ImmunoMap: A Bioinformatics Tool for T-cell Repertoire Analysis

Abstract

Despite a dramatic increase in T-cell receptor (TCR) sequencing, few approaches biologically parse the data in a fashion that both helps yield new information about immune responses and may guide immunotherapeutic interventions. To address this issue, we developed a method, ImmunoMap, that utilizes a sequence analysis approach inspired by phylogenetics to examine TCR repertoire relatedness. ImmunoMap analysis of the CD8 T-cell response to self-antigen (K-TRP2) or to a model foreign antigen (K-SIY) in naïve and tumor-bearing B6 mice showed differences in the T-cell repertoire of self- versus foreign antigen-specific responses, potentially reflecting immune pressure by the tumor, and also detected lymphoid organ-specific differences in TCR repertoires. When ImmunoMap was used to analyze clinical trial data of tumor-infiltrating lymphocytes from patients being treated with anti-PD-1, ImmunoMap, but not standard TCR sequence analyses, revealed a clinically predicative signature in pre- and posttherapy samples. .

DeepTCR: a deep learning framework for understanding T-cell receptor sequence signatures within complex T-cell repertoires

“…al in the ImmunoMap algorithm. Global sequence alignment was computed with BioPython's pairwise2.align.globalxx functionality 20 .…”

“…With this new sequencing technology, there has arisen a need to develop analytical tools to parse and draw meaningful concepts from the data, since antigen-specific T-cells exist within a sea of T-cells with specificities irrelevant to the microbe or tumor cell being assessed. In recent work, investigators have applied conventional sequence analytics, where either targeted motif searches or sequence alignment algorithms have been used to begin parsing the structural data within TCR-Seq [20][21][22] .…”

“…In order to demonstrate the utility of these algorithms, we collected two types of datasets ( Supplementary Fig. 1); samples sorted by antigen-specificity (Glanville_2017, Sidhom_2017, Dash_2017, Zhang_2018, 10x_Genomics), and samples taken from a heterogenous immune response with unknown specificities including cohorts of tumorbearing mice treated with various immunotherapies (Rudqvist_2017) and HLA-typed melanoma patients whose tumor biopsies were TCRβ CDR3 sequenced prior to the initiation of checkpoint blockade immunotherapy (CheckMate-038) 20,22,[27][28][29] . The antigen-specific datasets represent experiments where T-cells were tetramer-sorted for a specific antigen and where the ground truth label corresponds to a particular antigen-specificity.…”

“…In order to assess the value of using deep learning as method of TCR featurization, we collected data for tetramer-sorted antigen-specific cells for 9 murine and 7 human antigens where the ground truth label corresponds to a particular antigen-specificity for an individual sequence [20][21][22] . We benchmarked the VAE against featurizations of TCR's based on Hamming distances, K-mer representation, and global sequence alignment.…”

“…We benchmarked the VAE against featurizations of TCR's based on Hamming distances, K-mer representation, and global sequence alignment. Prior methods including GLIPH and TCRdist both use Hamming distances while ImmunoMap uses a global sequence alignment 20,22,27 . Our implementation of the Hamming distance method was directly benchmarked on the Glanville_2017 dataset against the original GLIPH algorithm demonstrating improved clustering accuracy as measured in the original manuscript ( Supplementary Fig.…”