Author Correction: Tryptophan depletion results in tryptophan-to-phenylalanine substitutants

Pataskar, Abhijeet; Champagne, Julien; Nagel, Remco; Kenski, Juliana C.N.; Laos, Maarja; Michaux, Justine; Pak, Hui Song; Bleijerveld, Onno B.; Mordente, Kelly; Navarro, Jasmine Montenegro; Blommaert, Naomi; Nielsen, Morten Milek; Lovecchio, Domenica; Stone, Everett; Georgiou, George; Gooijer, Mark C. de; Tellingen, Olaf van; Altelaar, Maarten; Joosten, Robbie P.; Perrakis, Anastassis; Olweus, Johanna; Bassani-Sternberg, Michal; Peeper, Daniel S.; Agami, Reuven

doi:10.1038/s41586-022-05097-y

Cited by 3 publications

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“…3 Recently, codon reassignment during translation has also been reported as a source of neoantigens, which can also be identified through searching immunopeptidomics data against customized protein databases including novel protein sequences derived from the codon reassignment of interest. 18 For DIA data analysis, RT and fragment ion intensity can be predicted for sequences in the customized protein databases using deep learning tools, and the predicted RT and MS/MS spectra can be used for the identification of both canonical and non-canonical peptides. 22 There are several key considerations in the proteogenomics-based approach, including accurate DNA, RNA and ribosomal sequencing data analysis, carefully designed plans for Open access incorporating predicted novel protein sequences into reference protein database, special quality control in MS data analysis due to the expanded and heterogeneous search space, cancer-specificity determination for the identified HLA peptides, and immunogenicity prediction.…”

Section: Identification Of Tumor Antigensmentioning

confidence: 99%

“…For example, post-transcriptional events, such as alternative splicing leading to intron retention, non-canonical translation initiation and codon read-through, as well as translation of long non-coding RNAs (lncRNAs), pseudogenes and transposablea elements (TEs) have been reported to generate non-canonical HLA peptides, some of which were demonstrated to be tumor-specific and immunogenic. 8 15 16 Furthermore, proteasomal splicing 17 and amino acid substitutions associated with deficiencies in translation 18 have been proposed as additional sources of HLA ligands. It is expected that once the existence of any of the above non-canonical sources will become more evident, common and thoroughly validated, they will gradually be considered and annotated as canonical.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to nsSNVs, codon reassignment during translation or translational infidelity may also lead to novel peptide sequences. 18 108 In this case, the translational alterations of interest could be introduced globally during reference protein database construction, but the canonical sequences should also be kept in the database to avoid false positive identifications caused by the lack of competition from canonical sequences 109 Out-of-frame INDELs cause frameshifts to coding sequence, which can lead to novel protein sequences. Of note, frameshift mutations frequently lead to premature termination codon (PTC), and PTC-bearing transcripts are often degraded by nonsense-mediated decay (NMD).…”

Section: Introductionmentioning

confidence: 99%

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Current perspectives on mass spectrometry-based immunopeptidomics: the computational angle to tumor antigen discovery

Zhang,

Bassani-Sternberg

2023

J Immunother Cancer

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Identification of tumor antigens presented by the human leucocyte antigen (HLA) molecules is essential for the design of effective and safe cancer immunotherapies that rely on T cell recognition and killing of tumor cells. Mass spectrometry (MS)-based immunopeptidomics enables high-throughput, direct identification of HLA-bound peptides from a variety of cell lines, tumor tissues, and healthy tissues. It involves immunoaffinity purification of HLA complexes followed by MS profiling of the extracted peptides using data-dependent acquisition, data-independent acquisition, or targeted approaches. By incorporating DNA, RNA, and ribosome sequencing data into immunopeptidomics data analysis, the proteogenomic approach provides a powerful means for identifying tumor antigens encoded within the canonical open reading frames of annotated coding genes and non-canonical tumor antigens derived from presumably non-coding regions of our genome. We discuss emerging computational challenges in immunopeptidomics data analysis and tumor antigen identification, highlighting key considerations in the proteogenomics-based approach, including accurate DNA, RNA and ribosomal sequencing data analysis, careful incorporation of predicted novel protein sequences into reference protein database, special quality control in MS data analysis due to the expanded and heterogeneous search space, cancer-specificity determination, and immunogenicity prediction. The advancements in technology and computation is continually enabling us to identify tumor antigens with higher sensitivity and accuracy, paving the way toward the development of more effective cancer immunotherapies.

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Section: Identification Of Tumor Antigensmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Current perspectives on mass spectrometry-based immunopeptidomics: the computational angle to tumor antigen discovery

Zhang,

Bassani-Sternberg

2023

J Immunother Cancer

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Proteomics to study cancer immunity and improve treatment

et al. 2023

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Cancer survival and progression depend on the ability of tumor cells to avoid immune recognition. Advances in the understanding of cancer immunity and tumor immune escape mechanisms enabled the development of immunotherapeutic approaches. In patients with otherwise incurable metastatic cancers, immunotherapy resulted in unprecedented response rates with the potential for durable complete responses. However, primary and acquired resistance mechanisms limit the efficacy of immunotherapy. Further therapeutic advances require a deeper understanding of the interplay between immune cells and tumors. Most high-throughput studies within the past decade focused on an omics characterization at DNA and RNA level. However, proteins are the molecular effectors of genomic information; therefore, the study of proteins provides deeper understanding of cellular functions. Recent advances in mass spectrometry (MS)-based proteomics at a system-wide scale may allow translational and clinical discoveries by enabling the analysis of understudied post-translational modifications, subcellular protein localization, cell signaling, and protein–protein interactions. In this review, we discuss the potential contribution of MS-based proteomics to preclinical and clinical research findings in the context of tumor immunity and cancer immunotherapies.

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Orally delivered 2D covalent organic frameworks releasing kynurenine generate anti-inflammatory T cell responses in collagen induced arthritis mouse model

Mohan Chandra Sekhar Jaggarapu,

Thumsi,

Nile

et al. 2023

Biomaterials

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Author Correction: Tryptophan depletion results in tryptophan-to-phenylalanine substitutants

Cited by 3 publications

References 0 publications

Current perspectives on mass spectrometry-based immunopeptidomics: the computational angle to tumor antigen discovery

Current perspectives on mass spectrometry-based immunopeptidomics: the computational angle to tumor antigen discovery

Proteomics to study cancer immunity and improve treatment

Orally delivered 2D covalent organic frameworks releasing kynurenine generate anti-inflammatory T cell responses in collagen induced arthritis mouse model

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