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) GenomicsProteomicsCGH Metab- olomics Patient Physiology -omics

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

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

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

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

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

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

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

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

15. 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

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

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

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

19. UMLS Semantic Network ??

20. SPSS – LexiMine and Clementine

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

22. 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

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

24. Cancer Progression 0IIIAIIBIIIAIIIBIV localizedregionalmetastatic

25. Tumor Progression 0 I IIA IIB IIIA IIIB IV

26. 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

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 T M N IIa GOOD POOR

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 81 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. 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

37. 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

38. 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

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

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. 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!

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 is 95% of the Way towards Solving the Right Problem

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