Diversity and divergence of the glioma-infiltrating T-cell receptor repertoire

Abstract

Although immune signaling has emerged as a defining feature of the glioma microenvironment, how the underlying structure of the glioma-infiltrating T-cell population differs from that of the blood from which it originates has been difficult to measure directly in patients. High-throughput sequencing of T-cell receptor (TCR) repertoires (TCRseq) provides a population-wide statistical description of how T cells respond to disease. We have defined immunophenotypes of whole repertoires based on TCRseq of the α-and β-chains from glioma tissue, nonneoplastic brain tissue, and peripheral blood from patients. Using information theory, we partitioned the diversity of these TCR repertoires into that from the distribution of VJ cassette combinations and diversity due to VJ-independent factors, such as selection due to antigen binding. Tumor-infiltrating lymphocytes (TILs) possessed higher VJ-independent diversity than nonneoplastic tissue, stratifying patients according to tumor grade. We found that the VJ-independent components of tumor-associated repertoires diverge more from their corresponding peripheral repertoires than T-cell populations in nonneoplastic brain tissue, particularly for low-grade gliomas. Finally, we identified a "signature" set of TCRs whose use in peripheral blood is associated with patients exhibiting low TIL divergence and is depleted in patients with highly divergent TIL repertoires. This signature is detectable in peripheral blood, and therefore accessible noninvasively. We anticipate that these immunophenotypes will be foundational to monitoring and predicting response to antiglioma vaccines and immunotherapy.T-cell receptor | immunoprofiling | glioma | glioblastoma | immunooncology T he potential for immunotherapy to alleviate progression and recurrence in glioma has inspired intense study of the immunological phenotypes underlying the regulation and selection of tissue-infiltrating lymphocytes (TILs). Motivated by the prospect of targeted therapy, previous studies of glioma have undertaken large-scale genomic and gene expression analysis. These studies revealed distinct phenotypic states or subtypes that stratify gliomas and resemble different glial lineages (1-3). Although immunological gene expression classifications have been associated with clinical outcomes and prognosis (4, 5), precision immunotherapy will ultimately rely on manipulation of the T-cell population that infiltrates gliomas and its underlying repertoire of T-cell receptors (TCRs). However, the structure and intertumoral heterogeneity of the TIL population in gliomas has not been described. Here, we use whole repertoire sequencing of TCRs from glioma tissue and matched peripheral blood to discover novel immunological phenotypes with implications for noninvasive patient monitoring.Local antitumor potential is, in part, a function of the TCR repertoire expressed by T cells that surveil the CNS beyond the bloodbrain barrier. Through somatic V(D)J recombination including random nucleotide insertion in each T-cell, the α-and β-cha...

Quantifying size and diversity of the human T cell alloresponse

“…Furthermore, a major advantage of S as a measure of T cell repertoire diversity is that it describes the physical distribution of a population such that it can be recreated and used for statistical modeling, which we apply in our approach for estimating the frequency of unseen clones. S emerges from the confirmation that the power law is the best model to describe the distribution for the bulk of TCR clones (22,25). The steepness of S reflects the difference in the number of rare clones on the left side of the distribution in an abundance plot compared with the number that are highly frequent on the right.…”

“…Scatter plots showing the number of individual clones shared between the 2 distinct alloreactive repertoires confirms their disparate nature for both CD4 + and CD8 + T cell repertoires ( Figure 4A). A standard quantitative measure of repertoire overlap is Jensen-Shannon divergence (JSD) (24,25), a tool that accounts for both clone number and frequency and is normalized on a scale of 0 to 1: a JSD of 1 indicates that all clones in 2 populations are distinct. A small group of high frequency shared clones was detected in the CD8 + repertoires of 2 of the 3 pairs (red in Figure 4A).…”

“…Interpopulation differences were determined by the JSD (47), which has previously been applied to TCR repertoire analysis (24,25). Rather than quantifying unevenness within a population, the JSD provides a measure of dissimilarity between the labeled frequencies of 2 populations.…”

Tumor and Microenvironment Evolution during Immunotherapy with Nivolumab

“…To explore whether the global TCR diversity dynamics described above could also be detected at the level of individual VJ cassette combinations, we determined CDR3 richness and evenness for every VJ pair, as previously described (Sims et al, 2016). This approach, although more rigorous, is an in-silico descendant of traditional spectratyping, in which specific V and J cassette primers are used to amplify all CDR3s encoded by specific VJ cassette combinations and the products are analyzed by electrophoresis (Arstila et al, 1999).…”

“…This approach, although more rigorous, is an in-silico descendant of traditional spectratyping, in which specific V and J cassette primers are used to amplify all CDR3s encoded by specific VJ cassette combinations and the products are analyzed by electrophoresis (Arstila et al, 1999). The benefits of this in-silico approach are two-fold: 1) by examining diversity dynamics at the VJ cassette level, bias potentially introduced by preferential V and J cassette primer binding is diminished, and 2) a greater understanding of the extent to which antigen-binding properties (CDR3 amino acid sequences) versus VJ cassette usage biases influence global changes in TCR diversity is gained (Sims et al, 2016). Kernel density estimate plots of the number of CDR3s per VJ versus evenness of CDR3s per VJ were generated using the seaborn statistical data visualization platform for Python.…”