Pharmacokinetics (exposure) Naesens & Anglicheau J Am Soc Nephrol 2018

All patients Patients with confirmed disease Risk/susceptibility Risk/susceptibility markers Prognostic markers Prognostic Low risk for disease High risk for disease Low risk / good prognosis High risk / bad prognosis Non-invasive Risk/susceptibility diagnostic markers Non-invasive diagnostic Low disease probability High disease probability Predicted benefit from treatment X, not Y Predictive Predictive markers Predicted benefit from treatment Y, not X Non-invasive diagnostic Invasive diagnostic Invasive diagnostic markers Start treatment X Start treatment Y No disease confirmation Disease confirmation Safety Pharmacodynamic / response Safety, pharmacodynamic, monitoring markers Monitoring - Disease activity - Pharmacokinetics (exposure)

Risk/susceptibility markers Prognostic markers Non-invasive diagnostic markers PERSONALIZED MEDICINE Predictive markers Invasive diagnostic markers Safety, pharmacodynamic, monitoring markers

KIDNEY TRANSPLANTATION

Risk markers Diagnostic markers Prognostic markers Predictive markers

Risk markers

We have several widely used susceptibility/risk s in kidney transplantation Number of HLA mismatches Pretransplant PRA% Pretransplant DSA De novo DSA occurrence Success story of HLA genotyping and antibody profiling TRANSPLANTATION MEDICINE = FRONTRUNNER IN PERSONALIZED MEDICINE

More personalization in allocation (AM program) leads to better outcome *AM = Eurotransplant Acceptable Mismatch program Heidt et al Kidney Int 2018

More personalization in allocation (AM* program) leads to better outcome *AM = Eurotransplant Acceptable Mismatch program Heidt et al Kidney Int 2018

More personalization in allocation: a role of high-resolution sequencing? Low resolution Acceptable MM at the antigen level High/allelic resolution Acceptable MM at the epitope level

New risk s in the pipeline for kidney transplantation? Epitope mismatch load 1 Genetic assessment for ahus recurrence Urinary or serum supar for FSGS recurrence 2 FSGS recurrence panel 3 PLA2R and THSD7A antibodies for recurrence of membranous glomerulopathy 4,5 Donor-reactive T-cell response 6 Risk markers 1 Wiebe et al Transplantation 2016; 2 Franco Palacios et al Transplantation 2013; 3 Delville et al Sci Transl Med 2014; 4 Sprangers et al Transplant Rev 2013; 5 Tomas et al J Clin Invest 2016; 6 Gandolfini et al Plos One 2018

Risk markers

Risk markers Diagnostic markers

We have several widely used diagnostic s in kidney transplantation Non-invasive: Serum creatinine/egfr Proteinuria Polyomavirus PCR Urinary mrna markers Urinary mirna markers Urinary protein markers Blood mrna markers

Non-invasive diagnostic markers in the pipeline for kidney transplantation 100 Urinary mrna Specificty for acute rejection (%) 75 50 25 0 0 25 50 75 100 Sensitivity for acute rejection (%) mrna Perforin Granzyme B PI-9 CD103 FOXP3 CXCL10 NKG2D TIM3 Granulysin Multigene signature Naesens and Anglicheau, J Am Soc Nephrol 2018

Non-invasive diagnostic markers in the pipeline for kidney transplantation 100 Urinary proteins Specificty for acute rejection (%) 75 50 25 0 0 25 50 75 100 Sensitivity for acute rejection (%) Proteins CXCL9 CXCL10 Fractalkine Naesens and Anglicheau, J Am Soc Nephrol 2018

Non-invasive diagnostic markers in the pipeline for kidney transplantation 100 Blood mrna Specificty for acute rejection (%) 75 50 25 0 0 25 50 75 100 Sensitivity for acute rejection (%) mrna Granzyme B Perforin FasL HLA-DRA Multigene signature Naesens and Anglicheau, J Am Soc Nephrol 2018

Non-invasive diagnostic markers in the pipeline for kidney transplantation 100 Blood mrna Trugraf Specificty for acute rejection (%) 75 50 25 0 0 25 50 75 100 Sensitivity for acute rejection (%) mrna Granzyme B Perforin FasL HLA-DRA ksort Multigene signature Naesens and Anglicheau, J Am Soc Nephrol 2018

