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Cancer Immunology Research
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Research Articles

ImmunoMap: A Bioinformatics Tool for T-cell Repertoire Analysis

John-William Sidhom, Catherine A. Bessell, Jonathan J. Havel, Alyssa Kosmides, Timothy A. Chan and Jonathan P. Schneck
John-William Sidhom
1Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland.
2Bloomberg∼Kimmel Institute for Cancer Immunotherapy, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland.
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Catherine A. Bessell
3Graduate Program in Immunology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
4Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland.
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Jonathan J. Havel
5Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York.
6Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, New York.
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Alyssa Kosmides
1Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland.
4Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland.
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Timothy A. Chan
5Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York.
6Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, New York.
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Jonathan P. Schneck
4Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland.
7Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
8Institute for Nanobiotechnology, Johns Hopkins Whiting School of Engineering, Baltimore, Maryland.
9Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland.
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  • For correspondence: jschnec1@jhmi.edu
DOI: 10.1158/2326-6066.CIR-17-0114 Published February 2018
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    Figure 1.

    Elements of the ImmunoMap algorithm. A, Weighted repertoire dendrogram visualizes the relatedness of sequences within repertoires along with the relative frequency of CDR3 amino acid sequences. B, Dominant motif analysis clusters homologous sequences and selects for clusters contributing to significant proportion of the response. Three dominant motifs are shown that are highly represented structural motifs in this individual's CMV response. C, Singular clone analysis defines sequences that expand significantly over the summation of all other homologous sequences. D, Novel clone analysis is implemented when comparing repertoires from different samples. A novel clone is defined as one that expands significantly over the summation of all homologous sequences in the other sample.

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    Figure 2.

    Naïve repertoire to Kb-SIY versus Kb-TRP2 antigens. A, Naïve C57BL/6 animals were harvested for CD8+ T cells from spleens. CD8+ T cells were enriched and expanded by incubation with nano-aAPCs and then were cultured ex vivo for 7 days. Antigen-specific cells were then selected by FACS and their TCR β-chains sequenced. B, Weighted repertoire dendrograms, in which the distance of the branch ends denotes sequence distance, the size of circles denotes frequency of sequence, and the color of circles denotes specific Vβ segment usage. C, Demonstration of dominant motifs detected for Kb-SIY (left) and Kb-TRP2 (right). Frequency and global sequence alignment is shown (red, fully conserved amino acids; green, semiconserved amino acids; black, nonconserved amino acids). D, Quantification of dominant motif analysis comparing the number of dominant motifs, the number of sequences per motif, the contribution of the sequences in the dominant motifs to the response, and the contribution to the response per sequence in a dominant motif (top). Singular structural clone and TCR diversity analysis metrics (bottom; n = 5). E, Shannon entropy calculations comparing endogenous Kb-SIY with Kb-TRP2 responses (n = 5 mice pooled, **, P ≤ 0.01; ***, P ≤ 0.001 using the unpaired two-tailed t test; bar, mean ± SEM).

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    Figure 3.

    Effects of tumor on TCR repertoire. A, Overlapped weighted repertoire dendrograms of tumor-bearing versus naïve antigen-specific splenic CD8 responses (red, tumor-bearing repertoire; blue, naïve repertoire). B, Dominant motif analysis for Kb-SIY and Kb-TRP2 responses before and after exposure to tumor (n = 5 mice). C, Maintenance of dominant motifs between naïve and tumor-bearing repertoire. D, Novel structural clone analysis (n = 5 mice). E, Vβ usage of Kb-SIY and Kb-TRP between naïve and tumor-bearing repertoire. F, Shannon entropy calculations comparing endogenous versus tumor-bearing responses with Kb-SIY and Kb-TRP2. (n = 3 mice pooled, *, P ≤ 0.05; ***, P ≤ 0.001 using the unpaired two-tailed t test; bar, mean ± SEM).

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    Figure 4.

