Is the Immunopeptidome Getting Darker?: A Commentary on the Discussion around Mishto et al., 2019

Purcell, Anthony W.

doi:10.3389/fimmu.2021.720811

Cited by 20 publications

(17 citation statements)

References 52 publications

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“…33 Specifically, we observed overlaps in predictions of ADHD and ASD as well as BD, MDD, and SCZ, thereby highlighting the known overlaps in preassessment predictors such as earlier diagnoses, genetics, and family history between the disorders. 34,35 Combining high-dimensional genetic data with clinical records increased prediction accuracy by 2% to 11% in our study. However, the overall low decrease in accuracy for each data set shows that the different data modalities have a high overlap in predictive information.…”

Section: Discussionmentioning

confidence: 55%

Deep Learning for Cross-Diagnostic Prediction of Mental Disorder Diagnosis and Prognosis Using Danish Nationwide Register and Genetic Data

et al. 2023

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ImportanceDiagnoses and treatment of mental disorders are hampered by the current lack of objective markers needed to provide a more precise diagnosis and treatment strategy.ObjectiveTo develop deep learning models to predict mental disorder diagnosis and severity spanning multiple diagnoses using nationwide register data, family and patient-specific diagnostic history, birth-related measurement, and genetics.Design, Setting, and ParticipantsThis study was conducted from May 1, 1981, to December 31, 2016. For the analysis, which used a Danish population-based case-cohort sample of individuals born between 1981 and 2005, genotype data and matched longitudinal health register data were taken from the longitudinal Danish population-based Integrative Psychiatric Research Consortium 2012 case-cohort study. Included were individuals with mental disorders (attention-deficit/hyperactivity disorder [ADHD]), autism spectrum disorder (ASD), major depressive disorder (MDD), bipolar disorder (BD), schizophrenia spectrum disorders (SCZ), and population controls. Data were analyzed from February 1, 2021, to January 24, 2022.ExposureAt least 1 hospital contact with diagnosis of ADHD, ASD, MDD, BD, or SCZ.Main Outcomes and MeasuresThe predictability of (1) mental disorder diagnosis and (2) severity trajectories (measured by future outpatient hospital contacts, admissions, and suicide attempts) were investigated using both a cross-diagnostic and single-disorder setup. Predictive power was measured by AUC, accuracy, and Matthews correlation coefficient (MCC), including an estimate of feature importance.ResultsA total of 63 535 individuals (mean [SD] age, 23 [7] years; 34 944 male [55%]; 28 591 female [45%]) were included in the model. Based on data prior to diagnosis, the specific diagnosis was predicted in a multidiagnostic prediction model including the background population with an overall area under the curve (AUC) of 0.81 and MCC of 0.28, whereas the single-disorder models gave AUCs/MCCs of 0.84/0.54 for SCZ, 0.79/0.41 for BD, 0.77/0.39 for ASD, 0.74/0.38, for ADHD, and 0.74/0.38 for MDD. The most important data sets for multidiagnostic prediction were previous mental disorders and age (11%-23% reduction in prediction accuracy when removed) followed by family diagnoses, birth-related measurements, and genetic data (3%-5% reduction in prediction accuracy when removed). Furthermore, when predicting subsequent disease trajectories of the disorder, the most severe cases were the most easily predictable, with an AUC of 0.72.Conclusions and RelevanceResults of this diagnostic study suggest the possibility of combining genetics and registry data to predict both mental disorder diagnosis and disorder progression in a clinically relevant, cross-diagnostic setting prior to clinical assessment.

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Section: Discussionmentioning

confidence: 55%

Deep Learning for Cross-Diagnostic Prediction of Mental Disorder Diagnosis and Prognosis Using Danish Nationwide Register and Genetic Data

et al. 2023

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“…Despite this seminal evidence of the immunological relevance of spliced peptides in HLA‐I immunopeptidomes, their frequency is still a controversial issue. After the publication of the first method for the identification of cis ‐spliced peptides in HLA‐I immunopeptidomes (Liepe et al., 2016 ), other groups published alternative methods and obtained contrasting results about their frequency (Admon, 2021 ; Faridi et al., 2021 ; Mishto, 2020 ; Purcell, 2021 ). Two recent studies re‐analysed the list of cis ‐spliced peptides, which we previously identified in HLA‐I spliced immunopeptidomes using Spliced Peptide Identifier (SPI) and SPI‐delta methods (Liepe et al., 2016 , 2019 ), by using PEAKS X software.…”

