Korea University

Yeul Hong Kim

Enough

amount

of data

Open data

platform

Low

sequencing

cost

Sustained

support of

policy

Optimal

level of

regulation

Improvement

of diagnostic

accuracy

Precision

Medicine

Implementation of Precision Medicine

Evaluation

Committee

MSICT & MOHW

Department of

cancer genomic

testing

Department of

Clinical trials

Department of

cancer data

management

Authentication

& security

team

Service

Solution

team

Director,

K-MASTER

Enterprise

Director, P-HIS

Enterprise

Advisory

Committee

Korea Precision Medicine Enterprise Project

Director, KPME

PM Steering Committee

Secretariat

OfficeAdvisory

Committee

Secretariat

Office

Medical

R&D team

C-HIS

team

Launched at June 2017

Total 70M$ for 5 years

K-MASTER

Cancer Precision Medicine

Diagnosis & Treatment Enterprise

Director

Yeul Hong Kim MD, PhD

K-MASTER overview Overview & purpose

“Cancer Precision Medicine Diagnosis & Treatment Enterprise

K-MASTER Korea’s precision Oncology Initiative”

Accelerated Application

Standardization

Master model

right Target

Equal Access & Quality

KOREA’s –

Recognition

K M A S T E RCancer Precision Medicine

Diagnosis & Treatment Enterprise

Korea’s Precision Oncology Initiative

-

Benchmarking of global leading program 1 Overview & purpose

• Managed by ASCO in USA since 2016

• Merck, AstraZeneca et al participate and offer

drug for free

• Clinical trial 16 drugs

• Participated 102 collaborator, 42 institutes

• France gov made investment $769 million for 5

year since 2016

• Cover nationwide until 2020

• To establish networdk for sequencing of 12 gene

• Plan fo sequencing 235,000 patients per year

• Managed by ECOG-ACRIN in USA since 2015

• Scheduled clinical study more than 40 drugs

with 25 participants

• Screen 5-6,000 and enroll 1,000 patients in

2,400 hospitals

• Include 143 Oncomine genes

• Managed by NCC-EPOC in japan since 2013

• Using Oncomine Cancer Research Panel

• Participated 200 institutes, 10 pharmaceuticals

• 143 gene Oncomine target sequencing results

are scheduled to report in 2 weeks

K-MASTER strategy through benchmarking 1 Overview & purpose

Strength Weakness

NCI-

MATCH

Extended

screening

time

• Time for screening extend

257%

• Single panel

Biopsy side

effect

• Side effect from tissue

biopsy(2.6%)

Low

accuracy

• Low registration rate of

rare mutation

Patient

involvement

• Patient online

participation in clinical

trial

Accessibility

• Clinical trial with

approved or developed

drug → shorten time

Public-

private

cooperation

•Activate more than 40

partnerships

Nationwide

network

•Establish network 200

hospitals focusing on

National Cancer Center

Sharing and

expansion of

results

•Pharmaceuticals

perform development

using SCRUM-Japan DB

SCRUM-

Japan

Single item

• High rigidity with

monopolistic panel

(Thermo inc.)

Low

accessibility

• Focus on new drug

development → high cost,

extended period, low

success rate

Big gap of

quality

control

• Focus on hospital – limited

patient participation

• Difference in clinical trial

quality between hospitals

cancer precision medicine

“Devise key successful factors for K-MASTER”

Outline of the Enterprise organization Expertise

“Infrastructure and network to carry out this project”Critical Success Factor

Through ‘Establishment and utilization of Cancer Precision Medicine Diagnosis & Treatment platform’

purpose-oriented business promotion and customized medical expansionVision

Construction of cancer

diagnosis NGS panel

• Three NGS analysis panels that are the only proven in Korea (KF1,

CancerScan, FIRST)

