Presenter: Huy Vuong, PhD Department of Biomedical Informatics Vanderbilt University 5/3/2013

1. Detection of somatic mutations: A data mining and a computational approach Presenter: Huy Vuong, PhD Department of Biomedical Informatics Vanderbilt University 5/3/2013

2. Somatic single nucleotide variants (sSNV) • Play major role in tumorigenesis and cancer development • Aim 1: Literature mining • Catalogue of Somatic Mutations In Cancer (COSMIC): the most comprehensive catalogue today • Aim 2: Tumor-specific mutations in tumor-normal pairs

3. Classes of somatic mutations • Point mutation: • Coding • Silent • Missense • Nonsense • Noncoding (UTR, ncRNA, miRNA…) • Intronic • Intergenic • Small scale mutation: • Small insertions • Small deletions • Large scale mutation: rearrangements • Intrachromosomal • Deletion • Invertion • Duplication • Interchromosomal • Translocation • Insertion

4. Aim1: Mining COSMIC For Protein Domain Interaction

5. History of COSMIC The Evolution of the Cosmos started with the Big Bang! http://en.wikipedia.org/wiki/Big_Bang

6. Yet, another COSMIC • History of the Catalogue Of Somatic Mutations In Cancer (Wellcome Trust Sanger Institute) COSMIC V64 (26th March, 2013) COSMIC V1 (4th February, 2004) Comparison V1 vs. V64

7. Advantages and Disadvantages Curation bias Many positive results, fewnegative results Other quality issues: experimental error, missing mutations Interpretation of mutation frequency • Bimonthly updates • Manual curated data, removed low quality data • Consistent vocabulary (histology and tissue) • Mutation maps to single version of gene (no alternative splicing) • FREE availability!!!

8. Typical workflow Histogram Distribution

9. Specific aims • Map somatic mutations (SM) in COSMIC to protein structural model • Identify SM in pocket region of protein • Use statistical analysis to score SM in the context of cancer (specificity, sensitivity)

10. Dataset and preprocessing step • Data are downloaded from COSMIC version 62 via Biomart interface as TSV file (http://cancer.sanger.ac.uk/biomart/martview/) • Use R to clean the data (i.e remove duplicates) and import to a SQLite database • Database contained 776,917 mutations and 15 variables: • Gene.Name • CDS.Mutation.Syntax • AA.Mutation.Syntax • Zygosity • Primary.Site • Primary.Histology • In.Cancer.Census • Tumour.Source Genomic.Coordinates.GRCh37 CDS.Mutation.Type AA.Mutation.Type Somatic.status Validation.status Entrez.Gene.ID COSMIC.Sample.ID

11. Protein pocket region • Li et al developed algorithm to identify functional pocket regions in protein Vast majority of disease-associated SNPs are located in Pockets. (Tseng and Li, PNAS, 2011)

12. A case study: KRAS About 64% of SM in KRAS is located on the functional pocket region Yu et al (Nature Biotechnology, 2012) also reported about 65% of disease associated in-frame mutations are located on the interaction surfaces of proteins associated with the diseases.

13. Aim2: Tumor-specific mutations in tumor-normal pairs

14. Outline • Challenges in detecting somatic single nucleotide variants (sSNV) • GATK pipelinefor calling sSNV • Installing and running MuTect • MuTect output • Summary

15. Detecting sSNV in cancer: challenge #1 Many sSNV occur at very low frequency in genome (0.1 to 100 mutations per megabase) Slide adapted from Mike Lawrence, TCGA Annual Symposium

16. Detecting sSNV in cancer: challenge #2 Tumors are impure (i.e. contain normal contaminating cells) and heterogeneous (i.e. contain sub-clones) C. Tri-clonal tumor Slide adapted from Christopher Miller, TCGA Annual Symposium and Mardis Elaine

17. GATK pipeline GATK Best Practices: http://www.broadinstitute.org/gatk/guide/topic?name=best-practices

18. NGS: Resources • SEQanswers (http://seqanswers.com/) • SEQanswers software list (http://seqanswers.com/wiki/Software/list • Galaxy (https://main.g2.bx.psu.edu/) • NGS Catalog (http://bioinfo.mc.vanderbilt.edu/NGS/) Slide adapted from Peilin Jia, PhD

19. Two types of error • USER ERRORS: • Due to wrong command line or incorrect user input files • Please do not post this error to the GATK forum • RUNTIME ERRORS: • Due to the program code • Do post this error to the GATK forum (together with the trace file)