The TruGraf assay needs further validation 200 peripheral blood mrna gene-set - early-access clinical programs started - large interventional trials ongoing - prospective, randomized, multi-center clinical trial ongoing Kurian et al Am J Transplant 2014

CTOT-08 study yields a novel 57-gene marker for subclinical rejection 57-gene marker Diagnostic accuracy for subclinical TCMR Training set (N=530): NPV: 88% PPV: 61% Validation set (N=138): NPV: 78-80% Sens: 48% PPV: 47-51% Friedewald et al Am J Transplant 2018

The ksort assay needs further validation 17 gene-set with published validation in case-control setting Roedder et al PLOS Med 2014

We have several widely used diagnostic s in kidney transplantation Non-invasive: Serum creatinine/egfr Proteinuria Polyomavirus PCR Clinical value of novel markers?

We have several widely used diagnostic s in kidney transplantation Non-invasive: Serum creatinine/egfr Proteinuria Polyomavirus PCR Clinical value of novel markers? Invasive: Histology of protocol biopsies Histology of for-cause (indication) biopsies

Currently used diagnostic s don t capture subclinical injury Creatinine Proteinuria Diagnostic threshold Subclinical Acute dysfunction acute rejection Chronic dysfunction Time BX for cause I-BX I-BX I-BX Nankivell et al. NEJM 2003 Lerut et al. Transplantation 2007 Naesens et al. JASN 2009 Ters et al. AJT 2013

Currently used diagnostic s don t capture subclinical injury Creatinine Proteinuria Treatment Diagnostic threshold Subclinical Acute dysfunction acute rejection Acute pathology Chronic dysfunction BX for cause Time Nankivell et al. NEJM 2003 Lerut et al. Transplantation 2007 Naesens et al. JASN 2009 Ters et al. AJT 2013

Currently used diagnostic s don t capture subclinical injury Creatinine Proteinuria Treatment Diagnostic threshold Subclinical Acute dysfunction acute rejection Acute pathology BX for cause Protocol BX P-BX P-BX P-BX P-BX Chronic pathology Time Nankivell et al. NEJM 2003 Lerut et al. Transplantation 2007 Naesens et al. JASN 2009 Ters et al. AJT 2013

Immune injury in kidney transplant biopsies is defined according to the Banff classification Haas et al. Am J Transplant 2018

Kidney transplant histology is problematic as a diagnostic Shinstock, Sapir-Pichhadze, Naesens et al Am J Transplant 2018

Innovative diagnostic s for transplant injury are being developed Haas et al. Banff 2017 paper - Am J Transplant 2018

Invasive diagnostic markers in the pipeline for kidney transplantation From MMDx website: www.molecular-microscope.com

Intrarenal NK cell mrna signatures accurately reflect ABMR disease activity 10 8 * NK cells total Activated NK cells Resting NK cells 100 ABMR vs NR Total NK cells 6 4 *** *** * * Sensitivity% 50 2 0 0 1/23/45/6 0 1/23/45/6 0 1/23/45/6 Microcirculation inflammation 0 0 50 100 100% - Specificity% AUC = 0.98 P = 1.1E-08 Yazdani, Callemeyn et al Kidney Int In press

Intrarenal NK cell mrna signatures accurately reflect ABMR disease activity and graft outcome 100 100 NK cells + Graft survival (%) 50 Low NK cells+ 196 High NK cells+ 86 Log-rank P = 1.7E-03 HR = 3.60 [1.98-6.55] Low NK cells+ High NK cells+ 0 0 500 1000 1500 Days after biopsy 150 56 58 34 7 10 Sensitivity% 50 AUC = 0.74 P = 2.2E-05 0 0 50 100 100% - Specificity% Graft failure 1-year post-biopsy Yazdani, Callemeyn et al Kidney Int In press

Risk markers Diagnostic markers

Risk markers Diagnostic markers Prognostic markers

egfr at 1 year is associated with graft outcome, and is a fair prognostic marker 100 MRDR egfr at 1 year and graft failure >70 ml/min 100 ROC for graft failure 5 year after biopsy according to 1 year MDRD egfr Graft survival (%) 80 60 40 20 0 log-rank P<0.0001 1 5 10 15 Time after biopsy (years) 60-70 ml/min 50-60 ml/min 40-50 ml/min 30-40 ml/min 20-30 ml/min <20 ml/min Sensitivity % 80 60 40 20 0 0 20 40 60 80 100 100% - Specificity% AUC=0.77 p<0.0001 Naesens et al (Unpublished)