• Establish a process group to be transferred to the researcher after

analysis of the holding organization and sample

Clinical trial experience

and know-how

accumulation

• Global Clinical Trial Center Consortium GREATS, PARTNERS

Infrastructure and Know-how

• Clinical trial recruitment route is already secured through the underlying

network

Provide treatment

opportunity to all patients

and access to new drugs

Building and sharing data

• Expansion of research results by securing a platform that can be utilized

by multiple facilities and data opening

• Spread of cooperation with Global programs NCI-MATCH, AACR,

Cancer Moonshot

High quality management

of multiple clinical trials

nationwide

• Linked to KCSG with more than 300 networks of oncology specialists

• Dual CRO-based clinical trial series and a high quality management

infrastructure

• Equal Access: K-MASTER consortium participating hospital + KCSG

nationwide network

Genome

screening

Clinical trial

Cancer data management

Goal 1

Goal 2

Goal 3

Goal 4

Goal 5

Goal 6

Goal 7

Goal 8

Goal 9

Goal 10

Core Detailed goal

“Consists of 3 cores and 10 detailed goals ”

• Development of NGS panel optimization and liquid

biopsy platform for cancer diagnosis

• Total profiling of cancer genome 10,000 patients,

20,000 samples

• Therapeutic target gene mutation detection 4,000

cases

NGS panel development

Planning and protocol

development

Insurance and drug

supply agreement

Recruitment of

Participating hospital

Liquid biopsy platform

Data collection

Monitoring and

Quality Control

Clinical Information

Integration Platform

Data sharing system

Performance goal

• Establishment of network of participating

hospitals nationwide

• Developed cooperation CRF

• Targeted therapy based on precision medical care

1 thousand cases (Repositioning/Repurposing)

• 20 clinical trials based on precision medical care,

1,000 people

• Expansion of indications for cancer drugs and

approval of new drugs

− IND 3 or more, NDA 1 or more

• Clinical genome analysis process management

system

• Precision Medical Hospital Information System (P-

HIS)

• Construction of clinical genetic information

registry

• Precise medical data resource sharing system

Concept and Scope of the Task Expertise

Result reporting and

licensing

Utilization and advancement of proven NGS panel

• Simultaneous analysis of 88

cancer genes

• Specified for liquid biopsy

• 1,500 data verification and

1,000 additional analysis

• Sensitivity more than 90%

• Simultaneous analysis of

381 cancer genes

• 7,000 data acquisition and

verification

• Acquisition of 1,200 cases of

5 major cancers (stomach,

liver, colon, breast, lung)

• Specified for immune-

profiling

Expertise

AXEN

1 2

CancerScan

3

FIRST

-

500

1.000

1.500

2.000

2.500

Analysis of genetic mutation by liquid biopsy Expertise

Provide opportunities of precision medicine to more patients using a platform that is more

accurate and more sensitive than normal tissue sequencing

Patient No.

EGFR Activating Mutations Status

EGFR

targeted

therapy

ResponseExon 19 deletion L858R

Tissue

sequencing

blood

cfDNA

NGS (%)

ddPCR (%)Tissue

sequencing

blood

cfDNA

NGS (%)

ddPCR (%)

#026 Wild Mut (7.413) 8.000 Wild W (0.029) 0 gefitinib RR

#059 Wild Mut (0.684) 5.600 Wild W (0.047) NA erlotinib RR

#006 Wild Mut (0.247) 0.034 Wild Mut (0.226) 0 erlotinib SD

#047 Wild W (0.012) 0.029 Wild Mut (2.103) 9.200 gefitinib SD

Number of

patients (N)

INTACT I-II TRIBUTE

15%21%

22%23% 25% 29%30% 17%22% 22%26%

TALENT BR.21 ISEL INTEREST

Total number of

patients (N)

Number of specimens capable

of EGFR mutation test

Number of specimens capable

of EGFR amplification test

• Patients possible to genetic

test using tissue : less than

30%

• Patients who can be tested by

liquid biopsy : 100%

Effective coordination between genome sequencing and

clinical trials part Expertise

MEBICA

Reporting system

Dapartment of

Clinical study

Nationwide network

Clinical study

Screening cancer patients

FFPE slides Blood

Core Lab

Lung

Colorectal

Gastric

Breast

Sample collection

Consent

Case reports

Report

• DNA extraction from tissue

and blood

• Genome sequencing

• Analysis and report

MATCH MASTER

• Identification of target gene

alteration

• Targeted therapy

• Report and enroll

Cooperation between collaborators, departments

Support by established Coordinating Center

Rapid protocol development and performing clinical trialsExpertise

Develop

protocol for

clinical study

Approve

synopsis for

clinical study

Review and

approve based

on scientific

theories

Enroll

Hospital and

institute

Progress

study

Complete

study

- Secure drug

- Optimize genome

sequencing

platform

- Study design

- Develop protocol

- Data collection

- E-CRF

Clinical study committee Evaluation committee

KCSG Commercial-CRO

- Result :

