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) GenomicsProteomicsCGH Metab- olomics Patient Physiology -omics

Bottom Up Approach GenomicsProteomicsCGH Metab- olomics Patient Physiology ????

Top Down Approach (Personalized Disease) GenomicsProteomicsCGH Metab- olomics Patient Physiology

Translational Medicine Clinical Practice “Bedside” Basic Research “Bench” Closing The Gap Training Job Function “Language” Culture Responsibilities “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) Time Polio Vaccine Menopause Influenza Measles Influenza Prostate Cancer PSA DES Influenza Pandemic 1918

Phenotype | Genotype | | Phenotype TIME Childhood Diseases Diabetes Cardiovascular Disease Smoking Overweight 2 nd 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 AGE Alcohol AGE Smoking AGE Obesity

Disease vs Aging Menopause Hormone Replacement Heart Disease Breast Cancer Ovarian Cancer Osteoporosis Alzheimer’s Menarche Child-bearing Peri- menopause {{ AgingDisease Quality of Life

Breast Development Menarche Child-bearing Peri-menopause Menopause Cumulative Development Lactation

Ontology: Breast Development Neo- Menarche Pregnancy Lactation Peri Menop Post natal menop Menop Parous NulliParous Buds Lobes Ducts Human Mouse? Terminal Buds Puberty

UMLS Semantic Network ??

SPSS – LexiMine and Clementine

Her2/neu (FISH) = Her2/neu (IHC) Her2/neu (IHC 1 ) = Her2/neu(IHC 2 ) Do Either Measure the Functional Form of Her2/neu?

Pathway of Disease Treatment Options Quality Of Life Genetic Risk Early Detection Patient Stratification Disease Staging Outcomes Natural History of DiseaseTreatment History Biomarkers Environment + Lifestyle

Stratifying Disease  Tumor Staging  T,M,N tumor scoring  Analysis of Outcomes

Cancer Progression 0IIIAIIBIIIAIIIBIV localizedregionalmetastatic

Tumor Progression 0 I 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?

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) N 2 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 CBCP Lymphedema 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 is 95% of the Way towards Solving the Right Problem

Gap INFORMATION KNOWLEDGE GAP TIME AMOUNT DATA CLINICAL UTILITY KNOWLEDGE