Clinical Genomics at Scale: Synthesizing and Analyzing Big Data From Thousands of Patients

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1 Clinical Genomics at Scale: Synthesizing and Analyzing Big Data From Thousands of Patients Brandy Bernard PhD Senior Research Scientist Institute for Systems Biology Seattle, WA Dr. Bernard s research interests are in cancer drug discovery and clinical genomics. He is currently a part of the ISB Genome Data Analysis Center (GDAC) within The Cancer Genome Atlas (TCGA) network. During this time, he has developed novel computational methods and analyses in support of TCGA network research and publications, and has provided scientific guidance for the data exploration tools and algorithms developed by the team. Dr. Bernard has led the group s research efforts and contributions to several TCGA Analysis Working Groups, particularly in the area of heterogeneous data integration and graph analysis. In collaboration with experts in functional genomics he has integrated TCGA and RNAi screening data to prioritize novel targets and tumor types for drug discovery and repurposing. His research in the area of cancer genomics has resulted in several proffered presentations at TCGA symposia and AACR meetings on distinct topics, a First Prize in the YarcData Graph Analytics Challenge, and a Life Science Discovery Fund grant to further the development of our cancer genomics web portals. In the area of clinical genomics, Dr. Bernard co-leads a collaboration with Inova Translational Medicine Institute (ITMI) to provide analytic support and develop scalable infrastructure for the integration of clinical data with whole genome sequences and molecular data from thousands of patients. Related to this effort, Dr. Bernard has worked with the PRE-EMPT Global Pregnancy Collaboration (CoLab) as well as the Crohn s and Colitis Foundation of America (CCFA) to advise in the study design and infrastructure of large-scale clinical genomics programs. Annual Quality Congress Breakout Session, Sunday, October 4, 2015 Clinical Genomics at Scale: Synthesizing and Analyzing Big Data From Thousands of Patients Objective: Define systems biology and relate this concept to the NICU context.

2 Disclosure Clinical Genomics: Scalable Analysis Across Thousands of Patients Sr. Research Scientist Brady Bernard does not have any financial arrangement or affiliations with a commercial entity. Brady Bernard will not be discussing the unlabeled use of a commercial product in her presentation. Example Big Science Projects The Cancer Genome Atlas (TCGA) Genomic and molecular characterization of 30 cancers across thousands of primary tumor samples Clinicians Researchers Software engineers Bioinformaticians TCGA Research Network Inova Translational Medicine Institute (ITMI) Analysis of thousands of whole genome sequences integrated with clinical data TCGA data and biospecimen flow Inova Translational Medicine Institute (ITMI) ITMI aims to assemble one of the world s largest collections of whole genome sequences in a single database to enable personalized healthcare and spur biomedical research Example projects: Families with full and preterm births Longitudinal study first 1000 days of life Congenital anomalies October 4,

3 Hi-level clinical genomics workflow Clinical EMR Survey Phenotypes Data cleansing Feature merging Functional cores Highly Collaborative Study design Phenotype prioritization Patient/family selection Data generation protocols Focused subgroup meetings Scalable models (dataset creation, analysis result exploration, ) Validation (predictors, variants, ) Publication and Visualization Genomic Sequencing QC/QA Data management Annotation Discussion topics Clinical Genomic & Molecular Computational / Informatics / Analysis Analysis Characterization (especially clinical) Genotype/phenotype associations Clinically relevant prediction Data integration Interactive exploration (web portals) Discussion topics Clinical Genomic & Molecular Computational / Informatics / Analysis Clinical considerations EMR and survey formulation Consistency (formalism) in the data Organization, LIMS, and metadata Aggregation of common data elements Precise phenotyping and sample size Prediction frameworks October 4,

4 EMR and Survey formulation Timing of events with respect to some reference point Easy to overlook Important for analysis and prediction Does lack of response mean no, don't know, didn't want to answer EMR and Survey formulation Timing of events with respect to some reference point Easy to overlook Important for analysis and prediction Does lack of response mean no, don't know, didn't want to answer Consistency (formalism) in the data Structured data dictionary for: Consistency across clinical or research sites More seamless automation Feature name matching (e.g., data dictionary and column name in ecrf data) Misspellings and synonyms (e.g., drugs) Mixed delimiters Data provenance and versioning Excel files? Organization, LIMS, and metadata Excel files are tempting, will not work for large consortium LIMS systems can capture meta-data in a structured and queryable form Metadata examples: source tissue known variations across batches (software changes etc.) mapped sample ids across data types date sample was taken date sample was processed Aggregation of common data elements Precise phenotyping and sample size Different sources of evidence for premature rupture of membranes Antenatan_Steroids_Indication:pprom Antenatan_Steroids_Indication:prom Delivery_Result_of_Other_Reason:pprom Delivery_Result_of_Other_Reason:prom Other_Medical_Conditions:pprom Other_Medication_Indication:pprom Prom Reason_for_C-Section_mc:pprom Reason_for_C-Section_mc:prom Tocolytic_Therapy_Indication:pprom Tocolytic_Therapy_Indication:prom Was_the_Delivery_a_Result_of:prom October 4,