Section: Introductionmentioning

confidence: 99%

Database search engines and target database features impinge upon the identification of post‐translationally cis‐spliced peptides in HLA class I immunopeptidomes

Mishto

Horokhovskyi²,

Cormican³

et al. 2022

Proteomics

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Unconventional epitopes presented by HLA class I complexes are emerging targets for T cell targeted immunotherapies. Their identification by mass spectrometry (MS) required development of novel methods to cope with the large number of theoretical candidates. Methods to identify post‐translationally spliced peptides led to a broad range of outcomes. We here investigated the impact of three common database search engines – that is, Mascot, Mascot+Percolator, and PEAKS DB – as final identification step, as well as the features of target database on the ability to correctly identify non‐spliced and cis‐spliced peptides. We used ground truth datasets measured by MS to benchmark methods’ performance and extended the analysis to HLA class I immunopeptidomes. PEAKS DB showed better precision and recall of cis‐spliced peptides and larger number of identified peptides in HLA class I immunopeptidomes than the other search engine strategies. The better performance of PEAKS DB appears to result from better discrimination between target and decoy hits and hence a more robust FDR estimation, and seems independent to peptide and spectrum features here investigated.

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“…However, numerous reports have shown that T cell epitopes can originate from cryptic sources, including polypeptides created by non-canonical translation [15][16][17][18][19][20][21][22][23][24] and post-translational peptide splicing [25][26][27][28] . While advancements in mass spectrometry have allowed for deeper probing of cryptic epitopes, uncertainties remain regarding the fraction of the immunopeptidome that is derived from non-canonical sources and their clinical relevance 29 . As nearly all cryptic epitopes described to date have been MHC-Ia-restricted, an additional unknown is the scope of unconventional epitope presentation on MHC-Ib and MHC class II (MHC-II).…”

Section: Introductionmentioning

confidence: 99%

Cryptic MHC-E epitope from influenza elicits a potent cytolytic T cell response

Hogan

Maheshwari

Begg

et al. 2022

Preprint

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The extent to which unconventional forms of antigen presentation drive T cell immunity is unknown. By convention, CD8 T cells recognize viral peptides in association with classical major histocompatibility complex (MHC) class I, or MHC-Ia, but immune surveillance can in some cases be directed against peptides presented by non-classical MHC-Ib, for example the MHC-E proteins (Qa-1 in mice and HLA-E in humans); however, the overall importance of non-classical responses in antiviral immunity remains unclear. Similarly uncertain is the importance of “cryptic” viral epitopes, defined as those undetectable by conventional mapping techniques. Here, we used an immunopeptidomics approach to search for unconventional epitopes that drive T cell responses in mice infected with influenza A virus (IAV). We identify a nine amino acid epitope, termed M-SL9, that drives a co-immunodominant, cytolytic CD8 T cell response that is unconventional in two ways: first, it is presented by Qa-1, and second, it maps to an alternative reading frame of the influenza matrix protein 1 (M1) mRNA and appears to derive mainly from an unannotated 16-residue peptide that is dispensable for viral replication. Presentation and immunogenicity of M-SL9 were dependent on the second AUG codon of the positive sense matrix RNA segment, supporting translation initiation by leaky ribosomal scanning. This work suggests that non-canonical translation products must be examined in order to fully reveal the T cell repertoire and adds to a growing body of evidence that MHC-E-restricted T cells may have significant therapeutic value.

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Is the Immunopeptidome Getting Darker?: A Commentary on the Discussion around Mishto et al., 2019

Cited by 20 publications

References 52 publications

Deep Learning for Cross-Diagnostic Prediction of Mental Disorder Diagnosis and Prognosis Using Danish Nationwide Register and Genetic Data

Deep Learning for Cross-Diagnostic Prediction of Mental Disorder Diagnosis and Prognosis Using Danish Nationwide Register and Genetic Data

Database search engines and target database features impinge upon the identification of post‐translationally cis‐spliced peptides in HLA class I immunopeptidomes

Cryptic MHC-E epitope from influenza elicits a potent cytolytic T cell response

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