The Curation of Molecular Pathology Cancer Samples - Kenneth Doig
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The Curation of Molecular Pathology Cancer Samples - Kenneth Doig

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Cancer researchers routinely use High Throughput Sequencing (HTS), but its uptake into the clinical environment has been slow, mainly because of sequencing costs and the complexity of data analysis. ...

Cancer researchers routinely use High Throughput Sequencing (HTS), but its uptake into the clinical environment has been slow, mainly because of sequencing costs and the complexity of data analysis. The former issue is being addressed by rapid technology improvements in HTS equipment but data complexity and problems associated with data analysis remain a serious impediment to the wider use of genomics in clinical pathology.


The Peter MacCallum Cancer Centre is the largest cancer hospital in Australia and has implemented HTS services as a routine assay for tumour and germline samples. To resolve the clinical sequencing analysis bottleneck we have developed PathOS, a web application that streamlines the clinical curation of HTS generated cancer variants. The integration of all aspects of the molecular pathology pipeline from sequencer through to clinical report facilitates a ‘curation workbench’ to structure workflows needed for managing HTS data. PathOS is now implemented as the internal curation system for the hospital’s Molecular Pathology laboratory. The lab performs routine sequencing analysis of blood and tumour samples from both in-patient and external customers using Illumina HiSeq and MiSeq instruments. Raw sequence data is automatically processed by an in-house developed bioinformatics pipeline to identify variants. Raw variants are loaded into the system where they are normalised, filtered and matched with previously curated variants and external databases. Stringent filtering is applied to raw variants based on the assay method. The system allows filters to be described in a flexible domain specific language (DSL) by the scientist creating the HTS assay. The sequencing QC data for runs, samples and variants are stored, and displayed as interactive charts. Integration of the IGV browser through
a web server allows the user to directly visualise reads giving rise to a variant. Designed with accreditation for clinical testing in mind, a full audit trail of source data and external evidence used to classify variants allows the pedigree of any clinical data to be ascertained. After curation, variants can be exported to a file to support external curation systems such as LOVD. PathOS generates clinical reports as PDF or Word documents using MS-Word templates giving the reporting scientist maximum presentation flexibility. Reclassification of previously reported variants causes PathOS to flag reports that need to be amended.

To validate PathOS, the Cancer 2015 study was used: a large-scale, prospective, longitudinal, multi-site cohort study of cancer in the Victorian population. As at January 2014, 769 patients were sampled and generated 141,657 unfiltered variants. PathOS has provided the pipeline framework, repository and curation for this data. Of these variants 1,618 were automatically flagged as inferred deleterious mutations.

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The Curation of Molecular Pathology Cancer Samples - Kenneth Doig The Curation of Molecular Pathology Cancer Samples - Kenneth Doig Presentation Transcript

