Victoria López Alonso PhD Medical Bioinformátics Area Instituto de Salud Carlos III Spain Bioinformatics challenges in a personalized medicine pipeline Workshop INBIOMEDvision, MIE 2011
Bridging gaps between Bioinformatics and MI BMI deals with the integrative management and synergic exploitation of the wide and inter-related scope of information that is generated and needed in healthcare settings, biomedical research institutions and health-related industry.
Overview: Personalized medicine in current practice 1- Processing large-scale genomic data 2- Interpretation of functional effect of genomic variation 3- Integration of systems data 4- Translation into medical practice Bioinformatics challenges for Personalized medicine
Personalized medicine in current practice Translational bioinformatics utilizes computational tools for the analysis of large biological databases and to fully comprehend disease mechanisms by not only understanding the genetics and the proteomics but also by associating them with the clinical data.
Finishing the euchromatic sequence of the human genome.
Nature 2004; 431 (7011): 931-945.
Phase I HapMap project in 2005
Phase II and Phase III
A haplotype map of the human genome.
Nature 2005: 437(7063):1299-1320
Encyclopedia of DNA Elements (ENCODE) project in 2007
Identification and analysis of functional elements in 1% of the human genome.
Nature 2007; 447(7146):799-816
1000 Genomes Project in 2008
DNA sequences. A plan to capture human diversity in 1000 genomes.
Science 2008; 319(5863):395
$1000 Genome in …2013 ??
Personalized 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, 2002). The inclusion of genetics in EHRs will provide risk assesment. Clinical assessment incorporating a personal genome . Ashley et al. Lancet (2010)
Bentley D. “Genomes for Medicine”. (2004). Nature Insight 429, p440-446
Today patient´s genetics are consulted only for few diagnoses and treatments and only in certain medical centers (cystic fribrosis , breast cancer)
With easy access to a well annotated human genome an individual could adquire a genetic health profile including risk and resistance factors that could be used to guide medical decisions. Personalized medicine in current practice
1- Processing large-scale genomic data 2- Interpretation of functional effect of genomic variation 3- Integration of systems data 4- Translation into medical practice Bioinformatics challenges for Personalized medicine
Different informatics challenges should be addressed to create the tools to tailor medical care to each individual genome and also to realize the potential of personalized medicine
Burrows-Wheeler Aligner (BWA) (Li and Homer, 2010).
Ideally performed in a cluster or by using cloud computing
Program must allow for mismatches without resulting in false alignments
Improving of quality control metrics: ratios of base transition, Mendelian inheritance errors (MIE), relative quality scores…
2- Interpretation of functional effect After genomic data has been processed, the functional effect and the impact of the genetic variation must be analyzed Genome-wide association studies (GWASs) have been used to assess the statistical associations of SNPs with many important common diseases. GWAS provides new insights but only a limited number of variants have been characterized and understanding the functional relationship between variants and phenotypes. https://www.wtccc.org.uk
Experimental test are required to validate genetic predictions.
There are is a need for fast and accurate methods for gene prioritization
Eleftherohorinou et al., 2010 Currently the most effective strategy uses the concept of genes that are linked to the biological process of interest. The input data for gene priorization is the functional annotation, the protein–protein interactions, biological pathways and literature.
Last year, the first edition of the Critical Assessment of Genome Interpretation (CAGI) was organized to assess the available methods for predicting phenotypic impact of genomic variation and to stimulate future research.
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 and
Multifactor Dimensionality Reduction
(ie. hypertension and familial amyloid polyneuropathy type I)
Ritchie and Monsimger, 2010
3- Integration of systems data Naylor and Chen, 2010
No external knowledge sources informs about the biology behind the interactions.
Systems biology and network approaches address to the problem of complexity integrating molecular data at multiple levels of biology including genomes, transcriptomes, metabolomes, proteomes and functional and regulatory networks.
Medical practice needs to be updated to include routine pharmacogenetic testing, educating and training physicians in personalized medicine, and futher clinical trials to prove the efficacy of predictions
Bioinformatics also translates discoveries to the clinic by disseminating discoveries through curated, searchable databases
4-Translation into medical practice http://pacdb.org/ http :// www.pharmgkb.org / The database of Genotypes and Phenotypes The Pharmakogenomics Knowledge Database Pharmacogenetics-Cell line database www.ncbi.nlm.nih.gov/gap The Adverse Event Reporting System (AERS) www.fda.gov/Drugs/
Biologically and medically focused text mining algorithms can speed the collection of this structured data, such as methods that use sentence syntax and natural language processing to derive drug–gene and gene–gene interactions from scientific literature.
Opportunities for bioinformatics to integrate with the electronic medical record (EMR)
4- Translation into medical practice www.mc.vanderbilt.edu/ www.phenx.org/ BioBank system at Vanderbilt RTI International with NHGRI