ASSESSING THE REAL RISK

IN COMPLEX DISEASES

Michael N. Liebman, PhDChief Scientific Officer

Windber Research Institute

Overview

Data, Information and Knowledge Systems Biology Defining Translational Research Understanding the Question(s) Clinical Breast Care Project (CBCP) Windber Research Institute Data Integration

Gap

INFORMATION

KNOWLEDGEGAP

GAP

GAP

TIME

AM

OU

NT

DATA

CLINICAL UTILITY

KNOWLEDGE

Systems Biology(Personalized Medicine)

Genomics Proteomics CGHMetab-olomics

Patient

Physiology

-omics

Bottom Up Approach

Genomics Proteomics CGHMetab-olomics

Patient

Physiology

????

Top Down Approach(Personalized Disease)

Genomics Proteomics CGHMetab-olomics

Patient

Physiology

Translational Medicine

Clinical Practice“Bedside”

BasicResearch“Bench”

TrainingJob Function“Language”

CultureResponsibilities

Translational Medicine

Clinical Practice“Bedside”

BasicResearch“Bench”

ClosingThe Gap

TrainingJob Function“Language”

CultureResponsibilities

“Crossing the Quality Chasm”

Humans as Detectors

Characteristics– Spectral sensitivity (visible region)– Sound sensitivity (audible range and volume)– Memory (retention is critical for comparison)– Perception (focus on what is known)– Analytical Capability (simple vs complex)– Ranks Importance of Change by Size (Bias)– Evolves slowly compared to other

technological advances– Does not perform uniformly over 24/7

“Discovery consists in seeing what Everyone else has seen and thinking What no one else has thought”

A. Szent-Gyorgi

Asking the Right Question is95% of the Way towards

Solving the Right Problem

Defining a Patient

A 48 year old woman, married, 2 children (ages 18, 24), presents with an abnormal mammogram, biopsy shows presence of cancer which, upon extraction, is diagnosed as invasive ductal carcinoma (T3,M1,N1). Her2/neu testing is +2

Disease as a State vs Disease as a Process Bias of Perspective Temporal Perspective

1. Modeling Disease

Modeling Disease

Lifestyle + Environment = F(t)

Disease(s){ } Risk(s){ }

| Genotype | Phenotype |

(SNP’s, Expression Data) (Clinical History and Data)

UMLS Semantic Network

??

Disease Etiology

Genetic Lifestyle Breast Survival Risk Factors Cancer (Chronic Disease)

DIAGNOSIS

Pathway of Disease

TreatmentOptions

QualityOf Life

GeneticRisk

EarlyDetection

Patient Stratification

DiseaseStaging

Outcomes

Natural History of Disease Treatment History

Biomarkers

Environment + Lifestyle

Her2/neu (FISH) = Her2/neu (IHC)

Her2/neu (IHC1) = Her2/neu(IHC2)

Do Either Measure the Functional Form of Her2/neu?

Phenotype

| Genotype | |

PhenotypeTIME

Childhood Diseases

Diabetes

Cardiovascular Disease

Smoking

Overweight

2nd Hand Smoke

Menarche

Breast Cancer (Age 48)Natural History ?

Longitudinal Interactions

in Breast Cancer Identify Environmental Factors Quantify Exposure

– When ?– How Long ?– How Much ?

Extract Dosing Model Compare with Stages of Biological

Development

Lifestyle Factors

5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

AGE

Alcohol

5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

AGE

Smoking

5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

AGE

Obesity

2. Genetics and Disease

Genetic Pre-Disposition– < 10 % of all breast cancers– Not all BRCA1 and BRCA2 mutations

result in breast cancer- Modifier genes?- Lifestyle or environmental factors?- Pedigree Analysis

Pedigree (modified)

Tim

e Polio Vaccine

Menopause

Influenza

Measles

Influenza

1940

1950

1960

1970

Prostate Cancer

1980

1990

2000

PSA

DES

Influenza Pandemic 1918

3. Aging and Disease

Processes of Aging vs Disease Processes Ongoing Breast Development Same Disease : Different Host? Text Data-mining Approaches

Disease vs Aging

Menopause

HormoneReplacement Heart Disease

Breast Cancer

Ovarian Cancer

Osteoporosis

Alzheimer’s

<50 years>

Menarche

Child-bearing

Peri-menopause

{ {

Aging Disease

Quality of Life

Breast Development

Menarche

Child-bearing Peri-menopause

Menopause

CumulativeDevelopment

Lactation

Ontology: Breast Development

Neo- Menarche Pregnancy Lactation Peri Menop Postnatal menop Menop

Parous

NulliParous

Buds

Buds

Lobes

Lobes

Ducts

Terminal Buds

Terminal Buds

Puberty

SPSS – LexiMine and Clementine

Puberty: •Two hormones – estrogen and progesterone signal the development of the glandular breast tissue.•In female estrogen acts on mesenchymal cells to stimulate further development.•The gland increases in size due to deposition of interlobular fat.•The ducts extend and branch into the expanding stroma.•The epithelial cell proliferation and basement membrane remodeling is controlled by interactions between the epithelium and the intra-lobular hormone sensitive zone of fibroblasts. •The smallest ducts, the intra-lobular ducts, end in the epithelial buds which are the prospective secretory alveoli.•Breast ducts begin to grow and this growth continues until menstruation begins.