  • Path-OS: The Curation of Cancer Samples Ken Doig – Bioinformatics Research Core Peter MacCallum Cancer Centre ken.doig@petermac.org
  • Agenda •  Context •  System overview •  Amplicon Panels •  Filtering •  Futures 22 May 2014 HVP5 Path-OS 2
  • The Context
  • What we do •  Peter MacCallum Cancer Centre –  Molecular Pathology Department •  Provide pathology services to the hospital and ext. labs. •  Blood and tumour tissue samples •  Targeted genetic sequencing using amplicon panels •  Between 4-50 cancer specific genes •  Looking for needles in haystacks •  Very sensitive assays ... ... AAAAGCAGGT TATATAGGCT AAATAGAACT AATCATTGTT TTAGACATAC TTATTGACTC TAAGAGGAAA TCATAATGCT TGCTCTGATA GGAAAATGAG ATCTACTGTT TTCCTTTACT TACTACACCT CAGATATATT TCTTCATGAA GACCTCACAG TAAAAATAGG TGATGTTGGT AGCTAGGAGT GAAATCTCGA TGGAGTGGGT CCCATCAGTT TGAACAGTTG TCTGGATCCA TTTTGTGGAT GGTAAGAATT GAGGCTATTT TTCCACTGAT TAGTTCCCAG TATTCACAAA AATCAGTGTT CTTATTTTTT ATGTAAATAG ATTTTTTAAC TTTTTTCTTT ... ... 22 May 2014 HVP5 Path-OS 4
  • Peter Mac Curation Scope •  Automate the processing from sequencer to draft report •  Automate curation evidence collection •  Sanitise data from external sources •  Automated reporting •  Best practice software engineering 22 May 2014 HVP5 Path-OS 5
  • The System
  • Patient Sample Genologics wet lab LIMS External Variant DBs •  COSMIC •  Ensembl •  Annovar •  UCSC •  Clinvar etc Loader Pipeline data repository FASTQ BAM VCF VEP Pipeline PipeCleaner PathOS Web Server Pipeline Validation QC Reporting Pipeline configuration Sequencers ETL configuration Periodic DB download and integration Sequencing QC Clinical Reporting Read QC Synthtetic Reads Known samples Filtering configuration Users •  molecular scientists •  clinicians •  researchers Export curated variants to global repositories Hospital Records 22 May 2014 HVP5 Path-OS 7 Path-OS Overview
  • Run QC This run in the context of past runs of the same panel Per sample read yield highlighting below average Amplicon performance read distribution 22 May 2014 HVP5 Path-OS 8
  • Classification Page 22 May 2014 HVP5 Path-OS 9 Automatically generated classification Justification free text field Check boxes for variant evidence Evidence type tool tip
  • Classifying variants for the clinic 22 May 2014 HVP5 Path-OS 10 C5: Pathogenic C4: Likely pathogenic C3: Unknown pathogenicity C2: Unlikely pathogenic C1: Not pathogenic 5 Level Classification Stand alone Strong Supporting Criteria or or Pathogenic evidence Stand alone Strong Supporting Benign evidence = or = or =or or = All other combinations =
  • Software Components Role Package Overview Language Groovy Java on steroids, powerful JVM language Web Framework Grails Rich Groovy based high productivity framework Code repository GitLab Private GitHub instance Database MySQL Widely adopted RDB, good performance User interactivity Javascript plugins Leverage best available js e.g. Jquery, Google Charts Object Persistence Hibernate Java standard for mapping POJOs to RDB Searching Lucene Full-featured text search engine IoC Layer Spring Java standard for inversion of control IDE IntelliJ Comprehensive developers environment for Java etc Build Management Gradle Groovy based DSL leverages Ant and CoC DB Migration Mgmt LiquiBase DSL based data migration tool for schema versioning Issue Management Jira Best of breed issue management tracker LIMS GenoLogics User friendly LIMS for NGS Aligner Primal Peter Mac in-house amplicon aligner, tuned for amplicons Variant Caller VarScan 2 Suitable for somatic and germline (for now) Annotation Ensembl, Annovar Rich set of annotations for multiple transcripts 22 May 2014 HVP5 Path-OS 11
