Breast Cancer: A Biomedical Informatics

Analysis

Michael N. Liebman, PhDChief Scientific Officer

Windber Research Institute

Overview

Systems Biology vs Systems of Biology Defining the Patient Defining the Disease Windber Research Institute Conclusions

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”

ClosingThe Gap

TrainingJob Function“Language”

CultureResponsibilities

“Crossing the Quality Chasm”

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

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 is a Process

Lifestyle + Environment = F(t)

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

| Genotype | Phenotype |

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

Disease Etiology

Genetic Lifestyle Breast Survival Risk Factors Cancer (Chronic Disease)

DIAGNOSIS

Pedigree (modified)

Tim

e Polio Vaccine

Menopause

Influenza

Measles

Influenza

1940

1950

1960

1970

Prostate Cancer

1980

1990

2000

PSA

DES

Influenza Pandemic 1918

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

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

Human Mouse?Terminal Buds

Terminal Buds

Puberty

UMLS Semantic Network

??

SPSS – LexiMine and Clementine

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

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

Do Either Measure the Functional Form of Her2/neu?

Pathway of Disease

TreatmentOptions

QualityOf Life

GeneticRisk

EarlyDetection

Patient Stratification

DiseaseStaging

Outcomes

Natural History of Disease Treatment History

Biomarkers

Environment + Lifestyle

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

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 Breast Diseases

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

Mission: Windber Research Institute

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• SNP arrays• 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 Resources Tissue Repository

– 4 (40,000 samples) N2 vapor freezers– 3 (-80 C) freezers– Breast Disease (>15,000 highly annotated specimens)

Genomics– Megabace 1000– Megabace 4000– Affymetrix (SNP analysis)– CodeLink (gene expression)

Proteomics– 4 mass spectrometers

Biomedical Informatics– Personnel (8)– LWS, CLWS– Data Warehouse– InforSense, SPSS partnerships

Diagnostic Equipment– Digital Mammography– 4-D Ultrasound– 16-slice PET/CT– 3T HD MRI

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

WRI Research Strategy

Women’s Health

Cardiovascular Disease

Aging(2005)

GDP

CADRE

CBCPLymphedema

Menopause

Obesity Synergies

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 improving treatment of 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

Hai Hu Yong Hong Zhang Wei Hong Zhang Song Yang Susan Maskery Sean Guo Leonid Kvecher

Patients, Personnel and Family!

m.liebman@wriwindber.org

“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

Gap

INFORMATION

KNOWLEDGEGAP

GAP

GAP

TIME

AM

OU

NT

DATA

CLINICAL UTILITY

KNOWLEDGE