Fernando J. Martin-Sanchez, Ph. D. Head, Health Bioinformatics Dept. National Institute of Health “Carlos III” Ministry of Health and Consumer Affairs

Fernando J. Martin-Sanchez, Ph. D.Head, Health Bioinformatics Dept. National Institute of Health “Carlos

III”Ministry of Health and Consumer

Affairs

Madrid, SPAIN EC IST BIOINFOMED Study

Coordinator

Health Information Systems in the age of Post-Genomic

Research

CEN/TC251 “Joint Working Group Meeting”

MadridJune 3, 2002

Dr. Fernando Martin-Sanchez - ISCIII – Jun. 2002 - Madrid

Agenda

Presentation

Overview of Bioinformatics

Molecular Medicine and Individualised Healthcare

The convergence between Medical Informatics and Bioinformatics

Issues on integrating genetic data into health information systems

EC IST BIOINFOMED Study


Institute of Health “Carlos III”

Ministry of Health and Consumer Affairs

• Public Research Institute• Scientific and technological support to the

National Health System• Competences in:

– Epidemiology, Public health laboratories (Food, Microbiology, Environmental Health)

– Health Technology Assesment– Biomedical research funding and coordination– School of Public Health, Health Sciences Library– New technologies - Telemedicine, Bioinformatics and

genomics, Health information systems


Forces driving the bioinformatics revolution

• The promise of health applications of the human genome information• Proof of concept with the human genome sequencing• Availability of new raw information

– Sequences (genome projects), SNPs (variability)– Gene expression data (DNA arrays), Proteomics

• Interest of new users– Pharma and Biotech– Biomedical research centers– IT firms– Clinical (not yet?)

• New tools– Internet– Data mining

• New discipline– Innovation brand– Search for funding


Overview of Bioinformatics

• Bioinformatics, Biocomputing, Computational Biology• Interface between biotechnology and computer science• “Flavours”

– Integration of relevant biomedical information– Platform for “in silico” biology– Focus on Health Applications– From Genetics to Genomics to Postgenomics to Molecular

Medicine


The role of bioinformatics supporting genetics

Sequences

Phylogenetic trees

Alignments

Structures


The role of Bioinformatics in support of genomics

ATCGCGCTA

Sequencing

Genome databases

Gene prediction

Annotation


The Post-Genomics Era

Genome Project(DNA

Consensussequence)

FunctionalGenomics(mRNAs)

Individual Genomics

(mutations, SNPs)

Comparative Genomics

(homology, evolution)

Proteomics(proteins)


Bioinformatics in support of Post-Genomic Research

Genomes

SNPs

Proteomics

DNA microarrays


Bioinformatics in support of Systems Biology

MetabolicPathways

GeneticNetworks

Signaling pathways

Interactions


New opportunities for health informatics

• Genome Project– Interest for

biologists– One gene at a time– Monogenic

diseases– Tedious genotyping– DNA level– Bioinformatics

explosion

• Post-Genomics– Clinical interest– Hundreds or thousands

of genes simultaneously

– Complex diseases– High throughput

genotyping– DNA, RNA, Proteins– Integration of clinical

and genetic information


Overview

Genome

Information

Individualisedhealthcare

Genotyping

Haplotyping

Functionalgenomics

proteomics

Individualgenomics (SNPs and mutations)

Gene Expression DNA arraysMS, 2D ef

Disease classificationPharmaco-genomics

Diagnosis

Pharmaco-genetics

Molecular medicine

HumanGenetic

Variation

Molecular causes of diseases

Technologies Data Applications

BIOINFORMATICS & MEDICAL INFORMATICSBIOINFORMATICS & MEDICAL INFORMATICS


Molecular Medicine and Individualised Healthcare

New approaches: Pharmacogenetics, DNA arrays, proteomics, SNPs, genetic diag.

