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ASSESSING THE REAL RISK IN COMPLEX DISEASES

ASSESSING THE REAL RISK IN COMPLEX DISEASES. Michael N. Liebman, PhD Chief Scientific Officer Windber Research Institute. Overview. Data, Information and Knowledge Systems Biology Defining Translational Research Understanding the Question(s) Clinical Breast Care Project (CBCP)

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ASSESSING THE REAL RISK IN COMPLEX DISEASES

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  1. ASSESSING THE REAL RISK IN COMPLEX DISEASES Michael N. Liebman, PhD Chief Scientific Officer Windber Research Institute

  2. Overview • Data, Information and Knowledge • Systems Biology • Defining Translational Research • Understanding the Question(s) • Clinical Breast Care Project (CBCP) • Windber Research Institute • Data Integration

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

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

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

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

  7. Translational Medicine Training Job Function “Language” Culture Responsibilities Basic Research “Bench” Clinical Practice “Bedside”

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

  9. Humans as Detectors • Characteristics • Spectral sensitivity (visible region) • Sound sensitivity (audible range and volume) • Memory (retention is critical for comparison) • Perception (focus on what is known) • Analytical Capability (simple vs complex) • Ranks Importance of Change by Size (Bias) • Evolves slowly compared to other technological advances • Does not perform uniformly over 24/7

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

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

  12. 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

  13. 1. Modeling Disease • Disease as a State vs Disease as a Process • Bias of Perspective • Temporal Perspective

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

  15. UMLS Semantic Network ??

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

  17. 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

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

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

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

  21. 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

  22. 2. Genetics and Disease • Genetic Pre-Disposition • < 10 % of all breast cancers • Not all BRCA1 and BRCA2 mutations result in breast cancer • Modifier genes? • Lifestyle or environmental factors? • Pedigree Analysis

  23. 1940 DES 1950 Measles Polio Vaccine 1960 Time Influenza Influenza 1970 1980 1990 Menopause PSA 2000 Prostate Cancer Pedigree (modified) Influenza Pandemic 1918

  24. 3. Aging and Disease • Processes of Aging vs Disease Processes • Ongoing Breast Development • Same Disease : Different Host? • Text Data-mining Approaches

  25. 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

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

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

  28. SPSS – LexiMine and Clementine

  29. Two hormones – estrogen and progesterone signal the • development of the glandular breast tissue. • In female estrogen acts on mesenchymal cells to stimulate further • development. • The gland increases in size due to deposition of interlobular fat. • The ducts extend and branch into the expanding stroma. • The epithelial cell proliferation and basement membrane • remodeling is controlled by interactions between the • epithelium and the intra-lobular hormone sensitive zone of • fibroblasts. • The smallest ducts, the intra-lobular ducts, end in the epithelial buds • which are the prospective secretory alveoli. • Breast ducts begin to grow and this growth continues until • menstruation begins. Puberty: Production of: Stroma, mesenchymal cells, epithelial cells

  30. Reality of Disease DNA RNA Amino Acids Genes Proteins Enzymes Substrates Co-Factors Pathways Tissues Cells Organelles Gene Ontology Processes: Tissue generation; Inflammation…. Physiological Systems Physiological Development (time) Disease Progression (time)

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

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

  33. Tumor Progression IIIA IIA I IV 0 IIB IIIB

  34. 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)

  35. 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

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

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

  38. 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

  39. Bayesian Network of Diagnoses

  40. Clinical Breast Care Project • Department of Defense • 20 % active duty personnel are female • 95 % active duty males are married • Tri-Care health system

  41. 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)

  42. 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

  43. Current CBCP Studies • LOH vs tumor location • Modifier gene analysis in BRCA1/2 • BC presentation in African Americans • Longitudinal Impact of Environmental/Lifestyle • MMG vs non-MMG detected BC and survival • Lymphedema • Quantitative diagnosis (3d-ultrasound) • Genomic and proteomic “risk” analysis • Mammography (GE, ICAD/CADx, SMDC) • Breast density factors • Integration of mammography and 3D ultrasound (“fusion”)

  44. Studying Environmental Factors Patients from JMBCC In CBCP vs (CBCP-JMBCC) CBCP JMBCC 1.Scranton 2.Landstuhl 3.Japan

  45. 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

  46. WRI’s Mission 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.

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