Strategies to improve long-term outcomes after kidney - - PowerPoint PPT Presentation

Miha Arnol

Strategies to improve long-term outcomes after kidney transplantation

5th Congres of Nephrology in BIH, Tuzla, October 18, 2019

Kidney transplant medicine

A story of success… A story of barriers… A story of how modern scientific medicine overcame several barriers

Long-term survival of ESKD patients Kidney Tx vs. Dialysis vs. General population

ERA-EDTA Registry Annual Report 2016, Amsterdam 2018

EU cohort: 2012-2016

Challenges in transplant medicine

Challenges in transplant medicine

◼ Lack of (appropriate) donors and organs ◼ Adverse events of immunosuppression

• metabolic, cardiovascular, infections, cancer

◼ Monitoring of patients/grafts is inaccurate ◼ No effective treatments for (chronic) rejection ◼ Immune tolerance not a near-future reality ◼ Long-term outcomes are suboptimal

Gondos A et al. Transplantation 2013; 95: 267; Kramer A et al. Clin Kidney J 2016; 9: 457

Challenges in kidney transplant medicine Kidney graft survival: Europe

◼ short-term vs. long-term survival

USRDS: Annual Data Report 2010

Challenges in kidney transplant medicine Number of patients returning to dialysis

USA

Buturović-Ponikvar J et al. Slovenian RRT Registry; ET period: 2000-2014

Challenges in kidney transplant medicine Number of patients returning to dialysis

SLO

Causes of kidney graft failure / loss

A model of kidney graft loss

(CNI toxicity)

Donor Brain death HLA MM (class II) IR injury DGF IF / TA TCMR

• clinical
• subclinical

de-novo DSA Graft failure ABMR

• clinical
• subclinical

Immunosuppression CG Patient death

Adverse events: metabolic, CV, infectious, malignant

Comorbidities Rejection

IR, ischemia-reperfusion; DGF, delayed graft function; DSA, donor-specific antibodies; TCMR, T-cell mediated rejection; ABMR, antibody-mediated rejection; CG, chronic glomerulopathy; IF/TA, interstital fibrosis/tubular atrophy

A model to improve outcomes

#1 quality of donor kidneys

statistics.eurotransplant.org/1086P_kidney

Deceased donors in Eurotransplant Donor age

statistics.eurotransplant.org/1229P_Slovenia_kidney

Deceased donors in Eurotransplant Donor age categories

> 50%

Deceased donors in Slovenia Donor type

standard criteria expanded criteria

statistics.eurotransplant.org/1086P_Slovenia_kidney

www.ctstransplant.org

Kidney graft survival Donor relationship

Since 2016: Living-Donor Kidney Program

• 6 Tx

(parents to children)

Arnol M et al. Ther Apher Dial 2016; 20: 229

Kidney transplants in Slovenia Donor relationship

#2: ischemia-reperfusion (IR) injury and delayed graft function (DGF)

(CNI toxicity)

Donor Brain (Circ.) death HLA MM (class II) IR injury DGF IF / TA TCMR

• clinical
• subclinical

de-novo DSA Graft failure ABMR

• clinical
• subclinical

Immunosuppression CG Patient death

Adverse events: metabolic, CV, infectious, malignant

Comorbidities Rejection

IR, ischemia-reperfusion; DGF, delayed graft function; DSA, donor-specific antibodies; TCMR, T-cell mediated rejection; ABMR, antibody-mediated rejection

SLO (2000-2017) CIT > 12h: 90 % pts CIT > 24h: 21 % pts

www.ctstransplant.org

Kidney graft survival Cold ischemia time

Hypothermic machine perfusion LifePort Kidney Transporter

• 1. Decreases the

risk for DGF

• 2. Attenuates

impact of DGF

• 3. Improves graft

survival

All donors ECD donors

Hypothermic machine perfusion LifePort Kidney Transporter

Moers C et al. N Engl J Med 2009; 360: 7; Treckman J et al. Transplant Int 2011; 24: 548

SCS, static cold storage; MP, machine perfusion

September 19, 2018

Hypothermic machine perfusion LifePort Kidney Transporter - UMC Ljubljana

Arnol M et al. UMCL Research Grant 2017-2019

Jochman I et al. ATC 2019 Congress

Machine perfusion Current perspectives: oxygenation

◼ oxygenated HMP improves 1-year graft function ◼ mediated via reduction in acute rejection