  • The Panels
  • Somatic Panel 22 May 2014 HVP5 Path-OS 13 Oncogenes Tumour suppressors Consequence type Other Missense Frame shift Splice site Stop gained Gene type
  • 6.2$%$ 25.5$%$ 0$ 20$ 40$ 60$ 80$ 100$ Single$ Duplicate$ Variant'Allele'Frequency'(%)' Variant'Allele'Frequency'for'Soma7c'Panel'Replicates' Somatic Replicates 22 May 2014 HVP5 Path-OS 14 0" 5" 10" 15" 20" 25" 30" 0'"<10" 10'<20" 20'"<30" 30'"<40" 40'"<50" 50'"<60" 60'"<70" 70'"<80" 80'"<90" 90'100" Mean%difference%in%variant%frequency%between%replicates%(%)% Variant%Read%Frequency%%%(Error:%S.E.M.)% Replicate%Variant%Frequency%Differences% 72% 28% n=14,771
  • Amplicon artifacts 22 May 2014 HVP5 Path-OS 15
  • The Filtering
  • Filtering Variants 22 May 2014 HVP5 Path-OS 17 ● ●● ● ● ●●●● ●●● ● ●●●●●●●● ● ● ●●●● ● ● ● ●●●●●● ● ● ●● ● ●●●● ● ● ● ● ●●●● ● ●●●●●●●●● ●● ●● ●●●● ●● ●● ● ● ● ● ●● ●●● ● ●●● ● ●● ● ●●●● ● ● ● ● ●●● ●●●●●● ●● ● ● ● ● ● ●● ●● ●● ● ● ● ●● ●●●●●● ●● ● ● ● ● ● ●● ●●●● ● ● ● ●●●● ●● ● ●● ●● ● ● ● ● ● ●● ● ●● ● ●● ●●● ● ●● ●●● ●● ●● ● ●● ● ●●● ●●● ● ●● ●●●●● ● ●● ● ● ●●● ● ● ● ● ● ●●● ● ● ●● ●●● ● ●●●●●●● ● ● ●●● ● ● ●● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ●●● ● ● ●● ●● ● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ●● ●● ● ● ● ● ● ●●● ●●●●●● ● ●●● ● ● ● ●● ● ● ● ● ● ●●● ● ● ● ●● ● ●● ●● ●● ● ●●● ● ● ●● ●● ●● ● ●● ●●● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●●●●●● ● ●● ●●● ●●● ●● ●●●●●● ● ●● ● ● ● ● ●● ●●● ● ● ● ●●● ● ●● ● ● ●●● ●●●● ●● ● ●●●● ●●● ● ● ● ●●●● ● ● ●●● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●●● ●●● ● ● ● ● ●● ● ● ● ● ● ● ●●●● ● ●● ● ●● ● ● ●● ● ● ● ● ●● ●●●● ● ●●●● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●●● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●●● ● ●●●● ● ●●● ● 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  • Sample Curation Page 22 May 2014 HVP5 Path-OS 18 In-silico and DB classifiers Filter Flags
  • Variant Searching and Filtering 22 May 2014 HVP5 Path-OS 19 “OR” clause Template searches Variant attribute Predicate Value
  • Variant Filter Attributes Filter Flag Description Filter Criteria pass Passed all filters no filters match vaf Low Variant Allele Frequency < 8% Somatic <15% Germline vrd Low Total Read Depth < 100 vad Low Variant Read Depth < 50 blk Assay panel specific black list user defined con Inferred Benign Consequences system defined gmaf High Global Minor Allele Frequency > 1% pnl High Variant Frequency in Panel > 50% sin Singleton variant in duplicate sample for run Not in both samples amp Uneven read distribution across amplicons Single amp. bias 22 May 2014 HVP5 Path-OS 20 Multiple filter flags may be assigned per variant
  • Filter effects 22 May 2014 HVP5 Path-OS 21 0.0# 1.0# 2.0# 3.0# 4.0# 5.0# 6.0# GMAF#>#1%# Benign#Consequences# Passed#all#filters# Reportable# Mean%CADD%scores%(scaled%scores%0%log)% Filtering%Criteria%(all%S.E.M.%<%0.1)% Effect%of%filtering%on%CADD%scores% Without#Filter# With#Filter# Average#CADD#scores# for#variants#(top#20%)#
  • The Future
  • In the Pipeline… •  Pluggable modular pipelines (using bpipe) •  Upload existing FASTQ, BAM and VCF files •  PipeCleaner for Pipeline validation •  Include CNV and fusion modules •  Drugability of variants and clinical trials •  Write paper… 22 May 2014 HVP5 Path-OS 23
  • Acknowledgements •  Molecular Pathology Department –  Andrew Fellowes –  Anthony Bell –  Stephen Wong •  Peter Mac Bioinformatics –  Jason Ellul –  Jason Li –  Maria Doyle –  Franco Caramia •  Cancer 2015 –  John Parisot 22 May 2014 HVP5 Path-OS 24 Email: ken.doig@petermac.org
  • Additional Slides
  • Column Chooser & Ordering 22 May 2014 HVP5 Path-OS 26