Improving Organ Transplant Management. Improving Organ Transplant Management

Size: px

Start display at page:

Download "Improving Organ Transplant Management. Improving Organ Transplant Management"

Wilfred Little
6 years ago
Views:

Improving Organ Transplant Management MODERATOR John Gill, MD Providence Health Care, Vancouver, Canada SPEAKERS Understanding Events in Organ Transplant: Lessons from the Molecular Microscope Philip

1 Improving Organ Transplant Management MODERATOR John Gill, MD Providence Health Care, Vancouver, Canada SPEAKERS Understanding Events in Organ Transplant: Lessons from the Molecular Microscope Philip F. Halloran, MD, PhD Alberta Transplant Applied Genomics Centre, Edmonton, Canada Impact of MMDx on Kidney Transplant Management Gaurav Gupta, MD Virginia Commonwealth University, Richmond, VA Understanding the Donor-Specific Antibody Phenotype Carmen Lefaucheur, MD Saint Louis Hospital, Paris, France Integrated Diagnostics Alexandre Loupy, MD, PhD Necker Hospital, Paris, France Improving Organ Transplant Management Dr. Phil Halloran Alberta Transplant Applied Genomics Centre, Edmonton, AB, Canada Dr. Gaurav Gupta Virginia Commonwealth University, Richmond, VA, USA Dr. Alex Loupy Hôpital Necker, Paris, France Dr. Carmen LeFaucheur Hôpital Saint Louis, Paris, France Launching the MMDx TM molecular diagnostic system Understanding events in organs transplants: lessons from the Molecular Microscope Diagnostic System (MMDx TM ) Phil Halloran Alberta Transplant Applied genomics Centre (ATAGC) ( Transcriptome sciences Inc (TSI) ( 1

2 Our center (ATAGC) through its company (TSI) has concluded a licensing agreement with One Lambda/Thermo Fisher to commercialize MMDx TM as a centralized diagnostic service for biopsies: service laboratory in USA. Learning objectives 1. The unmet need 2. What is MMDx TM 3. MMDx-kidney 4. MMDx-heart (EMB) 5. MMDx-lung (TBB) What the clinician needs to know about a troubled transplant Rejection T cell-mediated rejection (TCMR) Antibody-mediated rejection (ABMR) Probability of non-adherence Guidance for therapy Parenchymal injury ( wounding ) Recent parenchymal injury (AKI) Irreversible consequences of previous injury (atrophy-scarring) Risk of progression to failure 2

Problems with opinion-based diagnostics Empirical: no true gold standard Based on non-specific features Requires empirical guidelines (algorithms) Experts interpret guidelines: It depends Dichotomous

continuous Poor assessment of parenchymal injury Variability between observers Estimate of intrinsic error rate ( noise ) Histology: poor inter-observer agreement provides an estimate of noise : true

3 Problems with opinion-based diagnostics Empirical: no true gold standard Based on non-specific features Requires empirical guidelines (algorithms) Experts interpret guidelines: It depends Dichotomous ( 0 vs. 1 ) vs. continuous Poor assessment of parenchymal injury Variability between observers Estimate of intrinsic error rate ( noise ) Histology: poor inter-observer agreement provides an estimate of noise : true error rate Normal vs. abnormal : good diagnosis in abnormal: not good e.g. when one pathologist diagnoses T cell mediated rejection, will a second pathologist diagnose cell-mediated rejection: kidney transplant: 50% 1 heart transplant: 28% 2 lung transplant: 0-18% 3 liver transplant? Expectation: The degree to which MMDx TM agrees with histology will be limited by the noise in histology i.e. similar to the agreement of a second pathologist with the diagnosis by a first pathologist i.e. kidney 50% heart 28% lung 0-18% 1. J. Reeve et al. Molecular diagnosis of T cell-mediated rejection in human kidney transplant biopsies. Am J Transplant 13 (3): , M. G. Crespo-Leiro et al. Concordance Among Pathologists in the Second Cardiac Allograft Rejection Gene Expression Observational Study (CARGO II). Transplantation 94 (11): , S. M. Arcasoy et al. Pathologic interpretation of transbronchial biopsy for acute rejection of lung allograft is highly variable. Am.J.Transplant. 11 (2): , Clinical assessments (e.g. diagnoses) are predictions Predictions should be: Probabilistic: do not dichotomize Consensus-seeking Updatable 3

