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  • 1. Bioinformatics pipeline for detection of immunogenic cancer mutations by high throughput mRNA sequencing Jorge Duitama 1 , Ion Mandoiu 1 , and Pramod Srivastava 2 1 University of Connecticut. Department of Computer Sciences & Engineering 2 University of Connecticut Health Center
  • 2. Immunology Background J.W. Yedell, E Reits and J Neefjes. Making sense of mass destruction: quantitating MHC class I antigen presentation. Nature Reviews Immunology, 3:952-961, 2003
  • 3. Cancer Immunotherapy Tumor mRNA Sequencing Tumor Specific Epitopes Discovery Peptides Synthesis Immune System Training Mouse Image Source: http://www.clker.com/clipart-simple-cartoon-mouse-2.html Tumor Remission C T C AA TT G A T G AAA TT G TT C T G AAA C T G C A G A G A T A G C T AAA GG A T A CC GGG TT CC GG T A T CC TTT A G C T A T C T C T G CC T C C T G A C A CC A T C T G T G T GGG C T A CC A T G … A GG C AA G C T C A T GG CC AAA T C A T G A G A SYFPEITHI ISETDLSLL CALRRNESL …
  • 4. Analysis Pipeline Tumor mRNA (PE) reads CCDS Mapping Genome Mapping Read merging CCDS mapped reads Genome mapped reads Tumor-specific mutations Variants detection Epitopes Prediction Tumor-specific CTL epitopes Mapped reads Gene fusion & novel transcript detection Unmapped reads
  • 5. Read Merging Genome CCDS Agree? Hard Merge Soft Merge Unique Unique Yes Keep Keep Unique Unique No Throw Throw Unique Multiple No Throw Keep Unique Not Mapped No Keep Keep Multiple Unique No Throw Keep Multiple Multiple No Throw Throw Multiple Not Mapped No Throw Throw Not mapped Unique No Keep Keep Not mapped Multiple No Throw Throw Not mapped Not Mapped Yes Throw Throw
  • 6. Variant Calling Methods
    • Binomial: Test used in e.g. [Levi et al 07, Wheeler et al 08] for calling SNPs from genomic DNA
    • Posterior: Picks the genotype with best posterior probability given the reads, assuming uniform priors
  • 7. Epitopes Prediction
    • Predictions include MHC binding, TAP transport efficiency, and proteasomal cleavage
    C. Lundegaard et al . MHC Class I Epitope Binding Prediction Trained on Small Data Sets. In Lecture Notes in Computer Science, 3239:217-225, 2004
  • 8. Accuracy Assessment of Variants Detection
    • 63 million Illumina mRNA reads generated from blood cell tissue of Hapmap individual NA12878 (NCBI SRA database accession number SRX000566)
      • We selected Hapmap SNPs in known exons for which there was at least one mapped read by any method (22,362 homozygous reference, 7,893 heterozygous or homozygous variant)
      • True positives: called variants for which Hapmap genotype is heterozygous or homozygous variant
      • False positives: called variants for which Hapmap genotype is homozygous reference
  • 9. Comparison of Variant Calling Strategies Genome Mapping, Alt. coverage  1
  • 10. Comparison of Variant Calling Strategies Genome Mapping, Alt. coverage  3
  • 11. Comparison of Mapping Strategies Posterior , Alt. coverage  3
  • 12. Results on Meth A Reads
    • 6.75 million Illumina reads from mRNA isolated from a mouse cancer tumor cell line
    • We found 6775 variant candidates after hard merge mapping, posterior variant calling and a minimum of three reads per alternative allele
    • 934 variants produced 1439 epitopes with SYFPEITHI score higher than 15 for the mutated peptide
  • 13. SYFPEITHI Scores Distribution of Mutated Peptides
  • 14. Distribution of SYFPEITHI Score Differences Between Mutated and Reference Peptides
  • 15. Validation Results
    • We are using mass spectrometry for confirmation of presentation of epitopes in the surface of the cell
    • Mutations reported by [Noguchi et al 94] were found by this pipeline
    • We are performing Sanger sequencing of PCR amplicons to confirm reported mutations
  • 16. Conclusions & Ongoing Work
    • We implemented a bioinformatics pipeline for characterizing tumor immunomes
    • Identified tumor epitopes are being evaluated for therapeutic effect
    • Ongoing work:
      • Detect short immunogenic indels and novel transcripts
      • Include predictions of TAP transport efficiency, and proteasomal cleavage
      • Refine the posterior method to increase mutation detection robustness in the presence of differential allelic expression
  • 17. Acknowledgments
    • Brent Graveley and Duan Fei (UCHC)
    • NSF awards IIS-0546457, IIS-0916948, and DBI-0543365
    • UCONN Research Foundation UCIG grant
  • 18. Questions?
    • Thanks