Search bioRxiv⌕ Search

Biology subjects

Naradikian, M.

Publications and source records attributed to Naradikian, M..

2 recordsLinked to original sources

The spontaneous neoantigen-specific CD4+ T cell response to a growing tumor is functionally and phenotypically diverse.

CD4+ T cells play critical roles in the positive and negative regulation of cellular immunity through the many functional subsets they comprise. The progressive growth of immunogenic tumors which nonetheless generate mutation-specific T cells suggests that effective immune control may be avoided or suppressed at the level of the neoantigen-specific CD4+ T cell response. We used a tetramer specific for a validated neoantigen, CTLCH129>Q/I-Ek, to characterize the ontogeny of natural CD4+ T cell responses to an aggressive and poorly immunogenic Major Histocompatibility Complex Class II (MHCII)-deficient tumor, SCC VII, during progressive growth or following therapeutic peptide vaccination. We find that the natural CD4+ T cell response to a growing tumor is phenotypically and functionally diverse, with distinct subsets including type 1 helper (Th1), T follicular helper (Tfh)-like, and regulatory T cell (Treg) lineages appearing as early as 9 days after tumor implantation. Therapeutic vaccination using the CLTCH129>Q peptide in adjuvant plus -PD-1 sharply reduces the frequency of CLTCH129>Q-specific Treg frequency in both tumor and tumor-draining lymph node (tdLN). Single cell transcriptomic analysis of CLTC-specific CD4+ T cells recapitulated and extended the diversity of the response, with TCRs of varying affinity found within each functional subset. The TCR affinity differences did not strictly correlate with function, however, as even the lowest affinity TCRs isolated from Treg can mediate therapeutic efficacy against established tumors in the setting of adoptive cellular therapy (ACT). These findings offer unprecedented insight into the functional diversity of a natural neoantigen-specific CD4+ T cell response and show how immunotherapeutic intervention influences the phenotype, magnitude, and efficacy of the anti-tumor immune response. What is already known on this topicLittle is known about the ontogeny, architecture, development of the CD4+ NeoAg-specific repertoire induced by progressively-growing tumor. This study was performed to address this topic and contribute new information to aid in its understanding What this study addsThis study reveals that the NeoAg-specific CD4+ T cell response to a growing tumor is phenotypically and functionally diverse, featuring a range of functional T cells subsets including TH1, TFH, and Treg expressing a range of functional TCR avidities, and demonstrates how an immunotherapeutic NeoAg vaccine can alter their relative composition within the tumor and tumor-draining lymph node. How this study might affect research, practice or policyThis study offers new insights into the diversity of NeoAg-specific CD4+ T cells and their response to a tumor in the presence or absence of immunotherapeutic intervention. This information could lead to new approaches to immune monitoring in the clinical setting of checkpoint blockade immunotherapy and cancer vaccines. Furthermore, we show that Treg can be a potent source of TCRs that can mediate therapeutic benefit in the setting of adoptive cell therapy (ACT).

immunology↗

Machine learning of three-dimensional protein structures to predict the functional impacts of genome variation

Research in the human genome sciences generates a substantial amount of genetic data for hundreds of thousands of individuals, which concomitantly increases the number of variants with unknown significance (VUS). Bioinformatic analyses can successfully reveal rare variants and variants with clear associations to disease-related phenotypes. These studies have made a significant impact on how clinical genetic screens are interpreted and how patients are stratified for treatment. There are few, if any, comparable computational methods for variants to biological activity predictions. To address this gap, we developed a machine learning method that uses protein three-dimensional structures from AlphaFold to predict how a variant will influence changes to a genes downstream biological pathways. We trained state-of-the-art machine learning classifiers to predict which protein regions will most likely impact transcriptional activities of two proto-oncogenes, nuclear factor erythroid 2 (NFE2)-related factor 2 (Nrf2) and c-MYC. We have identified classifiers that attain accuracies higher than 80%, which have allowed us to identify a set of key protein regions that lead to significant perturbations in c-MYC or Nrf2 transcriptional pathway activities. SignificanceThe vast majority of mutations are either unspecified and/or their downstream biological implications are poorly understood. We have created a method that utilizes protein structure to cluster mutations from population-scale repositories to predict downstream functional impacts. The broader impacts of this approach include advanced filtering of mutations that are likely to impact genome function.

bioinformatics↗