Molecules can assess acute injury: histology cannot JASN 23:948, 2012 ATAGC The Molecular Microscope

changes in biopsies (stabilized in RNAlater shipped Fedex room temp) and applies equations to compare

Uses predefined diagnosis-trained or lesion-trained meta-classifiers to assign scores to reflect

Antibody-mediated rejection (ABMR) Extent of irreversible damage (Atrophy-scarring Score) Acute

score Molecular MicroscopeTM The ATAGC/TSI Edmonton team Anna Hutton Konrad Famulski Luis Hidalgo

4 Molecules can assess acute injury: histology cannot JASN 23:948, 2012 ATAGC The Molecular Microscope Diagnostic System (MMDx)TM A central diagnostic system that uses microarrays to measure transcript changes in biopsies (stabilized in RNAlater shipped Fedex room temp) and applies equations to compare the biopsy to a Reference Set, generating an automated report in 24 hours. Uses predefined diagnosis-trained or lesion-trained meta-classifiers to assign scores to reflect probabilities: Rejection Score (TCMR and/or ABMR) T cell-mediated rejection (TCMR) Score Antibody-mediated rejection (ABMR) Extent of irreversible damage (Atrophy-scarring Score) Acute kidney injury (AKI) Score Risk Score for progression to failure Probability of non-adherence: MH/time score Molecular MicroscopeTM The ATAGC/TSI Edmonton team Anna Hutton Konrad Famulski Luis Hidalgo Vido Ramassar Jessica Chang Michael parkes Michelle Ryan Rob Polakowski Jeff Reeve Katelynn Madill-Thomsen (Michael Trites) Brendan Halloran Kieran Halloran 4

Thank you to the current/recent MMDx collaborators Olivier Aubert Jessica Chang Gunilla Einecke Farsad Eskandary Konrad Famulski Kieran Halloran Luis Hidalgo Anna Hutton Jon Kobashigawa Carmen

Bromberg Patrick Bruneval Maria Crespo-Leiro Declan de Freitas Jean-Paul DuongVan-Huyen Bob Gaston John Gill Gaurav Gupta Daniel Kim Ornella Leone Andrew Malone Roslyn Mannon Arthur Matas Millie

5 Thank you to the current/recent MMDx collaborators Olivier Aubert Jessica Chang Gunilla Einecke Farsad Eskandary Konrad Famulski Kieran Halloran Luis Hidalgo Anna Hutton Jon Kobashigawa Carmen Lefaucheur Alexandre Loupy Katelynn Madill-Thomsen Luciano Potena Vido Ramassar Jeff Reeve Joana Sellares Jeffery Venner Enver Akalin Arezu Aliabadi Adam Bingaman Georg Bohmig Dan Brennan Jonathan Bromberg Patrick Bruneval Maria Crespo-Leiro Declan de Freitas Jean-Paul DuongVan-Huyen Bob Gaston John Gill Gaurav Gupta Daniel Kim Ornella Leone Andrew Malone Roslyn Mannon Arthur Matas Millie Sameniego Daniel Seron Andreas Zuckerman One Lambda/Thermo Fisher Scientific Industrial Research Assistance Program (IRAP) Muttart Chair in Clinical Immunology Roche Organ Transplantation Research Foundation Genome Canada Canada Foundation for Innovation Novartis University Hospital Foundation Capital Health/Alberta Health Services Astellas Pharma Inc. Roche Molecular Systems, Roche Canada Alberta Advanced Education and Technology Special thanks to: clinical collaborators and the patients >100 investigators in 3 international teams: INTERCOMEX Clinicaltrials.gov NCT INTERHEART Clinicaltrials.gov NCT INTERLUNG ClinicalTrials.gov: NCT Business development Robert Polakowski Hal Gibson Edmonton ATAGC Cedars-Sinai, LA St Louis San Antonio Ann Arbor Toronto Manchester/Dublin New York LaCaruna Baltimore Virginia Birmingham UAB Barcelona Hannover Paris Vienna Bologna Three teams of international investigators: INTERCOMEX Clinicaltrials.gov NCT INTERHEART Clinicaltrials.gov NCT INTERLUNG ClinicalTrials.gov: NCT Some collaborating centers in MMDx TM project Brisbane Sydney Melbourne Advantages and insights from MMDx Much less tissue needed: safety Objective, quantitative, probabilistic International standard for assessment Correct errors in histology C4d, v-lesions, need for DSA Estimates functional disturbance: injury Mechanisms and druggable targets Theranostic support: drug development 5

