Simultaneous targeted and discovery-driven clinical proteotyping using hybrid-PRM/DIA

BACKGROUND

Clinical samples are irreplaceable, and their transformation into searchable and reusable digital biobanks is critical for conducting statistically empowered retrospective and integrative research studies. Currently, mainly data-independent acquisition strategies are employed to digitize clinical sample cohorts comprehensively. However, the sensitivity of DIA is limited, which is why selected marker candidates are often additionally measured targeted by parallel reaction monitoring.

METHODS

Here, we applied the recently co-developed hybrid-PRM/DIA technology as a new intelligent data acquisition strategy that allows for the comprehensive digitization of rare clinical samples at the proteotype level. Hybrid-PRM/DIA enables enhanced measurement sensitivity for a specific set of analytes of current clinical interest by the intelligent triggering of multiplexed parallel reaction monitoring (MSxPRM) in combination with the discovery-driven digitization of the clinical biospecimen using DIA. Heavy-labeled reference peptides were utilized as triggers for MSxPRM and monitoring of endogenous peptides.

RESULTS

We first evaluated hybrid-PRM/DIA in a clinical context on a pool of 185 selected proteotypic peptides for tumor-associated antigens derived from 64 annotated human protein groups. We demonstrated improved reproducibility and sensitivity for the detection of endogenous peptides, even at lower concentrations near the detection limit. Up to 179 MSxPRM scans were shown not to affect the overall DIA performance. Next, we applied hybrid-PRM/DIA for the integrated digitization of biobanked melanoma samples using a set of 30 AQUA peptides against 28 biomarker candidates with relevance in molecular tumor board evaluations of melanoma patients. Within the DIA-detected approximately 6500 protein groups, the selected marker candidates such as UFO, CDK4, NF1, and PMEL could be monitored consistently and quantitatively using MSxPRM scans, providing additional confidence for supporting future clinical decision-making.

CONCLUSIONS

Combining PRM and DIA measurements provides a new strategy for the sensitive and reproducible detection of protein markers from patients currently being discussed in molecular tumor boards in combination with the opportunity to discover new biomarker candidates.

Substantial downregulation of mitochondrial and peroxisomal proteins during acute kidney injury revealed by data-independent acquisition proteomics

Acute kidney injury (AKI) manifests as a major health concern, particularly for the elderly. Understanding AKI-related proteome changes is critical for prevention and development of novel therapeutics to recover kidney function and to mitigate the susceptibility for recurrent AKI or development of chronic kidney disease. In this study, mouse kidneys were subjected to ischemia-reperfusion injury, and the contralateral kidneys remained uninjured to enable comparison and assess injury-induced changes in the kidney proteome. A ZenoTOF 7600 mass spectrometer was optimized for data-independent acquisition (DIA) to achieve comprehensive protein identification and quantification. Short microflow gradients and the generation of a deep kidney-specific spectral library allowed for high-throughput, comprehensive protein quantification. Upon AKI, the kidney proteome was completely remodeled, and over half of the 3945 quantified protein groups changed significantly. Downregulated proteins in the injured kidney were involved in energy production, including numerous peroxisomal matrix proteins that function in fatty acid oxidation, such as ACOX1, CAT, EHHADH, ACOT4, ACOT8, and Scp2. Injured kidneys exhibited severely damaged tissues and injury markers. The comprehensive and sensitive kidney-specific DIA-MS assays feature high-throughput analytical capabilities to achieve deep coverage of the kidney proteome, and will serve as useful tools for developing novel therapeutics to remediate kidney function.

Size-exclusion chromatography combined with DIA-MS enables deep proteome profiling of extracellular vesicles from melanoma plasma and serum

Extracellular vesicles (EVs) are important players in melanoma progression, but their use as clinical biomarkers has been limited by the difficulty of profiling blood-derived EV proteins with high depth of coverage, the requirement for large input amounts, and complex protocols. Here, we provide a streamlined and reproducible experimental workflow to identify plasma- and serum- derived EV proteins of healthy donors and melanoma patients using minimal amounts of sample input. SEC-DIA-MS couples size-exclusion chromatography to EV concentration and deep-proteomic profiling using data-independent acquisition. From as little as 200 µL of plasma per patient in a cohort of three healthy donors and six melanoma patients, we identified and quantified 2896 EV-associated proteins, achieving a 3.5-fold increase in depth compared to previously published melanoma studies. To compare the EV-proteome to unenriched blood, we employed an automated workflow to deplete the 14 most abundant proteins from plasma and serum and thereby approximately doubled protein group identifications versus native blood. The EV proteome diverged from corresponding unenriched plasma and serum, and unlike the latter, separated healthy donor and melanoma patient samples. Furthermore, known melanoma markers, such as MCAM, TNC, and TGFBI, were upregulated in melanoma EVs but not in depleted melanoma plasma, highlighting the specific information contained in EVs. Overall, EVs were significantly enriched in intact membrane proteins and proteins related to SNARE protein interactions and T-cell biology. Taken together, we demonstrated the increased sensitivity of an EV-based proteomic workflow that can be easily applied to larger melanoma cohorts and other indications.

MSstats Version 4.0: Statistical Analyses of Quantitative Mass Spectrometry-Based Proteomic Experiments with Chromatography-Based Quantification at Scale

The MSstats R-Bioconductor family of packages is widely used for statistical analyses of quantitative bottom-up mass spectrometry-based proteomic experiments to detect differentially abundant proteins. It is applicable to a variety of experimental designs and data acquisition strategies and is compatible with many data processing tools used to identify and quantify spectral features. In the face of ever-increasing complexities of experiments and data processing strategies, the core package of the family, with the same name MSstats, has undergone a series of substantial updates. Its new version MSstats v4.0 improves the usability, versatility, and accuracy of statistical methodology, and the usage of computational resources. New converters integrate the output of upstream processing tools directly with MSstats, requiring less manual work by the user. The package's statistical models have been updated to a more robust workflow. Finally, MSstats' code has been substantially refactored to improve memory use and computation speed. Here we detail these updates, highlighting methodological differences between the new and old versions. An empirical comparison of MSstats v4.0 to its previous implementations, as well as to the packages MSqRob and DEqMS, on controlled mixtures and biological experiments demonstrated a stronger performance and better usability of MSstats v4.0 as compared to existing methods.

Abstract 5059: High-throughput monitoring of proteoforms and pathways through multiplexed and customizable mass spectrometry assay panels

Proteins represent a key class of analytes in pharmacodynamic (PD) analysis, an essential component in preclinical and clinical studies which inform about a biological system’s response to drug treatment. Pharmacodynamic signatures typically comprise target engagement marker and downstream pathway markers. With recent development in targeted therapies and novel therapeutic modalities such as protein degraders, the scope of PD has widened to cover predictive and stratification biomarkers which can inform patient selection and contribute to further understanding of potential drug resistance.

Using peptides as surrogate for proteins of interest, mass spectrometry (MS)-based approach alleviates the barriers of reagent specificity of antibody-based methods which can be affected by species and matrices used in preclinical drug development. Moreover, the use of stable-isotope labeled standard (SIS) reference peptides grants targeted MS assays unraveled specificity and the ability to determine absolute quantity of a given analyte.

Harnessing the power of targeted MS, we present an oncology-focused assay repository which contains 804 peptides corresponding to 582 proteins. All 804 peptide surrogates were chosen under strict selection criteria such as uniqueness/proteotypicity and stability. Off-the-shelf availability (PQ500 kit) of SIS reference peptides for all 582 proteins facilitates the deployment of any of these assays in a plug-and-play manner. The collection of 582 protein targets cover > 180 cellular pathways (Reactome pathways e.g. cytokine signaling, cell-cell communication, activation of matrix metalloproteinases) together with 49 FDA-approved drug targets. To further evaluate the performance of these assays, we carried out a case control study comprising plasma samples from 5 patient groups (lung, colorectal, pancreatic, breast and prostate cancer) and age and gender-matched healthy subjects. In total, we measured absolute quantities of 582 proteins across 180 plasma samples while validating the expression profiles of well-characterized biomarkers in patient plasma.

To conclude, we’ve demonstrated the application of targeted MS approach for the routine analysis of pharmacodynamic biomarkers. Its multiplexing capability, together with the adaptability to various species allow this method to maneuver through preclinical and clinical scenarios.

Citation Format: Sebastian Müller, Véronique Laforte, Simonas Savickas, Maik Müller, Yuehan Feng, Lukas Reiter. High-throughput monitoring of proteoforms and pathways through multiplexed and customizable mass spectrometry assay panels. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5059.

Proteome-wide structural changes measured with limited proteolysis-mass spectrometry: an advanced protocol for high-throughput applications

Proteins regulate biological processes by changing their structure or abundance to accomplish a specific function. In response to a perturbation, protein structure may be altered by various molecular events, such as post-translational modifications, protein-protein interactions, aggregation, allostery or binding to other molecules. The ability to probe these structural changes in thousands of proteins simultaneously in cells or tissues can provide valuable information about the functional state of biological processes and pathways. Here, we present an updated protocol for LiP-MS, a proteomics technique combining limited proteolysis with mass spectrometry, to detect protein structural alterations in complex backgrounds and on a proteome-wide scale. In LiP-MS, proteins undergo a brief proteolysis in native conditions followed by complete digestion in denaturing conditions, to generate structurally informative proteolytic fragments that are analyzed by mass spectrometry. We describe advances in the throughput and robustness of the LiP-MS workflow and implementation of data-independent acquisition-based mass spectrometry, which together achieve high reproducibility and sensitivity, even on large sample sizes. We introduce MSstatsLiP, an R package dedicated to the analysis of LiP-MS data for the identification of structurally altered peptides and differentially abundant proteins. The experimental procedures take 3 d, mass spectrometric measurement time and data processing depend on sample number and statistical analysis typically requires ~1 d. These improvements expand the adaptability of LiP-MS and enable wide use in functional proteomics and translational applications.

