Pathology Imaging and Informatics as the Cornerstone of Precision Medicine: Two years of Experience with the NIDDK 

Kidney Precision Medicine Project (KPMP)

Jeff Hodgin, MD PhDAssistant Professor of Pathology

University of MichiganMay 2019

Disclosures

Receive funding from:AstraZeneca, MedImmune, Moderna, Gilead 

Kidney Disease 2019

Acute Kidney Injury Chronic Kidney Disease

• Lack of abundant human kidney biopsy tissue

Understand and treat human kidney diseases• Ethically and safely obtain kidney biopsies from participants with AKIs or CKDs• Create a kidney tissue atlas• Identify critical cells, pathways and targets for novel therapies• Find disease subgroups to stratify patients• Clinical assays to help devise individualized treatments• Improve scientific knowledge base

Goals/Deliverables of KPMP

Change patient management; improve health

Unique and challenging aspects of KPMP• Participant Collaborators (patients) intrinsic to research effort

• Maintain ethical, safety, and rigorous quality control standards• Research biopsies in people with common kidney diseases (hypertension, diabetes, AKI)

• Longitudinal 5‐10+ year biopsy cohorts for AKIs and CKDs• Need large cohorts with deep phenotyping to create personalized medicine metrics

• Finding out what we do not know (dark matter)• Create routine clinical assays for use in clinical practice

• Initially, better use of routine histology; then 20‐40 markers; then ??• Develop new patient categorizations (endophenotypes), find novel disease pathways and therapeutic targets, better predict outcome. 

Our journey begins with Version 0.5 

Phase 1• Establish clinical protocols, MOPs• Informed consent documents• Optimize/validate tissue processing• Establish Kidney Atlas• Start Biopsy cohorts

Phase 2• Proof of concept studies • Enrich Kidney Atlas• Next generation tissue interrogation assays

• Expand longitudinal cohort studies

Phase 3• Expand to larger cohort studies

2027202220192017

10+ Year KPMP Timeline

Ambitious long term project

Redefining disease categories and classifications

IntegratedPathology

Pathologists create disease categories, based on few pre-selected features,

and then test against outcome

Unsupervised learning and multiaxial datasets defining categories of patients

with similar biological trajectory

Identify Targets for Therapeutic

Intervention

Predict Clinical Outcome

CurrentPathology

“one size fits all” approach

Takes in account individual variability

KPMP: Three distinct but highly interactive activities

*

Goal of 3 kidney cores:• 1 diagnostic processing• 2 research 

- Transcriptomics- Proteomics- Metabolomics- Spatial molecular profiling

Research Biopsy Work Flow ‐ Recruitment Sites 

Recruitment Site Training

Tissue Interrogation Sites

UTHSA/PNNL/EMBL

IU/OSUUCSF/Stanford

UCSD/WashUUCSF/Stanford

UMich/Broad/Princeton

UCSD/WashUUCSF/Stanford

IU/OSU

IU/OSU

IU/OSUUCSF/StanfordUSCD/WashU

UMich/Broad/Princeton

OCT frozen

Cryopreserved

FFPE

CODEXUCSF

CytometryIU/OSU

3D

2D

Targeted profiling

High throughput unnbiased profiling

123 7

5

4

Assessment of gene expression correlation (by RNA Seq) at single nucleus vs single cell 

Comparison of transcriptomes measured by single cell RNA Seq (individual vs pooled)

Correlation analysis of gene expression at  single cell level vs region specific (by LCM)

Study of cell populations extracted by bulk RNA Seq vs single cell/nucleus

Generation of proteome map at near single cells and regions of kidney

Meta‐analysis of metabolome data and proteomics data at near single cell level

3D tissue/ whole slide spatial visualization and mapping

Integrative “Omic” analysis of transcriptome, proteome, and metabolome data

6

Integrated single cell functional Kidney Map

8

DART‐FISH UCSD/Wash U MIFISH 

UCSF Broad/U of M

3D Imaging

8

7

/EMBL

2D/

PREMIERE  TIS

30 cell clusters are identified in kidney tissue by single cell RNA‐seq

Samples: 27 total (16 nephrectomies and 11 biopsies; 3 samples from table 1 not used)

Major cell types: Residential kidney cells of the nephron, interstitium, arterioles, and inflammatory cells

