"Bridging the Gap between Bioinformatics and Medical Informatics"

Bridging the gap between
Bioinformatics and Medical
Informatics
INBIOMEDvision

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

2nd Consortium Meeting, Barcelona 16th May, 2011

MIE 2012 Workshop :
Why defining the Biomedical Informatics Field is so important
Dr Miguel Angel Mayer
Pompeu Fabra University – FIMIM
Joint Research Programme on Biomedical Informatics (GRIB)

Prospective analysis on Biomedical Informatics enabling
personalised medicine
Dra Victoria López-Alonso
Institute of Health Carlos III
Medical Bioinformatics Department

Personalised medicine: a legacy of promises without delivery –
can we get it right today?
Dra Nour Shublaq
University College London
Centre for Computational Science


INBIOMEDvision: Promoting & monitoring BMI in Europe

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

http://www.inbiomedvision.eu/


INBIOMEDvision: Promoting & monitoring BMI in Europe

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.


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.


Community building
activities
Researcher Directory
Consolidation of a Biomedical Informatics
Community

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

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


Think Tanks – Reports &
Summary and international experts, leaders in their own fields,
Different European
participated in three Think Tanks, in order to identify opportunities for future
collaborative work, and making recommendations for the wider scientific
community.


Prospective analysis on
Biomedical Informatics
enabling personalised medicine
Victoria López Alonso PhD
Bioinformátics Unit
Instituto de Salud Carlos III
Spain

Workshop INBIOMEDvision, MIE 2012


Overview
Personalised medicine
Biomedical Informatics (BMI) enabling personalised
medicine:


Personalised medicine in current practice
Chemotherapy
medications trastuzumab Incidence of adverse events for drugs
and Imatinib Abacavir, Carbamazepine and Clozapine
(Dettling et al., 2007; Ferrell and McLeod, 2008).
(Gambacorti-Passerini, 2008;
Hudis, 2007)

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


Personalised medicine and BMI
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

Information Personalised Medicine
DNA, RNA, proteins, small molecules, and lipids
Diagnosis
Genomics Individual genomics (SNPs, CNVs…), Functional genomics,
Disease Reclassification
Proteomics…
Pharmacogenomics

BIOMEDICAL INFORMATICS

patients, diseases, symptoms, laboratory tests, pathology
Clinical reports, clinical images, and drugs…

Population-based health data
&EHR

Powerful 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.
Clinical Bioinformatics Molecular & Clinical
Electronic Medical Records Databases
Genbank, Pubmed,
Standards for: GEO, PDB
Diseases: UMLS, MESH, UCSC, Ensembl
SNOMED… Human Genome
Adverse events: MedDRA Nomenclature
Drugs: RXNorm Veterans Committee
Affairs National Drug … HumanCyc and KEGG, The Adverse Event
Reference Laboratory tests: Reactome… Reporting System (AERS)
LOINC…
Health information: HL 7, The Standards:
Anatomical Therapeutic Gene Ontology…
Networkclassification…resources
Chemical of data


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)

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

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
BMI structure to
1 gen linked to disorder
store and process
genomic data



Evaluation of biomarkers for
Molecular Diagnostics and
Prognosis

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
Prognostics

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.



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, 2010


Methods to be able
20 million of references to extract
Literature mining could in natural lenguage information from
be used to create a set natural text and
represent it formally
of candidate genes: in databases that
methods that use allow automated
search, indexing and
sentence syntax and inference
natural language
processing to establish
the link between
molecular and clinical
entities and derive
drug-gene and gene-
gene interactions from
scientific literature.


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

BMI for Network-based decision support

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



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



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

Network of human diseases and the associated genes
from the Online Mendelian Inheritance in Man resource Goh et al., 2007

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 :

Genotype-phenotype mapping
Disease comorbidities
Patient stratification
Drug interactions
Clinical outcomes



EHR-based phenotyping in genetic
discovery is feasible and much
less expensive than specially
created study cohorts to :

replicate the GWAS results
generation 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).


BioBank system at Vanderbilt
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
RTI International with NHGRI
for statistical association against
hundreds of disease phenotypes of
patients to better understand the www.phenx.org/
clinical responses to diseases and
therapies.



Mining electronic health records

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

BMI for mining of EHR and other clinical information
Drug interactions and clinical outcome.

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 …

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 .


Limiting factors (key problems to overcome)
Restriction on access to existing data
to 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)

Interoperability across institutions, countries and continents
Biomedical standards (CEN–ISO 13606, HL7, SNOMED CT)
and Web standards
Integrative 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.


Thanks !!!


"Bridging the Gap between Bioinformatics and Medical Informatics"

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