Rejection of organ transplants: is molecular diagnosis ready for prime time?

Rejection of organ transplants:is molecular diagnosis ready for prime time? Yes and it will begin a new general approach to organ inflammatory diseases and injury Phil Halloran Alberta Transplant Applied Genomics Centre and Transcriptome Sciences Inc University of Alberta, Edmonton

Learning Objectives: molecular measurements in transplant biopsies • Interrelationships among molecular features • Relationship to histopathologic measurements • Clinical significance: function and outcome • Creating a system to make molecular measurements on biopsies interpretable for clinicians

Disclosures • Research collaborations: • Astellas, Stromedix • Support from Roche Canada: Affymetrix arrays arrays • Consulting agreements: • Stromedix, Astellas • Bristol-Myers Squibb • Shares in Transcriptome Sciences Inc (TSI)

Contents • Unmet need - potential of molecular diagnostics • Edmonton Genome Canada study: ATAGC TSI • The importance of stereotyping • Disease specificity: TCMR • Disease specificity: ABMR • (The injury twins: ARTI and CBOI) • Putting it all together

Contents • Unmet need - potential of molecular diagnostics • Edmonton Genome Canada study: ATAGC TSI • Disease specificity: TCMR • Disease specificity: ABMR

Symptoms Signs Laboratory tests Imaging e.g. MRI Organ function Outcomes Unmet need: diagnostic labels are unstable:they are only estimates of the probability of a disease Tissue biopsy read by histopathology 50-70% Accuracy? The Truth in the Tissue 3 Disease Elements: Mechanism of injury Active response to injury (ARTI) Cumulative burden of injury (CBOI) Assess features (lesions) Primary data e.g. inflammation, tubulitis, etc Assign diagnosis (label) Diagnoses are metadata Arbitrary rules based on context Define probability of a disease

Organ transplants are the Rosetta stonefor all organ injury and diseases Deconstructing disease elements makes system applicable to biopsies from any primary disease

Molecular diagnostics Fulfilling the promise of the Eagle pub Feb 28 1953 (Earles is the Eagle For ATAGC TSI)

Assessment of a biopsy • Disease assessment • process/mechanism • activity • stage • Active response to injury (ARTI) • Cumulative burden of injury (CBOI) • (see Friday March 26 Nephrology Grand Rounds)

Considerations in diagnostics What do we want the tests to do?

SSAPN

What is an ROC curve • True+R/D vsFalse+R/D (%FP=1-specificity) • Dependent on gold standard • is it is 20-30% wrong? Then the curve cannot be square • watch for limited challenge bias: cherry picking

SSAPN in English • True positive: Sick people correctly diagnosed as sick • False positive: Healthy people incorrectly identified as sick • True negative: Healthy people correctly identified as healthy • False negative: Sick people incorrectly identified as healthy.

SSAPN in English • Sensitivity: test finds the disease (%TP/D) • “can my test find the disease?” • Specificity: test finds non disease (%TN/ND) • “can my test rule out the disease?” • Accuracy: TP+TN/total tests • PPV: positive result is a true positive (TP/all P) • “is a positive result reliable?” • NPV: negative result is a true negative (TN/all N) • “is a negative result reliable?” • Important to consider probability of disease • select the right population to test: FP, FN

Type I (a) error and Type II (b) error • Type I error (false positives): rejecting a null hypothesis when it is true. Reporting a difference when there is none (poor specificity). • excessive credulity: e.g. tubulitis in protocol biopsies • Type II error (false negatives): failing to reject a null hypothesis when it is not true. Failing to report a difference when there is one (poor sensitivity). • excessive skepticism: missing C4d –ve ABMR

Building a diagnostic from data What features are present? What features are diagnostic? What validation is needed?

Developing a diagnostic from data requires dichotomous comparisons • Train: disease vs control comparison • sick versus well or sick versus sick • class comparisons, classifiers (weighted equation) • (sick vs well) • extreme phenotype vs normal • consensus definition versus normal • sick versus sick • index cases versus all others • consensus definition versus all others • Develop an algorithm • “e.g. if this is a transplant, greater than one year…” • Test on a real population! • avoid limited challenge bias

Contents • Unmet need - potential of molecular diagnostics • Edmonton Genome Canada study: ATAGC TSI • Disease specificity: TCMR • Disease specificity: ABMR