Substantial Downregulation of Mitochondrial and Peroxisomal Proteins during Acute Kidney Injury revealed by Data-Independent Acquisition Proteomics

Acute kidney injury (AKI) manifests as a major health concern, particularly for the elderly. Understanding AKI-related proteome changes is critical for prevention and development of novel therapeutics to recover kidney function and to mitigate the susceptibility for recurrent AKI or development of chronic kidney disease. In this study, mouse kidneys were subjected to ischemia-reperfusion injury, and the contralateral kidneys remained uninjured to enable comparison and assess injury-induced changes in the kidney proteome. A fast-acquisition rate ZenoTOF 7600 mass spectrometer was introduced for data-independent acquisition (DIA) for comprehensive protein identification and quantification. Short microflow gradients and the generation of a deep kidney-specific spectral library allowed for high-throughput, comprehensive protein quantification. Upon AKI, the kidney proteome was completely remodeled, and over half of the 3,945 quantified protein groups changed significantly. Downregulated proteins in the injured kidney were involved in energy production, including numerous peroxisomal matrix proteins that function in fatty acid oxidation, such as ACOX1, CAT, EHHADH, ACOT4, ACOT8, and Scp2. Injured mice exhibited severely declined health. The comprehensive and sensitive kidney-specific DIA assays highlighted here feature high-throughput analytical capabilities to achieve deep coverage of the kidney proteome and will serve as useful tools for developing novel therapeutics to remediate kidney function.

Limited Proteolysis-Mass Spectrometry to Identify Metabolite-Protein Interactions

Metabolite-protein interactions regulate diverse cellular processes, prompting the development of methods to investigate the metabolite-protein interactome at a global scale. One such method is our previously developed structural proteomics approach, limited proteolysis-mass spectrometry (LiP-MS), which detects proteome-wide metabolite-protein and drug-protein interactions in native bacterial, yeast, and mammalian systems, and allows identification of binding sites without chemical modification. Here we describe a detailed experimental and analytical workflow for conducting a LiP-MS experiment to detect small molecule-protein interactions, either in a single-dose (LiP-SMap) or a multiple-dose (LiP-Quant) format. LiP-Quant analysis combines the peptide-level resolution of LiP-MS with a machine learning-based framework to prioritize true protein targets of a small molecule of interest. We provide an updated R script for LiP-Quant analysis via a GitHub repository accessible at https://github.com/RolandBruderer/MiMB-LiP-Quant .

Global, in situ analysis of the structural proteome in individuals with Parkinson's disease to identify a new class of biomarker

Parkinson's disease (PD) is a prevalent neurodegenerative disease for which robust biomarkers are needed. Because protein structure reflects function, we tested whether global, in situ analysis of protein structural changes provides insight into PD pathophysiology and could inform a new concept of structural disease biomarkers. Using limited proteolysis-mass spectrometry (LiP-MS), we identified 76 structurally altered proteins in cerebrospinal fluid (CSF) of individuals with PD relative to healthy donors. These proteins were enriched in processes misregulated in PD, and some proteins also showed structural changes in PD brain samples. CSF protein structural information outperformed abundance information in discriminating between healthy participants and those with PD and improved the discriminatory performance of CSF measures of the hallmark PD protein α-synuclein. We also present the first analysis of inter-individual variability of a structural proteome in healthy individuals, identifying biophysical features of variable protein regions. Although independent validation is needed, our data suggest that global analyses of the human structural proteome will guide the development of novel structural biomarkers of disease and enable hypothesis generation about underlying disease processes.

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.

Abstract 2924: Target identification, selectivity profiling and mechanistic insights of a Cdk9 inhibitor using complementary proteomics methods

In an effort to map the selectivity and understand the mode of action of a CDK9 inhibitor (compound 1), we employed several orthogonal proteomics methods. Our results show a clear selectivity against the CDK family of kinases, with the highest specific affinity towards CDK9. In addition, our multi-method approach allows us to also map the protein binding sites of the inhibitor, identify non-kinase (off-) targets as well as detect the cellular molecular responses to the added inhibitor. Here we describe the methods used, their strengths and weaknesses, how they can be used in the drug discovery pipeline and how they synergize to provide mechanistic insights of compounds of interest. We have used chemoproteomics, kinase affinity tools (kinobeads), Cellular Thermal Shift Assay (CETSA) and Limited Proteolysis (LiP). The results obtained clearly show that CDK9 is the primary target of Compound 1, with affinity curves highly correlated between the different target deconvolution techniques. The chemoproteomic approach rely on a compound derivate, able to bind to a sepharose bead. Subsequently, the binding competition assays are performed on lysed cell material. The choice of a mild lysis buffer allowed us to identify, not only CDK9, but also it’s molecular partners in the p-Tefb complex (Cyclin T1, Cyclin T2 and Aff4) with similar concentration response behavior. The results for the kinases identified in the study were strikingly similar when also profiling the compound without the chemical modification using the kinobeads assay. In the CETSA experiments, where both lysed cells and intact cells were profiled, the lysate experiment most closely resembles that of the previous pull-downs. Here, only direct binders of Compound 1 show a thermal shift, for example several of the pulled down kinases but not the p-Tefb complex partners that were co-competed previously. In the intact cell version of CETSA, not only the direct binders of the compound show stability shifts, but also downstream events and other secondary modulatory effects leave thermal traces in the cell. For example, Compound 1 also binds to GSK3A/B, causing their melting temperature to increase. Inhibition of GSK3 further affects the phosphorylation state and cellular location of FOXK1, which in turn is identified as a destabilized protein. Finally, Limited Proteolysis was used to identify target protein and using the LiP-Quant approach their LiP scores were assigned. Further, out of the identified CDK targets, mapping of peptide cleavage pattern was performed for the members of the CDK family for which structural data is published. The result identified the peptides to be directly adjacent to the ATP binding pocket of CDK9 or regions of high homology. The use of complementary techniques, based on unique biological and biochemical processes, allow robust and confident characterization of inhibitor compounds.

Citation Format: Adam Hendricks, Nigel Beaton, Alexey Chernobrovkin, Eric Miele, Ghaith Hamza, Piero Ricchiuto, Ronald Tomlinson, Tomas Friman, Cassandra Borenstein, Bernard Barlaam, Sudhir Hande, Chris De Savi, Rick Davies, Martin Main, Joakim Hellner, Kristina Beeler, Yuehan Feng, Roland Bruderer, Lukas Reiter, Daniel Martinez Molina, Maria Paola Castaldi. Target identification, selectivity profiling and mechanistic insights of a Cdk9 inhibitor using complementary proteomics methods [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 2924.

Abstract 2136: Prediction of small molecule-protein binding events for BRD4 and EGFR inhibitors using HR-LiP, a novel structural proteomics approach

Background: Beyond phenotypic efficacy and safety categorization, high resolution profiling of drug-protein interactions and binding mechanisms remains a major hurdle during lead selection and optimization. A key milestone in structure-based drug design is compound binding site identification and characterization. Structure-activity relationship (SAR) studies utilize techniques such as nuclear magnetic resonance (NMR), x-ray crystallography (X-ray), cryo-electron microscopy (cryo-EM) and the mass spectrometry-based hydrogen-deuterium exchange (HDX) to address these hurdles but they are labor, time and cost intensive. Further, SAR studies are often complicated by protein size (i.e. large proteins) and location (i.e. membrane proteins), which can lead to protocol adaptations (e.g. recombinant protein usage and/or protein truncation) that can introduce artifacts. Using limited proteolysis (LiP) coupled to next-generation mass spectrometry we have developed a high-throughput, high-resolution approach (HR-LiP) that utilizes peptide-level resolution to characterize drug-protein interactions including for proteins that hindered by the previously mentioned limitations.

Methods: To boost protein abundance in their native environment, proteins of interest were overexpressed in HEK293 cells using a simple plasmid construct. Cell lysates were incubated with the compounds of interest at increasing concentrations. Samples were then subjected to a limited digest using proteinase K and further processed for data independent acquisition (DIA)-MS analysis using trypsin. Data was analyzed using a directDIA workflow in Spectronaut.

Results: Two well-characterized drug target proteins, bromodomain-containing protein 4 (BRD4) and epidermal growth factor receptor (EGFR), were selected for analysis. Using HR-LiP we identify the binding site of the BRD4 inhibitor JQ1 in the full-length protein, which is typically too large to be used directly in with conventional methods. Further, we map the intracellular binding location of both gefitinib and afatinib, two inhibitors of the membrane protein EGFR. Our data for both proteins are in good accordance with orthogonal data obtained by HDX-MS, NMR and X-ray studies.

Conclusions: We demonstrate that HR-LiP can be used to dissect small molecule-protein binding events, including compound binding site prediction for protein targets classically considered to be difficult. Given its biological power, broad applicability and ease of implementation, we envision the use of HR-LiP as a routine approach for target validation and lead optimization in small molecule drug discovery pipelines.

Citation Format: Nigel Beaton, Jagat Adhikari, Roland Bruderer, Ron Tomlinson, Yuehan Feng, Ivan Cornella-Taracido, Lukas Reiter. Prediction of small molecule-protein binding events for BRD4 and EGFR inhibitors using HR-LiP, a novel structural proteomics approach [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 2136.

