Wideband PRM: Highly Accurate and Sensitive Method for High-Throughput Targeted Proteomics.

Targeted mass spectrometry (MS) approaches, which are powerful methods for uniquely and confidently quantifying a specific panel of proteins in complex biological samples, play a crucial role in validating and clinically translating protein biomarkers discovered through global proteomic profiling. Common targeted MS methods, such as multiple reaction monitoring (MRM) and parallel-reaction monitoring (PRM), employ specific mass spectrometric technologies to quantify protein levels by comparing the transitions of surrogate endogenous (ENDO) peptides with those of stable isotope-labeled (SIL) peptide counterparts. These methods utilizing amino acid analyzed (AAA) SIL peptides warrant sensitive and precise measurements required for targeted MS assays. Compared with MRM, PRM provides higher experimental throughput by simultaneously acquiring all transitions of the target peptides and thereby compensates for different ion suppressions among transitions of a target peptide. However, PRM still suffers different ion suppressions between ENDO and SIL peptides due to spray instability, as the ENDO and SIL peptides were monitored at different liquid chromatography (LC) retention times. Here we introduce a new targeted MS method, termed wideband PRM (WBPRM), that is designed for high-throughput targeted MS analysis. WBPRM employs a wide isolation window for simultaneous fragmentation of both ENDO and SIL peptides along with multiplexed single ion monitoring (SIM) scans for enhanced MS sensitivity of the target peptides. Compared with PRM, WBPRM was demonstrated to provide increased sensitivity, precision, and reproducibility of quantitative measurements of target peptides with increased throughput, allowing more target peptide measurements in a shortened experiment time. WBPRM is a straightforward adaptation to a manufacturer-provided MS method, making it an easily implementable technique, particularly in complex biological samples where the demand for higher precision, sensitivity, and efficiency is paramount.

Novel Online Three-Dimensional Separation Expands the Detectable Functional Landscape of Cellular Phosphoproteome.

Protein phosphorylation is a prevalent post-translational modification that regulates essentially every aspect of cellular processes. Currently, liquid chromatography-tandem mass spectrometry (LC-MS/MS) with an extensive offline sample fractionation and a phosphopeptide enrichment method is a best practice for deep phosphoproteome profiling, but balancing throughput and profiling depth remains a practical challenge. We present an online three-dimensional separation method for ultradeep phosphoproteome profiling that combines an online two-dimensional liquid chromatography separation and an additional gas-phase separation. This method identified over 100,000 phosphopeptides (>60,000 phosphosites) in HeLa cells during 1.5 days of data acquisition, and the largest HeLa cell phosphoproteome significantly expanded the detectable functional landscape of cellular phosphoproteome.

PASS-DIA: A Data-Independent Acquisition Approach for Discovery Studies.

A data-independent acquisition (DIA) approach is being increasingly adopted as a promising strategy for identification and quantitation of proteomes. As most DIA data sets are acquired with wide isolation windows, highly complex MS/MS spectra are generated, which negatively impacts obtaining peptide information through classical protein database searches. Therefore, the analysis of DIA data mainly relies on the evidence of the existence of peptides from prebuilt spectral libraries. Consequently, one major weakness of this method is that it does not account for peptides that are not included in the spectral library, precluding the use of DIA for discovery studies. Here, we present a strategy termed Precursor ion And Small Slice-DIA (PASS-DIA) in which MS/MS spectra are acquired with small isolation windows (slices) and MS/MS spectra are interpreted with accurately determined precursor ion masses. This method enables the direct application of conventional spectrum-centric analysis pipelines for peptide identification and precursor ion-based quantitation. The performance of PASS-DIA was observed to be superior to both data-dependent acquisition (DDA) and conventional DIA experiments with 69 and 48% additional protein identifications, respectively. Application of PASS-DIA for the analysis of post-translationally modified peptides again highlighted its superior performance in characterizing phosphopeptides (77% more), N-terminal acetylated peptides (56% more), and N-glycopeptides (83% more) as compared to DDA alone. Finally, the use of PASS-DIA to characterize a rare proteome of human fallopian tube organoids enabled 34% additional protein identifications than DDA alone and revealed biologically relevant pathways including low abundance proteins. Overall, PASS-DIA is a novel DIA approach for use as a discovery tool that outperforms both conventional DDA and DIA experiments to provide additional protein information. We believe that the PASS-DIA method is an important strategy for discovery-type studies when deeper proteome characterization is required.