20,986 cells endothelial

Staining for top scRNA-seq markers validates endothelial cell subsets

PREMIERE  TIS

In situ hybridization for mRNA

PREMIERE  TIS

30 cell clusters are identified in kidney tissue by single cell RNA‐seq

Samples: 27 total (16 nephrectomies and 11 biopsies; 3 samples from table 1 not used)

Major cell types: Residential kidney cells of the nephron, interstitium, arterioles, and inflammatory cells

20,986 cells

Central Hub System Architecture

Data Generators

‐omics

Imaging & Histology

Longitudinal Clinical Data (REDCap)

…

Data Lake

Knowledge Environment

Identity and Access Management: Shibboleth/InCommon Authentication

Derived Data Analysis Pipelines

Data Viewing Tools

Kidney Tissue Atlas

Patient‐centric Tool

...

Raw Data Viewer

Data Integration and Metadata: Ontologies

Data Flow ‐ DVC

Specimen Tracking

KPMP Knowledge Environment

Framework for multiscalar data integration to mechanistically define AKI and CKD‐DKD kidney tissue disease manifestation for targeted therapeutic intervention

The Depth Defining data and metadata

Establishing ontology structures

Integrating molecular data types

Mapping molecular data to spatial data

Software development process

Requirements Gathering and Planning Development Sprint(s)     Deployment

Personas & Problem 

Statements

Design User Stories

Release Planning

Sprint  Kickoff Estimation

Show‐and‐Tell

Sprint Planning

Daily:• Tracking

• Stand‐up Meeting• JIRA updates

•Development• Pairing• CI

• Testing System‐Wide Testing

Application Release

User Feedback

Becky Steck – Michigan Nephrology

Ul Balis – Michigan Path Inf

Personas and Use CasesPersonas are representations of types of real users, which help us: - Understand our audience and answer the question, “Who are we

building for?” - Visualize/Humanize our user research- Prioritize features and align on strategy - Use the same language when we’re discussing our users- Create a shared understanding of how the product would be used by

customers- Provide a reference point throughout the design and development

process

Whole Slide Image Viewer – Patient Access

Whole Slide Image Viewer – Patient Access

Transcriptomics User Research Interviewees

Screenshots from the Mid‐Fidelity Mock‐ups

Gene Search Demo

Standing up an Atlas for theKidney Precision Medicine Project

Atlas of Histology,V. Eroschenko,Wolters Kluwer

Becomes…Matched Clinical Content

DemographicsComorbidities

OutcomeseGFR

ExposuresMedications

Matched Morphological Content

HistologyMolecular MarkersStructural Elements

Structural Ensembles 

Matched Molecular Content

Expression PatternsLinkages to PhenotypesMolec. Def. of Disease

Novel Targets 

Through the use of relatively recent image‐query and image analysis tools, histology data may now be promoted to serving as one of the primary starting points for interrogation, and not simply reside in the repository as passive information.  

Top Level Objectives of the Atlas (from an imaging perspective)

• Images will provide the central anatomic frame of reference, allowing for anchoring of all data types to each other, spatially

• Two key thrusts:• Visualization: for every data type across dimensionality (Resolution and Depth‐agnostic)

• Analytics: Tools will be tailored to personas• Will address the need to interrogate various length scales and biological units:

• Organ structures• Cells• Sub‐cellular spaces

• Image‐based analytics will be integral to the atlas

Prioritization of Atlas Features from a User‐Centric Perspective

• Most critically‐needed capabilities in the initial ensemble of functions:

• Allow for visualization and rendering of all expected TIS image‐based data types

• Provide for an integrated ecosystem of tools, representing  the most requested types of quantitative analyses, with candidate examples being: 

• VTEA: for tissue cytometry/ image analytics• VIPER Studio: for segmentation• TIS‐Provided Solutions

• Allow for data extraction and compilation of results into standardized formats 

• Exchangeability across TISs • Reproducibility (with analysis of metadata)• Dissemination

