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2nd Consortium Meeting, Barcelona 16th May, 2011

INBIOMEDvisionINBIOMEDvision

Bridging the gap between Bioinformatics and Medical

Informatics

Bridging the gap between Bioinformatics and Medical

Informatics

Workshop MIE 2012http://www.inbiomedvision.eu/


Why defining the Biomedical Informatics Field is so important Dr Miguel Angel Mayer

Prospective analysis on Biomedical Informatics enabling personalised medicine

Dra Victoria López-Alonso

Personalised medicine: a legacy of promises without delivery – can we get it right today?

Dra Nour Shublaq

MIE 2012 Workshop :

University College London Centre for Computational Science

Pompeu Fabra University – FIMIMJoint Research Programme on Biomedical Informatics (GRIB)

Institute of Health Carlos III Medical Bioinformatics Department


Partners: Universitat Pompeu Fabra (Coordination) Fundació IMIM (Managing) Danish Technical University Erasmus University Medical Center Universidad Politecnica de Madrid Instituto de Salud Calos III University College London

• + 40 additional experts participants

• Overseas scientific advisory board

INBIOMEDvision: Promoting & monitoring BMI in Europe

http://www.inbiomedvision.eu/


INBIOMEDvision provides overviews on the state-of-the-art, methods and models that connect biological systems described at the molecular level

with clinical physiopathology and compiles the existing knowledge on genotype-phenotype data resources.

INBIOMEDvision provides overviews on the state-of-the-art, methods and models that connect biological systems described at the molecular level

with clinical physiopathology and compiles the existing knowledge on genotype-phenotype data resources.

INBIOMEDvision: Promoting & monitoring BMI in Europe



Operational Objectives INBIOMEDvision

To consolidate a BMI community of researchers by congregating and promoting the interaction between scientists from a wide range of related fields.To develop and facilitate training activities promoting new generations of scientists and professionals having the BMI perspective.To widely disseminate the BMI knowledge and resources.


Researcher DirectoryConsolidation of a Biomedical Informatics Community

Training Activities – Training ChallengeTo promote cross-talk between disciplines to tackle a specific case study, by engagement of complementary expertise

Scientific EventsTo provide and facilitate interaction and collaboration between EU & international researchers from different related disciplines

Community building activities


Think Tanks – Reports & Summary

Different European and international experts, leaders in their own fields, participated in three Think Tanks, in order to identify opportunities for future

collaborative work, and making recommendations for the wider scientific community.


Why defining the Biomedical Informatics Field is so important Dr Miguel Angel Mayer

Prospective analysis on Biomedical Informatics enabling personalised medicine

Dra Victoria López-Alonso

Personalised medicine: a legacy of promises without delivery – can we get it right today?

Dra Nour Shublaq

MIE 2012 Workshop :

University College London Centre for Computational Science

Pompeu Fabra University – FIMIMJoint Research Programme on Biomedical Informatics (GRIB)

Institute of Health Carlos III Medical Bioinformatics Department


Victoria López Alonso PhDBioinformátics Unit

Instituto de Salud Carlos IIISpain

Victoria López Alonso PhDBioinformátics Unit

Instituto de Salud Carlos IIISpain

Prospective analysis on Biomedical Informatics

enabling personalised medicine

Prospective analysis on Biomedical Informatics

enabling personalised medicine

Workshop INBIOMEDvision, MIE 2012


Personalised medicine Biomedical Informatics (BMI) enabling personalised medicine:

Overview


Personalised medicine in current practice

Chemotherapy medications trastuzumab

and Imatinib (Gambacorti-Passerini, 2008;

Hudis, 2007)

Targeted pharmacogenetic dosing algorithm is used for warfarin (International Warfarin Pharmacogenetics Consortium et al., 2009)

Incidence of adverse events for drugs Abacavir, Carbamazepine and Clozapine

(Dettling et al., 2007; Ferrell and McLeod, 2008).


Personalised medicine and BMI

Genomics

Information Personalised MedicineDNA, RNA, proteins, small molecules, and lipids

Individual genomics (SNPs, CNVs…), Functional genomics, Proteomics…

DiagnosisDisease ReclassificationPharmacogenomics

BIOMEDICAL INFORMATICSBIOMEDICAL INFORMATICS

Clinicalpatients, diseases, symptoms, laboratory tests, pathology

reports, clinical images, and drugs…

Advancing biomedical research requires an overlap of genomic and clinical research. The assimilation of information at the molecular, cellular, tissue, organ, and personal level leads to the development of innovative BMI tools and technologies.

Advancing biomedical research requires an overlap of genomic and clinical research. The assimilation of information at the molecular, cellular, tissue, organ, and personal level leads to the development of innovative BMI tools and technologies.

High throughput biological measurements

Population-based health data &EHR


Network of data resources

Data sources coupled with clinical decision support systems(CDS), should become readily available at the bedside to support informed decision making and to improve patient safety.