Production of: Stroma, mesenchymal cells, epithelial cells

Reality of Disease

Tissues Cells Organelles Processes: Tissue generation;

Inflammation….

Pathways

Enzymes Substrates Co-Factors

Proteins

Genes

Gen

e

On

tolo

gy

Physiological Development

D

isease

Pro

gre

ssio

n

(time)

(tim

e)

Physiological Systems

DNA RNA Amino Acids

4. Stratifying Disease

Tumor Staging T,M,N tumor scoring Analysis of Outcomes

Cancer Progression

0 I IIA IIB IIIA IIIB IV

localized regional metastatic

Tumor Progression

0I

IIA

IIB

IIIA

IIIB

IV

Stage 0(Tis, N0, M0)

Stage IIA(T0, N1, M0 ); (T1,* N1,** M0); (T2, N0, M0) [*T1 includes T1mic ][**The prognosis of patients with pN1a disease is similar to that of patients with pN0 disease]

Stage IIB(T2, N1, M0) ; (T3, N0, M0)

Stage IIIA

(T0, N2, M0); (T1,* N2, M0); (T2, N2, M0); (T3, N1, M0); (T3, N2, M0) [*T1 includes T1mic ]

Stage IIIB(T4, Any N, M0) ; (Any T, N3, M0)

Stage IV(Any T, Any N, M1)

Stage I(T1,* N0, M0) ; [*T1 includes T1mic]

Tumor Staging

Stage IIIC(Any T, N3, Any M)

10/10/02

T, M, N Scoring T1: Tumor ≤2.0 cm in greatest

dimension – T1mic: Microinvasion ≤0.1 cm in

greatest dimension

– T1a: Tumor >0.1 cm but ≤0.5 cm in greatest dimension

– T1b: Tumor >0.5 cm but ≤1.0 cm in greatest dimension

– T1c: Tumor >1.0 cm but ≤2.0 cm in greatest dimension

T2: Tumor >2.0 cm but ≤5.0 cm in greatest dimension

T3: Tumor >5.0 cm in greatest

dimension

N0: No regional lymph node metastasis

N1: Metastasis to movable ipsilateral axillary lymph node(s)

N2: Metastasis to ipsilateral axillary lymph node(s) fixed or matted, or in clinically apparent ipsilateral internal mammary nodes in the absence of clinically evident lymph node metastasis

(T, M, N) Information Content

T

M

N

IIa

GOOD

POOR

5. Tumor Heterogeneity

Breast tumors are heterogeneous Diagnosis primarily driven from H&E Co-occurrences of breast disease? Co-morbidities with other diseases?

2 3 5 7 8 1 1 2 2 2 2 2 2 2 2 3 3 3 3 3 3 4 4 4 4 4 4 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6

0 2 2 3 4 5 6 7 8 9 0 4 6 7 8 9 1 5 6 7 8 9 0 1 2 3 4 5 9 0 1 2 3 4 5 6 7 8 9

Bayesian Network of Diagnoses

Clinical Breast Care Project

Department of Defense 20 % active duty personnel are female 95 % active duty males are married Tri-Care health system

Clinical Breast Care Project Collaboration between WRI and WRAMC

– 10,000 breast disease patients/year– Ethnic diversity; “transient– Equal access to health care for breast disease– All acquired under SINGLE PROTOCOL– All reviewed by a SINGLE PATHOLOGIST– 2 military, 1 non-military site added 2003– 6 military sites to be added 2006

Breast cancer vaccine program (her2/neu)

CBCP Repository

– Tissue, serum, lymph nodes (>15,000 samples)

– Patient annotation (500+data fields) – Patient Diagnosis = {130 sub-diagnoses}– Mammograms, 4d-ultrasound, PET/CT, 3T

MRI– Complementary genomics and proteomics,

IHC

Current CBCP Studies

LOH vs tumor location Modifier gene analysis in BRCA1/2 BC presentation in African Americans Longitudinal Impact of Environmental/Lifestyle MMG vs non-MMG detected BC and survival Lymphedema

