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Towards Personalized Genomics-Guided Cancer Immunotherapy

Towards Personalized Genomics-Guided Cancer Immunotherapy. Ion Mandoiu Department of Computer Science & Engineering Joint work with Sahar Al Seesi (CSE) Jorge Duitama (CIAT) Fei Duan , Tatiana Blanchard, Pramod K. Srivastava (UCHC). Mandoiu Lab. Main Research Areas:

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Towards Personalized Genomics-Guided Cancer Immunotherapy

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  1. Towards Personalized Genomics-Guided Cancer Immunotherapy Ion Mandoiu Department of Computer Science & Engineering Joint work with Sahar Al Seesi(CSE) Jorge Duitama(CIAT) FeiDuan, Tatiana Blanchard, Pramod K. Srivastava(UCHC)

  2. Mandoiu Lab • Main Research Areas: • Bioinformatics Algorithms • Development of Computational Methods for Next-Gen Sequencing Data Analysis • Ongoing Projects • RNA-Seq Analysis (NSF, NIH, Life Technologies) • Novel transcript reconstruction • Allele-specific isoform expression • Computational deconvolution of heterogeneous samples • Viral quasispecies reconstruction (USDA) • IBV evolution and vaccine optimization • Genome assembly and scaffolding, LD-based genotype calling, local ancestry inference, metabolomics, … • More info & software at http://dna.engr.uconn.edu

  3. Genomics-Guided Cancer Immunotherapy Peptide Synthesis mRNA Sequencing Tumor Specific Epitopes CTCAATTGATGAAATTGTTCTGAAACT GCAGAGATAGCTAAAGGATACCGGGTT CCGGTATCCTTTAGCTATCTCTGCCTC CTGACACCATCTGTGTGGGCTACCATG … AGGCAAGCTCATGGCCAAATCATGAGA Immune SystemStimulation T-Cell Response Tumor Remission SYFPEITHI ISETDLSLL CALRRNESL … Mouse Image Source: http://www.clker.com/clipart-simple-cartoon-mouse-2.html

  4. Bioinformatics Pipeline

  5. Hybrid Read Alignment Approach Transcript mapped reads Transcript Library Mapping mRNA reads Mapped reads Read Merging Genome mapped reads Genome Mapping http://en.wikipedia.org/wiki/File:RNA-Seq-alignment.png • More efficientcompared to spliced alignment onto genome • Stringent filtering: reads with multiple alignments are discarded

  6. Clipping Alignments

  7. Removal of PCR Artifacts

  8. VariantDetection and Genotyping Locus i AACGCGGCCAGCCGGCTTCTGTCGGCCAGCAGCCAGGAATCTGGAAACAATGGCTACAGCGTGC AACGCGGCCAGCCGGCTTCTGTCGGCCAGCCGGCAG CGCGGCCAGCCGGCTTCTGTCGGCCAGCAGCCCGGA GCGGCCAGCCGGCTTCTGTCGGCCAGCCGGCAGGGA GCCAGCCGGCTTCTGTCGGCCAGCAGCCAGGAATCT GCCGGCTTCTGTCGGCCAGCAGCCAGGAATCTGGAA CTTCTGTCGGCCAGCCGGCAGGAATCTGGAAACAAT CGGCCAGCAGCCAGGAATCTGGAAACAATGGCTACA CCAGCAGCCAGGAATCTGGAAACAATGGCTACAGCG CAAGCAGCCAGGAATCTGGAAACAATGGCTACAGCG GCAGCCAGGAATCTGGAAACAATGGCTACAGCGTGC Reference genome Ri

  9. VariantDetection and Genotyping • Pick genotype with the largest posterior probability

  10. Accuracy as Function of Coverage

  11. Haplotyping • Somatic cells are diploid, containing two nearly identical copies of each autosomal chromosome • Novel mutations are present on only one chromosome copy • For epitope prediction we need to know if nearby mutations appear in phase

  12. RefHapAlgorithm • Reduce the problem to Max-Cut • Solve Max-Cut • Build haplotypes according with the cut f4 -1 1 3 f1 f2 1 -1 f3 h1 00110 h2 11001

  13. Epitope Prediction Profile weight matrix (PWM) model 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 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

  14. Results on Tumor Data • Tumor rejection potential of identified epitopescurrentlyevaluated experimentally in the Srivastava lab P < 0.0001 Mean Tumor Diameter (mm) AUC (mm2) Days after tumor challenge

  15. Ongoing Work • Sequencing of spontaneous tumors (TRAMP mice) • Detecting other forms of variation: indels, gene fusions, novel transcripts • Incorporating predictions of TAP transport efficiency and proteasomal cleavage in epitope prediction • Integration of mass-spectrometry data • Monitoring immune response by TCR sequencing

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