Biophysicochemical Motifs in T-cell Receptor Sequences Distinguish Repertoires from Tumor-Infiltrating Lymphocyte and Adjacent Healthy Tissue

Abstract

Immune repertoire deep sequencing allows comprehensive characterization of antigen receptor-encoding genes in a lymphocyte population. We hypothesized that this method could enable a novel approach to diagnose disease by identifying antigen receptor sequence patterns associated with clinical phenotypes. In this study, we developed statistical classifiers of T-cell receptor (TCR) repertoires that distinguish tumor tissue from patient-matched healthy tissue of the same organ. The basis of both classifiers was a biophysicochemical motif in the complementarity determining region 3 (CDR3) of TCRb chains. To develop each classifier, we extracted 4-mers from every TCRb CDR3 and represented each 4-mer using biophysicochemical features of its amino acid sequence combined with quantification of 4-mer (or receptor) abundance. This representation was scored using a logistic regression model. Unlike typical logistic regression, the classifier is fitted and validated under the requirement that at least 1 positively labeled 4-mer appears in every tumor repertoire and no positively labeled 4-mers appear in healthy tissue repertoires. We applied our method to publicly available data in which tumor and adjacent healthy tissue were collected from each patient. Using a patient-holdout cross-validation, our method achieved classification accuracy of 93% and 94% for colorectal and breast cancer, respectively. The parameter values for each classifier revealed distinct biophysicochemical properties for tumor-associated 4-mers within each cancer type. We propose that such motifs might be used to develop novel immune-based cancer screening assays.Significance: This study presents a novel computational approach to identify T-cell repertoire differences between normal and tumor tissue. 1 We treat TCRb sequences with identical CDR3 sequences as being the same TCRb sequence, ignoring differences upstream of CDR3.

A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding

“…To our knowledge, a similar work on TCR-peptide interaction does not yet exist. Comparing motifs between TCR and antibodyantigen motifs would shed light on mechanistic similarities and differences in antibody and TCR antigen-interaction (Antunes et al, 2018;Bradley and Thomas, 2019;Dash et al, 2017;Glanville et al, 2017;Gowthaman and Pierce, 2018;Hellman et al, 2019Hellman et al, , 2019Lanzarotti et al, 2018;Ostmeyer et al, 2019;Riley and Baker, 2018;Turner et al, 2006). Furthermore, it remains to be investigated in how far the motif-based rules uncovered here for VH-VL-antigen complexes are transferable to scFV, nanobody, and other novel antibody constructs (Henry and MacKenzie, 2018;Mitchell and Colwell, 2018).…”

“…We identified the set of interacting residues at the interface of antibody-antigen structures by using a heavy-atom distance cutoff of <5Å (Ostmeyer et al, 2019) (see Methods and see Suppl Fig. S3 and S7 for the robustness of the distance cutoff).…”

“…These further investigations will elucidate the question of whether paratope sequence motifs may be used as in silico scanning devices for epitope-specific sequence regions in high-throughput antibody repertoire data for at least a subset of medically important antigens. Understanding the diversity and structure of antibody specificity on the repertoire level is also of importance for enhancing the precision of immune receptor-based immunodiagnostics where one of the most pressing current problems is a low signal-to-noise ratio (few antigen-specific sequences within a large pool of unrelated sequences) (Arora et al, 2019;Brown et al, 2019;Emerson et al, 2017;Ostmeyer et al, 2019).…”

“…Adaptive immune receptor repertoires represent a major target area for the application of machine learning in the hope that it may fast-track the in silico discovery and development of immunereceptor based immunotherapies and immunodiagnostics (Brown et al, 2019;Greiff et al, 2012;Mason et al, 2018Mason et al, , 2019Miho et al, 2018). The complexity of sequence dependencies that determine antigen binding (Dash et al, 2017;Glanville et al, 2017), immune receptor publicity (Greiff et al, 2017b) and immune status (immunodiagnostics) (Ostmeyer et al, 2019;Thomas et al, 2014) represent a perfect application ground for machine learning analysis (Arora et al, 2019;Cinelli et al, 2017;Greiff et al, 2017b;Liu et al, 2019;Mason et al, 2019;Sidhom et al, 2018;Sun et al, 2017). As discussed extensively in a recent literature review by us (Brown et al, 2019) the development of ML approaches for immune receptor datasets was and is still hampered by the lack of ground truth datasets.…”

A framework for annotation of antigen specificities in high-throughput T-cell repertoire sequencing studies

“…Immune repertoire profiling technology (AIRR-Seq, [1]) is an efficient technique that can be employed to study the structure and dynamics of the adaptive immune system. AIRR-Seq makes it possible to characterize the structure of both naive and antigen-experienced T-cell receptor (TCR) repertoires [2]- [4], tumor infiltrating T-cells [5], TCRs related to autoimmunity [6], leading to numerous downstream applications in both basic and applied immunological research [7]. While novel single-cell RNA sequencing methods allow coupling individual T-cell clones to their phenotype and function using their gene expression profiles [8], the actual antigen specificity (i.e.…”