Biology subjects

Wilhelm, M.

Publications and source records attributed to Wilhelm, M..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

To fly, or not to fly, that is the question: A deep learning model for peptide detectability prediction in mass spectrometry

Identifying detectable peptides, known as flyers, is key in mass spectrometry-based proteomics. Peptide detectability is strongly related with the peptide sequence and its resulting physicochemical properties. Moreover, the high variability in MS data, particularly in peptide detectability and intensity across multiple analyses and samples, makes the development of a generic model for detectability prediction unfeasible. This underlines the need for tools that can be refined for specific experimental conditions. To address this need, we present Pfly, a deep learning model developed to predicts peptide detectability based solely on peptide sequence. Pfly distinguishes itself as a versatile and reliable state-of-the-art tool, offering high performance, accessibility, and easy customizability for end-users. This adaptability allows researchers to tailor the model to their specific experimental conditions, facilitating the creation of lab-specific models. This, in turn, can lead to more accurate results and expand the models applicability across various research fields. The models architecture is an encoder-decoder with an attention mechanism. This tool classifies peptides as either flyers or non-flyers, providing both binary probabilities and detailed categorical probabilities for four distinct classes defined in this study: non-flyer, weak flyer, intermediate flyer, and strong flyer. The model was initially trained on a synthetic peptide library and subsequently fine-tuned with a biological dataset to mitigate bias towards synthesizability, improving the predictive capacity and outperforming state-of-the-art predictors in a benchmark comparison. The study further investigates the influence of protein abundance and the search engine, illustrating the negative impact on peptide identification due to misclassification. Pfly has been integrated in the DLOmix framework and it is accessible on GitHub at https://github.com/wilhelm-lab/dlomix.

bioinformatics↗

bioRxiv2024

Deep Learning Enhances Precision of Citrullination Identification in Human and Plant Tissue Proteomes

Citrullination is a critical yet understudied post-translational modification (PTM) implicated in various biological processes. Exploring its role in health and disease requires a comprehensive understanding of the prevalence of this PTM at a proteome-wide scale. Although mass spectrometry has enabled the identification of citrullination sites in complex biological samples, it faces significant challenges, including limited enrichment tools and a high rate of false positives due to the identical mass with deamidation (+0.9840 Da) and errors in monoisotopic ion selection. These issues often necessitate manual spectrum inspection, reducing throughput in large-scale studies. In this work, we present a novel data analysis pipeline that incorporates the deep learning model Prosit-Cit into the MS database search workflow to improve both the sensitivity and precision of citrullination site identification. Prosit-Cit, an extension of the existing Prosit model, has been trained on [~]53,000 spectra from [~]2,500 synthetic citrullinated peptides and provides precise predictions for chromatographic retention time and fragment ion intensities of both citrullinated and deamidated peptides. This enhances the accuracy of identification and reduces false positives. Our pipeline demonstrated high precision on the evaluation dataset, recovering the majority of known citrullination sites in human tissue proteomes and improving sensitivity by identifying up to 14 times more citrullinated sites. Sequence motif analysis revealed consistency with previously reported findings, validating the reliability of our approach. Furthermore, extending the pipeline to a tissue proteome dataset of the model plant Arabidopsis thaliana enabled the identification of [~]200 citrullination sites across 169 proteins from 30 tissues, representing the first large-scale citrullination mapping in plants. This pipeline can be seamlessly applied to existing proteomics datasets, offering a robust tool for advancing biological discoveries and deepening our understanding of protein citrullination across species.

bioinformatics↗

bioRxiv2024

Revisiting sour rot of grapevine through disease-associated microbiomes: a tripartite co-infection?

Sour rot in grapevines is thought to result from berry infection by yeasts, acetic acid bacteria and vinegar flies. To better characterize the role of each of the actors involved in sour rot expression, we conducted experiments involving the isolation of 1593 fungi and bacteria to understand the composition and dynamics of the microbiomes associated with healthy berries, diseased berries and insect vectors. As some grape clusters became symptomatic for sour rot in the absence of acetic acid bacteria, the latter might not necessarily be needed for disease expression. Similar to other yeast genera, the yeast genus Geotrichum is here also reported for the first time to be able to initiate sour rot in grapes, however, this finding has to be confirmed by further studies. By allowing or denying the access of insect vectors to intact or artificially wounded grapes we could emphasize that they accelerate the expression of the disease when berries are injured. Moreover, the microbial communities identified on native vinegar flies (Drosophila spp.) and the introduced Drosophila suzukii were similar and yeast dominated. This highlights the key role of these insect vectors in the transmission of microorganisms inflicting sour rot to wounded berries. Finally, our data also suggest that sour rot and grey mould can coexist in a vineyard at an advanced stage of grape decomposition, which runs counter to recent studies that emphasize the supremacy of sour rot over grey mould. IMPORTANCEThis study sheds new light on the complex interactions between microbiomes, insect vectors and physical factors favoring the development of sour rot. While previous studies suggested that acetic acid bacteria were mandatory for sour rot expression our results suggest that grape sour rot could also result solely from yeast infection. Moreover, native vinegar flies and the introduced D. suzukii host and vectorize a similar microbial community to injured berries in vineyards and their presence accelerates the infection process and consequently the expression of sour rot.