Abstract 3920: Precise solid tumor classification through unbiased quantification of proteoforms deep into tissue leakage

Blood is the most frequently used sample in diagnostic processes, and its liquid component is known as plasma. Plasma potentially contains molecular clues coming from all the tissues that make up an organism, thus possibly enable the holistic analysis of the health state of an individual. The major challenge in plasma protein analysis is the 1013- fold dynamic range and the strong prevalence of few, very abundant proteins. Depletion of the most abundant proteins is used to increase coverage of the plasma proteome. To be able to profile human plasma at large scale and at maximal depth, we developed and optimized two main aspects. Firstly, we automated the depletion of the top 14 most abundant proteins in human plasma to a throughput of 40 depletions per day per setup. A comparison of native and depletion workflows with a controlled quantitative experiment demonstrated that depletion led to a significant increase in the number of identifications (&gt200%) and number of true hits (&gt300% at controlled actual FDR &lt1%), while conserving quantitative accuracy. Secondly, we further optimized FAIMS-DIA methods for deep plasma proteome profiling and applied the optimized workflow to a cohort coming from the deadliest five solid tumor types. To research large cohorts’ feasibility, we analyzed a cohort comprising 30 each of healthy, lung, colorectal, pancreatic, breast and prostate cancer. Altogether, we processed 180 samples (plus 24 quality controls) and quantified 2,732 proteins, of which 1,804 in at least 50% of the runs. Based on quality control samples, we could characterize variance introduced on each level, all much lower than the inter-individual variability. We reached 8 orders of magnitude of dynamic range, within this range we covered extensively tissue leakage proteome, interleukins and signaling proteins such as Egf, Klk3 (PSA), Akt1, Cd86, Met, Erbb2 and Cd33. Using machine learning we reduced to an average of 129 (5% of the quantified proteins) biomarker candidates per cancer type, making biological interpretation more feasible while enabling classification of health and disease. The model performance was 86-100% on the 20% hold-out validation set when healthy and overall disease status were considered. Importantly, the biomarker candidates were predominantly (70%) coming from low abundance regions clearly demonstrating the need to measure deeply because they would be missed by shallow plasma profiling. Hence, we showed that our automated plasma depletion workflow has the potential to enable the unbiased and reproducible quantification of more than 2,700 proteins across very large cohorts and unlock biomarker candidate panels in different cancer types. Furthermore, with the latest analytical method iteration applied to a subset of the samples, we could quantify more than 3,500 proteins, demonstrating the full potential of the workflow.

Citation Format: Marco Tognetti, Kamil Sklodowski, Sebastian Mueller, Dominique Kamber, Jan Muntel, Yuehan Feng, Roland Bruderer, Lukas Reiter. Precise solid tumor classification through unbiased quantification of proteoforms deep into tissue leakage [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 3920.

Biomarker Candidates for Tumors Identified from Deep-Profiled Plasma Stem Predominantly from the Low Abundant Area

The plasma proteome has the potential to enable a holistic analysis of the health state of an individual. However, plasma biomarker discovery is difficult due to its high dynamic range and variability. Here, we present a novel automated analytical approach for deep plasma profiling and applied it to a 180-sample cohort of human plasma from lung, breast, colorectal, pancreatic, and prostate cancers. Using a controlled quantitative experiment, we demonstrate a 257% increase in protein identification and a 263% increase in significantly differentially abundant proteins over neat plasma. In the cohort, we identified 2732 proteins. Using machine learning, we discovered biomarker candidates such as STAT3 in colorectal cancer and developed models that classify the diseased state. For pancreatic cancer, a separation by stage was achieved. Importantly, biomarker candidates came predominantly from the low abundance region, demonstrating the necessity to deeply profile because they would have been missed by shallow profiling.

Mechanistic Insights into a CDK9 Inhibitor Via Orthogonal Proteomics Methods

Cyclin-dependent-kinases (CDKs) are members of the serine/threonine kinase family and are highly regulated by cyclins, a family of regulatory subunits that bind to CDKs. CDK9 represents one of the most studied examples of these transcriptional CDKs. CDK9 forms a heterodimeric complex with its regulatory subunit cyclins T1, T2 and K to form the positive transcription elongation factor b (P-TEFb). This complex regulates transcription via the phosphorylation of RNA polymerase II (RNAPolII) on Ser-2, facilitating promoter clearance and transcription elongation and thus remains an attractive therapeutic target. Herein, we have utilized classical affinity purification chemical proteomics, kinobeads assay, compressed CEllular Thermal Shift Assay (CETSA)-MS and Limited Proteolysis (LiP) to study the selectivity, target engagement and downstream mechanistic insights of a CDK9 tool compound. The above experiments highlight the value of quantitative mass spectrometry approaches to drug discovery, specifically proteome wide target identification and selectivity profiling. The approaches utilized in this study unanimously indicated that the CDK family of kinases are the main target of the compound of interest, with CDK9, showing the highest target affinity with remarkable consistency across approaches. We aim to provide guidance to the scientific community on the available chemical biology/proteomic tools to study advanced lead molecules and to highlight pros and cons of each technology while describing our findings in the context of the CDKs biology.

MhcVizPipe: A Quality Control Software for Rapid Assessment of Small- to Large-Scale Immunopeptidome Data Sets

Mass spectrometry (MS)-based immunopeptidomics is maturing into an automatized, high-throughput technology, producing small- to large-scale datasets of clinically relevant MHC class I- and II-associated peptides. Consequently, the development of quality control (QC) and quality assurance (QA) systems capable of detecting sample and/or measurement issues is important for instrument operators and scientists in charge of downstream data interpretation. Here, we created MhcVizPipe (MVP), a semi-automated QC software tool that enables rapid and simultaneous assessment of multiple MHC class I and II immunopeptidomic datasets generated by MS, including datasets generated from large sample cohorts. In essence, MVP provides a rapid and consolidated view of sample quality, composition and MHC-specificity to greatly accelerate the 'pass-fail' QC decision-making process toward data interpretation. MVP parallelizes the use of well-established immunopeptidomic algorithms (NetMHCpan, NetMHCIIpan and GibbsCluster) and rapidly generates organized and easy-to-understand reports in HTML format. The reports are fully portable and can be viewed on any computer with a modern web browser. MVP is intuitive to use and will find utility in any specialized immunopeptidomic laboratory and proteomics core facility that provides immunopeptidomic services to the community.

343 Multiomic biomarker signatures identify subsets of patients who may benefit from either nivolumab or sotigalimab in combination with chemotherapy in metastatic pancreatic cancer

Background

Gemcitabine/nab-Paclitaxel (GnP) is a standard of care regimen for first-line metastatic pancreatic ductal adenocarcinoma (PDAC) and has a 1-year overall survival (OS) rate of approximately 35%. There is an urgent need for novel therapeutics and precision medicine approaches in PDAC. PRINCE, a randomized phase 2 trial, reported an increased 1-year OS relative to historical data, for patients treated with nivolumab (nivo)/GnP (57.3%, p = 0.007, n=34) and sotigalimab (sotiga) (APX005M; CD40 agonist)/GnP (48.1%, p = 0.062, n= 36).

Methods

To investigate immune modulatory and pharmacodynamic (PD) effects of nivo or sotiga in combination with GnP we used several orthogonal minimally invasive, blood-based biomarker technologies. Immune population profiles were evaluated by CyTOF and features of T cell phenotype and function by multicolor flow cytometry. Soluble proteins were evaluated with predefined panels using the Olink platform (Immuno-oncology (IO) and Immune Response) along with an unbiased mass spectrometry proteomic approach (Biognosys) that identified circulating soluble proteins of significance.

Results

Relative to baseline, patients who received nivo/GnP had numerically increased frequencies of proliferating, activated CD8+ and CD4+ effector memory T cells in circulation across multiple timepoints. These patients also had significantly increased levels of soluble proteins associated with type II interferon responses and immune cell migration and T cell activation, as well as significantly decreased levels of immunomodulatory proteins.Patients who received sotiga/GnP had increased expression of the co-stimulatory molecule CD86 on conventional dendritic cells. These patients also had significantly increased concentrations of soluble proteins associated with mature antigen presenting cells, and the activation of helper CD4+ T cells, B cells, and monocytes. Significant increases in soluble proteins associated with type-1 cell-mediated effector immunity and decreases in immunosuppressive factors were observed in both arms. Significant proteins were defined as p ≤ 0.05, log2 expression fold change ≥ 0.5 (Olink) and Sparse PLS discriminant analysis was used with zero as a threshold (Biognosys).

Conclusions

This study is a first to use multiomic minimally invasive biomarker approaches in PDAC to demonstrate PD effects and immune modulation with immunotherapy/chemotherapy combinations. Orthogonal assays demonstrate that nivo/GnP and sotiga/GnP elicit unique immune responses and the observed effects are consistent with their distinct mechanisms of action. These data suggest that multiomic biomarker signatures may identify subsets of patients who may benefit from an immunotherapy/chemo approach in PDAC. Moreover, results from these analyses will support early phase clinical study development decisions.

Acknowledgements

We extend our gratitude to the patients, their families, the clinical investigators, and their site staff members who are making this trial possible. We would also like to thank Sultan Nawabi at Parker Institute for Cancer Immunotherapy (PICI) for operations leadership of the trial. We thank Bristol Myers Squibb (BMS) and Apexigen for collaboration and study drugs. The study was funded by Cancer Research Institute, BMS and PICI.

Trial Registration

NCT03214250

Ethics Approval

This study was approved by University of Pennsylvania Institutional Review Board; Federalwide assurance #00004028.

90 Unbiased proteomic profiling leads to the discovery of a novel non-invasive blood-based protein panel with significant positive predictive value in pancreatic and colorectal cancers

Background

Mass spectrometry-based discovery proteomics has recently emerged as a high-throughput method for the proteomic profiling in biofluid samples from large clinical and population screening cohorts. Despite this progress, a significant fraction of the plasma proteome is currently not covered by state-of-the-art discovery approaches and therefore not accessible for biomarker discovery. To close this analytical gap, we present a novel workflow combining automated plasma depletion and FAIMS-DIA-MS to bridge both sensitivity and scalability. We demonstrate the applicability of this workflow to support biomarker discovery and subject stratification in precision oncology in a case-control cohort.

Methods

The plasma samples were depleted in 96-well format using an automated MARS-14 depletion system. The depleted samples were processed to tryptic peptides and analyzed using a Thermo Scientific Orbitrap Exploris 480 equipped with a FAIMS Pro device. Data processing and analysis were performed using Biognosys’ SpectroMine and Spectronaut software.

Results

Using the unbiased discovery workflow, we investigated a cohort comprising of 180 plasma samples from healthy donors and subjects diagnosed with pancreatic, breast, prostate, colorectal and lung (NSCLC) cancer at either early or late stage of the disease. Overall, the optimized FAIMS-DIA-MS quantified 2,741 proteins across all samples and 1,849 proteins on average per sample measurement. Based on estimated plasma protein concentrations (Human Protein Atlas), quantified proteins span across 8 orders of magnitude, down to single digit pg/mL. Within this dynamic range, we could interrogate the tissue leakage proteome, interleukins and signaling proteins. Using classification algorithms, we were able to select candidates to build protein panels which provide significant positive predictive values associated with different disease stages, especially in the sub-cohorts for pancreatic and colorectal cancer.

