breast cancer: a biomedical informatics analysis
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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. - PowerPoint PPT PresentationTRANSCRIPT
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!
“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