High-throughput, targeted MHC class I immunopeptidomics using a functional genetics screening platform

Bruno, Peter M.; Timms, Richard T.; Abdelfattah, Nouran S.; Leng, Yumei; Lelis, Felipe J.N.; Wesemann, Duane R.; Yu, Xu G.; Elledge, Stephen J.

doi:10.1038/s41587-022-01566-x

Cited by 12 publications

(5 citation statements)

References 97 publications

(114 reference statements)

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“…In particular, there is a disconnect between BA or EL predictions and CD8+ reactivity based on data deposited in public databases such as CEDAR. There exists a potential bias in reactivity datasets due to the use of BA/EL models to determine test candidates from called mutations [13,14,22], since these tools have biases associated with their training data acquisition methods [30,31]. There is also a significant issue in the field with possible false negatives in datasets, with highly differential reactivity between patients [32].…”

Section: Discussionmentioning

confidence: 99%

Genesis: A modular protein language modelling approach to immunogenicity prediction

O’Brien,

Salm,

Morton

et al. 2024

Preprint

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Neoantigen immunogenicity prediction is a highly challenging problem in the development of personalised medicines. Low reactivity rates in called neoantigens result in a difficult prediction scenario with limited training datasets. Here we describe Genesis, a modular protein language modelling approach to immunogenicity prediction for CD8+ reactive epitopes. Genesis comprises of a pMHC encoding module trained on three pMHC prediction tasks, an optional TCR encoding module and a set of context specific immunogenicity prediction head modules. Compared with state-of-the-art models for each task, Genesis' encoding module performs comparably or better on pMHC binding affinity, eluted ligand prediction and stability tasks. Genesis outperforms all compared models on pMHC immunogenicity prediction (Area under the receiver operating characteristic curve=0.619, average precision: 0.514), with a 7% increase in average precision compared to the next best model. Genesis shows further improved performance on immunogenicity prediction with the integration of TCR context information. Genesis performance is further analysed for interpretability, which locates areas of weakness found across existing immunogenicity models and highlight possible biases in public datasets.

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Section: Discussionmentioning

confidence: 99%

Genesis: A modular protein language modelling approach to immunogenicity prediction

O’Brien,

Salm,

Morton

et al. 2024

Preprint

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“…For the manual selection of candidates, we mainly focused on in silico HLA binding predictions but also considered RNA expression data and dissimilarity to the corresponding wildtype peptide where applicable. In the future, the success rate of targeted immunopeptidomics workflows may be further improved as the predictive power of such prioritization improves due to the continuing advancements in understanding antigen processing and presentation 46,[60][61][62] . Here, especially the growing amount of available immunopeptidomics data for rare and thus poorly investigated HLA alleles might boost the predictive power.…”

Section: Discussionmentioning

confidence: 99%

optiPRM: A targeted immunopeptidomics LC-MS workflow with ultra-high sensitivity for the detection of mutation-derived tumor neoepitopes from limited input material

Salek,

Förster,

Becker

et al. 2023

Preprint

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Personalized cancer immunotherapies such as vaccines and T cell receptor (TCR)-transgenic T cells rely on the presentation of tumor-specific peptides by human leukocyte antigen (HLA) class I molecules to cytotoxic T cells. Such neoepitopes can for example arise from somatic mutations and their identification is crucial for the rational design of new therapeutic interventions. For their detection by liquid chromatography mass spectrometry (LC-MS), we have developed a parameter optimization workflow to tune targeted assays for maximum detection sensitivity on a per peptide basis, termed optiPRM. Optimization of collision energy using optiPRM allows for improved detection of low abundant peptides that are very hard to detect using standard parameters. Applying this to immunopeptidomics, we detected a neoepitope in a patient-derived xenograft (PDX) from as little as 2.5x10 to the 6 cells input. Application of the workflow on small patient tumor samples allowed for the detection of five mutation-derived neoepitopes in three patients. One neoepitope was confirmed to be recognized by patient T cells. In conclusion, we here present optiPRM, a targeted MS workflow reaching ultra-high sensitivity by per peptide parameter optimization, which allowed for the identification of actionable neoepitopes from sample sizes usually available in the clinic.

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“…cThe HIPP has organized several high-profile webinars. The second HUPO-HIPP Summer School in Oxford, UK, in September 2022 addressed HLA-antibody specificities and their impact on sample quality, optimized workflows for improved identification of PTMs, advantages in automating sample preparation now emerging in specialized laboratories, − improved HLA-peptide acquisition technology, , and the importance of accurate and improved spectral annotation and validation strategies, , specifically in the context of tumor-specific, noncanonical peptide discovery. , The third HUPO-HIPP Summer School will take place in Montreal in August 2024. The HIPP organized a pre-HUPO Congress workshop in Korea on the noncanonical proteomea novel class of clinically targetable T cell antigens in conjunction with genome annotation and Ribo-Seq experts.…”

Section: Biology and Disease-driven Hppmentioning

confidence: 99%

The 2023 Report on the Proteome from the HUPO Human Proteome Project

Omenn,

Lane,

Overall

et al. 2024

J. Proteome Res.

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Since 2010, the Human Proteome Project (HPP), the flagship initiative of the Human Proteome Organization (HUPO), has pursued two goals: (1) to credibly identify the protein parts list and (2) to make proteomics an integral part of multiomics studies of human health and disease. The HPP relies on international collaboration, data sharing, standardized reanalysis of MS data sets by PeptideAtlas and MassIVE-KB using HPP Guidelines for quality assurance, integration and curation of MS and non-MS protein data by neXtProt, plus extensive use of antibody profiling carried out by the Human Protein Atlas. According to the neXtProt release 2023-04-18, protein expression has now been credibly detected (PE1) for 18,397 of the 19,778 neXtProt predicted proteins coded in the human genome (93%). Of these PE1 proteins, 17,453 were detected with mass spectrometry (MS) in accordance with HPP Guidelines and 944 by a variety of non-MS methods. The number of neXtProt PE2, PE3, and PE4 missing proteins now stands at 1381. Achieving the unambiguous identification of 93% of predicted proteins encoded from across all chromosomes represents remarkable experimental progress on the Human Proteome parts list. Meanwhile, there are several categories of predicted proteins that have proved resistant to detection regardless of protein-based methods used. Additionally there are some PE1–4 proteins that probably should be reclassified to PE5, specifically 21 LINC entries and ∼30 HERV entries; these are being addressed in the present year. Applying proteomics in a wide array of biological and clinical studies ensures integration with other omics platforms as reported by the Biology and Disease-driven HPP teams and the antibody and pathology resource pillars. Current progress has positioned the HPP to transition to its Grand Challenge Project focused on determining the primary function(s) of every protein itself and in networks and pathways within the context of human health and disease.

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High-throughput, targeted MHC class I immunopeptidomics using a functional genetics screening platform

Cited by 12 publications

References 97 publications

Genesis: A modular protein language modelling approach to immunogenicity prediction

Genesis: A modular protein language modelling approach to immunogenicity prediction

optiPRM: A targeted immunopeptidomics LC-MS workflow with ultra-high sensitivity for the detection of mutation-derived tumor neoepitopes from limited input material

The 2023 Report on the Proteome from the HUPO Human Proteome Project

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