5 Precise phenotyping and sample size Precise phenotyping and sample size Precise phenotyping and sample size Prediction frameworks Goal: predict phenotypes or outcomes given clinical, genomic, and molecular data Non-linearity in data, classifiers should account for this Many clinical data elements highly correlated or irrelevant for prediction, should be black-listed Cross-validation and independent data sets should be considered in advance Discussion topics Clinical Genomic & Molecular Computational / Informatics / Analysis Genomic & Molecular Data Annotations Confounding factors Batch effects [will happen] Ancestry [is very important] October 4,

6 Annotations Current, common, and updated: Reference genome builds Gene definitions Software and annotation versions Batch effects: Methylation plate position Mendelian inheritance errors Extremely heterozygous variants Missing calls Commonly mutated segments Batch effects: Example variant associated with PTB Batch effects: Example variant associated with PTB 23% 45 Samples ordered by date variant variant preterm 5% 19 FTB PTB n=401 n=198 p = 2e-11 preterm A admixture A admixture , same pattern in ISB in house CGI genomes Graphic summary Ancestry and population stratification BATCH PTB October 4,

7 Ancestry and population stratification Family-based genomic study design Transmission disequilibrium Identify variants that are transmitted to effected offspring more frequently than expected by chance Population associated variants and class imbalance lead to likely false positives Advantage Accounts for population stratification Larger pedigrees can be helpful, though phenotypic and genomic data may not be available Family genomics: phasing and candidate genes Family genomics: phasing and candidate genes Roach et al. (2010). Science Roach et al. (2010). Science Mendelian Inheritance Errors (MIEs) Mendelian Inheritance Errors (MIEs) Can be real de novo mutations Most likely explanation is sequencing error MIEs, while infrequent, are observed orders of magnitude more than the expected de novo mutation rate October 4,

8 Accuracy and Variant call quality Percent accuracy (100 %MIE) Less than 0.05% of all calls are MIE Less then 0.002% MIE above quality score of 80 With family trios, sequencing errors can be identified and spurious associations/type 1 error is mitigated, enabling utility of whole genome sequences in the clinical setting Variant call quality score Genomic & Molecular Data Recommendations Clinical analysis can inform genomic study design Phenotype definition and prioritization is critical Matched and balanced cases and controls minimize batch effects improve statistical strength mitigate confounding factors Larger segregating pedigrees reduce number of candidate genes potentially hybrid approaches Run nuclear families (and multigenerational, if possible) together on the same batch Quality Control Re-run controls across batches Maintain highly detailed annotations on dates, reagents, sequencing runs, software versions, tissues, External data sets from data provider to mitigate batch effects SNP arrays for sample identifiability and case/control matching Additional data considerations Staging and production environments As data is being generated, upload to staging environment then QC and structure for analysis/consumption Data freezes Create common data sets, annotation pipelines, and files for collaborative analysis SNP arrays for sample identifiability and case/control matching, especially as number of data types and source sites increases Discussion topics Clinical Genomic & Molecular Computational / Informatics / Analysis De-identification, HIPAA, and Universally unique identifiers (UUIDs) Computing and collaborative projects NCI cancer cloud pilot Cloud computing Consolidate data to a centralized source Scalable computing Data backup Minimize IT Workflow management systems Web portals October 4,

9 NCI cancer cloud pilot Scalable Genomics Technologies and Architecture 7,000 genomes Staging File Server Google Standard Archival storage 100GB/subject Reads (BAM) 2GB/subject Variants (VCF) *Petabyte scale data Google Nearline Bioinformatic Pipelines Quality Control Re process Batch Assessment Data Normalization Organize/Structure Parallel Compute/Analysis Google Compute Engine In house cluster Distributed Databases Billions of Unique Variants Terabytes of Data (High Access) Google Genomics / BigQuery + Annotations Public (significant ETL and data modeling) ISB Proprietary (aggregated over thousands of control sequences) Open source workflow management systems Assist with provenance, data access, analysis, complex workflows, reproducible science Web portals Project summaries, reports, auditing Data access Research & dissemination Interactive exploration & dynamic analysis October 4,

10 Additional computational considerations Security/authentication/access controls Wikis/project pages (e.g., Confluence) and subteams (e.g., analysis working groups) Issue and project tracking (e.g., JIRA) Listservs and management Code repositories (e.g., GitHub) Discussion topics Clinical Genomic & Molecular Computational / Informatics / Analysis Concluding thoughts There are many potential [and avoidable] pitfalls Infrastructure required to establish and support Big Science consortium projects is significant and easy to underestimate Roles and responsibilities, maintenance, costs, support, data generation, QC, QA, With TCGA and ITMI, questions to be addressed with the data far exceeds the bandwidth of direct participants significant value to community in curated clinical, genomic, and molecular data what will consortium (and IRB?) guidelines be for access control and use by the broader community brady.bernard@systemsbiology.org 2bnh: alpha beta horseshoe 1hv9: left handed beta helix 1m30: SH3 like barrel October 4,