Conclusions

We demonstrate the capabilities of a novel discovery workflow for deep, quantitative profiling of plasma samples at large scale, providing a rich proteomic resource for precision oncology.

Abstract 21: LiP-MS, a machine learning-based chemoproteomic approach to identify drug targets in complex proteomes

Background

Target identification is a critical step in elucidating the mechanism of action (MoA) for bioactive compounds. For target-based and phenotypic drug discovery pipelines, extensive potential target knowledge for a lead compound provides essential insights that enable potency and selectivity optimization. The tedious process of target deconvolution for a compound often necessitates a plethora of biochemical and biophysical techniques. To expand the toolbox of unbiased target ID approaches, we developed a novel workflow combining limited proteolysis with quantitative mass spectrometry (LiP-MS) that exploits the drug binding phenomena of protein structural alterations and steric hindrance. Advantageously, LiP-MS's unique peptide-centric focus exploits signature peptide detection to discern ligand binding. Additionally, the LiP-MS workflow enables binding affinity estimation (EC50) and binding site prediction. Here we demonstrate the performance of LiP-MS using two well-characterized kinase inhibitors (KIs), an AstraZeneca CDK9 inhibitor (AZ) and Selumetinib (SE).

Methods

Mechanically sheared HeLa or U2OS cell lysate was incubated with compound at multiple concentrations. Next, a limited proteolytic digest was performed using proteinase K. After quenching this digestion, lysate was trypsin digested to peptides for mass spectrometry analysis. The resulting peptides were analyzed quantitatively using data-independent acquisition (DIA)-MS.

Results

Herein, we use LiP-MS to unbiasedly identify unique peptides generated by the binding of two distinct classes of kinase inhibitors in human cell lysate. For the ATP-competitive inhibitor AZ, LiP-Quant shows a strong enrichment for CDKs in the target space defined by LiP score, including CDK1, 2, 4, 6, 9 and 11A. In addition, our data indicates that this KI targets members of the CDKs with different selectivity, with CDK9 displaying the highest compound affinity (nM range). For Selumetinib, a non-competitive allosteric inhibitor, LiP-MS clearly identified the direct targets MEK1/2 as the main hits by LiP score. Both cases represent a highly specific enrichment given that we quantified > 120,000 peptides in each of the experiments. These findings confirm our approach's ability to identify genuine drug targets regardless of drug MoA in a complex biological matrix. Importantly, in both KI target ID studies, identified LiP peptides could be successfully deployed to map compound binding site, demonstrating the potential of LiP-MS to pinpoint regions involved in drug-protein interactions.

Conclusions

Collectively, this data demonstrates that LiP-MS can be used to effectively identify protein drug targets and characterize the binding properties in complex proteomes independent of the compound's MoA and without compound modification or labeling. These capabilities make LiP-MS a powerful addition to the target ID toolbox.

Citation Format: Nigel Beaton, Yuehan Feng, Roland Bruderer, Adam Hendricks, Ghaith Hamza, Eric Miele, Rick Davies, Kristina Beeler, Ilaria Piazza, Paola Picotti, Paola Castaldi, Lukas Reiter. LiP-MS, a machine learning-based chemoproteomic approach to identify drug targets in complex proteomes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 21.

Abstract 298: Dissection of drug-protein interactions by HR-LiP-MS in target validation and lead optimization

Background

A central focus of preclinical drug discovery is the thorough characterization of lead compounds. This is a key step that helps ensure that drug candidates are worthy of clinical testing. In addition to phenotypic characterization, quantitative profiling of drug-protein interactions is a major hurdle during preclinical lead optimization. Traditionally, the gold standard technique informing structure-based drug design has been x-ray crystallography, despite the fact that under crystallization conditions protein conformation is frozen and aspects of protein structural transitions are neglected by this approach. More recently, hydrogen-deuterium exchange (HDX) has emerged as an alternative tool to profile ligand-protein interactions. While correlation of HDX-profiles with functional readouts provides valuable insights into structure-activity relations (SAR), the method itself can be laborious with extensive optimization required to generate high quality data. To address some of these shortcomings, we developed a high-throughput approach based on limited proteolysis (LiP) and next-generation quantitative mass spectrometry that enables the dissection of drug-protein interactions at peptide-level resolution.

Methods

To simulate the complex protein mixture obtained from cell lysis, purified recombinant proteins were spiked into a cell lysate background. Next, the mixtures were incubated with the compounds of interest. The samples were subjected to limited digestion with proteinase K and subsequently processed to peptides with trypsin for DIA-MS analysis. MS data were analyzed using a DirectDIA-workflow.

Results

High-Resolution Limited Proteolysis (HR-LiP) was established using calmodulin and its robust interactions with Ca2+ ions and CAMKII peptide as a model system. From here, we expanded the technique to small molecule-protein interactions of established, druggable protein targets spanning several protein classes. Herein we demonstrate that using HR-LiP we are able to identify binding sites of various compound classes on their target proteins including well characterized small molecules such as the BRD4 inhibitor JQ1. HR-LiP data are in good accordance with orthogonal HDX-MS, NMR and X-ray studies.

Conclusions

We demonstrate that HR-LiP can be used to dissect small molecule-protein binding characteristics with a resolution of 5-10 amino acids. Quantitative properties of the binding events are accurately recapitulated in dosage series and can therefore be deployed to rank and compare different compounds and compound classes. The ability to deal with complex backgrounds and unpurified proteins enables its application on difficult-to-purify or unstable proteins, and potentially multi-protein complexes. We envision the application of HR-LiP as a routine approach for target validation and lead optimization in small molecule drug discovery pipelines.

Citation Format: Nigel Beaton, Jagat Adhikari, Yuehan Feng, Roland Bruderer, Ron Tomlinson, Ivan Cornella-Taracido, Lukas Reiter. Dissection of drug-protein interactions by HR-LiP-MS in target validation and lead optimization [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 298.

Clinical and histopathological features of myeloid neoplasms with concurrent Janus kinase 2 (JAK2) V617F and KIT proto-oncogene, receptor tyrosine kinase (KIT) D816V mutations

We report on 45 patients with myeloid neoplasms and concurrent Janus kinase 2 (JAK2) V617F and KIT proto-oncogene, receptor tyrosine kinase (KIT) D816V (JAK2^{pos .} /KIT^{pos .} ) mutations, which are individually identified in >60% of patients with classical myeloproliferative neoplasms (MPN) and >90% of patients with systemic mastocytosis (SM) respectively. In SM, the concurrent presence of a clonal non-mast cell neoplasm [SM with associated haematological neoplasm (SM-AHN)] usually constitutes a distinct subtype associated with poor survival. All 45 patients presented with a heterogeneous combination of clinical/morphological features typical of the individual disorders (e.g. leuco-/erythro-/thrombocytosis and elevated lactate dehydrogenase for MPN; elevated serum tryptase and alkaline phosphatase for SM). Overlapping features identified in 70% of patients included splenomegaly, cytopenia(s), bone marrow fibrosis and additional somatic mutations. Molecular dissection revealed discordant development of variant allele frequency for both mutations and absence of concurrently positive single-cell derived colonies, indicating disease evolution in two independent clones rather than monoclonal disease in >60% of patients examined. Overall survival of JAK2^{pos .} /KIT^{pos .} patients without additional somatic high-risk mutations [HRM, e.g. in serine and arginine-rich splicing factor 2 (SRSF2), additional sex combs like-1 (ASXL1) or Runt-related transcription factor 1 (RUNX1)] at 5 years was 77%, indicating that the mutual impact of JAK2 V617F and KIT D816V on prognosis is fundamentally different from the adverse impact of additional HRM in the individual disorders.

Proteome and phospho-proteome profiling for deeper phenotype characterization of colorectal cancer heterogeneity

e15536

Background: The rise of precision oncology therapeutics requires deep understanding of the molecular mechanisms implicated in cancer biology. Colorectal cancer (CRC) is one of the first solid tumors to be molecularly characterized by defined genes and pathways. Advances in tumor profiling have revealed a profound molecular heterogeneity in CRC leading to the definition of several consensus molecular subtypes (CMS). However, this molecular heterogeneity is still largely defined on the genomic and transcriptomics level. To complement the understanding of genetically defined molecular subgroups, we performed large-scale deep proteomic and phospho-proteomic profiling of CRC patient biopsies and adjacent healthy control tissue, which has enabled to explore the phenotype and obtain more functional insights in cancer biology. Methods: Sample processing from 5-10 mg of tissue per sample was performed using a liquid handling robot. Phospho-peptide enrichment was carried out with a Kingfisher Flex device and MagReSyn Ti-IMAC magnetic beads. Data-Independent Acquisition (DIA) LC-MS/MS was performed on multiple platforms consisting of a Thermo Scientific Q Exactive HF-X mass spectrometer coupled to a Waters M-Class LC. Chromatography was operating at 5 µL/min, and separation was achieved using 45 min (whole proteome) and 60 min (phospho-proteome) gradients. Results: Indivumed has built IndivuType, the world’s first multi-omics database for individualized cancer therapy, analyzing the highest quality cancer biospecimens to generate the most comprehensive dataset, including genomics, transcriptomics, proteomics, and clinical outcome information. Enabled by the DIA technology, a mass spectrometric method developed by Biognosys that obtains peptide fragmentation data in a highly parallelized way with high sensitivity, more than 7,000 proteins in the whole proteome and 20,000 phospho-peptides in the phospho-proteome workflow were profiled across more than 900 resected tissue samples of various CMS of CRC. The resulting proteome and phospho-proteome data were integrated into the IndivuType database and cross-analyzed with genomic and transcriptomic markers. Through this combined analysis, novel insights in clinically relevant signaling pathways in CRC subtypes were revealed. Conclusions: The deep phenotypic profiling of cancer samples, using next generation proteomics and phospho-proteomics, has enabled us to go beyond the genomic level in the characterization of tumor molecular heterogeneity. This multi-omics approach provides a solid foundation to advance the understanding of cancer biology, unravel key molecular events, and support the identification of novel therapeutic targets for precision medicine in CRC.