• Provide access to all raw data and its associated annotations

Descriptor LibraryConventional  Diagnosis Library

Vector Library

Diabetic Nephropathy

Mapped –omics Library

Omics mapping on structural normal and abnormal histologic primitives

Pathology

Text Search

Image Search

140 fields of view computed in less than 5 seconds, yielding an AUC of 0.96

Automated Interstitial Region Fractionation – VIPER Studio

University of Michigan, Pathology Informatics:• Ulysses Balis• Jerome Cheng

Proof‐of‐concept re: connecting molecular and spatial data

Identification of spatial gene expressionGene  Histology

Identification of spatial gene expressionHistology  Genes

Popup Query From 3D image volume to omics – Indiana Univ. TIS

DE AnalysisGeneXGeneY…MetabolomicsMetaboliteXMetaboliteY…LMD transcriptomicsGeneXGeneY

DE AnalysisGeneXGeneY…MetabolomicsMetaboliteXMetaboliteY…LMD transcriptomicsGeneXGeneY

Data Visualization CoreSystems‐Biology 

Ontologies

Digital Pathology

Team Science

Project Mgt.

Ravi IyengarMSSM

Olga TroyanskayaPrinceton

Matthias KretzlerMichigan

Laura MarianiMichiganCijiang He

MSSM

Rachel SealfonPrinceton

Evren AzelogluMSSM

Zidong ZhangPrinceton

Jian ZhouPrinceton

Barbara MirelMichigan

Heather AscaniMichigan

Sean MooneyU. Washington

Oliver HeMichigan

Becky SteckMichigan

Ul BalisMichigan

Jeff HodginMichigan

Laura BarisoniDuke

KPMP

Ruth DannenfelserPrinceton

Data Visualization CoreSystems‐Biology 

Ontologies

Digital Pathology

Team Science

Project Mgt.

Ravi IyengarMSSM

Olga TroyanskayaPrinceton

Matthias KretzlerMichigan

Laura MarianiMichiganCijiang He

MSSM

Rachel SealfonPrinceton

Evren AzelogluMSSM

Zidong ZhangPrinceton

Jian ZhouPrinceton

Barbara MirelMichigan

Heather AscaniMichigan

Sean MooneyU. Washington

Oliver HeMichigan

Becky SteckMichigan

Ul BalisMichigan

Jeff HodginMichigan

Laura BarisoniU. Miami

KPMP

Ruth DannenfelserPrinceton

Pathology Informatics:• Ulysses Balis• Jerome Cheng• Ross Smith

Nephrology:• Becky Steck• Michael Rose• Rachel Dull• Becky Reamy• Zach Wright

Acknowledgements & Collaborating Groups• Indiana University

• Tarek Ashkar• Seth Winfree• Ken Dunn• Micheal Ferkowicz• Angela Sabo• Pierre Dagher• Michael Eadon

• University of Michigan• Rajasree Menon• Edgar Otto • Celine Berthier• Matthias Kretzler• Keluo Yao

• Duke University• Laura Barisoni , MD

• University of Oklahoma• Christopher Williams, MD

• Funding Sources:1. 1U2CDK114886‐01    NIDDK

Central Hub for Kidney Precision  MedicineProject (KPMP)

2. UG3 DK114923‐01 NIDDK TIS3. P30 DK079312  3D Quantitative Core – Indiana O’Brien Center

Kidney PrecisionMedicine Project

Broad Institute• Paul J. Hoover• Tony D. Jones• Thomas Eisenhaure• Shuqiang Li• David Lieb• Nir Hacohen

Princeton• Rachel Sealfon• Olga Troyanskaya

KPMP.org

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Patients want to know•What do I have?•What will happen to me?•What can I do about it?•What does it mean for my family?

Prefer precision medicine approach•Right intervention for right patient at right time

Transcriptomics User Research Interviewees

Digital Pathology Repository

Overview on pathology work flow

RS

KPMP pathology

team

CRF:PathologyReport

CRF:Dx coreHigh levelassessment

CRF:TIS coreHigh levelassessment

Data Lake

Disease caterogyassignment

KIDNEY ATLAS

TIS

WSI & IF & EM Images

DescriptorMorphologic profile

REDCap

Links all in one place

• Participant web‐based slide viewer: https://mydata.kpmp.org/• Demo landing page: https://demo.kpmp.org/• Pathology web‐based slide viewer: https://demo.kpmp.org/dpr/• Cell type search prototype: https://demo.kpmp.org/atlas/• Gene search prototype: https://demo.kpmp.org/gene‐search/

• Associated mid‐fidelity mock‐ups