Preparing the scenario for the use of patient s genome sequences in clinic. Joaquín Dopazo

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1 Preparing the scenario for the use of patient s genome sequences in clinic Joaquín Dopazo Computational Medicine Institute, Centro de Investigación Príncipe Felipe (CIPF), Functional Genomics Node, (INB), Bioinformatics Group (CIBERER) and Medical Genome Project, Spain Arab Health. Big data pannel,30 January 2013

2 Background The road of excess leads to the palace of wisdom (William Blake, 28 November August 1827, poet, painter, and printmaker) The introduction and popularisation of high-throughput techniques has drastically changed the way in which biological problems can be addressed and hypotheses can be tested. But not necessarily the way in which we really address or test them

3 Personalized medicine: just about a better understanding of the relationship phenotype-genotype The future of personalized medicine is strongly based on genomics Allison, Is personalized medicine finally arriving? Nature. Personalized medicine is based on the availability of diagnostic biomarkers Genome sequencing offers ALL this information (if properly analyzed) Genome sequence prices are in free fall (exome price expected < 300 in 2-3 years) Over % of budget (>500 B $) per year, is spent on costs associated with overuse, underuse, misuse,...

4 Million Exome sequencing successfully used. NGS prices will be soon affordable. 100,000 10,000 First genome: 13 years ~3,000,000,000 1, Moore s Law < 2 weeks ~ While the cost falls down, the amount of data to manage and its complexity raise exponentially. Soon, costs will be competitive enough to be used in clinic The problem is are we ready to deal with this?

5 Personalized Genomic Medicine. Phase I: generating the knowledge database Patient List of variants sequencing Database. Query: variant/pathway Knowledge database Therapy System feedback Outcome Genetic variants are linked to therapies through the knowledge of their functional effects (systems biology) Genomic medicine Initially the system will need much feedback: Knowledge generation phase. Growing knowledge database

6 Personalized genomic medicine. Phase II: applying the knowledge database Patient Genomic core facility phase II 1) Genomic sequencing 2) Database of markers 3) Therapy prediction Prescription Other factors (risk, cost, etc.) + Clinician receives hints on possible prescriptions and therapeutic interventions Pre-symptomatic: Genetic predisposition of acquired diseases (>6000. some treatable) Early diagnosis of genetic diseases Symptomatic analysis Diagnostic of acquired diseases Early cancer detection Cancer treatment recommendation

7 From genetics to genomic medicine Test 1 Therapy 1 Test 2 Therapy 2 Genetic medicine Therapy 3? + Test Therapy 1 Therapy 2 Therapy 3 Genomic medicine feedback? Genomic analysis allows associating patients to therapies from the very beginning, saving time and costs and increasing the success of treatments.

8 Some examples Low initial investment Already existent infrastructure Quick implementation Easily implementation as a cloud service that guarantees sustainability Conventional sequencing Marfan syndrome genes, 75 exons Hereditary deafness genes 1500 exons NGS (with capture) genes, 237 exons genes > 1500 exons

9 FutureClinic: preparing the scenario for the introduction of the genome in clinics Acceleration of algorithms for data preprocessing. Data strorage optimization Patient ehr Integration of the data in the ehr Treatment feedback Decision support techniques: algorithms that relate biomarkers to treatments, outcomes, etc. (gene prioritization and predictors) Visualization and data presentation. Ready for the clinical interpretation Corporative systems Orion clinic Abucasis, Gaia, etc.

10 FutureClinic: preparing the scenario for the introduction of the genome in clinics Acceleration of algorithms for data preprocessing. Data strorage optimization Patient ehr Integration of the data in the ehr Treatment feedback Visualization and data presentation. Ready for the clinical interpretation Decision support techniques: algorithms that relate biomarkers to treatments, outcomes, etc. (gene prioritization and predictors) Corporative systems Orion clinic Abucasis, Gaia, etc.