pathology↗

bioRxiv2024

Koina: Democratizing machine learning for proteomics research

Recent developments in machine-learning (ML) and deep-learning (DL) have immense potential for applications in proteomics, such as generating spectral libraries, improving peptide identification, and optimizing targeted acquisition modes. Although new ML/DL models for various applications and peptide properties are frequently published, the rate at which these models are adopted by the community is slow, which is mostly due to technical challenges. We believe that, for the community to make better use of state-of-the-art models, more attention should be spent on making models easy to use and accessible by the community. To facilitate this, we developed Koina, an open-source containerized, decentralized and online-accessible high-performance prediction service that enables ML/DL model usage in any pipeline. Using the widely used FragPipe computational platform as example, we show how Koina can be easily integrated with existing proteomics software tools and how these integrations improve data analysis.

bioinformatics↗

bioRxiv2024

Unifying the analysis of bottom-up proteomics data with CHIMERYS

Proteomic workflows generate vastly complex peptide mixtures that are analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS), creating thousands of spectra, most of which are chimeric and contain fragment ions from more than one peptide. Because of differences in data acquisition strategies such as data-dependent (DDA), data-independent (DIA) or parallel reaction monitoring (PRM), separate software packages employing different analysis concepts are used for peptide identification and quantification, even though the underlying information is principally the same. Here, we introduce CHIMERYS, a novel, spectrum-centric search algorithm designed for the deconvolution of chimeric spectra that unifies proteomic data analysis. Using accurate predictions of peptide retention time, fragment ion intensities and applying regularized linear regression, it explains as much fragment ion intensity as possible with as few peptides as possible. Together with rigorous false discovery rate control, CHIMERYS accurately identifies and quantifies multiple peptides per tandem mass spectrum in DDA, DIA and PRM experiments.

biochemistry↗

bioRxiv2024

IS-PRM-based peptide targeting informed by long-read sequencing for alternative proteome detection

Alternative splicing is a major contributor of transcriptomic complexity, but the extent to which transcript isoforms are translated into stable, functional protein isoforms is unclear. Furthermore, detection of relatively scarce isoform-specific peptides is challenging, with many protein isoforms remaining uncharted due to technical limitations. Recently, a family of advanced targeted MS strategies, termed internal standard parallel reaction monitoring (IS-PRM), have demonstrated multiplexed, sensitive detection of pre-defined peptides of interest. Such approaches have not yet been used to confirm existence of novel peptides. Here, we present a targeted proteogenomic approach that leverages sample-matched long-read RNA sequencing (LR RNAseq) data to predict potential protein isoforms with prior transcript evidence. Predicted tryptic isoform-specific peptides, which are specific to individual gene product isoforms, serve as "triggers" and "targets" in the IS-PRM method, Tomahto. Using the model human stem cell line WTC11, LR RNAseq data were generated and used to inform the generation of synthetic standards for 192 isoform-specific peptides (114 isoforms from 55 genes). These synthetic "trigger" peptides were labeled with super heavy tandem mass tags (TMT) and spiked into TMT-labeled WTC11 tryptic digest, predicted to contain corresponding endogenous "target" peptides. Compared to DDA mode, Tomahto increased detectability of isoforms by 3.6-fold, resulting in the identification of five previously unannotated isoforms. Our method detected protein isoform expression for 43 out of 55 genes corresponding to 54 resolved isoforms. This LR RNA seq-informed Tomahto targeted approach, called LRP-IS-PRM, is a new modality for generating protein-level evidence of alternative isoforms - a critical first step in designing functional studies and eventually clinical assays.

molecular biology↗

bioRxiv2024

Base editing of Ptbp1 in neurons alleviates symptoms in a mouse model for Parkinson's disease