Isoform-resolved correlation analysis between mRNA abundance regulation and protein level degradation

Profiling of biological relationships between different molecular layers dissects regulatory mechanisms that ultimately determine cellular function. To thoroughly assess the role of protein post‐translational turnover, we devised a strategy combining pulse stable isotope‐labeled amino acids in cells (pSILAC), data‐independent acquisition mass spectrometry (DIA‐MS), and a novel data analysis framework that resolves protein degradation rate on the level of mRNA alternative splicing isoforms and isoform groups. We demonstrated our approach by the genome‐wide correlation analysis between mRNA amounts and protein degradation across different strains of HeLa cells that harbor a high grade of gene dosage variation. The dataset revealed that specific biological processes, cellular organelles, spatial compartments of organelles, and individual protein isoforms of the same genes could have distinctive degradation rate. The protein degradation diversity thus dissects the corresponding buffering or concerting protein turnover control across cancer cell lines. The data further indicate that specific mRNA splicing events such as intron retention significantly impact the protein abundance levels. Our findings support the tight association between transcriptome variability and proteostasis and provide a methodological foundation for studying functional protein degradation.

MassIVE.quant: a community resource of quantitative mass spectrometry-based proteomics datasets

MassIVE.quant is a repository infrastructure and data resource for reproducible quantitative mass spectrometry-based proteomics, which is compatible with all mass spectrometry data acquisition types and computational analysis tools. A branch structure enables MassIVE.quant to systematically store raw experimental data, metadata of the experimental design, scripts of the quantitative analysis workflow, intermediate input and output files, as well as alternative reanalyses of the same dataset.

Abstract 5135: Characterization of a data independent acquisition mass spectrometry-based workflow in the plasma of NSCLC subjects reveals host immune response mechanisms

Background: Measurement of circulating biomarkers in cancer has proven utility for early detection, differential diagnosis, and predicting pre-treatment response to therapy. More recently, circulating proteomic biomarkers for pre-treatment prediction of therapeutic response have received additional attention due to the heterogeneous responses to immunotherapies. To develop a greater understanding of the circulating plasma proteome in subjects with cancer we have optimized a depleted plasma proteomic workflow, based on label-free data independent acquisition (DIA) mass spectrometry, and applied it to plasma from subjects with late stage NSCLC. This approach provides a deep and unbiased description of the plasma proteome and the dysregulated biological pathways associated with lung cancer.

Methods: Plasma samples from subjects with Stage III-IV non-small cell lung cancer (NSCLC, n = 15) and age matched healthy donors (n = 15) were depleted of 14 high abundance proteins using MARS Hu-14 spin columns (Agilent). The resulting flow through was prepared for mass spectrometry using a filter aided sample preparation method (FASP). All samples were analyzed using 2h gradients on a C18 column coupled to a Thermo Scientific Q Exactive mass spectrometer operated in DIA mode. Data was extracted using Spectronaut (Biognosys) with combined sample specific and resource spectral libraries and statistical analysis was conducted to identify disease associated biomarker candidates. Pathway analysis highlights dysregulated biological functions.

Results: An overview of assay optimization will be presented which resulted in the final workflow. In summary, a comprehensive protein spectral library was created containing 1,827 unique proteins and in DIA acquisition, 1,304 proteins were quantified across all samples. Univariate statistical testing identified 162 dysregulated proteins (125 up-regulated and 37 down-regulated; q-value > 0.05 and log2 fold change > 0.58). In addition to the acute phase proteins (e.g. CRP and SAA1) which were previously verified to be elevated in subjects with NSCLC, multivariate analysis identified additional proteins that are differentially expressed between the sample groups. Most relevant to immune function was CLC (Galectin-10), which has been identified as key component supporting the suppressive function of Tregs1 and S100A9 which is a known therapeutic target associated myeloid-derived suppressor cells2. Furthermore, F13A1 was suppressed in the NSCLC samples which is known to be associated with macrophage activation.

Conclusions: Multiple plasma proteins were found to be dysregulated and associated with NSCLC reflecting the host immune response via acute phase response signaling and immunosuppressive mechanisms. Several of these markers have been linked to patient outcomes and represent known therapeutic targets.1. Kubach, J., et. al. Blood 2007 110:1550-1558 2. Shen, L., et. al. Cancer Immunol Res. 2015 3(2): 136-148.

Citation Format: Nicholas Dupuis, Linda Sensbach, Sebastian Muller, Lukas Reiter. Characterization of a data independent acquisition mass spectrometry-based workflow in the plasma of NSCLC subjects reveals host immune response mechanisms [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5135.

Abstract 4266: Proteomics for precision oncology: Profiling the proteome of matching tumor and adjacent normal tissue using data-independent acquisition

Precision oncology requires a deep understanding of the molecular mechanism in cancer biology. The predominant approach today focuses on genome structure and gene expression data, which have become available with the rise of next-gen sequencing technology. On a phenotypic level, however, protein expression and activation are arguably more directly related to cellular function. The availability of combined genome and proteome level data from the same tumor is therefore expected to provide a much more complete picture of a tumor in a particular state. Until recently, proteomics technology could not match the scale of next-gen sequencing and consequently, precision medicine research has almost exclusively been based on gene-level data. Here we show the first truly large-scale data set for protein expression and protein phosphorylation for a large collection of biospecimens derived from the IndivuType cohort of Indivumed, Germany. Matching fresh-frozen tumor and adjacent normal tissue samples from thousands of patients including various cancer entities were obtained from Indivumed's network of partner hospitals Enabled by the novel data-independent acquisition (DIA) workflow, a mass spectrometric method that obtains peptide fragmentation data in a highly parallelized way with high reproducibility and sensitivity, more than 7,000 proteins in the whole proteome (WP) and 20,000 phospho-peptides in the phospho-proteome (PP) workflow were analyzed. Sample processing from 5 mg of tissue per sample was performed on 96-well plates with the help of a liquid handling robot. Phospho-peptide enrichment was carried out using a Kingfisher Flex device and MagReSyn Ti-IMAC magnetic beads (ReSyn Biosciences). Data-independent acquisition (DIA) LC-MS/MS was performed on multiple platforms consisting of a Thermo Scientific Q Exactive HF-X mass spectrometer coupled to a Waters M-Class LC. Chromatography was operating at 5 µL/min, and separation was achieved using 45 min (WP) and 60 min (PP) gradients. With a throughput of 850 WP and 650 PP samples per month, thousands of samples were analyzed to date. The resulting proteome data is integrated into Indivumed's IndivuType multi-omics database, supporting the identification and validation of new molecular cancer drug targets and biomarkers.

Citation Format: Jakob Vowinckel, Karel Novy, Thomas Corwin, Tobias Treiber, Roland Bruderer, Lukas Reiter, Eike von Leitner, Oliver Rinner, Claudia Escher. Proteomics for precision oncology: Profiling the proteome of matching tumor and adjacent normal tissue using data-independent acquisition [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4266.

Abstract 4006: High-Resolution Limited Proteolysis (HR-LIP), a novel approach for target validation and lead compound optimization

Background

A central focus of preclinical drug discovery is the thorough characterization of lead compounds. This is a key step that helps ensure that drug candidates are worthy of clinical testing. In addition to phenotypic characterization, quantitative profiling of drug-protein interactions is a major hurdle during preclinical lead optimization. Traditionally, the gold standard technique informing structure-based drug design has been x-ray crystallography, despite the fact that under crystallization conditions protein conformation is frozen and aspects of protein structural transitions are neglected by this approach. More recently, hydrogen-deuterium exchange (HDX) has emerged as an alternative tool to profile ligand-protein interactions. While correlation of HDX-profiles with functional readouts provides valuable insights into structure-activity relations (SAR), the method itself can be laborious with extensive optimization required to generate high quality data. To address some of these shortcomings, we developed a high-throughput approach based on limited proteolysis (LiP) and next-generation quantitative mass spectrometry that enables the dissection of drug-protein interactions at peptide-level resolution.

Methods

To simulate the complex protein mixture obtained from cell lysis, purified recombinant proteins were spiked into a cell lysate background. Next, the mixtures were incubated with the compounds of interest at increasing concentrations. The samples were subjected to limited digestion with proteinase K and subsequently processed to peptides with trypsin for LC-DIA (data independent acquisition)-MS analysis. MS data were analyzed using a DirectDIA-workflow in Spectronaut Pulsar X.

Results

High-Resolution Limited Proteolysis (HR-LiP) was established using calmodulin and its robust interactions with Ca2+ ions and CAMKII peptide as a model system. From here, we expanded the technique to small molecule-protein interactions of established, druggable protein targets spanning several protein classes. Herein we demonstrate that using HR-LiP we are able to identify binding sites of various compound classes on their target proteins including well characterized small molecules such as the BRD4 inhibitor JQ1. HR-LiP data are in good accordance with orthogonal HDX-MS, NMR and X-ray studies.

Conclusions

We demonstrate that HR-LiP can be used to dissect small molecule-protein binding characteristics with a resolution of 5-10 amino acids. Quantitative properties of the binding events are accurately recapitulated in dosage series and can therefore be deployed to rank and compare different compounds and compound classes. The ability to deal with complex backgrounds and unpurified proteins enables its application on difficult-to-purify or unstable proteins, and potentially multi-protein complexes. We envision the application of HR-LiP as a routine approach for target validation and lead optimization in small molecule drug discovery pipelines.

Citation Format: Yuehan Feng, Nigel Beaton, Jagat Adhikari, Roland Bruderer, Ron Tomlinson, Ivan Cornella-Taracido, Lukas Reiter. High-Resolution Limited Proteolysis (HR-LIP), a novel approach for target validation and lead compound optimization [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4006.

Abstract 6404: LiP-Quant, an automated chemoproteomic approach to identify drug targets in complex proteomes

Background

Target identification is a critical step in elucidating the mechanism of action (MoA) for bioactive compounds. For phenotypic drug discovery pipelines, current unbiased, label-free chemoproteomics-based methods rely predominantly on the modulation of target thermal stability upon drug binding. We developed an automated drug target deconvolution workflow combining limited proteolysis with mass spectrometry (LiP-Quant) that exploits protein structural alterations, as well as steric effects driven by drug binding. A major advantage of LiP-Quant is its unique focus on the detection of signature peptides that discern ligand binding, peptides that are generated by a limited digestion and identified by proteomic analysis. Here we demonstrate the performance of LiP-Quant using two well-characterized kinase inhibitors (KIs), Selumetinib (SE) and Staurosporine (ST), as well as two natural product-derived phosphatase inhibitors (PIs) Calyculin A and Fostriecin in human cell lysate. Furthermore, LiP-Quant can be deployed to estimate in-lysate EC50 value of compound binding.