POMP trial: ECD COMPARE trial: DCD

#3: HLA matching

(CNI toxicity)

Donor Brain death HLA MM (class II) IR injury DGF IF / TA TCMR

• clinical
• subclinical

de-novo DSA Graft failure ABMR

• clinical
• subclinical

Immunosuppression CG Patient death

Adverse events: metabolic, CV, infectious, malignant

Comorbidities Rejection

HLA MM, human leukocyte antigen mismatch; DSA, donor-specific antibodies; TCMR, T-cell mediated rejection; ABMR, antibody-mediated rejection

www.ctstransplant.org

HLA matching Graft survival

Tambur AR & Claas FHJ. Am J Transplant 2015; 15: 1148

HLA matching Towards epitope matching

An HLA antigen has a unique epitope set - individual epitopes can also be present on other HLA antigens

HLA matching Towards epitope matching

Wiebe C et al. J Am Soc Nephrol 2017; 28: 3353

Wiebe C et al. Am J Transplant 2019; 19: 1708

HLA matching Towards epitope matching

◼ NGS methodology: expensive / time consuming

#4: immunosuppression discontinuation / minimization

(CNI toxicity)

Donor Brain death HLA MM (class II) IR injury DGF IF / TA TCMR

• clinical
• subclinical

de-novo DSA Graft failure ABMR

• clinical
• subclinical

Immunosuppression CG Patient death

Adverse events: metabolic, CV, infectious, malignant

Comorbidities Rejection

DSA, donor-specific antibodies; TCMR, T-cell mediated rejection; ABMR, antibody-mediated rejection

underimmunosuppression

Wiebe C et al. Am J Transplant 2012; 12: 1157

Immunosuppression minimization De-novo donor-specific antibodies (DSA)

Wiebe C et al. Am J Transplant 2015; 15: 2921

De-novo DSA (%)

Immunosuppression minimization Patient non-adherence and DSA

Visočnik N, Arnol M et al. ESOT 2019

Immunosuppression minimization Steroid withdrawal: rapid SW at day 5

All patients Rapid SW No rapid SW P

(N = 91) (n = 55) (n = 36)

Subclinical rejection at 1 year Incidence

26 (29) 12 (22) 14 (39) 0,127

T-cell mediated

22 (24) 10 (18) 12 (33) 0,099

Antibody-mediated

4 (4) 2 (4) 2 (6) 0,647

Banff classification T-cell mediated Banff 3

8 (9) 4 (7) 4 (11) 0,708

Banff 4/IA,B

8 (9) 3 (6) 5 (14) 0,256

Banff 4/IIA,B

6 (7) 3 (6) 3 (8) 0,678

Antibody-mediated Acute

4 (4) 2 (4) 2 (6) 0,647

Chronic

/

Silent de-novo DSA at 1 year Incidence

13 (14) 9 (16) 4 (11) 0,554

Specificity HLA class I

4 (4) 2 (4) 2 (6) 0,530

HLA class II

9 (10) 7 (13) 2 (6) 0,530

MFI values

2600 (1980–4855) 9100 (1825–17445) 2600 (2020–3620) 0,604

Adverse event All patients (N = 91) Rapid SW (n = 55) No rapid SW (n = 36) Metabolic post-transplant diabetes hyperlipidemia

• steopenia/osteoporosis

Infections CMV Polioma BK respiratory 12 (13%) 18 (20%) 8 (9%) 17 (19%) 12 (13%) 13 (14%) 1 (2%) 10 (19%) 2 (4%) 9 (16%) 4 (7%) 4 (7%) 11 (31%) 8 (22%) 6 (17%) 8 (22%) 8 (22%) 9 (25%)