Parkinsons disease (PD) is a multifactorial disease caused by irreversible progressive loss of dopaminergic neurons (DANs). Recent studies have reported successful conversion of astrocytes into DANs by repressing polypyrimidine tract binding protein 1 (PTBP1), which led to the rescue of motor symptoms in a chemically-induced mouse model of PD. However, several follow-up studies have questioned the validity of this astrocyte to DAN conversion model. In this study, we devised an adenine base editing strategy to downregulate PTBP1 in astrocytes and neurons in a chemically-induced PD mouse model. While PTBP1 downregulation in astrocytes had no effect, we observed that PTBP1 downregulation in neurons of the substantia nigra pars compacta and striatum resulted in the expression of the DAN marker tyrosine hydroxylase (TH) in non-dividing neurons, which was associated with an increase in striatal dopamine concentrations and a rescue of forelimb akinesia and spontaneous rotations. Phenotypic analysis using multiplexed iterative immunofluorescence imaging further revealed that most of the TH-positive cells in the striatum co-expressed the dopaminergic marker DAT and the pan-neuronal marker NEUN, with the majority of these triple-positive cells being classified as mature GABAergic neurons. Additional research is needed to fully elucidate the molecular mechanisms underlying the expression of the observed markers and understand how the formation of these cells contributes to the rescue of spontaneous motor behaviors. Nevertheless, our findings support a model where neuronal, but not astrocytic, downregulation of PTBP1 can mitigate symptoms in PD mice.

neuroscience↗

bioRxiv2024

Evaluating Bacterial Viability in Faecal Microbiota Transplantation: A Comparative Analysis of In Vitro Cultivation and Membrane Integrity Methods

BackgroundFaecal microbiota transplantation (FMT) is a developing therapy for disorders related to gut dysbiosis. Despite its growing application, standardized protocols for FMT filtrate preparation and quality assessment remain undeveloped. The viability of bacteria in the filtrate is crucial for FMTs efficacy and for validating protocol execution. We compared two methods--in vitro cultivation and membrane integrity assessment--for their accuracy, reproducibility, and clinical applicability in measuring bacterial viability in frozen FMT stool filtrate. MethodsBacterial viability in stool filtrate was evaluated using (i) membrane integrity through fluorescent DNA staining with SYTO9 and propidium iodide, followed by flow cytometry; and (ii) culturable bacteria counts (colony-forming units, CFU) under aerobic or anaerobic conditions. ResultsWe refined the bacterial DNA staining protocol integrated with flow cytometry for stool samples. Both the membrane integrity-based and cultivation-based methods exhibited significant variability in bacterial viability across different FMT filtrates, without correlation. The cultivation-based method showed a mean coefficient of variance of 17%, ranging from 5.3% to 52.9%. Conversely, the membrane integrity approach yielded highly reproducible results, with a median coefficient of variance for viable cells of 0.9%, ranging from 8.5% to 0.04%. ConclusionBacterial viability assessment using cultivation-dependent methods produces inconsistent outcomes. In contrast, the membrane integrity method offers robust and precise data, making it a viable option for routine faecal material evaluation in FMT.

microbiology↗

bioRxiv2023

Structural implications of BK polyomavirus sequence variations in the major viral capsid protein Vp1 and large T-antigen: a computational study

BK polyomavirus (BKPyV) is a double-stranded DNA virus causing nephropathy, hemorrhagic cystitis, and urothelial cancer in transplant patients. The BKPyV-encoded capsid protein Vp1 and large T-antigen (LTag) are key targets of neutralizing antibodies and cytotoxic T-cells, respectively. Our single-center data suggested that variability in Vp1 and LTag may contribute to failing BKPyV-specific immune control, and impact vaccine design. We therefore analyzed all available entries in GenBank (1516 VP1; 742 LTAG) and explored potential structural effects using computational approaches. BKPyV-genotype (gt)1 was found in 71.18% of entries, followed by BKPyV-gt4 (19.26%), BKPyV-gt2 (8.11%) and BKPyV-gt3 (1.45%), but rates differed according to country and specimen type. Vp1-mutations matched a serotype different than the assigned one or were serotype-independent in 43%, 18% affected more than one amino acid. Notable Vp1-mutations altered antibody-binding domains, interactions with sialic acid receptors, or were predicted to change conformation. LTag-sequences were more conserved, with only 16 mutations detectable in more than one entry and without significant effects on LTag-structure or interaction domains. However, LTag changes were predicted to affect HLA-class I presentation of immunodominant 9mers to cytotoxic T-cells. These global data strengthen single center observations and specifically our earlier findings revealing mutant 9mer epitopes conferring immune escape from HLA-I cytotoxic T cells. We conclude that variability of BKPyV-Vp1 and LTag may have important implications for diagnostic assays assessing BKPyV-specific immune control and for vaccine design. IMPORTANCEType and rate of amino acid variations in BKPyV may provide important insights into BKPyV diversity in human populations and an important step towards defining determinants of BKPyV-specific immunity needed to protect vulnerable patients from BKPyV diseases. Our analysis of BKPyV sequences obtained from human specimens reveals an unexpectedly high genetic variability for this double-stranded DNA virus that strongly relies on host cell DNA replication machinery with its proof reading and error correction mechanisms. BKPyV variability and immune escape should be taken into account when designing further approaches to antivirals, monoclonal antibodies and vaccines for patients at risk of BKPyV diseases.