Patient ID Biopsy Site ID INTERCOM Study ID Attending Physician XPA4756 XPA96B1 XPA96 Date Reported (Y-M-D) 2015-04-28 Date Received (Y-M-D) 2015-04-27 Date of Transplant (Y-M-D) 2007-01-27 Date of

6 Patient ID Biopsy Site ID INTERCOM Study ID Attending Physician XPA4756 XPA96B1 XPA96 Date Reported (Y-M-D) Date Received (Y-M-D) Date of Transplant (Y-M-D) Date of Biopsy (Y-M-D) NA General XPA96, The MMDxTM Kidney system is operational Reviewed in Nature Reviews Nephrology 12 (9): , 2016 Name: DOB (Y-M-D): Age at Bx: Clinical Information follow-up from previous biopsy Time of Biopsy Post-Tx 8.2 years Biopsy Indication (ABMR) Primary Disease Etiology uncertain Transplant Type Deceased-heart beating GFR (CG) at Biopsy 20 Tx before Biopsy Yes (PP/IVIG/Ritux) Proteinuria Pos DSA Status at Biopsy DSA II positive Haematuria PRA Status at Biopsy Unknown Local Histopathology Phenotype: the Banff System Inflammatory/TCMR ABMR Lesions Atrophy/Scarring Other Lesions i NA ptc NA ci NA BK NA t NA g NA ct NA v NA cg NA cv NA total i NA C4d NA ah NA mm NA Banff Diagnosis 1 NA Banff Diagnosis 2 NA The current MMDx report page 1 Integrated interpretation incorporating histo-clinical variables can follow as data is available Molecular Phenotype: the Edmonton Molecular Microscope System Biopsy Range in %ile compared to Classifier/PBT Interpretation Score Reference Set Reference Set Global Disturbance Score > 9.33 All:55.5 Late:55.6 Moderate Acute kidney injury (AKI) Score > 1.98 All:71.5 Late:78 High Atrophy-Fibrosis Score > 1.0 All:54.2 Late:36.2 Moderate Rejection Score > 1.0 All:79.4 Late:75.3 Moderate TCMR Score > 1.0 All:49.3 Late:56.2 Low ABMR Score > 1.0 All:95.1 Late:91.8 Very High Pure molecular interpretation Relatively severe ABMR, with ptc/g and cg features. No TCMR. Relatively mild atrophyfibrosis with considerable AKI and inflammation, suggesting recent injury. Histo. Dx. of the 50 NN Mol. Dx. of the 50 NN Prop. Of 50 NN surviving C4d-ABMR: 0.32 C4d+ABMR: 0.16 Borderline: 0.12 TG: 0.11 Mixed: 0.11 GN: 0.06 TCMR: 0.03 Other: 0.03 IFTA: 0.03 NOMOA: 0.02 AKI: 0.01 Rej: 0.62 ABMR: 0.54 IRRATs: 0.53 cigt1: 0.44 TCMR: year: years: 0.43 The current MMDx report page 3 Nearest neighbors analysis 6

7 XVA49B1-B2 Shift in phenotype in a non-adherent patient. Initial biopsy (July 15, 2015) : TCMR, no ABMR Second biopsy (July 29, 2015): no TCMR but early-stage ABMR. (Initial low ah molecular score: non-adherence) ID: XVA49B1 ID: XVA49B1 7

8 ID: XVA49B2 ID: XVA49B2 Bottom line for INTERCOMEX Clinicians frequently disagree with their own local histology assessments (20%). Clinician feedback indicated that MMDx agreed with their clinical assessment significantly more frequently than did the local histology assessments. P.F.Halloran et al: Real Time Assessment of Kidney Transplant Indication Biopsies by Microarrays: First Results of the INTERCOMEX Study (submitted) 8