    Effects of tumor on TCR repertoire in various lymphoid organs. A, Overlapped weighted repertoire dendrograms (blue, spleen; green, draining lymph node; red, TILs). B, Dominant motif and TCR diversity metrics (Kb-SIY TILs, n = 4 mice; Kb-TRP2 spleen, n = 4 mice; Kb-TRP2 dLN, n = 5 mice). C, Maintenance of dominant motifs between various lymphoid organs (n = 4 mice pooled by organ, *, P ≤ 0.05; ***, P ≤ 0.001 using the unpaired two-tailed t test; bar, mean ± SEM).

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    Figure 5.

    TCR repertoire analysis of patients undergoing anti–PD-1 (α-PD-1, nivolumab) therapy. A, Clinical protocol for sample collection and response stratification. Pretherapy biopsies were taken from tumor sites prior to initiation of therapy. Four weeks after initiation of anti–PD-1 therapy, on-therapy biopsies were taken from same tumor sites. TIL extraction was completed and sent to Adaptive Biotechnologies for CDR3 β-chain sequencing. B, Token weighted repertoire dendrograms for each of the cohorts of responders. C, Dominant motif and TCR diversity analysis. Complete response (CR) = 3, partial response (PR) = 5, stable response (SR) = 11, no response (NR) = 15. D, Shannon entropy calculations for responses before and after initiation of anti–PD-1 (*, P ≤ 0.05 using the Mann–Whitney test; bar, mean ± SEM).

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    • Supplementary Figures 1-8 - S1. In order to set thresholds for motif detection, two variables were optimized on a Naïve Adult B6 CD8 Repertoire (taken from Adaptive Biotechnologies ImmunoSeq Sample Data). Motif detection was completed across a range of phylogenetic distances and frequency thresholds and number of motifs detected was monitored. Since our analysis was looking for dominant motifs above what is present in an unexpanded population, we chose a frequency threshold of 0.03 and Phylogenetic Distance threshold of 0.35, at which 0 motifs were detected in Naïve B6 background. S2. A) FACS analysis of antigen-specific CD8 T cells on D7 in Naïve and Tumor-bearing lymphoid organs. B) ICS staining of antigen-specific CD8 T Cells confirming specificity and functionality. C) Comparison of Dimer+ and TNF+ CD8. D) Antigen-specific CD8 T cells staining from splenic CD8 T cells directly ex vivo compared to unloaded Kb-Ig staining. N = 3, Statistical 2-tailed T-test. S3. V-beta usage for Naïve Kb-SIY & Kb-TRP2 Response S4. V-beta usage for Tumor-Bearing Kb-SIY & Kb-TRP2 Response in Various Lymphoid Organs S5. In order to determine number of tumor-infiltrating lymphocytes that were sequenced for each patient, Adaptive reported amount of total DNA in nanograms that underwent sequencing and based on the assumption of 6.5pgDNA per cell, we were able to calculate the number of total cells that underwent sequencing. Furthermore, Adaptive calculated a %TIL metric based on number of non-recombined to recombined sequence reads. With this information, we were able to deduce the number of starting lymphocytes that were sequenced for each patient. S6. Calculation of TCR Diversity Score S7. Duplicates of Murine Experiments S8: ImmunoMap Graphical User Interface & Instructions for Use
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Cancer Immunology Research: 6 (2)
February 2018
Volume 6, Issue 2
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ImmunoMap: A Bioinformatics Tool for T-cell Repertoire Analysis
John-William Sidhom, Catherine A. Bessell, Jonathan J. Havel, Alyssa Kosmides, Timothy A. Chan and Jonathan P. Schneck
Cancer Immunol Res February 1 2018 (6) (2) 151-162; DOI: 10.1158/2326-6066.CIR-17-0114

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ImmunoMap: A Bioinformatics Tool for T-cell Repertoire Analysis
John-William Sidhom, Catherine A. Bessell, Jonathan J. Havel, Alyssa Kosmides, Timothy A. Chan and Jonathan P. Schneck
Cancer Immunol Res February 1 2018 (6) (2) 151-162; DOI: 10.1158/2326-6066.CIR-17-0114
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