Proteinuria is a risk factor for graft failure but a poor prognostic marker B Percent survival 100 80 60 40 20 0 No. at risk log-rank P <0.0001 Biopsy time points (N=1335) 1 5 10 Time after biopsy (years) Proteinuria < 0.3 g/24h 0.3-1.0 g/24h 1.0-3.0 g/24h >3.0 g/24h C True Positive Fraction (%) 100 80 60 40 Biopsy time points (N=1335) 20 AUC=0.66 (95% CI 0.63-0.69) P <0.0001 0 0 20 40 60 80 100 Naesens M et al J Am Soc Nephrol 2015

The CADI score is a bad prognostic marker, despite the association with graft failure All biopsies: ROC for 5y graft loss 100 Graft survival (%) 100 80 60 40 20 0 CADI score in indication biopsy log-rank P<0.0001 1 5 10 15 Time after biopsy (years) N=1335 indication biopsies Naesens et al (Unpublished) CADI 0 CADI 1 CADI 2-3 CADI 4-5 CADI 6-7 CADI 8-9 CADI >9 Sensitivity % Sensitivity % 80 60 40 20 0 0 20 40 60 80 100 80 60 40 20 100% - Specificity% AUC=0.65 p<0.0001 Late biopsies: ROC for 5y graft loss (>1y) 100 0 0 20 40 60 80 100 100% - Specificity% AUC=0.76 p<0.0001

We lack good prognostic s in kidney transplantation egfr Proteinuria Histology have on itself insufficient prognostic capacity We do not identify those the patients that need treatment

Several prognostic markers are independent risk factors for graft failure Hazard ratio (95% CI) for kidney graft loss Proteinuria 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 egfr microcirc. inflammation IFTA Transplant glomerulopathy GNF PVAN 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 Prognostic model? Decide who to treat From Naesens et al J Am Soc Nephrol 2015

Mathematical integration allows estimating individual prognosis Loupy et al under review

Prognostic markers in the pipeline for kidney transplantation Edmonton ABMR molecular score 1 Edmonton classifier for graft loss 2 GOCAR 13-geneset 3 1 Loupy et al JASN 2013; 2 Einecke et al J Clin Invest 2010; 3 O Connell Lancet 2016

Molecular ABMR score predicts graft failure better than histology of ABMR INTERCOM STUDY (multicenter) ABMR Score - Histology - ABMR Score - Histology + ABMR Score + Histology + ABMR Score + Histology - ABMR score for graft loss: Sensitivity = 75% Specificity = 81% PPV = 48% NPV = 93% Halloran et al Am J Transplant 2013 ROC AUC=0.81

Molecular Risk score predicts graft outcome better than histology or proteinuria Low risk score Survival probability High risk score AUC=0.83 Time after biopsy Einecke et al J Clin Invest 2010 Risk score for graft loss: Early biopsies: Late biopsies: Sensitivity = 100% Sensitivity = 83% Specificity = 41% Specificity = 63% PPV = 5% PPV = 47% NPV = 100% NPV = 90%

GoCAR score predicts CADI better than clinical and pathological parameters O Connell, Zhang et al Lancet 2016

GoCAR score predicts graft outcome with reasonable accuracy O Connell, Zhang et al Lancet 2016 GoCAR score for graft loss: PPV =? NPV =? ROC AUC=0.84

Risk markers Diagnostic markers Prognostic markers

Risk markers Diagnostic markers Prognostic markers Predictive markers

Prognostic test = risk for graft failure Predictive test = success of therapy Patients with confirmed disease Prognostic Prognostic Low risk / good prognosis High risk / bad prognosis Predictive Predictive Predicted benefit from treatment X, not Y Predicted benefit from treatment Y, not X Naesens & Anglicheau J Am Soc Nephrol 2018 Start treatment X Start treatment Y

Risk/susceptibility markers Prognostic markers Non-invasive diagnostic markers PERSONALIZED MEDICINE Predictive markers Invasive diagnostic markers Safety, pharmacodynamic, monitoring markers KIDNEY TRANSPLANTATION

Thank you! maarten.naesens@uzleuven.be

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