Sahar Al Seesi University of Connecticut CANGS 2017

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Sahar Al Seesi University of Connecticut CANGS 2017 GeNeo: Bioinformatics toolbox for Genomics guided Neo-epitope prediction Sahar Al Seesi University of Connecticut CANGS 2017

Class I endogenous antigen presentation

T-cell activation and proliferation

T-cell activation and proliferation

T-cell activation and proliferation

April, 2017

Read filtering

Normal Exome Reads Human reference * Tumor Exome Reads

Comparing Somatic Variant Callers Synthetic Tumors ION Torrent Proton exome sequencing of two 1K Genomes individual (mutations known) Downloaded from the public Torrent server Both exomes were sequenced on the same Proton chip Subset of the NA19240 sample was used as the normal sample Mixtures of NA19240 and NA12878 samples were used as the tumor samples Reads were mixed in different proportions to simulate allelic fractions, 0.1, 0.2, 0.3, 0.4 and 0.5.

Comparing Somatic Variant Callers

Results ATCC cell lines HCC1954: stage IIA, grade 3, ductal carcinoma Proton Exome Sequencing Illumina Exome Sequencing Proton RNA-Seq Illumina RNA-Seq HCC1954BL: matched blood

What will we assess? Sensitivity, specificity and PPV of the following filters (ways to select from CCCP call) 1 call SNVQ Illumina SNVQ Proton Strelka Illumina Strelka Proton 2 Calls Illumina SNVQ and Strelka Proton SNVQ and Strelka SNVQ Illumina and Proton (cross platform) Strelka Illumina and Proton (cross platform) 2CP : any 2 calls cross platform with relaxed condition for lack of coverage Any 3 calls

We need ground truth to assess accuracy of the calls 1) AccessArray/ Ion Torrent amplicon sequencing SNVs amplified using AccessArray and sequenced on PGM 81 called by CCCP 35 from Cancer Cell Line Encyclopedia (CCLE) 3 from an older CCCP run Total: 111 Output analyzed using AccessArray demultiplexing tool

AccessArray demultiplexing tool

Access Array Validation 111 SNVs tested – 85 validated – 26 not validated

We need ground truth to assess accuracy of the calls AccessArray/ Ion Torrent amplicon sequencing -108 SNVs Validation by single cell sequencing (Fluidigm) – 84 SNVs

We need ground truth to assess accuracy of the calls AccessArray/ Ion Torrent amplicon sequencing -108 SNVs Validation by single cell sequencing (Fluidigm) – 84 SNVs SNVs supported by RNA-Seq

Fluidigm Validation 84 validated

RNA Support as a proxy for PPV

Criteria for selecting candidate epitopes Mutated epitope is expressed in the cell: RNA Support filter

Criteria for selecting candidate epitopes Mutated epitope is expressed in the cell: RNA Support filter Peptide will bind to an MHC molecule that will chaperone it to the cell surface - EpitopeFinder/NetMHC

Criteria for selecting candidate epitopes Mutated epitope is expressed in the cell: RNA Support filter Peptide will bind to an MHC molecule that will chaperone it to the cell surface - EpitopeFinder/NetMHC - Compute Differential Agretopic Index (DAI)

Duan et. al., JEM 2014

Criteria for selecting candidate epitopes Mutated epitope is expressed in the cell: RNA Support filter Peptide will bind to an MHC molecule that will chaperone it to the cell surface - EpitopeFinder/NetMHC - Compute Differential Agretopic Index (DAI) Mutated epitope is presented on significant number of tumor cells - Amplicon sequencing of selected mutations at the single cell level

SNV Support Matrix Cells SNVs Low High Cells with less than 500 mapped reads filtered out Mouse data (CM5 tumor cell line)

Summary CCCP: Consensus Caller Cross Platform somatic variant callers Can be used with one technology or two Set of filters to allow user to select variants at the desired stringency level RNA support filter and dbSNP filter Validation Primer design tools ( Sanger sequencing and AccessArray) AmpliSeq ION Torrent demultiplexing tool Genome visualization tool EpitopeFinder

Future Work 1000 Epitope project Pipeline enhancements Build and train a predictive model for tumor rejection based on the peptide attributes Pipeline enhancements Incorporate indels in CCCP and AmpliSeq demultiplexing tool Add flanking sequences to predicted epitopes

Acknowledgments Ion Mandoiu Pramod Srivastava Availability: Anas Alokaily http://mhc1.engr.uconn.edu:8080/ Tatiana Shcheglova Adam Hagymasi Anu Kaur