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

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Presentation on theme: "ASSESSING THE REAL RISK IN COMPLEX DISEASES Michael N. Liebman, PhD Chief Scientific Officer Windber Research Institute."— Presentation transcript:

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


4 Systems Biology (Personalized Medicine) GenomicsProteomicsCGH Metab- olomics Patient Physiology -omics

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

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

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

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

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 is 95% 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  Disease as a State vs Disease as a Process  Bias of Perspective  Temporal Perspective 1. Modeling Disease

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

15 UMLS Semantic Network ??

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

17 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

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

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

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

21 Lifestyle Factors AGE Alcohol AGE Smoking AGE Obesity

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 Pedigree (modified) Time Polio Vaccine Menopause Influenza Measles Influenza Prostate Cancer PSA DES 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 Disease vs Aging Menopause Hormone Replacement Heart Disease Breast Cancer Ovarian Cancer Osteoporosis Alzheimer’s Menarche Child-bearing Peri- menopause {{ AgingDisease Quality of Life

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

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

28 SPSS – LexiMine and Clementine

29 Puberty: 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. Production of: Stroma, mesenchymal cells, epithelial cells

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

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

32 Cancer Progression 0IIIAIIBIIIAIIIBIV localizedregionalmetastatic

33 Tumor Progression 0 I IIA IIB IIIA IIIB IV

34 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

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 T M N IIa GOOD POOR

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





42 Bayesian Network of Diagnoses

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

44 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)

45 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


47 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”)

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

49 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

50 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.

51 WRI’s Core Technologies: Tissue Banking Histopathology Immunohistochemistry Laser Capture Micro-dissection DNA Sequencing 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

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

53 WRI Partnerships Genomics/Proteomics Clinical Studies Infrastructure Amersham Thermo-Finnegan Waters Teradata MSA Dept of Defense USASMDC Cimarron InforSense Oracle MDR GE Healthcare ICAD/CADx Correlogic CiraSciences Walter Reed Army Medical Center University of Pittsburgh(UPMC, UPCI) Georgetown University Creighton University University of Hawaii Penn State University Uniformed Services University-Health Sciences UCSF- Breast Center Preventative Medicine Research Institute Pittsburgh Tissue Engineering Institute University of Nevada-Las Vegas University of Pittsburgh

54 Core 2 Biomedical Research Core 1 Computational Research Pedigree Analysis Patient Synchron. Disease Stratific. Information Content Co- Morbidities Pathway Simulation Data Mining Text Mining Breast/Melanoma Risk (Wen-Jen Hwu) Race/Ethnicity (Yudell) Co-Morbidity/Risk (Esserman) Core 3 Driving Biomedical Projects Core 4 Infrastructure Core 5 Training Core 6 Dissemination BioSimBioWebBioSoft Reasoning Environment

55 Data Integration  Data Warehouse Model –Teradata  Oracle  Cimarron’s Scierra LIMS –Amersham LWS  Creation of CLWS  InforSense and SPSS

56 A Patient is: Patient Family History……Nurse Genomics………….Genetic Couns. Demographics…….Epidemiologist Environment………Envir. Scientist Lifestyle……………Social Scientist Clinical History…..Physician Therapeutic History.. Pharmacist Tissue Samples……Pathologist Cost of Treatment…Insurer Quality of Life…….Patient ………. A Patient is a Mother, Sister, Wife, Daughter…..

57 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

58 Windber Storage Area Network Windber SAN ? ? ? ? Digital Mammo 4d Ultra- Sound Pet/CT3T MRI Mega- bace MALDI Code- Link Pathol PACS 1PACS 2PACS 3PACS 4 NAS CLWS Hospital WRI Hospital/WRI OC-3, OC-48 PittsburghPhiladelphia Washington, DC

59 WRI 7/2005

60 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 recognizing breast cancer as a chronic disease

61 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 Patients, Personnel and Family!


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