Statistical

analysis

- Report :

write paper

- Collect tissue or biopsy sample nationally

Academic-CRO

Design

- Monitoring

- Quality control

Review∙Management∙

Supervision

- Manage and direct whole procedure until end-point- Conduction of pre-design

No. Cancer type No. pt Target gene Drug 2017 2018 2019 2020 2021

KM-00 Any solid 10,000Any Targetable

geneScreening

KM-01 MSI-H CRC 33 MSI Avelumab

KM-02

-1Any solid 25

PIK3CA/Akt/

PTEN Sirolimus

KM-02

-2

Any solid27 TSC1/2

Vistusertib

(AZD2014)

KM-02

-3Any solid 27 RICTOR

Vistusertib

(AZD2014)

KM-05 NSCLC 198EGFR, ALK

Liquid biopsy

Gefitinib

ALK TKI

KM-06 Any solid 48 dDDR Nivolumab

KM-07NSCLC

adjuvant225 EGFR

Gefitinib

Pemetrexed

KM-08 CRC 48 TBRSPembrolizumab

TEW-7197

KM-09 GC 50EBV, MSI-H,

PD-L1

Nivolumab

Paclitaxel

KM-10HER-2 +

Breast48

PIK3CA/Akt/

PTEN

Trastuzumab

Gedatolisib

KM-11 Salivary 41 Her2Nanoxel

Herzuma

KM-12 Esophagus 45MCS110

PDR001

Planned GU, GY

Ongoing Clinical Trials

Control of Integrated genome-clinical data Expertise

Common

collaborator /

Institution

Access data

Public type

Public open limited data with

specific gene variants and

simple patients information

Consortium

collaborator

/ institution

Data

uploader

Access data

authorized

Upload data

Data Collection

(merge data)

Review data by

streering

committee

Revision of

authority to

access ID

(standardization)

Public open

data after 6

months

Provide online tool

for data analysis

Access data

authorized

collaborator /

institution

Extension of data utilization

Extension of data utilization

Firewall

DB construction plan based on Common Data Model for extension of global data utilization

Control of Integrated genome-clinical data Expertise

Search for a gene or variant or region

Genome

variant

list

Genome

information

Tracking link for

related research

and latest trends

Control of Integrated genome-clinical data Expertise

Establish pipiline for

automatic analysis

Develop algorithm with

minimum error

Tool for evaluation of

panel performance

Korean

analysis

solution

K-MASTER

Clinical study

team

SGI

MACROGEN

SNUH

IT collaborator

Clinical-

genome

DB

Knowledge DB

of genomic

variant

Cancer panel for

diagnosis

Cinical study Data

Genomic data

Panel design

Analytic service

Information of clinical experience

genomic knowledge

Open Source Data

Link to P-HIS

Utilization of translational research network : active

participation of researchers Expertise

K-MASTER

: IO clinical study

Support of sample analysis

Scientific consultations

KCSG-KIMC

: immune response before

and after immunotherapy

Bio-repository

FACS

Proteomics

Genomics

WRII

: Institute of immunology

Seoul National University

1. Support of translational research using human-derived material bank of K-MASTERS

2. Support of Immuno-translational research using KCSG Immune Monitoring Core systems

Remaining sample

storage

Bio-Bank

Integration of

genomic/clinical

information

Systematic

information

Sys.

Cancer

Genome

Analysis

Identification of

target gene

mutations

(Actionable Mutations)

TAT: 11~14 days

Target agent

MATCH-MASTER t

r

e

a

t

m

e

n

t

Response

(CR+PR)

No

response

(PD)

Report

/

Enroll

No

response

(PD)

Additional

cancer

genome

analysis

Cancer

genome

analysis after

re-biopsy/

liquid biopsy

No

identification

of target gene

mutations

Follow-up

Y N

A series of integrated processes100-1-2 Preemptive response model

Screening

registration

Simple

information

registration

Sample

delivery

Analysis

(Cenral

laboratory)

Sample DNA

acquisition

Gene

analysis

1 2

Clinical

trial

Results

report

Research

progress

Research

participation

Clinical trial

registration

Collecting

and analyzing

results

4

5678

Achievement

20% of

clinical trial

enrollmentthrough

MATCH-

MASTER

Gene/Clinical

information

Pharmaceutical

information

MATCH-MASTER

Report

results

(Database)

3

Coordinating

clinical trials Patient

management

system

Central lab:

tissue and

blood sample

collection

Analyze 12

samples a day

MEBICA system:

Track the analysis

process

Operating

3 or more

central lab.

