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Tzatsos, A.

Publications and source records attributed to Tzatsos, A..

2 recordsLinked to original sources

Large-Scale Assessment of NF1 Single Amino Acid Variants as HLA Class I Neoantigens

Neoantigens are cancer-specific antigens arising from genomic alterations. Single Amino Acid Variants (SAAVs) represent a primary class of these neoantigens. To evaluate the therapeutic potential of Neurofibromin 1 (NF1)-derived SAAVs - given that NF1 is frequently mutated in malignant brain tumors - we prioritized the 40 NF1 SAAVs determined to be HLA-A*02:01 binders using computational prediction coupled with experimental validation. To validate these predicted neoepitopes, we employed a two-tiered experimental approach in HLA-A*02:01 homozygous U87-MG cells. We first synthesized minigene constructs encoding the predicted neoepitopes, introduced them via lentiviral transfection and confirmed their expression by mass spectrometry (MS). Subsequently, we performed endogenous validation using pan-HLA immunoprecipitation mass spectrometry (IP-MS), confirming 4 (10 neoepitopes) of the 40 candidate SAAVs. We observed a discrepancy between in silico predictions and the observed sequences. Our endogenous peptidomics further revealed conserved peptide motifs and demonstrated that peptide selection for HLA presentation is transient. While our study substantiates the therapeutic feasibility of T-cell immunotherapies targeting NF1 mutations, these results underscore a limitation in current computational prediction. Our study highlights the necessity of experimental validation to refine neoantigen prioritization strategies.

immunology↗

Predicting CTCF-mediated chromatin interactions by integrating genomic and epigenomic features

The CCCTC-binding zinc finger protein (CTCF)-mediated network of long-range chromatin interactions is important for genome organization and function. Although this network has been considered largely invariant, we found that it exhibits extensive cell-type-specific interactions that contribute to cell identity. Here we present Lollipop--a machine-learning framework--which predicts CTCF-mediated long-range interactions using genomic and epigenomic features. Using ChIA-PET data as benchmark, we demonstrated that Lollipop accurately predicts CTCF-mediated chromatin interactions both within and across cell-types, and outperforms other methods based only on CTCF motif orientation. Predictions were confirmed computationally and experimentally by Chromatin Conformation Capture (3C). Moreover, our approach reveals novel determinants of CTCF-mediated chromatin wiring, such as gene expression within the loops. Our study contributes to a better understanding about the underlying principles of CTCF-mediated chromatin interactions and their impact on gene expression.

systems biology↗