Accurate Precursor Mass Assignment Improves Peptide Identification in Data-Independent Acquisition Mass Spectrometry.

Proteomics research today no longer simply seeks exhaustive protein identification; increasingly, it is also desirable to obtain robust, large-scale quantitative information. To accomplish this, data-independent acquisition (DIA) has emerged as a promising strategy largely owing to developments in advanced mass spectrometers and sophisticated data analysis methods. Nevertheless, the highly complex multiplexed MS/MS spectra produced by DIA remain challenging to interpret. Here, we present a novel strategy to analyze DIA data, based on unambiguous precursor mass assignment through the mPE-MMR (multiplexed post-experimental monoisotopic mass refinement) procedure and combined with complementary multistage database searching. Compared to conventional spectral library searching, the accuracy and sensitivity of peptide identification were significantly increased by incorporating precise precursor masses in DIA data. We demonstrate identification of additional peptides absent from spectral libraries, including sample-specific mutated peptides and post-translationally modified peptides using MS-GF+ and MODa/MODi multistage database searching. This first use of unambiguously determined precursor masses to mine DIA data demonstrates considerable potential for further exploitation of this type of experimental data.

Comprehensive Proteome Profiling of Platelet Identified a Protein Profile Predictive of Responses to An Antiplatelet Agent Sarpogrelate.

Sarpogrelate is an antiplatelet agent widely used to treat arterial occlusive diseases. Evaluation of platelet aggregation is essential to monitor therapeutic effects of sarpogrelate. Currently, no molecular signatures are available to evaluate platelet aggregation. Here, we performed comprehensive proteome profiling of platelets collected from 18 subjects before and after sarpogrelate administration using LC-MS/MS analysis coupled with extensive fractionation. Of 5423 proteins detected, we identified 499 proteins affected by sarpogrelate and found that they strongly represented cellular processes related to platelet activation and aggregation, including cell activation, coagulation, and vesicle-mediated transports. Based on the network model of the proteins involved in these processes, we selected three proteins (cut-like homeobox 1; coagulation factor XIII, B polypeptide; and peptidylprolyl isomerase D) that reflect the platelet aggregation-related processes after confirming their alterations by sarpogrelate in independent samples using Western blotting. Our proteomic approach provided a protein profile predictive of therapeutic effects of sarpogrelate.

Multiplexed Post-Experimental Monoisotopic Mass Refinement (mPE-MMR) to Increase Sensitivity and Accuracy in Peptide Identifications from Tandem Mass Spectra of Cofragmentation.

Mass spectrometry (MS)-based proteomics, which uses high-resolution hybrid mass spectrometers such as the quadrupole-orbitrap mass spectrometer, can yield tens of thousands of tandem mass (MS/MS) spectra of high resolution during a routine bottom-up experiment. Despite being a fundamental and key step in MS-based proteomics, the accurate determination and assignment of precursor monoisotopic masses to the MS/MS spectra remains difficult. The difficulties stem from imperfect isotopic envelopes of precursor ions, inaccurate charge states for precursor ions, and cofragmentation. We describe a composite method of utilizing MS data to assign accurate monoisotopic masses to MS/MS spectra, including those subject to cofragmentation. The method, "multiplexed post-experiment monoisotopic mass refinement" (mPE-MMR), consists of the following: multiplexing of precursor masses to assign multiple monoisotopic masses of cofragmented peptides to the corresponding multiplexed MS/MS spectra, multiplexing of charge states to assign correct charges to the precursor ions of MS/MS spectra with no charge information, and mass correction for inaccurate monoisotopic peak picking. When combined with MS-GF+, a database search algorithm based on fragment mass difference, mPE-MMR effectively increases both sensitivity and accuracy in peptide identification from complex high-throughput proteomics data compared to conventional methods.

Efficient Exploitation of Separation Space in Two-Dimensional Liquid Chromatography System for Comprehensive and Efficient Proteomic Analyses.

