There is a pressing need to identify therapeutic targets in tumors with low mutation rates such as the malignant pediatric brain tumor medulloblastoma. To address this challenge, we quantitatively ...profiled global proteomes and phospho-proteomes of 45 medulloblastoma samples. Integrated analyses revealed that tumors with similar RNA expression vary extensively at the post-transcriptional and post-translational levels. We identified distinct pathways associated with two subsets of SHH tumors, and found post-translational modifications of MYC that are associated with poor outcomes in group 3 tumors. We found kinases associated with subtypes and showed that inhibiting PRKDC sensitizes MYC-driven cells to radiation. Our study shows that proteomics enables a more comprehensive, functional readout, providing a foundation for future therapeutic strategies.
•Deep proteomic profiling reveals mechanistic differences among medulloblastomas•Proteomic-defined SHH subtypes do not differ in RNA expression•MYC phosphorylation events define a higher risk subset of group 3 patients•Inhibiting PRKDC may sensitize MYC-activated medulloblastoma tumors to radiation
Archer et al. quantitatively profile global proteomes of medulloblastomas (MB). They identify different pathways associated with subsets of SHH MB and post-translational modifications of MYC associated with poor outcomes in group 3 MB. Inhibition of PRKDC sensitizes MYC-driven cells to radiation.
The Clinical Proteomic Tumor Analysis Consortium (CPTAC) of the National Cancer Institute (NCI) has launched an Assay Portal (http://assays.cancer.gov) to serve as an open-source repository of ...well-characterized targeted proteomic assays. The portal is designed to curate and disseminate highly characterized, targeted mass spectrometry (MS)-based assays by providing detailed assay performance characterization data, standard operating procedures, and access to reagents. Assay content is accessed via the portal through queries to find assays targeting proteins associated with specific cellular pathways, protein complexes, or specific chromosomal regions. The position of the peptide analytes for which there are available assays are mapped relative to other features of interest in the protein, such as sequence domains, isoforms, single nucleotide polymorphisms, and posttranslational modifications. The overarching goals are to enable robust quantification of all human proteins and to standardize the quantification of targeted MS-based assays to ultimately enable harmonization of results over time and across laboratories.
Cell-surface proteins (CSPs) mediate intercellular communication throughout the lives of multicellular organisms. However, there are no generalizable methods for quantitative CSP profiling in ...specific cell types in vertebrate tissues. Here, we present in situ cell-surface proteome extraction by extracellular labeling (iPEEL), a proximity labeling method in mice that enables spatiotemporally precise labeling of cell-surface proteomes in a cell-type-specific environment in native tissues for discovery proteomics. Applying iPEEL to developing and mature cerebellar Purkinje cells revealed differential enrichment in CSPs with post-translational protein processing and synaptic functions in the developing and mature cell-surface proteomes, respectively. A proteome-instructed in vivo loss-of-function screen identified a critical, multifaceted role for Armh4 in Purkinje cell dendrite morphogenesis. Armh4 overexpression also disrupts dendrite morphogenesis; this effect requires its conserved cytoplasmic domain and is augmented by disrupting its endocytosis. Our results highlight the utility of CSP profiling in native mammalian tissues for identifying regulators of cell-surface signaling.
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•iPEEL: a method for cell-type-specific in situ cell-surface proteomics in mice•iPEEL labels cell-surface proteomes of a variety of cell types from diverse organs•Cell-surface proteomes of developing and mature cerebellar Purkinje cells profiled•Armh4, a proteome-informed candidate, has a critical role in dendrite morphogenesis
Shuster and Li et al. introduce iPEEL, a method for cell-type-specific in situ cell-surface proteome profiling in mice. Using iPEEL, they profile the cell-surface proteomes of developing and mature cerebellar Purkinje cells and identify a multifaceted dendritic morphogenesis function for Armh4, a transmembrane protein preferentially expressed in developing Purkinje cells.
Proteogenomics involves the integrative analysis of genomic, transcriptomic, proteomic and post-translational modification data produced by next-generation sequencing and mass spectrometry-based ...proteomics. Several publications by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and others have highlighted the impact of proteogenomics in enabling deeper insight into the biology of cancer and identification of potential drug targets. In order to encapsulate the complex data processing required for proteogenomics, and provide a simple interface to deploy a range of algorithms developed for data analysis, we have developed PANOPLY—a cloud-based platform for automated and reproducible proteogenomic data analysis. A wide array of algorithms have been implemented, and we highlight the application of PANOPLY to the analysis of cancer proteogenomic data.