9 Counts of the histologic diagnoses in the six archetype clusters (% of columns) Histologic diagnosis N (% of 1208) A NR N=774 Archetype cluster A TCMR N=81 A Mixed N=27 A4,A5,A6 All ABMR Potential errors in histology diagnoses 395/ % (BD as error) ABMR-related N=279 (200 called ABMR by MMDx) ABMR 215 (18%) /215=27% ABMR suspected 24 (2%) /24=54% TG 40 (3%) /40=60% Mixed N=41 Mixed 41 (3%) /41=78% TCMR-related N=196 TCMR 87 (7%) /87=57% Borderline 109 (9%) all NOMOA 274 (23%) /274=12% AKI 96 (8%) /96=6% IFTA 145 (12%) /145=15% Not rejection-related N=692 GN 97 (8%) /97=21% Diabetic Neph. 18 (1%) /18=17% BK nephropathy 37 (3%) /37=46% Other 25 (2%) /25=24% Unsupervised archetype analysis Assign 6 states for kidney: Using classifier input 1. No rejection 2. TCMR 3. Mixed 4. ABMR early-stage 5. ABMR fully-developed 6. ABMR late-stage Assign 3 states for all organs Using rejection-associated transcripts 1. No rejection 2. TCMR (or TCMR-like) 3. ABMR (or ABMR-like) Distribution of rejection phenotypes (archetype scores) over time Need a legend, or better line annotations as in next fig. Also y-label: Prevalence. Olive colour has to change to cyan. Fully-developed ABMR Early-stage ABMR Early-stage ABMR (Pre-existing DSA) TCMR Late-stage ABMR 9

10 MMDx-Heart The INTERHEART study Reading human EMBs by their mrna expression Endomyocardial biopsy Drs. Caves and Billingham developed the concept of scheduled endomyocardial biopsies to diagnose ACR before the onset of graft dysfunction Circulation in press 2017 The molecular landscape of ABMR in heart transplants is similar to that in kidney transplants 10

Endomyocardial biopsies of heart transplants show molecular ABMR changes similar to those in kidney transplants with ABMR (Loupy et al Circulation in press 2017) Comparing the A123 Archetypes for

11 Endomyocardial biopsies of heart transplants show molecular ABMR changes similar to those in kidney transplants with ABMR (Loupy et al Circulation in press 2017) Comparing the A123 Archetypes for kidney, heart, and lung as estimated by expression of rejection-associated transcripts (RATs) identified in kidney A1: no rejection; A2:TCMR; A3: ABMR (or ABMR-like) A1=no rejection; A2=TCMR or TCMR-like; A3=ABMR or ABMR-like Use expression of transcripts associated with rejection in kidney to classify endomyocardial biopsies for probability of No rejection, TCMR, or ABMR in a single EMB bite 11

12 A no rejection heart transplant endomyocardial biopsy MMDx-Heart report EMB from Cedars Sinai 1.1 years post tx Molecular Phenotype Classifier/PBT Biopsy IQR in Normal Interpretation Score DSAST > 0.03 above range ENDAT > above range GRIT > above range HT > 0.15 below range IRRAT > 0.03 above range CDH > 9.05 above range CDH > normal ADAMDEC > 2.91 above range CXCL > 5.12 normal IFNG > 4.10 normal CTLA > 3.56 above range MMDx-Heart signout Abnormal biopsy. ABMR (archetype probability 80%). No TCMR (archetype probability 0%). Some dedifferentiation (below average HT1s) with some injury (elevated IRRATs). Reproducibility of MMDx in heart transplant for two EMB bites: TCMR EMB bite 1 vs. EMB bite 2 12

Assess the performance of microarray analysis of lung tissue, using kidneyderived molecular

13 MMDx-Lung The INTERLUNG study Reading human TBBs by their mrna expression Study Objectives for transbronchial biopsies (TBBs) 1. Assess the performance of microarray analysis of lung tissue, using kidneyderived molecular signatures in TBB 2. Assess the potential of getting the same information from mucosal tissue (MB) TBB 3B-MB: X 2B-MB? Transbronchial biopsy Mucosal biopsy ATAGC ID: LED36TBB1 13

14 Thank you Hold questions until the end 14

Special thanks to our clinical collaborators Special thanks to our patients. Administration. Andre Baretto

Special thanks to our clinical collaborators Special thanks to our patients. Administration. Andre Baretto Antibody-mediated rejection: a prototype for antibody-mediated diseases Phil Halloran Alberta Transplant Applied Genomics Centre Edmonton, Canada Disclosures PFH has shares in TSI a university spinoff