Proteomics aims to achieve complete profiling of the protein content and protein modifications in cells, tissues, and biofluids and to quantitatively determine changes in their abundances. This information serves to elucidate cellular processes and signaling pathways and to identify candidate protein biomarkers and/or therapeutic targets. Analyses must therefore be both comprehensive and efficient. Here, we present a novel online two-dimensional reverse-phase/reverse-phase liquid chromatography separation platform, which is based on a newly developed online noncontiguous fractionating and concatenating device (NCFC fractionator). In bottom-up proteomics analyses of a complex proteome, this system provided significantly improved exploitation of the separation space of the two RPs, considerably increasing the numbers of peptides identified compared to a contiguous 2D-RP/RPLC method. The fully automated online 2D-NCFC-RP/RPLC system bypassed a number of labor-intensive manual processes required with the previously described offline 2D-NCFC RP/RPLC method, and thus, it offers minimal sample loss in a context of highly reproducible 2D-RP/RPLC experiments.

A protein profile of visceral adipose tissues linked to early pathogenesis of type 2 diabetes mellitus.

Adipose tissue is increasingly recognized as an endocrine organ playing important pathophysiological roles in metabolic abnormalities, such as obesity, cardiovascular disease, and type 2 diabetes mellitus (T2DM). In particular, visceral adipose tissue (VAT), as opposed to subcutaneous adipose tissue, is closely linked to the pathogenesis of insulin resistance and T2DM. Despite the importance of VAT, its molecular signatures related to the pathogenesis of T2DM have not been systematically explored. Here, we present comprehensive proteomic analysis of VATs in drug-naïve early T2DM patients and subjects with normal glucose tolerance. A total of 4,707 proteins were identified in LC-MS/MS experiments. Among them, 444 increased in abundance in T2DM and 328 decreased. They are involved in T2DM-related processes including inflammatory responses, peroxisome proliferator-activated receptor signaling, oxidative phosphorylation, fatty acid oxidation, and glucose metabolism. Of these proteins, we selected 11 VAT proteins that can represent alteration in early T2DM patients. Among them, up-regulation of FABP4, C1QA, S100A8, and SORBS1 and down-regulation of ACADL and PLIN4 were confirmed in VAT samples of independent early T2DM patients using Western blot. In summary, our profiling provided a comprehensive basis for understanding the link of a protein profile of VAT to early pathogenesis of T2DM.

A simple dual online ultra-high pressure liquid chromatography system (sDO-UHPLC) for high throughput proteome analysis.

We report a new and simple design of a fully automated dual-online ultra-high pressure liquid chromatography system. The system employs only two nano-volume switching valves (a two-position four port valve and a two-position ten port valve) that direct solvent flows from two binary nano-pumps for parallel operation of two analytical columns and two solid phase extraction (SPE) columns. Despite the simple design, the sDO-UHPLC offers many advantageous features that include high duty cycle, back flushing sample injection for fast and narrow zone sample injection, online desalting, high separation resolution and high intra/inter-column reproducibility. This system was applied to analyze proteome samples not only in high throughput deep proteome profiling experiments but also in high throughput MRM experiments.

Compact variant-rich customized sequence database and a fast and sensitive database search for efficient proteogenomic analyses.

In proteogenomic analysis, construction of a compact, customized database from mRNA-seq data and a sensitive search of both reference and customized databases are essential to accurately determine protein abundances and structural variations at the protein level. However, these tasks have not been systematically explored, but rather performed in an ad-hoc fashion. Here, we present an effective method for constructing a compact database containing comprehensive sequences of sample-specific variants--single nucleotide variants, insertions/deletions, and stop-codon mutations derived from Exome-seq and RNA-seq data. It, however, occupies less space by storing variant peptides, not variant proteins. We also present an efficient search method for both customized and reference databases. The separate searches of the two databases increase the search time, and a unified search is less sensitive to identify variant peptides due to the smaller size of the customized database, compared to the reference database, in the target-decoy setting. Our method searches the unified database once, but performs target-decoy validations separately. Experimental results show that our approach is as fast as the unified search and as sensitive as the separate searches. Our customized database includes mutation information in the headers of variant peptides, thereby facilitating the inspection of peptide-spectrum matches.

Estimating influence of cofragmentation on peptide quantification and identification in iTRAQ experiments by simulating multiplexed spectra.