100Samples

Registration

Analysis

1week

2weeks

“100 samples:

Registration for 1 week and

Analysis within 2 weeks”

Reinforce the efficient

management through the

construction of the

preemptive response

system

Management of operation

Expansion of K-MASTER project

Activation of cancer precision medicine clinical research

and spread of outcome

Becoming the global leader of Cancer Precision Medicine!

Expansion of indications and

approval of new drugs

• Establish the data of disease and

treatment progress in 10,000

advanced cancer patients

• Large-scale clinical trial of

domestic and foreign

pharmaceutical companies and

subsequent support of PMS

Expansion of new drug

accessibility for cancer patients

• Significant reduction in period and

cost of new drug development

(development period: shortened

from 20 years to 4 years)

• Reduce the burden of patient

illness and expand treatment

opportunities

Activation of national clinical

network

• Using K-MASTER’s clinical network

including KCSG and application of

‘Partners Care Me’

• Improving participation rates and

expanding network among national

researchers

• Development of NGS panel

optimization and liquid biopsy

platform for cancer diagnosis

• Total profiling of cancer genome:

10,000 patients, 20,000 samples

• Detection of therapeutic target gene

mutation 4,000 cases

• Targeted therapy 1,000 cases

(Repositioning/Repurposing)

• 20 Clinical trials, 1,000 patients

• Expansion of indications for cancer

drugs and approval of new drugs

- IND 3 or more, NDA 1 or more

• Clinical genome analysis process

management system (MEBICA)

• Precision Medical Hospital Information

System (P-HIS)

• Construction of clinical genetic

information registry

• Precise medical data resource sharing

system

Management of operation

DNA isolation from fragmented, contaminated cfDNA

Cell free DNA isolation

- Using Qiagen QIAamp circulating nucleic

acid kit with efficient and stable

performance

Removal of genomic DNA from Cell free

DNA

- gDNA removal protocol using AMPure XP

Bead

Cell free DNA quantification (more

accurate than Nanodrop)

- Picogreen (Fluorescence-based method),

Qubit 2.0 (Fluorescence-based method)

Cell free DNA QC

- Bioanalyzer (cfDNA pattern confirmation)

NGS applicable cfDNA extraction and gDNA removal

Liquid Biopsy Project in KU

Treatment resistance marker

22

Identification of Tx

resistance marker

in NSCLC patient

Monitoring of EGFR activating mutation and

resistance marker T790M, PIK3CA, PTEN in cfDNA

AfatinibTime

Progression

Disease

Time Time

Progression

Disease

Progression

Disease

Tx resistance

marker

(PIK3CA)

Gefitinib Gefitinib

Tx resistance

marker

(PIK3CA)Tx resistance

marker

(PIK3CA)

Development of Liquid Biopsy Diagnostics based Cancer Treatment

Resistance Diagnosis and Treatment Strategy (2015.06.01~2020.05.31)

(KNRF funding)

• Target marker identification using Whole Exome Sequencing (WES) in

ctDNA of lung cancer and colorectal cancer patients

• Verification of technological usefulness of developed nanosensor

• Clinical usefulness verification through analysis of target marker and

clinical information integration algorithm

• Analysis and exploration of network type knowledge base

Development of personalized drug target selection technology through

integration analysis with cancer genome data

• Identification of acquired resistance mutation using ctDNA of lung and

colorectal cancer patients.

