Statistical Learning of Next-Generation Sequencing T cell Repertoire Data Li Zhang, Ph.D. Associate Professor Department of Medicine Department of Epidemiology and Biostatistics Helen Diller Family Comprehensive Cancer Center University of California San Francisco li.zhang@ucsf.edu

Acknowledgements Lawrence Fong, MD (PI), Immunologist and oncologist Dave Oh, MD Ph.D. , Jason Cham and Alan Parcicok San Francisco State University Dr. Tao He, Assistant Professor

Combinatorial Diversity of Human β Locus Freeman et. al Genome Research TCRBeta locus at human chromosome 7q34 Recombination first occurs between TRBJ and TRBD genes, followed by recombination to a TRBV gene (Red lines). After transcription, intervening sequences are spliced out so that a TRBC is adjacent to the recombined V-D-J sequence. V(D)J recombination is not entirely random, and the prevalence of specific gene segments and combinations of gene segments shows marked variation in the repertoire. T-cell repertoire is not static, but constantly molded by immune challenge CDRs: Recognition specificity for diverse peptide-MHC (pMHC) complexes is provided by the three complementarity-determining regions (CDRs) of the TCR . CDR1 and CDR2 are coded by germline sequences. CDR3: the highly polymorphic principal recognition site, is created when TCR genomic loci undergo somatic recombination between gene segments during development of T lymphocytes in the thymus. V(D)J recombination is not entirely random, and the prevalence of specific gene segments and combinations of gene segments shows marked variation in the repertoire. T-cell repertoire is not static, but constantly molded by immune challenge.

T-cell Clonotype Tracking by TCRb Sequencing V D J ~340bp Consensus primers VH-FR1 ~270bp JH VH-FR2 ~140bp VH-FR3 Multiplexed PCR Next generation sequence CCCAGTAAC T cells express T cell receptors (TCR) on the surface that determines the cell’s specificity. TCRs are formed by splicing together V, D, and J segments. The junctions between the V-D and D-J segments have insertions and deletions of random nucleotides, which add even more diversity. We sequenced the CDR3 region of the TCRβ region that spans these junctions by a next-generation sequencing-based method. We are able to identify unique T cell clones based on their TCRβ nucleotide sequence and determine frequency of the detected sequence. This allows us to track clonal expansion or contraction of the T cell repertoire in response to the Sip-T treatment. Blue and purple TCR Yellow and orange: NHC Primer for each V,J segment Primer uses library Spike-in a multiplex polymerase chain reaction (PCR) system was used to amplify the rearranged CDR3β sequences from sample DNA We only look at beta chain (half of TCR) ImmunoSEQ assay (Adaptive Biotechnologies, Seattle, WA) Blue and purple represent the TCR, yellow is MHC, grey is the part MHC show to TCR, red arrows are primers beta chain is generated by VDJ recombination (both involving a somewhat random joining of gene segments to generate the complete TCR chain TCRb is TCR beta chain GGGTCATTG Count 1 AAAGCGACATTGGGATCTGTCAGTTGTCATTCGCG 1321 2 GCGGTTTTGTAGAAGGTTAGGGGAATAGGTTAGAT 1122 3 TGAGTGGCTTAAGAATGTAAAATCTGGGATTATAG 901 4 TGTAGTAATCTCTGATTAACGGTGACGGTTTTAAG 534 5 GAAGAATAATTAAGAAAAAAGCACCCCTCGTCGCC 421 6 TAGAATTACCTACCGCGGTCCACCATACCTTCGAT 132 7 TATCGCGCCCACTCTCCCATTAGTCGGCAGAGGTG 113 106- 108 Independent Sequence Reads Per Run (Adapted from Aaron Logan)

Typical TCR Sequencing Data Clone_Sequence Clone_Protein_Sequence CDR3_Sense_Sequence J_Segment_Major_ Gene V_Segment_Major_ Count Frequency CAAGACAGAGAACCGATCACTGAGCAGCCTTGATTTTTCTAGTTGAGCTTCATTCTGGAAGTAAGTCAGAAACTCTGGGCCCTGCCCCAGGGTT TLGQGPEFLTYFQNEAQLEKSRLLSDRFSVL TGGGGCAGGGCCCAGAGTTTCTGACTTACTTCCAGAATGAAGCTCAACTAGAAAAATCAAGG TRBJ1-4 TRBV7-9 3 -5.189 CAAGACAGAGAACTGTTGTGCGGAGAATCGTTCAAGAATGTTTCCTTTTGCTCTCTCTTCTCCATTATAATACTGAATGAGGAACTGGAGGCCC GLQFLIQYYNGEERAKGNILERFSAQQFSVL AGTATTATAATGGAGAAGAGAGAGCAAAAGGAAACATTCTTGAACGA TRBV9 2 -5.365 CAAGACAGAGACACTGTACCCCTCAGGAATATCTCCTTTTTCTTTCATTTTAACATCATATGAGAAATAGATCAGCCGTAGCCCCAGACCTGGG PGLGLRLIYFSYDVKMKEKGDIPEGYSVSVL ATGAAAGAAAAAGGAGATATT TRBV28 CAAGACAGAGAGCTGGGTTCCACTGCCAAAAAAACAGTTTTTCATTAGTTGTGGGACTGCTGGCACAGAAGTACATGGCTGAGTCCTCCAGCTT SWRTQPCTSVPAVPQLMKNCFFGSGTQLSVL TGTGCCAGCAGTCCCACAACTAATGAAAAACTGTTTTTTT TRBV2 Should I explain what count frequency ? One subject one file > 20 MB Large number of unique nucleotides: 0.1M~1M Uniqueness and dynamics: limited overlap nucleotides 10%-20%

Analysis Pipeline Clone-level VJ gene-level: 3D Analysis: Diversity, Dynamics & Differential Testing VJ gene-level: Feature selection by random forest: to select the important V and J gene Hierarchical Clustering: to distinguish the subjects based on the selected important V and J genes Pattern recognition by time-point change analysis: to recognize the gene abundance change across time

Study Design Ipi: once every 4 weeks GM-CSF: everyday first 2 weeks for every 4 weeks mCRPC and melanoma patients

VJ gene usage from baseline to post-treatment significantly different between mCRPC patients and melanoma patients The first part of the genes show more changes in 03015 and the 2nd part of genes show more changes in 02558. Jason, can you find some biological relevalnce.

At-most-one (AMO) change point detection for gene usage shows that more changes happened from C1 to C2 in melanoma patients, but more changes happened from C2 to C3 in mCRPC patients.