Download Genomics

Breast Cancer:
A Biomedical Informatics
Analysis
Michael N. Liebman, PhD
Chief 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)
Patient
Physiology
Genomics Proteomics
Metabolomics
CGH
-omics
Bottom Up Approach
Patient
Physiology
Genomics Proteomics
Metabolomics
CGH
????
Top Down Approach
(Personalized Disease)
Patient
Physiology
Genomics Proteomics
Metabolomics
CGH
Translational Medicine
“Crossing the Quality Chasm”
Closing
The Gap
Clinical
Practice
“Bedside”
Basic
Research
“Bench”
Training
Job Function
“Language”
Culture
Responsibilities
Clinical Breast Care Project

Collaboration between WRI and WRAMC
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
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
{ Risk(s)}
{Disease(s)}
Lifestyle + Environment = F(t)
| Genotype |
(SNP’s, Expression Data)
Phenotype
(Clinical History and Data)
|
Disease Etiology
DIAGNOSIS
Genetic
Risk
Lifestyle
Factors
Breast
Survival
Cancer (Chronic Disease)
Pedigree (modified)
Influenza Pandemic 1918
1940
DES
Time
1950
1960
1970
Measles
Polio Vaccine
Influenza
Influenza
1980
1990
2000
Menopause
Prostate Cancer
PSA
Phenotype
Childhood Diseases
| Genotype | |
Smoking
Menarche
Overweight
Diabetes
Cardiovascular Disease
2nd Hand Smoke
Natural History ?
Breast Cancer
(Age 48)
Phenotype
TIME
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
Smoking
5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95
AGE
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
Disease vs Aging
Hormone
Replacement
Menarche
Menopause
Perimenopause
Child-bearing
<50 years>
Heart Disease
Breast Cancer
Ovarian Cancer
Osteoporosis
{
{
Alzheimer’s
Aging
Disease
Quality of Life
Breast Development
Cumulative
Development
Lactation
Menopause
Menarche
Peri-menopause
Child-bearing
Ontology: Breast Development
Parous
Terminal Buds
Buds
Lobes
Ducts
Puberty
Neo- Menarche Pregnancy
natal
Buds
Lactation Peri Menop Post
menop
Menop
Lobes
Terminal Buds
Human Mouse?
NulliParous
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
Natural History of Disease
Treatment History
Outcomes
Treatment
Options
Environment
+ Lifestyle
Disease
Staging
Patient
Stratification
Early
Detection
Genetic
Risk
Biomarkers
Quality
Of Life
Stratifying Disease
Tumor Staging
 T,M,N tumor scoring
 Analysis of Outcomes

Cancer Progression
localized
0
I
regional
metastatic
IIA IIB IIIA IIIB
IV
Tumor Progression
IIIA
IIA
0
I
IV
IIB
IIIB
Tumor Staging
Stage 0
(Tis, N0, M0)
Stage I
(T1,* N0, M0) ; [*T1 includes T1mic]
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 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
IIa
M
N
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:
Central Dogma of Molecular Biology:
DNA  RNA  Protein
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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
WRI Resources

Tissue Repository
–
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Genomics
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4 mass spectrometers
Biomedical Informatics
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Megabace 1000
Megabace 4000
Affymetrix (SNP analysis)
CodeLink (gene expression)
Proteomics
–

4 (40,000 samples) N2 vapor freezers
3 (-80 C) freezers
Breast Disease (>15,000 highly annotated specimens)
Personnel (8)
LWS, CLWS
Data Warehouse
InforSense, SPSS partnerships
Diagnostic Equipment
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Digital Mammography
4-D Ultrasound
16-slice PET/CT
3T HD MRI
Modular Data Model
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Socio-demographics(SD)
Reproductive History(RH)
Family History (FH)
Lifestyle/exposures (LE)
Clinical history (CH)
Pathology report (P)
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Tissue/sample repository (T/S)
Outcomes (O)
Genomics (G)
Biomarkers (B)
Co-morbidities (C)
Proteomics (Pr)
Swappable based on Disease
WRI Research Strategy
Cardiovascular Disease
Synergies
Obesity
CADRE
Lymphedema
CBCP
GDP
Women’s Health
Menopause
Aging
(2005)
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
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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

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Hai Hu
Yong Hong Zhang
Wei Hong Zhang
Song Yang
Susan Maskery
Sean Guo
Leonid Kvecher
Patients, Personnel and Family!
[email protected]
“Discovery consists in seeing what
Everyone else has seen and thinking
What no one else has thought”
A. Szent-Gyorgi
Asking the Right Question is
95% of the Way towards
Solving the Right Problem
Gap
AMOUNT
DATA
INFORMATION
GAP
GAP
GAP
KNOWLEDGE
KNOWLEDGE
CLINICAL UTILITY
TIME