Isobaric tag-based quantification such as iTRAQ and TMT is a promising approach to mass spectrometry-based quantification in proteomics as it provides wide proteome coverage with greatly increased experimental throughput. However, it is known to suffer from inaccurate quantification and identification of a target peptide due to cofragmentation of multiple peptides, which likely leads to under-estimation of differentially expressed peptides (DEPs). A simple method of filtering out cofragmented spectra with less than 100% precursor isolation purity (PIP) would decrease the coverage of iTRAQ/TMT experiments. In order to estimate the impact of cofragmentation on quantification and identification of iTRAQ-labeled peptide samples, we generated multiplexed spectra with varying degrees of PIP by mixing the two MS/MS spectra of 100% PIP obtained in global proteome profiling experiments on gastric tumor-normal tissue pair proteomes labeled by 4-plex iTRAQ. Despite cofragmentation, the simulation experiments showed that more than 99% of multiplexed spectra with PIP greater than 80% were correctly identified by three different database search engines-MODa, MS-GF+, and Proteome Discoverer. Using the multiplexed spectra that have been correctly identified, we estimated the effect of cofragmentation on peptide quantification. In 74% of the multiplexed spectra, however, the cancer-to-normal expression ratio was compressed, and a fair number of spectra showed the "ratio inflation" phenomenon. On the basis of the estimated distribution of distortions on quantification, we were able to calculate cutoff values for DEP detection from cofragmented spectra, which were corrected according to a specific PIP and probability of type I (or type II) error. When we applied these corrected cutoff values to real cofragmented spectra with PIP larger than or equal to 70%, we were able to identify reliable DEPs by removing about 25% of DEPs, which are highly likely to be false positives. Our experimental results provide useful insight into the effect of cofragmentation on isobaric tag-based quantification methods. The simulation procedure as well as the corrected cutoff calculation method could be adopted for quantifying the effect of cofragmentation and reducing false positives (or false negatives) in the DEP identification with general quantification experiments based on isobaric labeling techniques.

Targeted proteomics data interpretation with DeepMRM.

Targeted proteomics is widely utilized in clinical proteomics; however, researchers often devote substantial time to manual data interpretation, which hinders the transferability, reproducibility, and scalability of this approach. We introduce DeepMRM, a software package based on deep learning algorithms for object detection developed to minimize manual intervention in targeted proteomics data analysis. DeepMRM was evaluated on internal and public datasets, demonstrating superior accuracy compared with the community standard tool Skyline. To promote widespread adoption, we have incorporated a stand-alone graphical user interface for DeepMRM and integrated its algorithm into the Skyline software package as an external tool.

Proteogenomic landscape of human pancreatic ductal adenocarcinoma in an Asian population reveals tumor cell-enriched and immune-rich subtypes.

We report a proteogenomic analysis of pancreatic ductal adenocarcinoma (PDAC). Mutation-phosphorylation correlations identified signaling pathways associated with somatic mutations in significantly mutated genes. Messenger RNA-protein abundance correlations revealed potential prognostic biomarkers correlated with patient survival. Integrated clustering of mRNA, protein and phosphorylation data identified six PDAC subtypes. Cellular pathways represented by mRNA and protein signatures, defining the subtypes and compositions of cell types in the subtypes, characterized them as classical progenitor (TS1), squamous (TS2-4), immunogenic progenitor (IS1) and exocrine-like (IS2) subtypes. Compared with the mRNA data, protein and phosphorylation data further classified the squamous subtypes into activated stroma-enriched (TS2), invasive (TS3) and invasive-proliferative (TS4) squamous subtypes. Orthotopic mouse PDAC models revealed a higher number of pro-tumorigenic immune cells in TS4, inhibiting T cell proliferation. Our proteogenomic analysis provides significantly mutated genes/biomarkers, cellular pathways and cell types as potential therapeutic targets to improve stratification of patients with PDAC.

Fully automated multifunctional ultrahigh pressure liquid chromatography system for advanced proteome analyses.

A multifunctional liquid chromatography system that performs 1-dimensional, 2-dimensional (strong cation exchange/reverse phase liquid chromatography or SCX/RPLC) separations and online phosphopeptide enrichment using a single binary nanoflow pump has been developed. With a simple operation of a function selection valve equipped with a SCX column and a TiO2 (titanium dioxide) column, a fully automated selection of three different experiment modes was achieved. Because the current system uses essentially the same solvent flow paths, the same trap column, and the same separation column for reverse-phase separation of 1D, 2D, and online phosphopeptides enrichment experiments, the elution time information obtained from these experiments is in excellent agreement, which facilitates correlating peptide information from different experiments. The final reverse-phase separation of the three experiments is completely decoupled from all of the function selection processes; thereby salts or acids from SCX or TiO2 column do not affect the efficiency of the reverse-phase separation.

