1. 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)

2. 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 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.eduhttp://dna.engr.uconn.edu -Computational deconvolution of heterogeneous samples

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

4. Bioinformatics Pipeline

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

6. Clipping Alignments

7. Removal of PCR Artifacts

8. Variant Detection and Genotyping AACGCGGCCAGCCGGCTTCTGTCGGCCAGCAGCCAGGAATCTGGAAACAATGGCTACAGCGTGC AACGCGGCCAGCCGGCTTCTGTCGGCCAGCCGGCAG CGCGGCCAGCCGGCTTCTGTCGGCCAGCAGCCCGGA GCGGCCAGCCGGCTTCTGTCGGCCAGCCGGCAGGGA GCCAGCCGGCTTCTGTCGGCCAGCAGCCAGGAATCT GCCGGCTTCTGTCGGCCAGCAGCCAGGAATCTGGAA CTTCTGTCGGCCAGCCGGCAGGAATCTGGAAACAAT CGGCCAGCAGCCAGGAATCTGGAAACAATGGCTACA CCAGCAGCCAGGAATCTGGAAACAATGGCTACAGCG CAAGCAGCCAGGAATCTGGAAACAATGGCTACAGCG GCAGCCAGGAATCTGGAAACAATGGCTACAGCGTGC Reference genome Locus i RiRi

9. Variant Detection 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 LocusMutationAlleles 1SNVC,T 2DeletionC,- 3SNVA,G 4Insertion-,GC LocusMutationHaplotype 1 Haplotype 2 1SNVTC 2DeletionC- 3SNVAG 4Insertion-GC

12. RefHap Algorithm Reduce the problem to Max-Cut Solve Max-Cut Build haplotypes according with the cut Locus12345 f1f1 *0110 f2f2 110*1 f3f3 1**0* f4f4 *00*1 3 f1f1 1 1 f4f4 f2f2 f3f3 h 1 00110 h 2 11001

13. Epitope Prediction 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 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 Profile weight matrix (PWM) model

14. Results on Tumor Data Tumor TypeMethACMS5 RNA-Seq Reads (Million)105.823.4 Genome Mapped75%54% Transcriptome Mapped83%59% HardMerge Mapped50%36% HardMerge Mapped Bases (Gb)3.180.41 High-Quality Heterozygous SNVs in CCDS Exons 1,504 232 Non-synonymous 1,160 182 Missense 1,096 178 Nonsense 63 4 No-stop 1 - NetMHC Predicted Epitopes 836 142 Mean Tumor Diameter (mm) Days after tumor challenge AUC (mm 2 ) P < 0.0001 Tumor rejection potential of identified epitopes currently evaluated experimentally in the Srivastava lab

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