• Development of nano-sensoring diagnostics for acquired resistance in

blood and whole period treatment strategy for these cancer using

targeted nano-aptamer

Final Goal

Study Goal

Sample-to-Answer System for multiplex detection

24

Total inspection time: 2hr 25min

Microfluidic

cfDNA

extraction

Microfluidic

fast PCR

ctDNA

detection

assay

SPRi based

multiplexed

analysis

10min 15min 90min 30min

1. Blood prep 2. Target amplification 3. Detection assay 4. Starting product

Identification and Functional Study of Metastasis related Genetic

Variances using NGS analysis using Paired Tissue and Liquid Biopsy

with ctDNA and Exosome in Metastatic Colorectal Cancer patients

(2017.03.01~2022.02.28) (KMHW funding)

• Discovery and validation of metastatic specific gene variations in NGS

analysis of primary and metastatic tumor tissues in patients with

metastatic colorectal cancer

• Discovery and validation of metastasis-related genes by NGS analysis

using serial ctDNA and exoRNA through liquid biopsy

• Identification of metastasis mechanism by functional study of

metastatic specific genes discovered using NGS analysis in tumor

tissues and serial liquid biopsy

Development of therapeutic strategy through the investigation of metastatic

specific genes using primary and metastatic tumor tissues and ctDNA and

exosome in metastatic colorectal cancer

Final Goal

Study Goal

Cancer Panel RNA Sequencing

Development of therapeutic strategy through the investigation of

metastatic specific genes using primary and metastatic tumor tissues

and ctDNA and exosome in metastatic colorectal cancer

Serial Liquid BiopsyPrimary–metastatic

tumor pair

Identification of

metastatic-related

variations

Identification of

metastatic-related genes

Discovery set 100 case

Validation set 80 case

ctDNA Exosomal RNA

Discovery of metastatic tumor specific gene

variationsddPCR validation

Variations and transcriptome

analysisBiomarker discovery

Functional StudyStudy on the function of metastasis-specific genes using colorectal cancer cell lines

연구의 내용 및 방법Study Contents

연구의 내용 및 방법Study scheme

Metastatic tumor

29%

29%

14%

0%

11%

0%

3%

0%

0%

9%

7%

3%

A

B

C

D

E

F

G

H

I

J

K

L

Genomic variations Transcriptome analysis

0

20

40

60

80

100

120

0

10

20

30

40

50

60

70

Cont NC shRNA

Via

bilit

y(%

)

No

of

ce

ll%

)

Primary Tumor Metastatic Tumor

Serial Liquid Biopsy

ctDNA Exosomal RNATissue gDNA

Cancer Panel RNA sequencing

Development of Therapeutic Strategy by Functional Study

Cont shScr shGene

actin

gene

NC shRNA

Cancer Panel

ddPCR Validation

28/60

Sang-Heon Lee, MD, PhD

Professor, Dept. of PM & R

Korea University College of Medicine

The 4th Industrial Revolution and Medical Personnel Training

- Precision Medicine and Big data

29/60

Dream Team

Formation of a 'Dream Team', composed of 14 prestigious and specialized

institutions – hospitals, companies, and public institutions/universities

Public institutions/universities3

Hospital1 Establishment of Precision medicine analysis model and service

Plan of core Data Set

Conversion of Data based CDM

Introduction and verification of P-HIS

Certification - JCI, ISO27001, ISMS

Companies2 Cloud –HIS development

Precision medicine analysis service development

Cloud HIE development

Medicine Analysis Platform development

30/60

Development strategy

Composed of five major areas which include Cloud HIS and development

of Precision medicine analysis service.

Standard

Authentication

Security

Establishment and

Expansion of P-HIS

Overseas export

Medical treatment

Medical treatment

Support

Administration

Mobile EMR

HIE Platform

Cancer

Early Warning of

Critical patient

Emergency patient

information

Guide of Cardiovasc

ularDisease Patients

AI-based antibiotics selection

Medicine Analysis

Platform

2

Clinical

Data

Genomic

Data

Life Log

Data

3

4 51

31/60

Cloud HIS Is designed to allow selective inclusion of modules to fit for

each medical institution of specific size and type.