microbiology↗

bioRxiv2023

PTMNavigator: Interactive Visualization of Differentially Regulated Post-Translational Modifications in Cellular Signaling Pathways

Post-translational modifications (PTMs) play pivotal roles in regulating cellular signaling, fine-tuning protein function, and orchestrating complex biological processes. Despite their importance, the lack of comprehensive tools for studying PTMs from a pathway-centric perspective has limited our ability to understand how PTMs modulate cellular pathways on a molecular level. Here, we present PTMNavigator, a tool integrated into the ProteomicsDB platform, which offers an interactive interface for researchers to overlay experimental PTM data with pathway diagrams. PTMNavigator provides [~]3000 canonical pathways from manually curated databases and further enables users to modify and create custom diagrams, tailored to their data. Additionally, PTMNavigator automatically runs multiple kinase and pathway enrichment algorithms whose results are directly integrated into the visualization. This offers a comprehensive view of the intricate relationship between PTMs and signaling pathways. To demonstrate the utility of PTMNavigator, we applied it to two phosphoproteomics perturbation datasets. First, PTMNavigator enhanced pathway enrichment analysis by showing how the regulated peptides and proteins are distributed in the pathways with high enrichment scores. Second, it visualized how drug treatments result in a discernable flow of PTM-driven signaling within pathways. Third, PTMNavigator aided in proposing extensions to an existing pathway by suggesting putative new links between both PTMs and pathway components. By enhancing our understanding of cellular signaling dynamics and facilitating the discovery of novel PTM-pathway interactions, PTMNavigator advances our knowledge of PTM biology and its implications in health and disease.

systems biology↗

bioRxiv2023

Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in TimsTOF

Immunopeptidomics plays a crucial role in identifying targets for immunotherapy and vaccine development. Because the generation of immunopeptides from their parent proteins does not adhere to clear-cut rules, rather than being able to use known digestion patterns, every possible protein subsequence within human leukocyte antigen (HLA) class-specific length restrictions needs to be considered during sequence database searching. This leads to an inflation of the search space and results in lower spectrum annotation rates. Peptide-spectrum match (PSM) rescoring is a powerful enhancement of standard searching that boosts the spectrum annotation performance. Low abundant peptides often occur in the field of immunopeptidomics, which is why the highly sensitive timsTOF instruments are increasingly gaining popularity. To improve PSM rescoring for immunopeptides measured using timsTOF instruments, we trained a deep learning-based fragment ion intensity prediction model. 302,105 unique synthesized non-tryptic peptides from the ProteomeTools project were analyzed on a timsTOF-Pro to generate a ground-truth dataset, containing 93,227 MS/MS spectra of 74,847 unique peptides, that was used to fine-tune an existing Prosit model. By applying our fragment ion intensity prediction model, we demonstrate up to 3-fold improvement in the identification of immunopeptides. Furthermore, our approach increased detection of immunopeptides even from low input samples.

bioinformatics↗

bioRxiv2023

Stimulation of VTA dopamine inputs to LH upregulates orexin neuronal activity in a DRD2-dependent manner

