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
Authors: Shapiro Ilja E. , Raess Luca , Tognetti Marco , Temu Tikira , Bernhardt Oliver M. , Feng Yuehan , Bruderer Roland , Reiter Lukas 
Published: June 2022

Abstract
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.