11 Acceleration of data processing steps 8-10 hours* 8-12 hours* 8-12 hours* Automatic QC Sequence cleansing Mapping + QC Variant calling + QC Accelerations > 10x over current algorithms. Scalability with the number of CPUs/cores Makes use of GPUs Exome/genome sequencing data processing from days to a few hours. Close to what it is expected from an analytical test. Use of GPUs

12 New Big Data storage strategies 8-10 hours* 8-12 hours* 8-12 hours* Automatic QC Sequence cleansing Mapping + QC Variant calling + QC FASTQ (50GB) BAM (30GB) VCF (200MB) Remote visualization of big data. Data production phase CLOUD Final human supervision of data QC Data sizes for exomes. In case of whole genomes sizes are >20x e-health record

13 What are the real storage requirements? Automatic QC Sequence cleansing FASTQ (50GB) Mapping + QC BAM (30GB) Variant calling + QC VCF (200MB) Raw data 80GB/genome Processed data 200MB/genome Hereditary diseases: 1 patient = 1 genome Cancer: 1 patient = 1 genome + x disease genomes Now we store everything (>80GB/genome). Once QC and software reach an acceptable standard of quality we will store only VCF files (or similar)

14 FutureClinic: preparing the scenario for the introduction of the genome in clinics Acceleration of algorithms for data preprocessing. Data strorage optimization Patient ehr Integration of the data in the ehr Treatment feedback Visualization and data presentation. Ready for the clinical interpretation Decision support techniques: algorithms that relate biomarkers to treatments, outcomes, etc. (gene prioritization and predictors) Corporative systems Orion clinic Abucasis, Gaia, etc.

15 Finding new biomarkers Therapy 1 Therapy 2 Test Therapy 3 Feedback: treatment failures are reanalyzed to search for: 1) Biomarkers (of failure) 2) Subgroups (to search for new personalized and rational therapeutic interventions? feedback Rationale design of therapies rely on Systems Biology concepts. Pathways are complex and must be understood with the proper bioinformatic tools Protein interaction Regulation Treatables Non treatables Irrelevant Failure treatment biomarkers Group A biomarkers Group A biomarkers Signaling Variants are used as biomarkers to distinguish between responders and non-responders and to sub-classify non-responders

16 Software and algorithms for finding biomarkers Software is proprietary. Algorithms to deal with genomic data must be public and validated by the scientific community. All of them have been published in high impact factor journals. See interactive map of for the last 24h use HPC on CPU, SSE4, GPUs on NGS data processing Speedups up to 40X Ultrafast genome viewer with google technology Different programs that cover distinct relevant aspects in genomic data analysis Regulation Gepas is the most cited tool for microarray data analysis. Composed of more than 80 tools for different analysis purposes Babelomics is the third most cited tool for functional analysis. Includes more than 30 tools for advanced, systemsbiology based data analysis More than experiments were analyzed in our tools during the last year Protein interaction Signaling

17 FutureClinic: preparing the scenario for the introduction of the genome in clinics Acceleration of algorithms for data preprocessing. Data strorage optimization Patient ehr Integration of the data in the ehr Treatment feedback Visualization and data presentation. Ready for the clinical interpretation Decision support techniques: algorithms that relate biomarkers to treatments, outcomes, etc. (gene prioritization and predictors) Corporative systems Orion clinic Abucasis, Gaia, etc.

18 Report for the clinician and relevant data for the ehr Findings: total variants 1000 new variants 100 LOF variants (30 homozygous) 100 known variants associated to disease The report must contain: 1) Diagnostic variants 2) Therapy-related variants 3) Susceptibility variants 4) Unsolicited information on variants of risk for the patient List of variants

19 FutureClinic: preparing the scenario for the introduction of the genome in clinics Acceleration of algorithms for data preprocessing. Data strorage optimization Patient ehr Integration of the data in the ehr Treatment feedback Visualization and data presentation. Ready for the clinical interpretation Decision support techniques: algorithms that relate biomarkers to treatments, outcomes, etc. (gene prioritization and predictors) Corporative systems Orion clinic Abucasis, Gaia, etc.

20 Curent status of the FutureClinic initiative.. Pilot project with 20 leukemias Upload image gateway Retrieve (by patient ID) Genomic gateway 1PB DB ehr We have taken advantage of the already operative corporative medical image system using a quite similar philosophy.

21 Medical image Metabolomics Proteomics Methylomics Transcriptomics Sequencing Image data Genomic data FutureClinic General Overview and future steps (in grey) Diagnostic and treatment Data pre-processing Integration Applications Diagnostic Personalized medicine Report, diagnostic and teratment hint feedback Database Of known biomarkers Outcome Diagnostic Algorithms that relate biomarkers to prognosis or treatment outcomes Pharmacogenomics Nutrigenomics