Aß-induced mitochondrial dysfunction in neural progenitors controls KDM5A to influence neuronal differentiation.

Mitochondria in neural progenitors play a crucial role in adult hippocampal neurogenesis by being involved in fate decisions for differentiation. However, the molecular mechanisms by which mitochondria are related to the genetic regulation of neuronal differentiation in neural progenitors are poorly understood. Here, we show that mitochondrial dysfunction induced by amyloid-beta (Aß) in neural progenitors inhibits neuronal differentiation but has no effect on the neural progenitor stage. In line with the phenotypes shown in Alzheimer's disease (AD) model mice, Aß-induced mitochondrial damage in neural progenitors results in deficits in adult hippocampal neurogenesis and cognitive function. Based on hippocampal proteome changes after mitochondrial damage in neural progenitors identified through proteomic analysis, we found that lysine demethylase 5A (KDM5A) in neural progenitors epigenetically suppresses differentiation in response to mitochondrial damage. Mitochondrial damage characteristically causes KDM5A degradation in neural progenitors. Since KDM5A also binds to and activates neuronal genes involved in the early stage of differentiation, functional inhibition of KDM5A consequently inhibits adult hippocampal neurogenesis. We suggest that mitochondria in neural progenitors serve as the checkpoint for neuronal differentiation via KDM5A. Our findings not only reveal a cell-type-specific role of mitochondria but also suggest a new role of KDM5A in neural progenitors as a mediator of retrograde signaling from mitochondria to the nucleus, reflecting the mitochondrial status.

Label-Free Quantitative Proteome Profiling of Cerebrospinal Fluid from a Rat Stroke Model with Stem Cell Therapy.

Human adipose-derived mesenchymal stem cells (hAMSCs) are capable of immunomodulation and regeneration after neural injury. For these reasons, hAMSCs have been investigated as a promising stem cell candidate for stroke treatment. However, noninvasive experiments studying the effects of grafted stem cells in the host brain have not yet been reported. Cerebrospinal fluid (CSF), which can be collected without sacrificing the subject, is involved in physiological control of the brain and reflects the pathophysiology of various neurological disorders of the central nervous system (CNS). Following stem cell transplantation in a stroke model, quantitative analysis of CSF proteome changes can potentially reveal the therapeutic effect of stem cells on the host CNS. We examined hAMSC-secreted proteins obtained from serum-free culture medium by liquid chromatography-tandem mass spectrometry (LC-MS/MS), which identified several extracellular matrix proteins, supporting the well-known active paracrine function of hAMSCs. Subsequently, we performed label-free quantitative proteomic analysis on CSF samples from rat stroke models intravenously injected with hAMSC (experimental) or phosphate buffered saline (control). In total, 524 proteins were identified; among them, 125 and 91 proteins were increased and decreased with hAMSC treatment, respectively. Furthermore, gene set enrichment analysis revealed three proteins, 14-3-3 theta, MAG, and neurocan, that showed significant increases in the hAMSC-treated model; these proteins are core members of neurotrophin signaling, nerve growth factor (NGF) signaling, and glycosaminoglycan metabolism, respectively. Subsequent histological and neurologic function experiments validated proliferative neurogenesis in the hAMSC-treated stroke model. We conclude that (i) intravenous injection of hAMSCs can induce neurologic recovery in a rat stroke model and (ii) CSF may reflect the therapeutic effect of hAMSCs. Additionally, proteins as 14-3-3 theta, MAG, and neurocan could be considered as potential CSF biomarkers of neuroregeneration. These CSF proteome profiling results would be utilized as valuable resource in further stroke studies.

Integrated post-experiment monoisotopic mass refinement: an integrated approach to accurately assign monoisotopic precursor masses to tandem mass spectrometric data.