• Molecular Medicine - Effort in explaining life and disease in terms of the presence and regulation of molecular entities

• Individualised Healthcare – Application of genomics to identify individual predispositions to disease and to design therapies adapted to the genetic profiles of patients and that could be prescribed with guarantee of security and efficiency

Art by Doug Struthers


The convergence between MI and BI


A model for studying

interactions

To apply IT to facilitate molecular medicine

To adapt medical informatics systems to the genetics paradigm

To foster the application of bioinformatics in health


?

Molecular Medicine

Informatics

Medical Informatics

Medicine

Bioinformatics

Genomics

The application of informatics in Molecular Medicine


Chris Sander, Bioinformatics (Editorial). Vol 17. Nº1. 2001, p1-2

Bioinformatics in Health


Karolinska Institute, Sweden


Bioinformatics and medical information

• EBI - SOFG - Standards and Ontologies for Functional Genomics, November, 2002, Hinxton, Cambridge, UK

• Topics include vocabularies such as chemical and biochemical nomenclature and the molecular biology vocabularies developed by the Gene Ontology Consortium

• But also: vocabularies for phenotypes, anatomies and developmental stages; and other areas such as diseases, pathology and toxicology


Health Information levels

Bioinformatics

Medical Imaging

MedicalInformatics

Public HealthInformatics

Martin-Sanchez et al, Methods. Inf. Med. 2002, 41:25-30


Health information level

Classical health informatics applications

New genomic data and information

New health informatics applications

Population • Public Health & epidemiology databases and surveillance networks

• Technology assessment, outcomes research

Genome epidemiology

• Genetic Screening

Genome epidemiology databases and networks (CDC-HuGeNet)

Disease Computerized clinical practice guidelines (CCPGs) Information systems in clinical trials

New classification of disease based on its molecular causes Clinical trials in pharmacogenetics

•CCPGs including genetic tests and therapy follow-up based on genetic data•Decision-making support tools•Pharmacogenetics databases

Patient Computerized patient health record (CPHR)

Genetic individual profiles (SNPs, mutations)

Genetic data in the CPHR

Tissue, organ Pathology lab systems, medical image processing

Physiological genomics Genetic networks

Disease models Tumour databanks (Integration of clinical markers and genomic analysis)

Cell Imaging in Cytogenetics, histology Microbiology lab information systems

Gene expression profiling Proteomics

Molecular classification of disease Information systems in pharmacogenomics (drug R&D)• Molecular imaging

Molecule Biochemistry and genetic tests and laboratory information management systems

DNA and protein sequences Macromolecular structures

Facilitating integrated and guided access to relevant databases to health professionals


Types of medical data

• lab results• administrative orders, appointments• images• signals, EKG• microbiology results• demographic• familiar• history of prescriptions• GENETIC


Genetic data special features

• multiple sources• large amounts• More static• probabilistic• Multilevel (DNA, RNA, Prot)• Accumulative (SNPs,

multifactorial diseases)• Context-dependent • Needs comparison with

public databases

• Not quantitative• Complex• Informs about relatives,

not only about patients• Predictive power, even in

the absence of clinical signs or symptoms;

• Has the potential to generate a unique identifier profile for individuals.


Sources of Genetic Data

• Genome and sequence databases– EMBL

• Protein sequence and structures– PDB, SwissProt

• Genetic diseases – OMIM, GeneCards, GeneReviews

• Genetic tests – Geneclinics, EddNal

• Mutations – Central variation databases – HGMD– Single Locus Databases

• SNPs – (dbSNP)


Examples • Gene - RPS6KA4• Mutations

– [76A>C; 83G>C] – 112_117delAGGTCAinsTG– K29_M29insQSK

• SNP: rs1472728 – Submitter Handle: TSC-CSHL – Submitter Method ID: TSC-WUGSC-1-2 – GenBanK Accession: AC011382.2 – Length: 673 – Flanking Sequence Information: GATGGGACCA CTGGTAGGAG... – Observed: C/T

– No. of Chromosomes Sampled: 16 – Allele: C = 0.437 / T = 0.563


Genetic data in medical coding systems

• ICD• SNOMED• UMLS• MeSH• LOINC• GALEN


Knowledge representationin Biology

• The MGED ontology - ontologies for describing gene expression experiments and data.