Immunosuppression minimization Steroid withdrawal: rapid SW at day 5

Visočnik N, Arnol M et al. ESOT 2019

#5: immunosuppression

• ptimization

Vincenti F et al. N Engl J Med 2016; 374: 333

Graft function

Immunosuppression optimization Novel IS drugs: belatacept

Graft and patient survival

Vincenti F et al. N Engl J Med 2016; 374: 333

De-novo DSA

Immunosuppression optimization Novel IS drugs: belatacept

Immunosuppression optimization Novel IS drug combinations: mTOR & CNI

TRANSFORM Study

Pascual J et al. J Am Soc Nephrol 2018; 29: 1979

#6: biomarkers: diagnostic/prognostic/predictive

Biomarkers for optimizing outcomes Towards precision medicine in transplantation

Adapted after Naesens M et al. J Am Soc Nephrol 2018; 29: 24

Surveillance /Indication graft biopsy triple IS novel agents

Banff & Molecular scores of graft injury

Adjusting / Optimizing IS

eGFR / Proteinuria CVE / Infections / Tumors

Weeks Years

Acute Post-Transplant Immunosuppression Maintenance Immunosuppression Optimal Immunosuppression

Induction

• noninv. biomarkers of over/under IS and graft injury:
• clinical, molecular (genomic/trascriptomic/proteomic)

Weeks Years

Biomarkers for optimizing outcomes Towards precision medicine in transplantation

6 / 12 months after transplant

UMC Ljubljana: since 2014 > 300 outpatient allograft Bx

subclinical histologic changes

Biomarkers for optimizing outcomes Surveillance biopsies

Biomarkers for optimizing outcomes Molecular microscope (MMDx)

Halloran P et al. Alberta Transplant Applied Genomic Centre (ATAGC)

◼ ATAGC – research collaboration since 2018

Biomarkers for optimizing outcomes Noninvasive biomarkers: cell-free DNA

Bloom RD et al. J Am Soc Nephrol 2017; 28: 2221

dd-cf-DNA discriminates active rejection dd-cf-DNA levels are higher in ABMR than TCMR

Biomarkers for optimizing outcomes Noninvasive biomarkers: cell-free DNA

dd-cf-DNA discriminates active ABMR in DSA+ patients

Jordan SC et al. Transplantation Direct 2018; 4: e379

Biomarkers for optimizing outcomes Noninvasive biomarkers: cell-free DNA

Biomarkers for optimizing outcomes Noninvasive biomarkers: cell-free DNA

◼ Transplant-Check study: non-selected cohort of

kidney Tx recipients within the first year after Tx

Tx 1 12 2 3 4 5 6 7 8 9 10 11 months after transplant surveillance visits with dd-cf-DNA tests in the serum Biopsy DSA Biopsy DSA Bx, DSA, dd-cf-DNA for cause

Key problem: standardized collection and storage

• f biological samples: blood, urine, tissue =

“BioBank“ Possible solution = cooperation with other partners: Faculty of Medicine, UL - Institute for Biochemistry: Laboratory for farmacogenomics & Centre for functional genomics and biochips

Biomarkers for optimizing outcomes Biomarkers: molecular

post-transplant integrative assessment: diagnosis / prognosis / treatment

Clinical data Epidemiology Histopathology Transcriptomic Immunology Integrative assessment

• Immunology
• Clinical biomarkers

Routine Lab

• Lesions
• Diagnoses

Histopathology

• Transcriptome

profiling Molecular phenotyping

Diagnosis Risk prediction Prognosis Response to therapy

Optimizing transplant outcomes Biomarkers, surrogates, clinical endpoints

Translational Kidney Transplant Research Group

Clinical data & Epidemiology

Centre for Kidney Tx

• Dpt. of Nephrology

Histopathology

Institute of Pathology

Transcriptomic

• Inst. for Biochemistry

Medical Exp. Centre National Inst. of Biology

Immunology

Tissue Typing Centre IMI

Integrative assessment Diagnosis Risk prediction Prognosis Response to therapy

Optimizing transplant outcomes Biomarkers, surrogates, clinical endpoints

UMCL, Centre for Kidney Tx Department of Nephrology Medical Faculty, University of Ljubljana

• Institute of Pathology
• Institute of Biochemistry
• Medical Experimental Centre
• Institute for Microbiology & Immunology

Tissue Typing Centre, BTC of Slovenia National Institute of Biology

Optimizing transplant outcomes Biomarkers, surrogates, clinical endpoints

post-transplant integrative assessment: research collaboration with ATAGC