– Quantitative diagnosis (3d-ultrasound)– Genomic and proteomic “risk” analysis

Mammography (GE, ICAD/CADx, SMDC)– Breast density factors – Integration of mammography and 3D ultrasound (“fusion”)

Studying Environmental Factors

CBCP JMBCC

Patients from JMBCCIn CBCP vs (CBCP-JMBCC)

1.Scranton2.Landstuhl3.Japan

Windber Research Institute

Founded in 2001, 501( c) (3) corporation Genomic, proteomic and informatics

collaboration with WRAMC 45 scientists (8 biomedical informaticians) 36,000 sq ft facility under construction Focus on Women’s Health, Cardiovascular

Disease, Processes of Aging

WRI’s Mission

WRI intends to be a catalyst in the creation

of the “next-generation” of medicine,

integrating basic and clinical research

with an emphasis on improving patient

care and the quality of life for the patient

and their family.

WRI’s Core Technologies:

• Tissue Banking• Histopathology• Immunohistochemistry• Laser Capture Micro-dissection• DNA Sequencing• Genotyping • Gene Expression• Array CGH• Proteomic Separation• Mass Spectrometry• Tissue Culture• Biomedical Informatics • Data Integration and Modeling

Central Dogma of Molecular Biology: DNA RNA Protein

WRI Research Strategy

Women’s Health

Cardiovascular Disease

Aging(2005)

GDP

CADRE

CBCPLymphedema

Menopause

Obesity Synergies

WRI Partnerships

Genomics/Proteomics

Clinical Studies

Infrastructure

AmershamThermo-FinneganWaters

TeradataMSADept of DefenseUSASMDCCimarronInforSenseOracle

MDRGE HealthcareICAD/CADxCorrelogicCiraSciences

Walter Reed Army Medical CenterUniversity of Pittsburgh(UPMC, UPCI) Georgetown UniversityCreighton UniversityUniversity of HawaiiPenn State UniversityUniformed Services University-Health SciencesUCSF- Breast Center

Preventative Medicine Research Institute Pittsburgh Tissue Engineering Institute University of Nevada-Las Vegas

University of Pittsburgh

Core 2 Biomedical Research

Core 1 Computational Research

PedigreeAnalysis

PatientSynchron.

DiseaseStratific.

InformationContent

Co-Morbidities

PathwaySimulation

DataMining

TextMining

Breast/Melanoma Risk(Wen-Jen Hwu)

Race/Ethnicity(Yudell)

Co-Morbidity/Risk(Esserman)

Core 3Driving

BiomedicalProjects

Core 4 Infrastructure

Core 5 Training

Core 6 Dissemination

BioSim BioWeb BioSoft

Reasoning Environment

Data Integration

Data Warehouse Model– Teradata Oracle

Cimarron’s Scierra LIMS– Amersham LWS

Creation of CLWS InforSense and SPSS

A Patient is:

Patient

Family History…… Nurse

Genomics………….Genetic Couns.Demographics…….EpidemiologistEnvironment………Envir. ScientistLifestyle……………Social ScientistClinical History…..PhysicianTherapeutic History.. PharmacistTissue Samples……PathologistCost of Treatment…InsurerQuality of Life…….Patient……….

A Patient is a Mother, Sister, Wife, Daughter…..

Modular Data Model Socio-demographics(SD) Reproductive History(RH) Family History (FH) Lifestyle/exposures (LE) Clinical history (CH) Pathology report (P)

Tissue/sample repository (T/S) Outcomes (O) Genomics (G) Biomarkers (B) Co-morbidities (C) Proteomics (Pr)

Swappable based on Disease

Windber Storage Area Network

Windber SAN

?

?

?

?

DigitalMammo

4d Ultra-Sound

Pet/CT 3T MRI

Mega-bace

MALDI

Code-Link

Pathol

PACS 1 PACS 2 PACS 3 PACS 4

NAS

CLWS

Ho

spit

alW

RI

Hospital/WRI

OC-3, OC-48Pittsburgh Philadelphia

Washington, DC

WRI 7/2005

Conclusions

Personalized Disease will improve Patient Care, Today; Personalized Medicine, Tomorrow

Disease is a Process, not a State Translational Medicine must be both:

– Bedside-to-bench, and– Bench-to-bedside

The processes of aging are critical:– For accurate diagnosis of the patient– For recognizing breast cancer as a chronic disease

Acknowledgements

Windber Research Institute Joyce Murtha Breast Care Center Walter Reed Army Medical Center Immunology Research Center Malcolm Grow Medical Center Landstuhl Medical Center Henry Jackson Foundation USUHS MRMC-TATRC Military Cancer Institute

Patients, Personnel and Family!

m.liebman@wriwindber.org