Accurate assignment of monoisotopic precursor masses to tandem mass spectrometric (MS/MS) data is a fundamental and critically important step for successful peptide identifications in mass spectrometry based proteomics. Here we describe an integrated approach that combines three previously reported methods of treating MS/MS data for precursor mass refinement. This combined method, "integrated post-experiment monoisotopic mass refinement" (iPE-MMR), integrates steps (1) generation of refined MS/MS data by DeconMSn; (2) additional refinement of the resultant MS/MS data by a modified version of PE-MMR; and (3) elimination of systematic errors of precursor masses using DtaRefinery. iPE-MMR is the first method that utilizes all MS information from multiple MS scans of a precursor ion including multiple charge states, in an MS scan, to determine precursor mass. With the combination of these methods, iPE-MMR increases sensitivity in peptide identification and provides increased accuracy when applied to complex high-throughput proteomics data.

Postexperiment monoisotopic mass filtering and refinement (PE-MMR) of tandem mass spectrometric data increases accuracy of peptide identification in LC/MS/MS.

Methods for treating MS/MS data to achieve accurate peptide identification are currently the subject of much research activity. In this study we describe a new method for filtering MS/MS data and refining precursor masses that provides highly accurate analyses of massive sets of proteomics data. This method, coined "postexperiment monoisotopic mass filtering and refinement" (PE-MMR), consists of several data processing steps: 1) generation of lists of all monoisotopic masses observed in a whole LC/MS experiment, 2) clusterization of monoisotopic masses of a peptide into unique mass classes (UMCs) based on their masses and LC elution times, 3) matching the precursor masses of the MS/MS data to a representative mass of a UMC, and 4) filtration of the MS/MS data based on the presence of corresponding monoisotopic masses and refinement of the precursor ion masses by the UMC mass. PE-MMR increases the throughput of proteomics data analysis, by efficiently removing "garbage" MS/MS data prior to database searching, and improves the mass measurement accuracies (i.e. 0.05 +/- 1.49 ppm for yeast data (from 4.46 +/- 2.81 ppm) and 0.03 +/- 3.41 ppm for glycopeptide data (from 4.8 +/- 7.4 ppm)) for an increased number of identified peptides. In proteomics analyses of glycopeptide-enriched samples, PE-MMR processing greatly reduces the degree of false glycopeptide identification by correctly assigning the monoisotopic masses for the precursor ions prior to database searching. By applying this technique to analyses of proteome samples of varying complexities, we demonstrate herein that PE-MMR is an effective and accurate method for treating massive sets of proteomics data.

Prognostic plasma protein panel for Aß deposition in the brain in Alzheimer's disease.

Alzheimer's disease (AD) is the most common age-associated dementia. Many studies have sought to predict cerebral amyloid deposition, the major pathological hallmark of AD, using body fluids such as blood or cerebral spinal fluid (CSF). The use of blood in diagnostic procedures is widespread in medicine; however, existing blood biomarkers for AD remain unreliable. We sought to discover blood biomarkers that discriminate Aß deposition status in the brain. This study used 107 individuals who were cognitively normal (CN), 107 patients with mild cognitive impairment (MCI), and 40 AD patients with Pittsburg compound B positron emission tomography (PiB-PET) amyloid imaging data available. We found five plasma biomarker candidates via mass spectrometry (MS) based-proteomic analysis and validated these proteins using enzyme-linked immunosorbent assay (ELISA). Our integrated models were highly predictive of brain amyloid deposition, exhibiting 0.871 accuracy with 79% sensitivity and 84% specificity overall, and 0.836 accuracy with 68% sensitivity and 90% specificity in patients with MCI. These results indicated that a combination of proteomic-based blood proteins might be a possible biomarker set for predicting cerebral amyloid deposition.

Proteogenomic Characterization of Human Early-Onset Gastric Cancer.

We report proteogenomic analysis of diffuse gastric cancers (GCs) in young populations. Phosphoproteome data elucidated signaling pathways associated with somatic mutations based on mutation-phosphorylation correlations. Moreover, correlations between mRNA and protein abundances provided potential oncogenes and tumor suppressors associated with patient survival. Furthermore, integrated clustering of mRNA, protein, phosphorylation, and N-glycosylation data identified four subtypes of diffuse GCs. Distinguishing these subtypes was possible by proteomic data. Four subtypes were associated with proliferation, immune response, metabolism, and invasion, respectively; and associations of the subtypes with immune- and invasion-related pathways were identified mainly by phosphorylation and N-glycosylation data. Therefore, our proteogenomic analysis provides additional information beyond genomic analyses, which can improve understanding of cancer biology and patient stratification in diffuse GCs.