Off-target toxicity prediction in cellular cancer immunotherapies

Pienkowski, V.A. Murcia; Mazzocco, Giovanni; Niemiec, Iga; Sanecka-Duin, Anna; Krol, P.; Myronov, O.; Skoczylas, Piotr; Kaczmarczyk, Jan; Blum, Amy E.

doi:10.1016/s1465324921004229

Cited by 1 publication

(1 citation statement)

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“…Overall, this study demonstrated how an optimized isolation strategy improves the isolation of MHC-bound peptides when hydrophobicity and modification status of the peptides are considered. Additionally, we developed a novel MHC ligand prediction algorithm ARDisplay-I, which outperforms the current state-of-the-art and will support not only the definition of new targets for T cell immunotherapies but might also help to improve, for example, predictors of neoantigen fitness 20 , off-target immunotoxicity 18,19 and response to immunotherapies, which rely on MHC ligand prediction algorithms 21,22 .…”

Section: Introductionmentioning

confidence: 99%

Identification of tumor-specific MHC ligands through improved biochemical isolation and incorporation of machine learning

Mecklenbräuker,

Skoczylas,

Biernat

et al. 2023

Preprint

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Isolation of MHC ligands and subsequent analysis by mass spectrometry is considered the gold standard for defining targets for TCR-T immunotherapies. However, as many targets of high tumor-specificity are only presented at low abundance on the cell surface of tumor cells, the efficient isolation of these peptides is crucial for their successful detection. Here, we demonstrate how different isolation strategies, which consider hydrophobicity and post-translational modifications, can improve the detection of MHC ligands, including cysteinylated MHC ligands from cancer germline antigens or point-mutated neoepitopes. Furthermore, we developed a novel MHC class I ligand prediction algorithm (ARDisplay-I) that outperforms the current state-of-the-art and facilitates the assignment of peptides to the correct MHC allele. The model has other applications, such as the identification of additional MHC ligands not detected from mass spectrometry or determining whether the MHC ligands can be presented on the cell surface via MHC alleles not included in the study. The implementation of these strategies can augment the development of T cell receptor-based therapies (i.a. TIL-derived T cells, genetically engineered T cells expressing tumor recognizing receptors or TCR-mimic antibodies) by facilitating the identification of novel immunotherapy targets and by enriching the resources available in the field of computational immunology.

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