In modern clinical neuro-oncology, histopathological diagnosis affects therapeutic decisions and prognostic estimation more than any other variable. Among high-grade gliomas, histologically classic ...glioblastomas and anaplastic oligodendrogliomas follow markedly different clinical courses. Unfortunately, many malignant gliomas are diagnostically challenging; these nonclassic lesions are difficult to classify by histological features, generating considerable interobserver variability and limited diagnostic reproducibility. The resulting tentative pathological diagnoses create significant clinical confusion. We investigated whether gene expression profiling, coupled with class prediction methodology, could be used to classify high-grade gliomas in a manner more objective, explicit, and consistent than standard pathology. Microarray analysis was used to determine the expression of approximately 12000 genes in a set of 50 gliomas, 28 glioblastomas and 22 anaplastic oligodendrogliomas. Supervised learning approaches were used to build a two-class prediction model based on a subset of 14 glioblastomas and 7 anaplastic oligodendrogliomas with classic histology. A 20-feature k-nearest neighbor model correctly classified 18 of the 21 classic cases in leave-one-out cross-validation when compared with pathological diagnoses. This model was then used to predict the classification of clinically common, histologically nonclassic samples. When tumors were classified according to pathology, the survival of patients with nonclassic glioblastoma and nonclassic anaplastic oligodendroglioma was not significantly different (P = 0.19). However, class distinctions according to the model were significantly associated with survival outcome (P = 0.05). This class prediction model was capable of classifying high-grade, nonclassic glial tumors objectively and reproducibly. Moreover, the model provided a more accurate predictor of prognosis in these nonclassic lesions than did pathological classification. These data suggest that class prediction models, based on defined molecular profiles, classify diagnostically challenging malignant gliomas in a manner that better correlates with clinical outcome than does standard pathology.
Herein, zinc oxide nanoparticles (ZnO NPs) were greenly synthesized from Tridax procumbens aqueous leaf extract (TPE) and characterized physically (e.g., Fourier-transform infrared (FTIR) ...spectroscopy and scanning electron microscopy (SEM)) and biologically (test of their anti-diabetic activity). Anti-diabetic activities of TPE and TPE-derived ZnO NPs have been carried out in a streptozotocin (STZ)—induced diabetic rat model. Diabetes mellitus (DM) was induced with a single intraperitoneal dosage of the glucose analogue STZ (55 mg/Kg) known to be particularly toxic to pancreatic insulin-producing beta-cells. TPE and TPE-derived ZnO NPs were administered orally, once every day for 21 days in diabetic rats, at 100 and 200 mg/Kg, respectively. The standard antidiabetic medication, glibenclamide, was used as a control at a dose of 10 mg/Kg. Various parameters were investigated, including bodyweight (bw) variations, glycemia, lipidaemia, glycated hemoglobin (HbA1c), and histopathological alterations in the rat’s liver and pancreas. The TPE-mediated NPs were small, spherical, stable, and uniform. Compared to TPE and, to a lesser extent, glibenclamide, TPE-derived ZnO NPs lowered blood glucose levels considerably (p < 0.05) and in a dose-dependent manner while preventing body weight loss. Further, positive benefits for both the lipid profile and glycated hemoglobin were also noticed with TPE-derived ZnO NPs. The histopathological assessment revealed that synthesized TPE-derived ZnO NPs are safe, non-toxic, and biocompatible. At 200 mg/Kg/day, TPE-derived ZnO NPs had a more substantial hypoglycemic response than at 100 mg/Kg/day. Thus, in this first reported experimental setting, ZnO NPs biosynthesized from the leaf extract of Tridax procumbens exert more potent anti-diabetic activity than TPE and glibenclamide. We conclude that such a greenly prepared nanomaterial may be a promising alternative or complementary (adjuvant) therapy, at least to the current Indian’s traditional medicine system. Translational findings are prompted in human populations to determine the efficacy of these NPs.
Clear cell renal cell carcinomas (ccRCCs) represent ∼75% of RCC cases and account for most RCC-associated deaths. Inter- and intratumoral heterogeneity (ITH) results in varying prognosis and ...treatment outcomes. To obtain the most comprehensive profile of ccRCC, we perform integrative histopathologic, proteogenomic, and metabolomic analyses on 305 ccRCC tumor segments and 166 paired adjacent normal tissues from 213 cases. Combining histologic and molecular profiles reveals ITH in 90% of ccRCCs, with 50% demonstrating immune signature heterogeneity. High tumor grade, along with BAP1 mutation, genome instability, increased hypermethylation, and a specific protein glycosylation signature define a high-risk disease subset, where UCHL1 expression displays prognostic value. Single-nuclei RNA sequencing of the adverse sarcomatoid and rhabdoid phenotypes uncover gene signatures and potential insights into tumor evolution. In vitro cell line studies confirm the potential of inhibiting identified phosphoproteome targets. This study molecularly stratifies aggressive histopathologic subtypes that may inform more effective treatment strategies.