Methods

Mechanically sheared HeLa cell lysate was incubated with compound at multiple concentrations. Next, a limited digest was performed using proteinase K. Finally, the limited digests were processed to peptides with trypsin for mass spectrometry analysis. A project-specific spectral library was generated using data-dependent acquisition (DDA) mass spectrometry and for quantitative analysis data-independent acquisition (DIA) data were recorded and analyzed using Spectronaut Pulsar X.

Results

Herein, we demonstrate the ability of our LiP-Quant approach to identify unique peptides generated by the binding of either a highly specific (selumetinib) or broad specificity (staurosporine) KI in human cell line lysate. While > 20 kinases met the qualifying LiP-score cutoff for staurosporine, the direct targets MEK1 and MEK2 were clearly identified as main hits in the unbiased ranking by LiP scores. Both cases represent a highly specific enrichment given that we quantified > 100,000 peptides in each of the experiments. These findings confirm our approach's ability to identify genuine drug targets regardless of drug specificity in a complex biological matrix. To characterize the specificity of LiP-Quant, we treated lysate with two separate protein PIs. According to literature calyculin A targets protein phosphatase 1 and 2 (PP1A and PP2A) and fostriecin also targets PP2A, in addition to protein phosphatase 4 (PP4C) but does not bind PP1A. Robust phosphatase identification was achieved for both calyculin A and fostriecin treatment. Importantly, with LiP-Quant, we could recapitulate the known relative affinities of the PIs towards their respective targets.

Conclusions

Collectively, this data demonstrate that LiP-Quant can be used to effectively identify protein drug targets and characterize the binding properties in complex proteomes, without compound modification or labeling, and regardless of the specificity of the compound. These capabilities make LiP-Quant a powerful target deconvolution and identification strategy.

Citation Format: Yuehan Feng, Nigel Beaton, Roland Bruderer, Ilaria Piazza, Paola Picotti, Lukas Reiter. LiP-Quant, an automated chemoproteomic approach to identify drug targets in complex proteomes [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6404.

Next generation proteomics in precision oncology: 1000s of proteome and phosphoproteome profiles of tumors and matching healthy tissues as meaningful layer in multi-omics database

e15672

Background: The rise of precision oncology therapeutics requires deep understanding of all molecular mechanisms involved in cancer biology. IndivuType offers the world’s first multi-omics database for individualized cancer therapy, analyzing the highest quality cancer biospecimens to generate the most comprehensive dataset, including genomics (WGS), transcriptomics, proteomics, and clinical outcome information. Indivumed is committed to the quality of the IndivuType ecosystem starting with stringent SOP-driven sample collection combined with thorough validation of clinical information and data integrity. The availability of multi-omics data from the same tumor can provide a comprehensive molecular picture of cancer for a given patient. Protein expression and activation are directly related to cellular function and hence provide actionable information about druggable targets. Until recently, the proteomics technology could not match the scale of next-gen sequencing and consequently precision medicine has almost exclusively been based on gene level data. Here we present the first large-scale data set for protein expression and phosphorylation. Enabled by the data independent acquisition (DIA) workflow, a mass spectrometric method provided by Biognosys that obtains peptide fragmentation data in a highly parallelized way with high sensitivity, more than 7,000 proteins in the whole proteome (WP) and 20,000 phospho-peptides in the phospho-proteome (PP) workflow were profiled. Methods: Sample processing from 5 mg of tissue per sample was performed using liquid handling robot. Phospho-peptide enrichment was carried out with a Kingfisher Flex device and MagReSyn Ti-IMAC magnetic beads. DIA LC-MS/MS was performed on multiple platforms consisting of a Thermo Scientific Q Exactive HF-X mass spectrometer coupled to a Waters M-Class LC. Chromatography was operating at 5 µL/min, and separation was achieved using 45 min (WP) and 60 min (PP) gradients. Results: Several thousands of high-quality patient samples of various cancer types have been analyzed to date. The resulting proteome and phospho-proteome data has been integrated into the IndivuType database, thereby providing a solid foundation to advance our understanding of cancer. Conclusions: With the ongoing addition of more samples and associated deep and rich data, the platform could unravel key molecular events and is expected to transform knowledge into actionable treatments and personalized therapies.

Importance of Adequate Diagnostic Workup for Correct Diagnosis of Advanced Systemic Mastocytosis

BACKGROUND

Little is known about the epidemiology of advanced systemic mastocytosis (advSM).

OBJECTIVES

To investigate epidemiologic features and diagnostic pitfalls of advSM in Germany.

METHODS

Therefore, 140 patients from a single German reference center of the European Competence Network on Mastocytosis between 2003 and 2018 were analyzed.

RESULTS

The patients' median age was 68 years (range, 26-86 years), and male versus female ratio was 2:1. An elevated serum tryptase, a KIT D816 mutation, and additional somatic mutations, for example, in SRSF2, ASXL1, or RUNX1, were identified in 95%, 91%, and 74% of patients, respectively. Median overall survival was 3.5 years (range, 0.03-14.3 years; male vs female 2.6 vs 4.2 years; P = .02). Two categories of misdiagnoses were identified in 51 of 140 (36%) patients: First, systemic mastocytosis (SM) was overlooked in 28 of 140 (20%) patients primarily diagnosed with various subtypes of myeloid neoplasms. Second, 23 of 140 (16%) patients were diagnosed with supposed progression from indolent SM to advSM; however, combination of an elevated KIT D816V variant allele frequency in peripheral blood (n = 22), monocytosis (n = 9), eosinophilia (n = 6), and/or mutations in SRSF2, ASXL1, or RUNX1 (n = 10) suggest that distinct signs of potential advSM were overlooked in virtually all patients. Based on locally diagnosed patients in an area of 2.5 million inhabitants, but obviously without considering more, yet unrecognized cases, the incidence and prevalence of advSM is at least 0.8 and 5.2, respectively, per 1 million inhabitants.

CONCLUSIONS

Adequate analyses of tryptase levels, bone marrow morphology, and genetics in patients with myeloid neoplasms or SM would help to prevent the significant underdiagnosis of advSM.

Revealing Dynamic Protein Acetylation across Subcellular Compartments

Protein acetylation is a widespread post-translational modification implicated in many cellular processes. Recent advances in mass spectrometry have enabled the cataloging of thousands of sites throughout the cell; however, identifying regulatory acetylation marks have proven to be a daunting task. Knowledge of the kinetics and stoichiometry of site-specific acetylation is an important factor to uncover function. Here, an improved method of quantifying acetylation stoichiometry was developed and validated, providing a detailed landscape of dynamic acetylation stoichiometry within cellular compartments. The dynamic nature of site-specific acetylation in response to serum stimulation was revealed. In two distinct human cell lines, growth factor stimulation led to site-specific, temporal acetylation changes, revealing diverse kinetic profiles that clustered into several groups. Overlap of dynamic acetylation sites among two different human cell lines suggested similar regulatory control points across major cellular pathways that include splicing, translation, and protein homeostasis. Rapid increases in acetylation on protein translational machinery suggest a positive regulatory role under progrowth conditions. Finally, higher median stoichiometry was observed in cellular compartments where active acetyltransferases are well described. Data sets can be accessed through ProteomExchange via the MassIVE repository (ProteomExchange: PXD014453; MassIVE: MSV000084029).

Rapid and site-specific deep phosphoproteome profiling by data-independent acquisition without the need for spectral libraries

Quantitative phosphoproteomics has transformed investigations of cell signaling, but it remains challenging to scale the technology for high-throughput analyses. Here we report a rapid and reproducible approach to analyze hundreds of phosphoproteomes using data-independent acquisition (DIA) with an accurate site localization score incorporated into Spectronaut. DIA-based phosphoproteomics achieves an order of magnitude broader dynamic range, higher reproducibility of identification, and improved sensitivity and accuracy of quantification compared to state-of-the-art data-dependent acquisition (DDA)-based phosphoproteomics. Notably, direct DIA without the need of spectral libraries performs close to analyses using project-specific libraries, quantifying > 20,000 phosphopeptides in 15 min single-shot LC-MS analysis per condition. Adaptation of a 3D multiple regression model-based algorithm enables global determination of phosphorylation site stoichiometry in DIA. Scalability of the DIA approach is demonstrated by systematically analyzing the effects of thirty kinase inhibitors in context of epidermal growth factor (EGF) signaling showing that specific protein kinases mediate EGF-dependent phospho-regulation.

Localizing phosphorylation sites by data-independent acquisition (DIA)-based proteomics is still challenging. Here, the authors develop algorithms for phosphosite localization and stoichiometry determination, and incorporate them into single-shot DIA-phosphoproteomics workflows.

Combining Precursor and Fragment Information for Improved Detection of Differential Abundance in Data Independent Acquisition

In bottom-up, label-free discovery proteomics, biological samples are acquired in a data-dependent (DDA) or data-independent (DIA) manner, with peptide signals recorded in an intact (MS1) and fragmented (MS2) form. While DDA has only the MS1 space for quantification, DIA contains both MS1 and MS2 at high quantitative quality. DIA profiles of complex biological matrices such as tissues or cells can contain quantitative interferences, and the interferences at the MS1 and the MS2 signals are often independent. When comparing biological conditions, the interferences can compromise the detection of differential peptide or protein abundance and lead to false positive or false negative conclusions.We hypothesized that the combined use of MS1 and MS2 quantitative signals could improve our ability to detect differentially abundant proteins. Therefore, we developed a statistical procedure incorporating both MS1 and MS2 quantitative information of DIA. We benchmarked the performance of the MS1-MS2-combined method to the individual use of MS1 or MS2 in DIA using four previously published controlled mixtures, as well as in two previously unpublished controlled mixtures. In the majority of the comparisons, the combined method outperformed the individual use of MS1 or MS2. This was particularly true for comparisons with low fold changes, few replicates, and situations where MS1 and MS2 were of similar quality. When applied to a previously unpublished investigation of lung cancer, the MS1-MS2-combined method increased the coverage of known activated pathways.Since recent technological developments continue to increase the quality of MS1 signals (e.g. using the BoxCar scan mode for Orbitrap instruments), the combination of the MS1 and MS2 information has a high potential for future statistical analysis of DIA data.

