Breast Cancer: A Biomedical Informatics Analysis

1. Breast Cancer: A Biomedical Informatics Analysis Michael N. Liebman, PhD Chief Scientific Officer Windber Research Institute

2. Overview • Systems Biology vs Systems of Biology • Defining the Patient • Defining the Disease • Windber Research Institute • Conclusions

3. Systems Biology (Personalized Medicine) Patient Physiology Genomics Proteomics Metab- olomics CGH -omics

4. Bottom Up Approach Patient Physiology Genomics Proteomics Metab- olomics CGH ????

5. Top Down Approach(Personalized Disease) Patient Physiology Genomics Proteomics Metab- olomics CGH

6. Translational Medicine “Crossing the Quality Chasm” Closing The Gap Clinical Practice “Bedside” Basic Research “Bench” Training Job Function “Language” Culture Responsibilities

7. 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)

8. 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

9. 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

10. { } { } Disease(s) Risk(s) | Genotype | Phenotype | Disease is a Process Lifestyle + Environment = F(t) (SNP’s, Expression Data) (Clinical History and Data)

11. Disease Etiology DIAGNOSIS Genetic Lifestyle Breast Survival Risk Factors Cancer (Chronic Disease)

12. 1940 DES 1950 Measles Polio Vaccine 1960 Time Influenza Influenza 1970 1980 1990 Menopause PSA 2000 Prostate Cancer Pedigree (modified) Influenza Pandemic 1918

13. Phenotype TIME Phenotype Childhood Diseases | Genotype | | Smoking Menarche Overweight Diabetes Cardiovascular Disease 2nd Hand Smoke Breast Cancer (Age 48) Natural History ?

14. Longitudinal Interactionsin Breast Cancer • Identify Environmental Factors • Quantify Exposure • When ? • How Long ? • How Much ? • Extract Dosing Model • Compare with Stages of Biological Development

15. Smoking 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 AGE 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

16. Hormone Replacement Menarche Heart Disease Breast Cancer Ovarian Cancer Osteoporosis Alzheimer’s Peri- menopause Menopause Child-bearing <50 years> { { Aging Disease Disease vs Aging Quality of Life

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

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

19. UMLS Semantic Network ??

20. SPSS – LexiMine and Clementine

21. Her2/neu (FISH) = Her2/neu (IHC) Her2/neu (IHC1) = Her2/neu(IHC2) Do Either Measure the Functional Form of Her2/neu?

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

23. Stratifying Disease • Tumor Staging • T,M,N tumor scoring • Analysis of Outcomes

24. localized regional metastatic Cancer Progression 0 I IIA IIB IIIA IIIB IV

25. Tumor Progression IIIA IIA I IV 0 IIB IIIB

26. Tumor Staging Stage I(T1,* N0, M0) ; [*T1 includes T1mic] 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)

27. 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

28. (T, M, N) Information Content POOR T IIa GOOD M N

29. Tumor Heterogeneity • Breast tumors are heterogeneous • Diagnosis primarily driven from H&E • Co-occurrences of breast disease? • Co-morbidities with other diseases?

31. 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

33. Bayesian Network of Breast Diseases

34. 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

35. 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.

36. Central Dogma of Molecular Biology: DNA  RNA  Protein 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

37. 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

38. 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) Modular Data Model Swappable based on Disease

39. WRI Research Strategy Cardiovascular Disease Synergies Obesity CADRE CBCP Lymphedema GDP Women’s Health Menopause Aging (2005)

40. WRI 7/2005

41. 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

42. 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 Acknowledgements Patients, Personnel and Family!

43. m.liebman@wriwindber.org

44. “Discovery consists in seeing what Everyone else has seen and thinking What no one else has thought” A. Szent-Gyorgi

45. Asking the Right Question is95% of the Way towards Solving the Right Problem

46. DATA INFORMATION GAP AMOUNT KNOWLEDGE KNOWLEDGE GAP GAP CLINICAL UTILITY TIME Gap