Dopamine and orexins (hypocretins) play important roles in regulating reward-seeking behaviors. It is known that hypothalamic orexinergic neurons project to dopamine neurons in the ventral tegmental area (VTA), where they can stimulate dopaminergic neuronal activity. Although there are reciprocal connections between dopaminergic and orexinergic systems, whether and how dopamine regulates the activity of orexin neurons is currently not known. Here we implemented an opto-Pavlovian task in which mice learn to associate a sensory cue with optogenetic dopamine neuron stimulation to investigate the relationship between dopamine release and orexin neuron activity in the LH. We found that dopamine release can be evoked in LH upon optogenetic stimulation of VTA dopamine neurons, and is also naturally evoked by cue presentation after opto-Pavlovian learning. Furthermore, orexin neuron activity could also be upregulated by local stimulation of dopaminergic terminals in the LH in a way that is partially dependent on dopamine D2 receptors (DRD2). Our results reveal previously unknown orexinergic coding of reward expectation and unveil an orexin-regulatory axis mediated by local dopamine inputs in the LH. HighlightsO_LIOptical VTA DA neuron stimulation is sufficient to elicit a Pavlovian-like dopamine transient in the NAc C_LIO_LIDopamine in the LH encodes both negative and positive reward prediction errors C_LIO_LIDopamine in the LH positively modulates orexin neuronal activity locally in a D2R dependent way C_LI

neuroscience↗

bioRxiv2023

A systemic approach allows to identify the pedoclimatic conditions most critical in the susceptibility of a grapevine cultivar to esca/Botryosphaeria dieback

Esca and/or Botryosphaeria dieback (esca-BD) are two of the most destructive grapevine trunk diseases in the world, disease complex which remains poorly understood. As some vine cultivars show highly variable susceptibility to esca-BD, we designed a four-year experiment to identify which environmental factors influence the expression of the disease. We collected epidemiological and physiological data once a year for four consecutive years in 19 vineyard plots located in four wine-growing regions of Western Switzerland. We compared these data with climatic data obtained from weather stations for these same plots for four years and over the long term. We also estimated the soil water holding capacity of each plot. Confounding factors were minimal because all vineyards were planted in 2003 with the same cultivar and all plants grafted in the same nursery with genetically homogeneous grafting material. Principal component and regression analyses of combined epidemiological, biotic and pedoclimatic data identified a positive correlation between soil water retention capacity and plant mortality due to esca-BD. These analyses also showed that leaf disease symptoms and apoplexy are more frequent when cold, wet periods are followed - or alternate with - hot, dry periods, and that apoplexy occurs more frequently when weather conditions change abruptly (cold, wet May followed by a hot June) and deviate significantly from long-term climatic conditions. Regression analyses show that the soil water holding capacity impacts less the disease expression when the climate is warm and dry, both at the regional and at year-specific levels. Having identified the most important environmental factors towards expression of esca-BD, this study allows recommendations to be given to the winegrowers for the cultivar studied but can also be used as a model to identify the environmental factors that influence the expression of fungal diseases in other grapevine cultivars, other grapevine trunk diseases and even in other woody plants.

plant biology↗

bioRxiv2023

Prime editing of the beta-1 adrenoceptor in the brain reprograms mouse behavior

Prime editing is a highly versatile genome editing technology that holds great potential for treating genetic diseases1, 2. While in vivo prime editing has recently been conducted in the brain, liver, heart, and retina3-6, application of this technology to modulate neural circuits in the brain has not been reported yet. Here, we employ adeno-associated viral vectors to deliver optimized intein-split prime editors into the brain of mice. Delivery into newborn pups via intracerebroventricular injection resulted in up to 44.0% editing at the Dnmt1 locus in the cortex (on average 34.8{+/-}9.8% after 6 months). In addition, we obtained up to 28.1% editing at the Adrb1 locus in the cortex (on average 14.7{+/-}11.6% after 6 months). The introduced Adrb1A187V mutation is a naturally occurring variant of the {beta}1-adrenergic receptor, which has previously been linked to increased activity and natural short sleep7. Similarly, we observed an increase in the activity and exploratory behavior of treated animals. This study demonstrates the potential of prime editing for treating genetic diseases in the central nervous system and for reprogramming molecular pathways that modulate animal behavior.

genetics↗

bioRxiv2023

Rheological behavior of Pluronic/Pluronic diacrylate hydrogels used for bacteria encapsulation in living materials