Systematic Comparison of Strategies for the Enrichment of Lysosomes by Data Independent Acquisition

In mammalian cells, the lysosome is the main organelle for the degradation of macromolecules and the recycling of their building blocks. Correct lysosomal function is essential, and mutations in every known lysosomal hydrolase result in so-called lysosomal storage disorders, a group of rare and often fatal inherited diseases. Furthermore, it is becoming more and more apparent that lysosomes play also decisive roles in other diseases, such as cancer and common neurodegenerative disorders. This leads to an increasing interest in the proteomic analysis of lysosomes for which enrichment is a prerequisite. In this study, we compared the four most common strategies for the enrichment of lysosomes using data-independent acquisition. We performed centrifugation at 20,000 × g to generate an organelle-enriched pellet, two-step sucrose density gradient centrifugation, enrichment by superparamagnetic iron oxide nanoparticles (SPIONs), and immunoprecipitation using a 3xHA tagged version of the lysosomal membrane protein TMEM192. Our results show that SPIONs and TMEM192 immunoprecipitation outperform the other approaches with enrichment factors of up to 118-fold for certain proteins relative to whole cell lysates. Furthermore, we achieved an increase in identified lysosomal proteins and a higher reproducibility in protein intensities for label-free quantification in comparison to the other strategies.

Abstract C015: Limited proteolysis coupled to mass spectrometry (LiP-Quant), a novel drug target deconvolution strategy

Background: High attrition rates in target-centric drug development approaches, as well as a limited number of targets, have shifted the focus of drug development back towards phenotypic screening. In parallel, novel proteomics-based target deconvolution approaches to drug target identification have gained popularity. Limited proteolysis coupled with mass spectrometry (LiP-MS) is a new target deconvolution technique that exploits protein structural alterations, as well as steric effects driven by drug binding. A major advantage of LiP-MS is its unique focus on detection of signature peptides that report on ligand binding, which are generated by a limited digestion and identified by proteomic analysis. Here we demonstrate the performance of LiP-MS using two well-characterized kinase inhibitors, Selumetinib (SE) and Staurosporine (ST), as well as two natural product-derived phosphatase inhibitors Calyculin A and Fostriecin in HeLa cell lysate. Additionally, we introduce a LiP workflow that can be used to dissect protein-drug interactions in a more nuanced manner. Results Herein, we demonstrate the ability of our LiP-MS approach to identify unique peptides generated by the binding of either a highly-specific (SE) or broad specificity (ST) kinase inhibitor. Taking the top 200 identified target candidate peptides ranked by LiP score from the broad inhibitor (staurosporine), GO enrichment analysis confirmed a highly significant 3-fold enrichment for kinase targets (p < 2E-5). For the more challenging selumetinib experiment, the direct targets MEK1 and MEK2 were clearly identified as main hits with 5 peptides in the top 16 (sorted by LiP score). This represents a highly specific enrichment given that we quantified > 100,000 peptide precursors in this experiment. These findings confirm our approach’s ability to identify genuine drug targets regardless of drug specificity. To characterize the specificity of LiP-MS, we treated lysate with two separate protein phosphatase inhibitors. According to literature calyculin A targets protein phosphatase 1 and 2 (PP1A and PP2A) and fostriecin also targets PP2A, in addition to protein phosphatase 4 (PP4C) but does not bind PP1A. We identified both targets of calyculin A, with 14 of the top 15 peptides by LiP score being from either PP2A or PP1A. Robust phosphatase identification (three main targets PP2A, PP4C and PP6C) was also observed with fostriecin treatment with a different profile from calyculin A. In line with literature, the hits of fostriecin did not include PP1A, despite the high homology of the protein phosphatase family. Finally, taking advantage of the peptide resolution of LiP-MS, we employed this workflow to investigate the Ca2+-induced conformational switch in the recombinant protein calmodulin by monitoring structure-specific peptides which cover its entire amino acid sequence. Conclusions Collectively, this data demonstrates that LiP-MS can be used to effectively identify protein drug targets and characterize the binding properties, regardless of the specificity of the compound. These capabilities make LiP-MS a powerful target deconvolution and identification strategy. In addition, we envision the development of LiP-MS for recombinant proteins as a high-throughput strategy for binding site characterization of protein-small molecule and protein-protein interactions.

Citation Format: Yuehan Feng, Nigel Beaton, Roland Bruderer, Ilaria Piazza, Paola Picotti, Lukas Reiter. Limited proteolysis coupled to mass spectrometry (LiP-Quant), a novel drug target deconvolution strategy [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr C015. doi:10.1158/1535-7163.TARG-19-C015

Abstract 2755: Limited proteolysis coupled to mass spectrometry (LiP-MS), a novel drug target deconvolution strategy

Background High attrition rates in target-centric drug development approaches, as well as a limited number of targets, have shifted the focus of drug development back towards phenotypic screening. In parallel, novel proteomics-based target deconvolution approaches to drug target identification have gained popularity. Limited proteolysis coupled with mass spectrometry (LiP-MS) is a new target deconvolution technique that exploits protein structural alterations driven by drug binding. A major advantage of LiP-MS is its unique focus on detection of peptides that report on ligand binding induced structural changes that are generated by a limited digestion and identified by proteomic analysis. Here we demonstrate the performance of LiP-MS using the protein phosphatase inhibitor Calyculin A, as well as two well known kinase inhibitors, selumetinib (SE) and staurosporine (ST), in HeLa cell lysate.

Materials and Methods Mechanically sheared HeLa cell lysate was incubated with compound at multiple concentrations. Next, a limited digest was performed using proteinase K. Finally, the limited digests were processed to peptides with trypsin for mass spectrometry analysis. A project-specific spectral library was generated using data-dependent acquisition (DDA) mass spectrometry and for quantitative analysis data-independent acquisition (DIA) data were recorded and analyzed using Spectronaut Pulsar X.

Results LiP-MS identifies several peptides for the phosphatase inhibitor Calyculin A, with IC50 values of 52 nM and 17 nM for PP1A and PP2A respectively. Additionally, a previously unknown target PP1B was also identified among the same family, although with a higher IC50 (74 nM). Through structural inspection of ligand-sensitive peptides we were able to map the drug’s binding site within the phosphatases and predict distal conformational changes, demonstrating that LiP-MS can be used to provide structural insights to ligand-protein binding. Similar dose response relationships were observed for both specific (SE) and broad (ST) kinase inhibitors. Amongst the top 200 identified target candidate peptides ranked by LiP score, GO enrichment analysis confirmed a highly significant 3-fold enrichment for kinase targets (p < 0.00002) in ST-treated lysate, while no such enrichment was observed for SE. However, in SE-treated lysate robust identification of multiple MEK1 peptides, one of the compound’s main targets, was observed. In the case of both kinase inhibitors LiP peptides could be successfully mapped to ATP binding sites, confirming the ability of LiP-MS to model drug-bound protein structure.

Conclusions This data demonstrates that LiP-MS can be used to effectively identify protein drug targets and characterize the binding properties, regardless of the specificity of the compound. These capabilities make LiP-MS a powerful target deconvolution and identification strategy.

Citation Format: Nigel Beaton, Roland Bruderer, Kristina Beeler, Nicholas Dupuis, Ilaria Piazza, Paola Picotti, Lukas Reiter. Limited proteolysis coupled to mass spectrometry (LiP-MS), a novel drug target deconvolution strategy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2755.

Analysis of 1508 Plasma Samples by Capillary-Flow Data-Independent Acquisition Profiles Proteomics of Weight Loss and Maintenance

Comprehensive, high throughput analysis of the plasma proteome has the potential to enable holistic analysis of the health state of an individual. Based on our own experience and the evaluation of recent large-scale plasma mass spectrometry (MS) based proteomic studies, we identified two outstanding challenges: slow and delicate nano-flow liquid chromatography (LC) and irreproducibility of identification of data-dependent acquisition (DDA). We determined an optimal solution reducing these limitations with robust capillary-flow data-independent acquisition (DIA) MS. This platform can measure 31 plasma proteomes per day. Using this setup, we acquired a large-scale plasma study of the diet, obesity and genes dietary (DiOGenes) comprising 1508 samples. Proving the robustness, the complete acquisition was achieved on a single analytical column. Totally, 565 proteins (459 identified with two or more peptide sequences) were profiled with 74% data set completeness. On average 408 proteins (5246 peptides) were identified per acquisition (319 proteins in 90% of all acquisitions). The workflow reproducibility was assessed using 34 quality control pools acquired at regular intervals, resulting in 92% data set completeness with CVs for protein measurements of 10.9%.The profiles of 20 apolipoproteins could be profiled revealing distinct changes. The weight loss and weight maintenance resulted in sustained effects on low-grade inflammation, as well as steroid hormone and lipid metabolism, indicating beneficial effects. Comparison to other large-scale plasma weight loss studies demonstrated high robustness and quality of biomarker candidates identified. Tracking of nonenzymatic glycation indicated a delayed, slight reduction of glycation in the weight maintenance phase. Using stable-isotope-references, we could directly and absolutely quantify 60 proteins in the DIA.In conclusion, we present herein the first large-scale plasma DIA study and one of the largest clinical research proteomic studies to date. Application of this fast and robust workflow has great potential to advance biomarker discovery in plasma.