20. USER ERROR • ##### ERROR ------------------------------------------------------------------------------------------ • ##### ERROR A USER ERROR has occurred (version 2.2-25-g2a68eab): • ##### ERROR The invalid arguments or inputs must be corrected before the GATK can proceed • ##### ERROR Please do not post this error to the GATK forum • ##### ERROR • ##### ERROR See the documentation (rerun with -h) for this tool to view allowable command-line arguments. • ##### ERROR Visit our website and forum for extensive documentation and answers to • ##### ERROR commonly asked questions http://www.broadinstitute.org/gatk • ##### ERROR • ##### ERROR MESSAGE: SAM/BAM file SAMFileReader{/scratch/vuongh/Lungevity_Project/GATK/bwa/13_karosorted_RG_MarkDup_Realigned_Recal.bam} is malformed: read starts with deletion. Cigar: 9D18M15I38M26S. Although the SAM spec technically permits such reads, this is often indicative of malformed files. If you are sure you want to use this file, re-run your analysis with the extra option: -rfBadCigar

21. BEST OF RUNTIME ERROR • ##### ERROR ------------------------------------------------------------------------------------------ • ##### ERROR A GATK RUNTIME ERROR has occurred (version 2.4-7-g5e89f01): • ##### ERROR • ##### ERROR Please visit the wiki to see if this is a known problem • ##### ERROR If not, please post the error, with stack trace, to the GATK forum • ##### ERROR Visit our website and forum for extensive documentation and answers to • ##### ERROR commonly asked questions http://www.broadinstitute.org/gatk • ##### ERROR • ##### ERROR MESSAGE: START (0) > (-1) STOP -- this should never happen -- call Mauricio!

22. MuTect: a highly sensitive and specific sSNV caller • Distinct Features • Focus on identifying low allelic fraction mutations due to tumor heterogeneity, normal contaminating cell, sub-clones • Use Bayesian model with allelic fraction as parameter  yield high sensitivity • Carefully tuned , elaborated set of filters  yield high specificity

23. Overview of the detection of a somatic point mutation using MuTect Bayesian model Panel of Normal Filter Variant Filter Cibulskis, K. et al.NatBiotechnology (2013).doi:10.1038/nbt.2514

24. Benchmarking mutation-detection methods • Advantages: • High sensitivity at low allelic fraction (f=0.1) • High specificity achieved by filters Cibulskis, K. et al.NatBiotechnology (2013).doi:10.1038/nbt.2514

25. Filter options Strand bias • Proximal gap • Poor mapping • Triallelic site • Strand bias • Clustered position • Observed in Control • Panel of normal samples Good Bad Jia et al. PLoS ONE 7(6): e38470

26. Installing MuTect • Installation (Linux) • Version 1.1.4 available for download at http://www.broadinstitute.org/cancer/cga/mutect_download (must register an account at Broad) • Can also be built from source available for download at http://www.nature.com/nbt/journal/v31/n3/extref/nbt.2514-S3.zip

27. Preparing input • Resources: • COSMIC VCF file: use b37_cosmic_v54_120711.vcf • dbSNP VCF file: use dbsnp_132_b37.leftAligned.vcf.gz • Human reference fasta: downloaded from GATK reference bundle, use Homo_sapiens_assembly19.fasta, *.fai, *.dict files • Inputs: • Tumor bam file and matched normal bam file from read alignment tool output (e.g. BWA, Tophat) • Bam files needed to be sorted and indexed. • Recommendation: corrected for local indels realignment, marked for PCR duplicates according to GATK best practice variant detection

28. Running MuTect • Command line with all default parameter java -Xmx4g -jar /scratch/vuongh/mutect_latest/muTect-1.1.4.jar \ --analysis_typeMuTect\ --reference_sequence/ref/Homo_sapiens_assembly19.fasta \ -cosmic /ref/hg19_cosmic_v54_120711.vcf \ -dbsnp/ref/dbsnp_132_b37.leftAligned.vcf \ --input_file:normal/Huy-RNAseq/1/accepted_hits.sorted.RG.bam\ --input_file:tumor/Huy-RNAseq/2/accepted_hits.sorted.RG.bam \ --out /out/1_2_cal_stats.out \ --vcf/out/1_2_mutation.vcf \ -cov/out/1_2_coverage.wig.txt \ --enable_extended_output • Notes: • Put all resource files (COSMIC, dbSNP and reference fasta) in folder ref • Normal bam file and index in folder 1, turmor bam and index in folder 2. • Output call stats and vcf file of mutation candidates in folder out

29. Result • Test data: RNA-seq data from squamous cell lung cancer patients (tumor/normal pair) • Total run time: 6 hours on 8 Intel Nehalem CPUs (2.4 GHz) and, processed 65.1 million reads per sample • View the result with Excel

30. Example of Mutect output Keep: 1143 (0.5%) %Reject: 213000 (99.5%)

31. Distribution of keep versus reject calls Density plot with cutoff threshold = 6.3 • Most reject calls are high allelic fraction sSNV • Keep most of the low-allelic fraction sSNV • Mono-clonal ??? density Allelic fraction f

32. Variant annotation (Annovar) Display 10 out of 432 genes

33. Summary • MuTect is a highly sensitive and specific tool for somatic SNVs calling • Designed to detect low allelic fraction somatic mutations in as few as 10% of cancer cells • Easy to install and run on all OS • Work on all NGS data • Limitations: • Computational intensive • Can’t call indels

34. THANK YOU