Data sources coupled with clinical decision support systems(CDS), should become readily available at the bedside to support informed decision making and to improve patient safety.

Clinical Bioinformatics Molecular & Clinical

Electronic Medical Records

Standards for:Diseases: UMLS, MESH, SNOMED… Adverse events: MedDRA Drugs: RXNorm Veterans Affairs National Drug …Reference Laboratory tests: LOINC…Health information: HL 7, The Anatomical Therapeutic Chemical classification…

DatabasesGenbank, Pubmed, GEO, PDBUCSC, Ensembl Human Genome Nomenclature CommitteeHumanCyc and KEGG, Reactome…

Standards:Gene Ontology…

Powerful Network of data resources

The Adverse Event Reporting System (AERS)


BMI for Health-Related Genomics

Bentley D. “Genomes for Medicine”. (2004). Nature Insight 429, p440-446


Personalised medicine in current practice

Today patient´s genetics are consulted only for few diagnoses and treatments and only in certain medical centers (cystic fibrosis , breast cancer)

Today patient´s genetics are consulted only for few diagnoses and treatments and only in certain medical centers (cystic fibrosis , breast cancer)

Clinical assessment incorporating a personal genome. Ashley et al. Lancet (2010)

They assessed his risk for common diseases and his response to hundred of drugs based on information about the presence of certain genetic alleles

Clinical assessment incorporating a personal genome. Ashley et al. Lancet (2010)

They assessed his risk for common diseases and his response to hundred of drugs based on information about the presence of certain genetic alleles


The ability to measure human genetic information creates opportunities for translational bioinformatics.

BMI structure to store and process genomic data


1,63 millionSNPs shared by twins that differ from reference human genome

9,531 Variants that code for proteins

4,605 Variants that change aa seq

77 Rare variants

3 Candidate genes

Sequence of entire genomes and exomes, measures of genetic variations…

1 gen linked to disorder



Diagnostic classifiers that can identify subclasses of disease with different prognoses or therapeutic sensitivities. (i.e. expression data clustering).

Evaluation of biomarkers for Molecular Diagnostics and

Prognosis


Genome wide association studies (GWAS) for discovering genetic association between a disease and a biomarker (case-control design).The most basic analyses include characterizing cellular populations and clustering them on the basis of similar profiles. It is important to collect data on exposure to potential non-genetic (environmental) risk factors.


Evaluation of biomarkers for Molecular Diagnostics and

Prognostics



The Wellcome Trust Consortium published a landmark paper: 14,000 cases & 3,000 controls in a GWAS analysis of seven common diseases using 500,000 SNPs.They found 24 independent associations, and made the data available for the development of additional methods for GWAS analysis


Model Selection Methods have been successful with disease and trait GWAS studies using selection techniques to choose multifactorial models that balance the false positive rate, statistical power and computational requirements of the search

Dimensionality reduction methods•Principal Components Analysis•Information Gain •Multifactor Dimensionality Reduction (ie. hypertension and familial amyloid polyneuropathy type I)

Ritchie and Monsimger, 2010Ritchie and Monsimger, 2010



Literature mining could be used to create a set of candidate genes: methods that use sentence syntax and natural language processing to establish the link between molecular and clinical entities and derive drug-gene and gene-gene interactions from scientific literature.


20 million of references in natural lenguage

Methods to be able to extract information from natural text and represent it formally in databases that allow automated search, indexing and inference


Informatics for Health-Related Genomics

A key obstacle in the use of genome data for decision making in the clinic is the billions of features that are contained in a single human genome.

Difficulty to discriminate between ‘causal’ variation that has predictive value in the clinic and the substantial amount of ‘passenger’ variation that travels along in an uncorrelated manner.

Systems-level analyses can drastically reduce the combinatorial problem:

• grouping individual genetic variants that affect the same molecular machinery•turning EHR data into valuable clinical markers relative to gene approaches


Systems biology and network approaches: integration of molecular data at multiple levels (genomes, transcriptomes, metabolomes, proteomes and functional and regulatory networks

BMI for Network-based decision support


Systems medicine: characterizing disease states at the molecular level.Systems pharmacology: network of molecules that interact with one another and with drugs. “The network is the target”


•Disease-Gene Networks •Chemical structures, Diseases and Protein sequences •Epigenetic data and Drug Phenotypes•Pathways and Gene sets

2nd Consortium Meeting, Barcelona 16th May, 2011Goh et al., 2007 Goh et al., 2007

Network of human diseases and the associated genes from the Online Mendelian Inheritance in Man resourceNetwork of human diseases and the associated genes from the Online Mendelian Inheritance in Man resource

Recent work has focused on networks for human metabolism, cancer, and stem cells. Combining “top down” use of text and “bottom up” use of genomic information.

Diseases are clustered based on shared associated genes (comobidities).