• TAMBIS ontology (TaO) an ontology of bioinformatics and molecular biology.

• RiboWeb an ontology describing ribosomal components, associated data and computations for processing those data.

• EcoCyc an ontology describing the genes, gene product function, metabolism and regulation within E. coli.

• Gene Ontology (GO) an ontology describing the function, the process and cellular location of gene products from eukaryotes.


Related scientific activities

• Dec, 2001. Brussels – EC – Synergy between Research in Medical Informatics, Bioinformatics and Neuroinformatics

• Manchester March 2002 meeting: – “Genotype To Phenotype: Linking Bioinformatics and

Medical Informatics Ontologies”

• Nov 2002. AMIA Conference: – “Bio-medical informatics: one discipline”

• EC IST BIOINFOMED• ACMI - NLM


BIOINFOMED

• Prospective Analysis on the Relationships and Synergy between Medical Informatics and Bioinformatics

• URL: http://bioinfomed.isciii.es (starting from January 2002)

• Institute of Health “Carlos III” – Madrid – SPAIN

• Polytechnical University of Madrid (Prof. Victor Maojo) - SPAIN

• Linkoping University (Prof. Ankica Babic) - SWEDEN

• State of the Art and Inventory of resources of interest and standardisation initiatives

• Identification of key groups and priority research lines

• Collaboration between experts and groups

• Final report and Workshop (Nov 2002)


Modelling

• MI – Top-down approach from clinical manifestations to the underlying pathophysiological processes

• BI - Bottom-up approach, from genomic information to physiological function

• An integrated approach could provide a unified vision

Maojo, Martin-Sanchez et al, Journal of Biomedical Informatics. In press


Methods and tools

Medical Inform.

Bioinform.

Application

Individualised Healthcare

MolecularMedicine

BIOINFOMED

Synergy

Synergy


Integrating genetic data into health information

systems• Telemedicine – genetic diagnosis

networks -- telegenetics• Accessing genetic databases using

clinical inputs• Integrating genetic data into clinical

trials infrastructures and methods• Genetic data in clinical records• Genetics in Clinical practice guidelines• Adapting terminologies, vocabularies,

ontologies


Bioinformatics. Health applications

• SNP & haplotype information management and analysis

• Disease reclassification based on gene expression data

• Clinical proteomics• Systems Biology• Pharmacogenomics• Clinical-genetic databases

• Genomics of pathogenic micro-organisms


Synergy (integrated use of genetic and clinical

information with an application in individualised healthcare and/or molecular

medicine)

• Virtual Tumor databases (clinical-genetic analysis)

• Decision-making support tools • Integrated clinical-genetic workstations• Interoperability between genetic lab and health

information systems• Linking phenotype to genotype in patients and

populations• Pharmacogenetics Databases• Genome epidemiology• Molecular imaging • Computer models of disease


Molecular imaging

• Medical imaging + genomics• Imaging molecular alterations

that are the basis of disease rather than their effects

• Weissleder, R. Radiology 2001; 219:316-333


Levels and tech´s

AnatomyPhysiologyMetabolismMolecular

Today Tomorrow

CTUS

MRINuclearOpticalNano


Molecular imaging

• New markers for early disease detection

• Specific markers for therapy assesment

• Drug screening• Imaging of gene expression


Conclusions

• Interaction between MI and BI is needed:– Not reinventing the wheel– Not making the same mistakes

• Collaboration is better – integrated approach to disease

• Synergy – will it be possible?– New developments from scratch

• ¿Birth of a new discipline?– BIOMEDICAL INFORMATICS

MI BI

MI BI

MI BI

Documents

Fernando J. Martin-Sanchez, Ph. D. Head, Health Bioinformatics Dept. National Institute of Health “Carlos III” Ministry of Health and Consumer Affairs