Pluronic (Plu) hydrogels containing Pluronic diacrylate (PluDA) have become popular matrices to encapsulate bacteria in engineered living materials. For this purpose, 30 wt% Plu/PluDA hydrogels with variable fraction of covalently crosslinkable PluDA in the hydrogel composition are used. The degree of covalent crosslinking and the consequent different mechanical properties of the hydrogels have been shown to affect bacteria growth, but a systematic investigation of the mechanical response of the hydrogels is still missing. Here we study the rheological response of 30 wt.% Plu/PluDA hydrogels with increasing PluDA fraction between 0 and 1. We quantify the range of viscoelastic properties that can be covered in this system by varying in the PluDA fraction. We present stress relaxation and creep-recovery experiments, and analyze the variation of the critical yield strain/stress, relaxation and recovery parameters of Plu/PluDA hydrogels as function of the covalent crosslinking degree using the Burgers and Weilbull models. We expect this study to help users of Plu/PluDA hydrogels to estimate the mechanical properties of their systems, and eventually to correlate them with the behaviour of bacteria in future Plu/PluDA devices of similar composition.

bioengineering↗

bioRxiv2023

Real-time spectral library matching for sample multiplexed quantitative proteomics.

Sample multiplexed quantitative proteomics has proved to be a highly versatile means to assay molecular phenotypes. Yet, stochastic precursor selection and precursor co-isolation can dramatically reduce the efficiency of data acquisition and quantitative accuracy. To address this, intelligent data acquisition (IDA) strategies have recently been developed to improve instrument efficiency and quantitative accuracy for both discovery and targeted methods. Towards this end, we sought to develop and implement a new real-time library searching (RTLS) workflow that could enable intelligent scan triggering and peak selection within milliseconds of scan acquisition. To ensure ease of use and general applicability, we built an application to read in diverse spectral libraries and file types from both empirical and predicted spectral libraries. We demonstrate that RTLS methods enable improved quantitation of multiplexed samples, particularly with consideration for quantitation from chimeric fragment spectra. We used RTLS to profile proteome responses to small molecule perturbations and were able to quantify up to 15% more significantly regulated proteins in half the gradient time as traditional methods. Taken together, the development of RTLS expands the IDA toolbox to improve instrument efficiency and quantitative accuracy in sample multiplexed analyses.

cell biology↗

bioRxiv2023

Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing

Unlike for DNA and RNA, accurate and high-throughput sequencing methods for proteins are lacking, hindering the utility of proteomics in applications where the sequences are unknown including variant calling, neoepitope identification, and metaproteomics. We introduce Spectralis, a new de novo peptide sequencing method for tandem mass spectrometry. Spectralis leverages several innovations including a new convolutional neural network layer connecting peaks in spectra spaced by amino acid masses, proposing fragment ion series classification as a pivotal task for de novo peptide sequencing, and a new peptide-spectrum confidence score. On spectra for which database search provided a ground truth, Spectralis surpassed 40% sensitivity at 90% precision, nearly doubling state-of-the-art sensitivity. Application to unidentified spectra confirmed its superiority and showcased its applicability to variant calling. Altogether, these algorithmic innovations and the substantial sensitivity increase in the high-precision range constitute an important step toward broadly applicable peptide sequencing.

bioinformatics↗

bioRxiv2022

Proteogenomic analysis reveals RNA as an important source for tumor-agnostic neoantigen identification correlating with T-cell infiltration

Systemic pan-tumor analyses may reveal the significance of common features implicated in cancer immunogenicity and patient survival. Here, we provide a comprehensive multi-omics data set for 32 patients across 25 tumor types by combining proteogenomics with phenotypic and functional analyses. By using an optimized computational approach, we discovered a large number of novel tumor-specific and tumor-associated antigens including shared common target candidates. To create a pipeline for the identification of neoantigens in our cohort, we combined deep DNA and RNA sequencing with MS- based immunopeptidomics of tumor specimens, followed by the assessment of their immunogenicity. In fact, we could detect a broad variety of non-wild type HLA-binding peptides in the majority of patients and confirmed the immunogenicity of 24 neoantigens. Most interestingly, the majority of total and immunogenic neoantigens originated from variants identified in the RNA dataset, illustrating the importance of RNA as a still understudied source of cancer antigens. Moreover, the amount of these mainly RNA-based immunogenic neoantigens correlated positively with overall CD8+ tumor-infiltrating T cells. This study therefore underlines the importance of RNA-centered variant detection for the identification of shared biomarkers and potentially relevant neoantigen candidates. Statement of significanceThe significance of this study lies not only in the potential of our optimized proteogenomic workflow for the discovery of neoantigens (in particular RNA-derived neoantigens) for clinical application, but sheds light on the entity-agnostic prevalence of HLA class I peptide presentation of RNA processing events to be used for tumor targeting.

cancer biology↗