Temporal aspects of phenotypes



BMI for use of EHR and other clinical information BMI for use of EHR and other clinical information

Mining electronic health records using statistical, machine-learning text mining and computational data-mining methods for :

Mining electronic health records using statistical, machine-learning text mining and computational data-mining methods for :

Genotype-phenotype mappingDisease comorbiditiesPatient stratificationDrug interactions Clinical outcomes



EHR-based phenotyping in genetic discovery is feasible and much less expensive than specially created study cohorts to : replicate the GWAS resultsgeneration of clinically actionable knowledge that can inform the tailoring of treatments partly automate the process of recruiting patients for clinical trials and case-control studies (health-care-sector data is linked with biobanks and genetic data).

EHR-based phenotyping in genetic discovery is feasible and much less expensive than specially created study cohorts to : replicate the GWAS resultsgeneration of clinically actionable knowledge that can inform the tailoring of treatments partly automate the process of recruiting patients for clinical trials and case-control studies (health-care-sector data is linked with biobanks and genetic data).


Linking EHR data to biobanked blood samples have been collected during routine clinical care by the Vanderbilt University. Phenome-wide association study (PheWAS) checks individual SNPs for statistical association against hundreds of disease phenotypes of patients to better understand the clinical responses to diseases and therapies.

Linking EHR data to biobanked blood samples have been collected during routine clinical care by the Vanderbilt University. Phenome-wide association study (PheWAS) checks individual SNPs for statistical association against hundreds of disease phenotypes of patients to better understand the clinical responses to diseases and therapies.

www.phenx.org/ www.phenx.org/

BioBank system at VanderbiltBioBank system at Vanderbilt

RTI International with NHGRIRTI International with NHGRI



Disease comorbidities:Correlating clinical features or disease co-occurrence (Charlson index) to interpret confounding effects of diseases in cohort studies Patient Stratification: using clustering methods and semantic similarity metrics

Disease comorbidities:Correlating clinical features or disease co-occurrence (Charlson index) to interpret confounding effects of diseases in cohort studies Patient Stratification: using clustering methods and semantic similarity metrics


Peter et al., 2012 Peter et al., 2012

Mining electronic health records


Pharmacogenomics: Drug efficacy is influenced by genetic variation.

The detailed patient profile that can be assembled from EHR data enables drug exposure profiles to be correlated with treatment outcome measures, efficacy and toxicity.

Prediction of drug-gen interactions using text extraction relationships contained in EHR, PubMed, US Food and Drug Administration (FDA) data …

Pharmacogenomics: Drug efficacy is influenced by genetic variation.

The detailed patient profile that can be assembled from EHR data enables drug exposure profiles to be correlated with treatment outcome measures, efficacy and toxicity.

Prediction of drug-gen interactions using text extraction relationships contained in EHR, PubMed, US Food and Drug Administration (FDA) data …

Drug interactions and clinical outcome.

BMI for mining of EHR and other clinical information BMI for mining of EHR and other clinical information

Dose response to the anticoagulant warfarin affected by at least three genetic variants


Predictive clinical outcomes


EHR data mining and conventional epidemiology on registry data provide the basis for predicting patient outcomes using machine-learning methods (surgery outcome, breast cancer survival and coronary heart disease risk from variables such as age, sex, smoking status, hypertension and various biomarkers).

Establishing patterns of directionality in comorbidity and disease progression is a first step .

EHR data mining and conventional epidemiology on registry data provide the basis for predicting patient outcomes using machine-learning methods (surgery outcome, breast cancer survival and coronary heart disease risk from variables such as age, sex, smoking status, hypertension and various biomarkers).

Establishing patterns of directionality in comorbidity and disease progression is a first step .


Restriction on access to existing datato make data available to researchers (patient

databases: Kaiser RPGEH, Million Veterans Program, PatientsLikeMe…)

Restriction on access to existing datato make data available to researchers (patient

databases: Kaiser RPGEH, Million Veterans Program, PatientsLikeMe…)

Privacy, autonomy and consent is required. to de-identify research data according to specifications (Health Insurance Portability and Accountability Act (HIPAA) privacy rule)

Privacy, autonomy and consent is required. to de-identify research data according to specifications (Health Insurance Portability and Accountability Act (HIPAA) privacy rule)

Interoperability across institutions, countries and continents Biomedical standards (CEN–ISO 13606, HL7, SNOMED CT) and Web standardsIntegrative international Centers as “Informatics for integrating biology and the bedside system (i2b2)”Cloud computing

By addressing the challenges outlined in this review, BMI will create the tools to tailor medical care to each individual genome.

Interoperability across institutions, countries and continents Biomedical standards (CEN–ISO 13606, HL7, SNOMED CT) and Web standardsIntegrative international Centers as “Informatics for integrating biology and the bedside system (i2b2)”Cloud computing

By addressing the challenges outlined in this review, BMI will create the tools to tailor medical care to each individual genome.

Limiting factors (key problems to overcome)


Thanks !!!


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