Development strategy

Application of C-HIS Utilization of Open API

Tertiary medical institutions

• Full functional

accessibilitySecondary medical

institutions

• Treatment, patient

information and

treatment support

Primary medical institutions

• Diagnosis/Prescription,

Patient data

• E-Chart

Application of specialized modules are possible

• E-Chart

• Flow Chart

• Anesthesia record

• Infection control

• Health care quality

indicator management

Primary, secondaryMedical Institutes

• Utilization of UI development

function based on

standardized api by each

HIS company

Spreading of individual service

• Possibility of optional

functionality

expansion from Open

API

- E-consent

- IoT Patient Guide

Standard API

DiagnosisClinic/

Prescri-

ption

E-Chart Nursing Operation

Patient

Data

Patient

manag-

ement

Fee

claim

Insurance/

review

Certifi-

cates

Treatment

support

Drug NutritionRadiol-

ogy

Health

screen

ClinicLabora-

tory

Medical

recordLIS

Functional

modules for

professional

tasks

CommonMaster

data

Authority

manage

Connection

manage

E-ChartFlow

Chart

Anesth

record

Insurance

reimburse

QA/QCInfection

control

Mobile

PHR

Mobile

EMR

32/60

Diagnosis(412)

Nursing(323)

Surgical

Anesthesia

(121)

Pharmacy (188)

Insurance(169)

Hospital

expenses(58)

…

Analysis of user

experience(UX*)Process

Design

기능 개발(모듈화)

Application to SaaS

Platform

Cloud HIS

Test-Bed

Construction

Functional

Development

(modulization)

Range of P-HIS Tasks - Precision medicine Analysis Service

33/60

Securement of interoperability and interface flexibility based on international

standard technique

- Accept international standard technique regulation (HL7 V2.x, CDA/C-CDA, FHIR, CDM and so on)

Framework

WAS

JVM

OS

ReceiveService

HIEAgent

SendService

P-HIS

OS

JVM

MOM(Message Oriented Middleware)

MessageListen

MessageRouting

MessageTransform / Encrypt-Decrypt

MessageBuild

MessageParse

Common(Exception / Logging / Retry…)

Adaptor

HL7/CDA

CDM

XML

JSON

Fixed

RESTFul

SOAP

HL7 & FHIR Engine

Cloud HIE Platform

Hospitals Government

Insurance Bank/Card

Other Organizations

External agency system

MIS / ERP Portal

Mail / SMS Other Legacy

Internal system

Clinical InformationStorage

(Repository)

Nationwide

hub hospital

Clinical Information

Exchange System

MPI

Clinical Information

Transmission System

Registry

National Computing

and Information

Agency

TCP/IP TCP/IP

HTTP HTTP

Range of P-HIS Tasks - Precision medicine Analysis Service

34/60

Construction of an open integrative analysis platform of medical data that

allows convenient expandability of its features

Easier build of

newly developed precision service

Hospital

Clinical data

Dielectric

Patient

ECG, Heart rate

Blood sugar

Food, nutrient

Exercise

Taking medicine

Health-risk

Prediction

Disease diagnosis

/Classification

Personalized care

/Prevention

Preventive

Management

Guide

Optimal Patient

Selection

Linkage

(HL7)

Linkage

(Batch/

Stream)

Solid

cancer

Infectious

Diseases

Heart

disease

Chronic

disease

Data

Extraction

Transformation

Loading

(ETL)

Analytics modeling

Analytics

Data

Standard

Service

Linkage

(API)

Analytics Model

Evaluation

AI, Statistic

Data

Standard

Integrated

Data Model

Open

API

Mobile

API

Range of P-HIS Tasks - Precision medicine Analysis Service

35/60

Providing various medical services based on precision medicine Analytics results

PopulationProfiling

PR

Reguest selection

IoT

AI basedAnalytics

results

Inspection/Measurement

Insurance

company

Genomic

Data

Clinical

Data

Provision of information

Company

Pharmacist

Patient

Hospital

Patient

Range of P-HIS Tasks - Precision medicine Analysis Service

36/60

application of CDM system that allows spreading of analysis service even in

various HIS environments

Centralized data model to convert data from each hospital into standardized form and terminologies

Provides analysis result when needed from dispersed data storage without personal identification information

Will utilize OHDSI* consortium’s data model in this particular project

A HISCDM

Apply

B HIS

C HIS

A HISCDM

Apply

B HISCDM

Apply

C HISCDM

Apply

(60 Hospitals)(15 Hospitals)Domestic Foreign

Range of P-HIS Tasks - Construction of Big Data for Precision Medicine

CDM

Apply

CDM

Apply

Thank you for your attention!

Precision, Innovation, Big data, Collaboration, Technology, Participation…