breast cancer: a biomedical informatics analysis

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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. - PowerPoint PPT Presentation

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  • Breast Cancer: A Biomedical Informatics Analysis Michael N. Liebman, PhDChief Scientific OfficerWindber Research Institute

  • OverviewSystems Biology vs Systems of BiologyDefining the PatientDefining the DiseaseWindber Research InstituteConclusions

  • Systems Biology (Personalized Medicine)GenomicsProteomics

    CGH

    Metab-olomicsPatientPhysiology-omics

  • Bottom Up ApproachGenomicsProteomics

    CGH

    Metab-olomicsPatientPhysiology????

  • Top Down Approach(Personalized Disease)GenomicsProteomics

    CGH

    Metab-olomicsPatientPhysiology

  • Translational Medicine Clinical PracticeBedsideBasicResearchBenchClosingThe GapTrainingJob FunctionLanguageCultureResponsibilitiesCrossing the Quality Chasm

  • Clinical Breast Care ProjectCollaboration between WRI and WRAMC10,000 breast disease patients/yearEthnic diversity; transientEqual access to health care for breast diseaseAll acquired under SINGLE PROTOCOLAll reviewed by a SINGLE PATHOLOGIST2 military, 1 non-military site added 20036 military sites to be added 2006Breast cancer vaccine program (her2/neu)

  • CBCP RepositoryTissue, serum, lymph nodes (>15,000 samples)Patient annotation (500+data fields) Patient Diagnosis = {130 sub-diagnoses}Mammograms, 4d-ultrasound, PET/CT, 3T MRIComplementary genomics and proteomics, IHC

  • Defining a PatientA 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)(SNPs, Expression Data)(Clinical History and Data)

  • Disease EtiologyGenetic Lifestyle Breast Survival Risk Factors Cancer (Chronic Disease)DIAGNOSIS

  • Pedigree (modified)Influenza Pandemic 1918

  • Phenotype| Genotype | |Childhood DiseasesDiabetesCardiovascular DiseaseSmokingOverweight2nd Hand SmokeMenarcheBreast Cancer (Age 48)Natural History ?

  • Longitudinal Interactionsin Breast CancerIdentify Environmental FactorsQuantify ExposureWhen ?How Long ?How Much ?Extract Dosing ModelCompare with Stages of Biological Development

  • Lifestyle Factors 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95AGEAlcohol 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95AGEObesity

  • Disease vs AgingQuality of Life

  • Breast DevelopmentMenarcheChild-bearing Peri-menopauseMenopause CumulativeDevelopmentLactation

  • Ontology: Breast Development

    Neo- Menarche Pregnancy Lactation Peri Menop Postnatal menop MenopParousNulliParousBudsBudsLobesLobesDuctsHuman Mouse?Terminal BudsTerminal BudsPuberty

  • UMLS Semantic Network

    ??

  • SPSS LexiMine and Clementine

  • Her2/neu (FISH) = Her2/neu (IHC)

    Her2/neu (IHC1) = Her2/neu(IHC2)

    Do Either Measure the Functional Form of Her2/neu?

  • Pathway of DiseaseTreatmentOptionsQualityOf LifeGeneticRiskEarlyDetectionPatient StratificationDiseaseStagingOutcomesTreatment HistoryBiomarkersEnvironment + Lifestyle

  • Stratifying DiseaseTumor StagingT,M,N tumor scoringAnalysis of Outcomes

  • Cancer Progression0IIIAIIBIIIAIIIBIV

  • Tumor Progression0IIIAIIBIIIAIIIBIV

  • 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 IV(Any T, Any N, M1) Tumor StagingStage 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 ContentTMNIIaGOODPOOR

  • Tumor HeterogeneityBreast tumors are heterogeneousDiagnosis primarily driven from H&ECo-occurrences of breast disease?Co-morbidities with other diseases?

  • 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

  • Bayesian Network of Breast Diseases

  • Windber Research InstituteFounded in 2001, 501( c) (3) corporationGenomic, proteomic and informatics collaboration with WRAMC45 scientists (8 biomedical informaticians)36,000 sq ft facility under constructionFocus on Womens Health, Cardiovascular Disease, Processes of Aging

  • Mission: Windber Research InstituteWRI intends to be a catalyst in the creationof the next-generation of medicine,integrating basic and clinical research with an emphasis on improving patientcare and the quality of life for the patient and their family.

  • WRIs Core Technologies:

    Tissue BankingHistopathologyImmunohistochemistryLaser Capture Micro-dissectionDNA SequencingSNP arraysGenotyping Gene ExpressionArray CGHProteomic SeparationMass SpectrometryTissue CultureBiomedical Informatics Data Integration and ModelingCentral Dogma of Molecular Biology: DNA RNA Protein

  • WRI ResourcesTissue Repository4 (40,000 samples) N2 vapor freezers3 (-80 C) freezersBreast Disease (>15,000 highly annotated specimens)GenomicsMegabace 1000Megabace 4000Affymetrix (SNP analysis)CodeLink (gene expression)Proteomics4 mass spectrometersBiomedical InformaticsPersonnel (8)LWS, CLWSData WarehouseInforSense, SPSS partnershipsDiagnostic EquipmentDigital Mammography4-D Ultrasound16-slice PET/CT3T HD MRI

  • Modular Data ModelSocio-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 Womens HealthCardiovascular DiseaseAging(2005)GDPCADRECBCPLymphedemaMenopauseObesitySynergies

  • WRI 7/2005

  • ConclusionsPersonalized Disease will improve Patient Care, Today; Personalized Medicine, TomorrowDisease is a Process, not a StateTranslational Medicine must be both:Bedside-to-bench, andBench-to-bedsideThe processes of aging are critical:For accurate diagnosis of the patientFor improving treatment of chronic disease

  • AcknowledgementsWindber Research InstituteJoyce Murtha Breast Care CenterWalter Reed Army Medical CenterImmunology Research CenterMalcolm Grow Medical CenterLandstuhl Medical CenterHenry Jackson FoundationUSUHSMRMC-TATRCMilitary Cancer InstituteHai HuYong Hong ZhangWei Hong ZhangSong YangSusan MaskerySean GuoLeonid Kvecher

    Patients, Personnel and Family!

  • m.liebman@wriwindber.org

  • Discovery consists in seeing what Everyone else has seen and thinking What no one else has thoughtA. Szent-Gyorgi

  • Asking the Right Question is95% of the Way towards Solving the Right Problem

  • Gap

    3

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