Bortone, D. | Search bioRxiv

bioRxiv2022

Shared Graft Versus Leukemia Minor Histocompatibility Antigens in DISCOVeRY-BMT

T cell responses to minor histocompatibility antigens (mHAs) mediate graft versus leukemia (GvL) effects and graft versus host disease (GvHD) in allogeneic hematopoietic cell transplant (alloHCT). Therapies that boost T cell responses improve the efficacy of alloHCT; however, these have been limited by concurrent increases in the incidence and severity of GvHD. mHAs with expression restricted to hematopoietic tissue (GvL mHAs) are attractive targets for driving GvL without causing GvHD. Prior work to identify mHAs has focused on a small set of mHAs or population-level SNP association studies. We report here the discovery of a large set of novel GvL mHAs based on predicted peptide immunogenicity, restriction of expression to hematopoietic tissue or GvHD target organs, and degree of sharing among donor-recipient pairs (DRPs) in the DISCOVeRY-BMT dataset of 3231 alloHCT DRPs. The total number of predicted mHAs and count within each class of predicted mHAs significantly differed by recipient genomic ancestry group, with European American>Hispanic>African American for each. The number of mHAs also differed markedly by HLA allele, even among alleles of the same gene. From the pool of predicted mHAs, we identified the smallest sets of GvL mHAs needed to cover 100% of DRPs with a given HLA allele. We then used mass spectrometry to search for high population frequency mHAs for three common HLA alleles. We validated a total of 24 novel predicted GvL mHAs that cumulatively are found within 98.8%, 60.7%, and 78.9% of DRPs within DISCOVeRY-BMT that express HLA-A*02:01, HLA-B*35:01, and HLA-C*07:02 respectively. We also confirmed in vivo immunogenicity of one example novel mHA via coculture of healthy human CD8 T cells with mHA-pulsed dendritic cells. This work demonstrates that identification of shared mHAs is a feasible and promising technique for expanding mHA-targeting immunotherapeutics to larger numbers of patients.

cancer biology↗

bioRxiv2020

STIG: Generation and simulated sequencing of synthetic T cell receptor repertoires

T cell receptor repertoire inference from DNA and RNA sequencing experiments is frequently performed to characterize host immune responses to disease states. Existing tools for repertoire inference have been compared across publicly available biological datasets or unpublished simulated sequencing data. Evaluation and comparison of these tools is challenging without common data sets created from a known repertoire with well-defined biological and sequencing characteristics. Here we introduce STIG, a tool to create simulated T cell receptor sequencing data from a customizable virtual T cell repertoire, with clear attribution of individual reads back to locations within their respective T-cell receptor clonotypes. STIG allows for robust performance evaluation of T cell repertoire inference and downstream analysis methods. STIG is implemented in Python 3 and is freely available for download at https://github.com/Benjamin-Vincent-Lab/stig Author summaryAs part of the acquired immune system, T cells are integral in the host response to microbes, tumors and autoimmune disease. These cells each have a semi-unique T cell receptor that serves to bind a set of antigens that will in turn stimulate that cell to perform its particular pro- (or anti) inflammatory role. This receptor is the product of DNA rearrangement of germline gene segments, similar to B cell receptor loci rearrangement, which provides a wide variety of potential T cell receptors to respond to antigens. At the site of an immune reaction, T cells can increase their number through clonal expansion and methods have been developed to analyze bulk genetic sequencing data to infer the individual receptors and the relative size of their clonal subpopulations present within a sample. To date, these methods and tools have been tested and compared using either biological samples (where the true quantitiy and types of T cells is unknown) or unshared synthetic datasets. In this paper I describe a new tool to generate biologically-inspired T-cell repertoires in-silico and generate simulated sequencing data from them.

genomics↗