ASSESSING%20THE%20REAL%20RISK%20IN%20COMPLEX%20DISEASES

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ASSESSING THE REAL RISK IN COMPLEX DISEASES

• Michael N. Liebman, PhD
• Chief Scientific Officer
• Windber Research Institute

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Overview

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

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Gap
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Systems Biology(Personalized Medicine)
Patient
Physiology
Genomics
Proteomics
CGH
Metab- olomics
-omics
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Bottom Up Approach
Patient
Physiology
Genomics
Proteomics
CGH
Metab- olomics
????
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Top Down Approach(Personalized Disease)
Patient
Physiology
Genomics
Proteomics
CGH
Metab- olomics
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Translational Medicine
Training Job Function Language Culture Responsib
ilities
Clinical Practice Bedside
Basic Research Bench
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Translational Medicine
Crossing the Quality Chasm
Closing The Gap
Clinical Practice Bedside
Basic Research Bench
Training Job Function Language Culture Responsib
ilities
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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

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Discovery consists in seeing what Everyone else
has seen and thinking What no one else has
thought
A. Szent-Gyorgi
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Asking the Right Question is95 of the Way
towards Solving the Right Problem
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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

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1. Modeling Disease

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

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Modeling Disease
Lifestyle Environment F(t)
(SNPs, Expression Data) (Clinical History and
Data)
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UMLS Semantic Network

??
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Disease Etiology
DIAGNOSIS
Genetic Lifestyle
Breast Survival Risk
Factors Cancer
(Chronic Disease)
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Pathway of Disease
Quality Of Life
Treatment History
Outcomes
Treatment Options
Environment Lifestyle
Disease Staging
Patient Stratification
Early Detection
Biomarkers
Genetic Risk
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Her2/neu (FISH) Her2/neu (IHC) Her2/neu
(IHC1) Her2/neu(IHC2) Do Either Measure the
Functional Form of Her2/neu?
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Phenotype
Childhood Diseases
Genotype
Smoking
Menarche
Overweight
Diabetes
Cardiovascular Disease
2nd Hand Smoke
Breast Cancer (Age 48)
Natural History ?
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Longitudinal Interactionsin Breast Cancer

• Identify Environmental Factors
• Quantify Exposure
• When ?
• How Long ?
• How Much ?
• Extract Dosing Model
• Compare with Stages of Biological Development

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Lifestyle Factors
Obesity
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
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2. Genetics and Disease

• Genetic Pre-Disposition
• lt 10 of all breast cancers
• Not all BRCA1 and BRCA2 mutations
• result in breast cancer
• Modifier genes?
• Lifestyle or environmental factors?
• Pedigree Analysis

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Pedigree (modified)
Influenza Pandemic 1918
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3. Aging and Disease

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

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Disease vs Aging
Quality of Life
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Breast Development
Cumulative Development
Lactation
Menopause
Menarche
Peri-menopause
Child-bearing
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Ontology Breast Development
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SPSS LexiMine and Clementine
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Puberty
Production of Stroma, mesenchymal cells,
epithelial cells
 
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Reality of Disease
DNA RNA Amino Acids
Genes
Proteins
Enzymes Substrates Co-Factors
Pathways
Tissues Cells Organelles
Gene Ontology
Processes Tissue generation Inflammation.
Physiological Systems
Physiological Development
(time)
Disease Progression
(time)
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4. Stratifying Disease

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

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Cancer Progression
0
I
IIA
IIB
IIIA
IIIB
IV
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Tumor Progression
IIIA
IIA
I
IV
0
IIB
IIIB
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Stage I(T1, N0, M0) T1 includes T1mic
Tumor Staging
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 IIIC (Any T, N3, Any M)
10/10/02
Stage IV (Any T, Any N, M1)
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T, M, N Scoring

• T1 Tumor 2.0 cm in greatest dimension
• T1mic Microinvasion 0.1 cm in greatest
  dimension
• T1a Tumor gt0.1 cm but 0.5 cm in greatest
  dimension
• T1b Tumor gt0.5 cm but 1.0 cm in greatest
  dimension
• T1c Tumor gt1.0 cm but 2.0 cm in greatest
  dimension
• T2 Tumor gt2.0 cm but 5.0 cm in greatest
  dimension
• T3 Tumor gt5.0 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

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(T, M, N) Information Content
POOR
T
IIa
M
GOOD
N
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5. Tumor Heterogeneity

• Breast tumors are heterogeneous
• Diagnosis primarily driven from HE
• Co-occurrences of breast disease?
• Co-morbidities with other diseases?

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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
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Bayesian Network of Diagnoses
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Clinical Breast Care Project

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

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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)

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CBCP Repository

• Tissue, serum, lymph nodes (gt15,000 samples)
• Patient annotation (500data fields)
• Patient Diagnosis 130 sub-diagnoses
• Mammograms, 4d-ultrasound, PET/CT, 3T MRI
• Complementary genomics and proteomics, IHC

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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)

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Studying Environmental Factors
Patients from JMBCC In CBCP vs (CBCP-JMBCC)
CBCP
JMBCC
1.Scranton 2.Landstuhl 3.Japan
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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 Womens Health, Cardiovascular Disease,
  Processes of Aging

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WRIs 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.

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• WRIs 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
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WRI Research Strategy
Cardiovascular Disease
Synergies
Obesity
CADRE
CBCP
Lymphedema
GDP
Womens Health
Menopause
Aging (2005)
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WRI Partnerships
Amersham Thermo-Finnegan Waters
University of Pittsburgh
Teradata MSA Dept of Defense USASMDC Cimarron Info
rSense Oracle
Walter Reed Army Medical Center University of
Pittsburgh(UPMC, UPCI) Georgetown
University Creighton University University of
Hawaii Penn State University Uniformed Services
University-Health Sciences UCSF- Breast Center
Preventative Medicine Research Institute
Pittsburgh Tissue Engineering Institute
University of Nevada-Las Vegas
MDR GE Healthcare ICAD/CADx Correlogic CiraScience
s
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Reasoning Environment
BioSim
BioWeb
BioSoft
Core 1 Computational Research
Core 2 Biomedical Research
Pedigree Analysis
Patient Synchron.
Disease Stratific.
Information Content
Co- Morbidities
Pathway Simulation
Data Mining
Text Mining
Breast/Melanoma Risk (Wen-Jen Hwu)
Race/Ethnicity (Yudell)
Co-Morbidity/Risk (Esserman)
Core 3 Driving Biomedical Projects
Core 4 Infrastructure
Core 5 Training
Core 6 Dissemination
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Data Integration

• Data Warehouse Model
• Teradata ? Oracle
• Cimarrons Scierra LIMS
• Amersham LWS
• Creation of CLWS
• InforSense and SPSS

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A Patient is
A Patient is a Mother, Sister, Wife, Daughter..
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Modular Data Model

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

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

Swappable based on Disease
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Windber Storage Area Network
Hospital/WRI
Digital Mammo
4d Ultra- Sound
Pet/CT
3T MRI
PACS 1
PACS 2
PACS 3
PACS 4
Mega- bace
Windber SAN
?
MALDI
?
WRI
NAS
Hospital
Code- Link
?
Pathol
?
OC-3, OC-48
CLWS
Pittsburgh
Philadelphia
Washington, DC
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WRI 7/2005
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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

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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!
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m.liebman_at_wriwindber.org