Data-independent Acquisition Improves Quantitative Cross-linking Mass Spectrometry

Quantitative cross-linking mass spectrometry (QCLMS) reveals structural detail on altered protein states in solution. On its way to becoming a routine technology, QCLMS could benefit from data-independent acquisition (DIA), which generally enables greater reproducibility than data-dependent acquisition (DDA) and increased throughput over targeted methods. Therefore, here we introduce DIA to QCLMS by extending a widely used DIA software, Spectronaut, to also accommodate cross-link data. A mixture of seven proteins cross-linked with bis[sulfosuccinimidyl] suberate (BS3) was used to evaluate this workflow. Out of the 414 identified unique residue pairs, 292 (70%) were quantifiable across triplicates with a coefficient of variation (CV) of 10%, with manual correction of peak selection and boundaries for PSMs in the lower quartile of individual CV values. This compares favorably to DDA where we quantified cross-links across triplicates with a CV of 66%, for a single protein. We found DIA-QCLMS to be capable of detecting changing abundances of cross-linked peptides in complex mixtures, despite the ratio compression encountered when increasing sample complexity through the addition of E. coli cell lysate as matrix. In conclusion, the DIA software Spectronaut can now be used in cross-linking and DIA is indeed able to improve QCLMS.

Surpassing 10 000 identified and quantified proteins in a single run by optimizing current LC-MS instrumentation and data analysis strategy

Optimization of chromatography and data analysis resulted in more than 10 000 proteins in a single shot at a validated FDR of 1% (two-species test) and revealed deep insights into the testis cancer physiology.

Plasma Proteomics for Epidemiology: Increasing Throughput With Standard-Flow Rates

BACKGROUND

Mass spectrometry is selective and sensitive, permitting routine quantification of multiple plasma proteins. However, commonly used nanoflow liquid chromatography (LC) approaches hamper sample throughput, reproducibility, and robustness. For this reason, most publications using plasma proteomics to date are small in study size.

METHODS AND RESULTS

Here, we tested a standard-flow LC mass spectrometry (MS) method using multiple reaction monitoring for the application to large epidemiological cohorts. We have reduced the LC-MS run time to almost a third of the nanoflow LC-MS approach. On the basis of a comparison of the quantification of 100 plasma proteins in >1500 LC-MS runs, the SD range of the retention time during continuous operation was substantially lower with the standard-flow LC-MS (<0.05 minutes) compared with the nanoflow LC-MS method (0.26-0.44 minutes). In addition, the standard-flow LC method also offered less variation in protein measurements. However, 5× more sample volume was required to achieve similar sensitivity. Two different commercial multiple reaction monitoring kits and an antibody-based multiplexing kit were used to compare the apolipoprotein measurements in a subset of samples. In general, good agreement was observed between the 2 multiple reaction monitoring kits, but some of the multiple reaction monitoring-based measurements differed from antibody-based assays.

CONCLUSIONS

The multiplexing capability of LC-MS combined with a standard-flow method increases throughput and reduces the costs of large-scale protein measurements in epidemiological cohorts, but protein rather than peptide standards will be required for defined absolute proteoform quantification.

The taxonomic composition of the donor intestinal microbiota is a major factor influencing the efficacy of faecal microbiota transplantation in therapy refractory ulcerative colitis

BACKGROUND

Faecal microbiota transplantation is an experimental approach for the treatment of patients with ulcerative colitis. Although there is growing evidence that faecal microbiota transplantation is effective in this disease, factors affecting its response are unknown.

AIMS

To establish a faecal microbiota transplantation treatment protocol in ulcerative colitis patients, and to investigate which patient or donor factors are responsible for the treatment success.

METHODS

This is an open controlled trial of repeated faecal microbiota transplantation after antibiotic pre-treatment (FMT-group, n = 17) vs antibiotic pre-treatment only (AB-group, n = 10) in 27 therapy refractory ulcerative colitis patients over 90 days. Faecal samples of donors and patients were analysed by 16SrRNA gene-based microbiota analysis.

RESULTS

In the FMT-group, 10/17 (59%) of patients showed a response and 4/17 (24%) a remission to faecal microbiota transplantation. Response to faecal microbiota transplantation was mainly influenced by the taxonomic composition of the donor's microbiota. Stool of donors with a high bacterial richness (observed species remission 946 ± 93 vs no response 797 ± 181 at 15367 rps) and a high relative abundance of Akkermansia muciniphila (3.3 ± 3.1% vs 0.1 ± 0.2%), unclassified Ruminococcaceae (13.8 ± 5.0% vs 7.5 ± 3.7%), and Ruminococcus spp. (4.9 ± 3.5% vs 1.0 ± 0.7%) were more likely to induce remission. In contrast antibiotic treatment alone (AB-group) was poorly tolerated, probably because of a sustained decrease of intestinal microbial richness.

CONCLUSIONS

The taxonomic composition of the donor's intestinal microbiota is a major factor influencing the efficacy of faecal microbiota transplantation in ulcerative colitis patients. The design of specific microbial preparation might lead to new treatments for ulcerative colitis.

Optimization of Experimental Parameters in Data-Independent Mass Spectrometry Significantly Increases Depth and Reproducibility of Results

Comprehensive, reproducible and precise analysis of large sample cohorts is one of the key objectives of quantitative proteomics. Here, we present an implementation of data-independent acquisition using its parallel acquisition nature that surpasses the limitation of serial MS2 acquisition of data-dependent acquisition on a quadrupole ultra-high field Orbitrap mass spectrometer. In deep single shot data-independent acquisition, we identified and quantified 6,383 proteins in human cell lines using 2-or-more peptides/protein and over 7100 proteins when including the 717 proteins that were identified on the basis of a single peptide sequence. 7739 proteins were identified in mouse tissues using 2-or-more peptides/protein and 8121 when including the 382 proteins that were identified based on a single peptide sequence. Missing values for proteins were within 0.3 to 2.1% and median coefficients of variation of 4.7 to 6.2% among technical triplicates. In very complex mixtures, we could quantify 10,780 proteins and 12,192 proteins when including the 1412 proteins that were identified based on a single peptide sequence. Using this optimized DIA, we investigated large-protein networks before and after the critical period for whisker experience-induced synaptic strength in the murine somatosensory cortex 1-barrel field. This work shows that parallel mass spectrometry enables proteome profiling for discovery with high coverage, reproducibility, precision and scalability.

Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses

Liquid chromatography coupled to tandem mass spectrometry is the main method for high-throughput identification and quantification of peptides and inferred proteins. Within this field, data-independent acquisition (DIA) combined with peptide-centric scoring, exemplified by SWATH-MS, emerged as a scalable method to achieve deep and consistent proteome coverage across large-scale datasets. Here we discuss the adaptation of statistical concepts developed for discovery proteomics based on spectrum-centric scoring to large-scale DIA experiments analyzed with peptide-centric scoring strategies and provide guidance on their application. We show that optimal tradeoffs between sensitivity and specificity require careful considerations of the relationship between proteins in the samples and proteins represented in the spectral library. We propose the application of a global analyte constraint to prevent accumulation of false positives across large-scale datasets. Furthermore, to increase the quality and reproducibility of published proteomic results, well-established confidence criteria should be reported for detected peptide queries, peptides and inferred proteins.

New targeted approaches for the quantification of data-independent acquisition mass spectrometry

The use of data-independent acquisition (DIA) approaches for the reproducible and precise quantification of complex protein samples has increased in the last years. The protein information arising from DIA analysis is stored in digital protein maps (DIA maps) that can be interrogated in a targeted way by using ad hoc or publically available peptide spectral libraries generated on the same sample species as for the generation of the DIA maps. The restricted availability of certain difficult-to-obtain human tissues (i.e., brain) together with the caveats of using spectral libraries generated under variable experimental conditions limits the potential of DIA. Therefore, DIA workflows would benefit from high-quality and extended spectral libraries that could be generated without the need of using valuable samples for library production. We describe here two new targeted approaches, using either classical data-dependent acquisition repositories (not specifically built for DIA) or ad hoc mouse spectral libraries, which enable the profiling of human brain DIA data set. The comparison of our results to both the most extended publically available human spectral library and to a state-of-the-art untargeted method supports the use of these new strategies to improve future DIA profiling efforts.

WITHDRAWN: Heralds of parallel MS: Data-independent acquisition surpassing sequential identification of data dependent acquisition in proteomics

This article has been withdrawn by the authors. This article did not comply with the editorial guidelines of MCP. Specifically, single peptide based protein identifications of 9-19% were included in the analysis and discussed in the results and conclusions. We wish to withdraw this article and resubmit a clarified, corrected manuscript for review.

A multicenter study benchmarks software tools for label-free proteome quantification

Consistent and accurate quantification of proteins by mass spectrometry (MS)-based proteomics depends on the performance of instruments, acquisition methods and data analysis software. In collaboration with the software developers, we evaluated OpenSWATH, SWATH 2.0, Skyline, Spectronaut and DIA-Umpire, five of the most widely used software methods for processing data from sequential window acquisition of all theoretical fragment-ion spectra (SWATH)-MS, which uses data-independent acquisition (DIA) for label-free protein quantification. We analyzed high-complexity test data sets from hybrid proteome samples of defined quantitative composition acquired on two different MS instruments using different SWATH isolation-window setups. For consistent evaluation, we developed LFQbench, an R package, to calculate metrics of precision and accuracy in label-free quantitative MS and report the identification performance, robustness and specificity of each software tool. Our reference data sets enabled developers to improve their software tools. After optimization, all tools provided highly convergent identification and reliable quantification performance, underscoring their robustness for label-free quantitative proteomics.

High-precision iRT prediction in the targeted analysis of data-independent acquisition and its impact on identification and quantitation

Targeted analysis of data‐independent acquisition (DIA) data is a powerful mass spectrometric approach for comprehensive, reproducible and precise proteome quantitation. It requires a spectral library, which contains for all considered peptide precursor ions empirically determined fragment ion intensities and their predicted retention time (RT). RTs, however, are not comparable on an absolute scale, especially if heterogeneous measurements are combined. Here, we present a method for high‐precision prediction of RT, which significantly improves the quality of targeted DIA analysis compared to in silico RT prediction and the state of the art indexed retention time (iRT) normalization approach. We describe a high‐precision normalized RT algorithm, which is implemented in the Spectronaut software. We, furthermore, investigate the influence of nine different experimental factors, such as chromatographic mobile and stationary phase, on iRT precision. In summary, we show that using targeted analysis of DIA data with high‐precision iRT significantly increases sensitivity and data quality. The iRT values are generally transferable across a wide range of experimental conditions. Best results, however, are achieved if library generation and analytical measurements are performed on the same system.