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•Integrated multi-omics and histopathology reveal intratumoral heterogeneity in ccRCCs•Signatures of aggressive sarcomatoid and rhabdoid histology are uncovered by snRNA-seq•High-grade ccRCCs have specific glycoproteomic, metabolomic, and methylation signatures•UCHL1 correlates with methylation, genome instability, BAP1 mutation, and poor survival
Li et al. integrate histopathologic, proteogenomic, and metabolomic data from 305 tumor segments and reveal intratumoral heterogeneity in at least 90% of clear cell renal cell carcinomas, signatures for sarcomatoid and rhabdoid features, and prognostic value of UCHL1. This study molecularly stratifies aggressive histopathologic subtypes to inform effective treatment strategies.
Adoption of targeted mass spectrometry (MS) approaches such as multiple reaction monitoring (MRM) to study biological and biomedical questions is well underway in the proteomics community. Successful ...application depends on the ability to generate reliable assays that uniquely and confidently identify target peptides in a sample. Unfortunately, there is a wide range of criteria being applied to say that an assay has been successfully developed. There is no consensus on what criteria are acceptable and little understanding of the impact of variable criteria on the quality of the results generated. Publications describing targeted MS assays for peptides frequently do not contain sufficient information for readers to establish confidence that the tests work as intended or to be able to apply the tests described in their own labs. Guidance must be developed so that targeted MS assays with established performance can be made widely distributed and applied by many labs worldwide. To begin to address the problems and their solutions, a workshop was held at the National Institutes of Health with representatives from the multiple communities developing and employing targeted MS assays. Participants discussed the analytical goals of their experiments and the experimental evidence needed to establish that the assays they develop work as intended and are achieving the required levels of performance. Using this “fit-for-purpose” approach, the group defined three tiers of assays distinguished by their performance and extent of analytical characterization. Computational and statistical tools useful for the analysis of targeted MS results were described. Participants also detailed the information that authors need to provide in their manuscripts to enable reviewers and readers to clearly understand what procedures were performed and to evaluate the reliability of the peptide or protein quantification measurements reported. This paper presents a summary of the meeting and recommendations.
Quantitative proteomics holds considerable promise for elucidation of basic biology and for clinical biomarker discovery. However, it has been difficult to fulfill this promise due to over-reliance ...on identification-based quantitative methods and problems associated with chromatographic separation reproducibility. Here we describe new algorithms termed “Landmark Matching” and “Peak Matching” that greatly reduce these problems. Landmark Matching performs time base-independent propagation of peptide identities onto accurate mass LC-MS features in a way that leverages historical data derived from disparate data acquisition strategies. Peak Matching builds upon Landmark Matching by recognizing identical molecular species across multiple LC-MS experiments in an identity-independent fashion by clustering. We have bundled these algorithms together with other algorithms, data acquisition strategies, and experimental designs to create a Platform for Experimental Proteomic Pattern Recognition (PEPPeR). These developments enable use of established statistical tools previously limited to microarray analysis for treatment of proteomics data. We demonstrate that the proposed platform can be calibrated across 2.5 orders of magnitude and can perform robust quantification of ratios in both simple and complex mixtures with good precision and error characteristics across multiple sample preparations. We also demonstrate de novo marker discovery based on statistical significance of unidentified accurate mass components that changed between two mixtures. These markers were subsequently identified by accurate mass-driven MS/MS acquisition and demonstrated to be contaminant proteins associated with known proteins whose concentrations were designed to change between the two mixtures. These results have provided a real world validation of the platform for marker discovery.
Multiple reaction monitoring mass spectrometry (MRM-MS) with stable isotope dilution (SID) is increasingly becoming a widely accepted assay for the quantification of proteins and peptides. These ...assays have shown great promise in relatively high throughput verification of candidate biomarkers. While the use of MRM-MS assays is well established in the small molecule realm, their introduction and use in proteomics is relatively recent. As such, statistical and computational methods for the analysis of MRM-MS data from proteins and peptides are still being developed. Based on our extensive experience with analyzing a wide range of SID-MRM-MS data, we set forth a methodology for analysis that encompasses significant aspects ranging from data quality assessment, assay characterization including calibration curves, limits of detection (LOD) and quantification (LOQ), and measurement of intra- and interlaboratory precision. We draw upon publicly available seminal datasets to illustrate our methods and algorithms.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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