Abstract 1374: Discovery of MHC class I and class II neoantigens in lung cancer in needle biopsy tissue samples using an optimized high-throughput workflow

Human leukocyte antigen-associated peptides, known as immunopeptides, play an essential role in adaptive immunity by activating and ensuring the specificity of T-cells. The identification and quantification of the immunopeptidome bear the potential to enable personalized treatments, especially in cancers, vaccines, infectious, and autoimmune diseases. Mass spectrometry is currently the only technology that can reliably measure and identify immunopeptide profiles of biological samples on a large scale. However, the usually high sample input amount and poor scalability are limiting. Here, we introduce a semi-automated workflow to robustly identify immunopeptides from low amounts of cultured cells and tissue samples by systematically optimizing each step of the sample preparation and acquisition. We optimized the native lysis and immunoprecipitation workflow while ensuring scalability and reproducibility. Leveraging the magnetic properties of the beads, 1,000 samples can be processed within a week by a single operator. The established sample preparation offers high reproducibility and identifications of good quality. For class-I immunopeptides, &gt60% of the peptides identified are 9-mers, &gt80% predicted strong binders, and the expected amino acids are enriched at the anchor positions. For class-II, &gt50% of the peptides identified are 14-to-16-mers, and &gt50% are predicted strong binders. Furthermore, the pipeline is highly sensitive as we could still identify over 2,800 class-I immunopeptides when processing as little as 2.5 mg fresh frozen tissue and &gt9,000 class-I and &gt12,000 class-II immunopeptides when preparing 10 million JY cells. Overall, the pipeline is scalable, highly reproducible, and results in high-quality identifications while supporting very limited sample input. Finally, we measured a cohort of 12 cancerous and matched healthy lung tissues from as little as 15 mg tissue, whereby we could identify &gt11,000 class-I immunopeptides and &gt9,000 class-II on average. For class-I, matched samples clustered together, while &gt3,000 immunopeptides were upregulated in the cancer tissues, with a significant enrichment for proteins related to lung cancer. Overall, we established a scalable, efficient pipeline for cell line and tissues immunopeptidomics for class-I and II that generates high-quality identifications and that only requires small amounts of input material and is ready to shed light into immunopeptidomics heterogeneity through large-scale profiling of patients.

Citation Format: Ilja E. Shapiro, Luca Raess, Marco Tognetti, Tikira Temu, Oliver M. Bernhardt, Yuehan Feng, Roland Bruderer, Lukas Reiter. Discovery of MHC class I and class II neoantigens in lung cancer in needle biopsy tissue samples using an optimized high-throughput workflow [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1374.

The MaxQuant computational platform for mass spectrometry-based shotgun proteomics

MaxQuant is one of the most frequently used platforms for mass-spectrometry (MS)-based proteomics data analysis. Since its first release in 2008, it has grown substantially in functionality and can be used in conjunction with more MS platforms. Here we present an updated protocol covering the most important basic computational workflows, including those designed for quantitative label-free proteomics, MS1-level labeling and isobaric labeling techniques. This protocol presents a complete description of the parameters used in MaxQuant, as well as of the configuration options of its integrated search engine, Andromeda. This protocol update describes an adaptation of an existing protocol that substantially modifies the technique. Important concepts of shotgun proteomics and their implementation in MaxQuant are briefly reviewed, including different quantification strategies and the control of false-discovery rates (FDRs), as well as the analysis of post-translational modifications (PTMs). The MaxQuant output tables, which contain information about quantification of proteins and PTMs, are explained in detail. Furthermore, we provide a short version of the workflow that is applicable to data sets with simple and standard experimental designs. The MaxQuant algorithms are efficiently parallelized on multiple processors and scale well from desktop computers to servers with many cores. The software is written in C# and is freely available at http://www.maxquant.org.

Homology-driven assembly of NOn-redundant protEin sequence sets (NOmESS) for mass spectrometry

To enable mass spectrometry (MS)-based proteomic studies with poorly characterized organisms, we developed a computational workflow for the homology-driven assembly of a non-redundant reference sequence dataset. In the automated pipeline, translated DNA sequences (e.g. ESTs, RNA deep-sequencing data) are aligned to those of a closely related and fully sequenced organism. Representative sequences are derived from each cluster and joined, resulting in a non-redundant reference set representing the maximal available amino acid sequence information for each protein. We here applied NOmESS to assemble a reference database for the widely used model organism Xenopus laevis and demonstrate its use in proteomic applications.

AVAILABILITY AND IMPLEMENTATION

NOmESS is written in C#. The source code as well as the executables can be downloaded from http://www.biochem.mpg.de/cox Execution of NOmESS requires BLASTp and cd-hit in addition.

CONTACT

cox@biochem.mpg.de

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

DNA repair. Proteomics reveals dynamic assembly of repair complexes during bypass of DNA cross-links

DNA interstrand cross-links (ICLs) block replication fork progression by inhibiting DNA strand separation. Repair of ICLs requires sequential incisions, translesion DNA synthesis, and homologous recombination, but the full set of factors involved in these transactions remains unknown. We devised a technique called chromatin mass spectrometry (CHROMASS) to study protein recruitment dynamics during perturbed DNA replication in Xenopus egg extracts. Using CHROMASS, we systematically monitored protein assembly and disassembly on ICL-containing chromatin. Among numerous prospective DNA repair factors, we identified SLF1 and SLF2, which form a complex with RAD18 and together define a pathway that suppresses genome instability by recruiting the SMC5/6 cohesion complex to DNA lesions. Our study provides a global analysis of an entire DNA repair pathway and reveals the mechanism of SMC5/6 relocalization to damaged DNA in vertebrate cells.

Visualization of LC-MS/MS proteomics data in MaxQuant

Modern software platforms enable the analysis of shotgun proteomics data in an automated fashion resulting in high quality identification and quantification results. Additional understanding of the underlying data can be gained with the help of advanced visualization tools that allow for easy navigation through large LC‐MS/MS datasets potentially consisting of terabytes of raw data. The updated MaxQuant version has a map navigation component that steers the users through mass and retention time‐dependent mass spectrometric signals. It can be used to monitor a peptide feature used in label‐free quantification over many LC‐MS runs and visualize it with advanced 3D graphic models. An expert annotation system aids the interpretation of the MS/MS spectra used for the identification of these peptide features.