Acknowledgements: Edmonton Genome Canada study Kara Allanach Dina Badr Sakarn Bunnag Patricia Campbell Jessica Chang Declan de Freitas Gunilla Einecke Konrad Famulski Luis Hidalgo Herman Haller Anna Hutton Zija Jacaj Bruce Kaplan Bert Kasiske Nathalie Kayser Daniel Kayser Daniel Kim Rob Leduc Arthur Matas Michael Mengel Vido Ramassar Jeff Reeve Gui Renesto Joana Sellares Banu Sis Jeffery Venner Lin-Fu Zhu Stromedix Astellas Roche Molecular Systems, Roche Canada Alberta Health Services University Hospital Foundation Roche Organ Transplantation Research Foundation Genome Canada University of Alberta Alberta Advanced Education and Technology Canada Foundation for Innovation Canadian Institutes of Health Research Kidney Foundation of Canada Alberta Heritage Foundation for Medical Research Muttart Chair in Clinical Immunology Canada Research Chair in Life Sciences Special thanks to our clinical collaborators Special thanks to our patients

Clinical Function Imaging Infections Virus load Using independent phenotypes in unselected patients to understand the molecular phenotype Biopsy Pathology lesions diagnosis Primary data Metadata >800 fully solved biopsies so far Biopsy Molecules Transcripts • PBTs miRNA Many new iterative analyses But all are part of a global view of mechanisms HLA antibodies Class I Class II Outcomes Attribute causes of failure

Executive - admin support: Legal/IP Project Management Administrative support Scientific leadership: coordination, fund-raising, publication Technology platforms Sample prep, QI Standard methods Transcriptomics miRNA Analysis: Computational biology team Data storage Data retrieval Data queries Data analysis Bioinformatics Biostatistics Clinical Team IRB Subject identification Subject consent Sample acquisition Sample stabilization Documentation Follow-up Detailed outcomes Reports to clinic In vivo and in vitro models Microsurgery Animal models In vitro models Ex vivo perfusion In silico models Simulations Conventional phenotyping Histopathology Aperio images Image analysis Lab medicine Anti HLA Imaging Iterative analysis by embedded teams“The Magic Table” requires five capabilities

Pathogenesis-based transcript sets (PBTs): a system for interrogating mechanisms and annotating changed transcriptsin any biopsy of a diseased tissue

Two approaches are complementary Pathogenesis-based Transcript sets: measurements Unsupervised methods Lists of individual molecule They find the same transcripts: they represent the same biology Annotating transcripts coming from unsupervised methods Biology ultimately guides the understanding

Two approaches are complementary Transcriptome Histopathology They teach each other Histopathology needs external standard They represent the same biology

Contents • Unmet need - potential of molecular diagnostics • Edmonton Genome Canada study: ATAGC TSI • The importance of stereotyping • (see NGR Friday) • Disease specificity: TCMR • Disease specificity: ABMR

All biopsies aligned by T cell burden (QCATs): Standardized with 8 control kidneys, PBTs from IQR filtered set Jeff Reeve’s ICON Standardized with 8 control kidneys The jaws of death

No molecule change is completely disease specific No molecule changes alone They move in herds

Contents • Unmet need - potential of molecular diagnostics • Edmonton Genome Canada study: ATAGC TSI • The importance of stereotyping • Disease specificity: TCMR • Disease specificity: ABMR

T cell mediated rejection Prototype for cognate T cell recognition diseases Overdiagnosed by histopathology (25%) When treated properly TCMR is benign (but may indicate non-compliance)

TCMR: DiapedesisT cells and macrophages enter the interstitium

Non cognate T cells act as inflammatory cells – innate immune functions ARTI!

Non cognate T cells ARTI! How much cell death occurs?

Defining features of TCMR(cognate T cell mediated inflammation) • Infiltrate (i) tubulitis (t) endothelial arteritis (V) • High T cell burden • High macrophage burden • Selective alternative macrophage activation features • Intense IFNG effects

Approaching analysis • Sick vswell or sick vs sick • e.g. TCMR • Banff TCMR vs everything else • Banff TCMR + hi-quintile CAT vs everything else • Banff TCMR vs Banff ABMR • cTCMR v everything else • Index case TCMR vs ?everything else • Extreme phenotypes (sick vs well) • Class comparison or classifier

How to train the classifier for TCMR To test the classifier, any candidate classifier must be tested against wild type unselected population not an artificial cherry picked population It is extremely unlikely that you will ever get a “Square ROC”…clinical phenotypes are usually only 80%

Rejection in 234 biopsies for cause in Edmonton and Chicago Mixed C4d+ ABMR PVN Borderline TCMR Non rejecting Imagine all of the possible comparisons and what their diagnostic uses would be Biopsies for cause

Mixed C4d+nopath ABMR TCMR Borderline BK Other

Rejection of organ transplants: is molecular diagnosis ready for prime time?