XMAn v2-a database of Homo sapiens mutated peptides

ProtGraph: a tool for the quick and comprehensive exploration and exploitation of the peptide search space derived from protein sequence databases using graphs

Due to computational resource limitations, in mass spectrometry based proteomics only a limited set of peptide sequences is used for the matching against measured spectra. We present an approach to represent proteins by graphs and allow not only the canonical sequences but also known isoforms and annotated amino acid variations, e.g. originating from genomic mutations, and further common protein sequence features contained in Uniprot KB or other protein databases. Our C++ and Python implementation enables a groundbreaking comprehensive characterization of the peptide search space, encompassing for the first time all available annotations in a protein database (in combination more than $10^{200}$ possibilities). Additionally, it can be used to quickly extract the relevant subset of the search space for peptide to spectrum matching, e.g. filtering by the peptide mass. We demonstrate the advantages and innovative findings of our implementation compared to previous workflows by re-analysing publicly available datasets.

Fallopian tube lesions as potential precursors of early ovarian cancer: a comprehensive proteomic analysis

Ovarian cancer is the leading cause of death from gynecologic cancer worldwide. High-grade serous carcinoma (HGSC) is the most common and deadliest subtype of ovarian cancer. While the origin of ovarian tumors is still debated, it has been suggested that HGSC originates from cells in the fallopian tube epithelium (FTE), specifically the epithelial cells in the region of the tubal-peritoneal junction. Three main lesions, p53 signatures, STILs, and STICs, have been defined based on the immunohistochemistry (IHC) pattern of p53 and Ki67 markers and the architectural alterations of the cells, using the Sectioning and Extensively Examining the Fimbriated End Protocol. In this study, we performed an in-depth proteomic analysis of these pre-neoplastic epithelial lesions guided by mass spectrometry imaging and IHC. We evaluated specific markers related to each preneoplastic lesion. The study identified specific lesion markers, such as CAVIN1, Emilin2, and FBLN5. We also used SpiderMass technology to perform a lipidomic analysis and identified the specific presence of specific lipids signature including dietary Fatty acids precursors in lesions. Our study provides new insights into the molecular mechanisms underlying the progression of ovarian cancer and confirms the fimbria origin of HGSC.

Detection of Mutant Peptides of SARS-CoV-2 Variants by LC/MS in the DDA Approach Using an In-House Database

Equipped with a dramatically high mutation rate, which happens to be a signature of RNA viruses, SARS-CoV-2 trampled across the globe infecting individuals of all ages and ethnicities. As the variants of concern (VOC) loomed large, definitive detection of SARS-CoV-2 strains became a matter of utmost importance in epidemiological and clinical research. Besides, unveiling the disease pathogenesis at the molecular level and deciphering the therapeutic targets became key priorities since the emergence of the pandemic. Mass spectrometry has been largely used in this regard. A critical part of mass spectrometric analyses is the proteome database required for the identification of peptides. Presently, the mutational information on proteins available on SARS-CoV-2 databases cannot be used to analyze data extracted from mass spectrometers. Hence, we developed the novel Mutant Peptide Database (MPD) for the mass spectrometry (MS)-based identification of mutated peptides, which contains information from 11 proteins of SARS-CoV-2 from a total of 21,549 SARS-CoV-2 variants across different regions of India. The database was validated using clinical samples, and its applicability was also demonstrated with the mutated peptides extracted from the literature. We believe that MPD will support broad-spectrum MS-based studies like viral detection, disease pathogenesis, and therapeutics with respect to SARS-CoV-2 and its variants.

Path to Clonal Theranostics in Luminal Breast Cancers

Integrating tumor heterogeneity in the drug discovery process is a key challenge to tackle breast cancer resistance. Identifying protein targets for functionally distinct tumor clones is particularly important to tailor therapy to the heterogeneous tumor subpopulations and achieve clonal theranostics. For this purpose, we performed an unsupervised, label-free, spatially resolved shotgun proteomics guided by MALDI mass spectrometry imaging (MSI) on 124 selected tumor clonal areas from early luminal breast cancers, tumor stroma, and breast cancer metastases. 2868 proteins were identified. The main protein classes found in the clonal proteome dataset were enzymes, cytoskeletal proteins, membrane-traffic, translational or scaffold proteins, or transporters. As a comparison, gene-specific transcriptional regulators, chromatin related proteins or transmembrane signal receptor were more abundant in the TCGA dataset. Moreover, 26 mutated proteins have been identified. Similarly, expanding the search to alternative proteins databases retrieved 126 alternative proteins in the clonal proteome dataset. Most of these alternative proteins were coded mainly from non-coding RNA. To fully understand the molecular information brought by our approach and its relevance to drug target discovery, the clonal proteomic dataset was further compared to the TCGA breast cancer database and two transcriptomic panels, BC360 (nanoString^®) and CDx (Foundation One^®). We retrieved 139 pathways in the clonal proteome dataset. Only 55% of these pathways were also present in the TCGA dataset, 68% in BC360 and 50% in CDx. Seven of these pathways have been suggested as candidate for drug targeting, 22 have been associated with breast cancer in experimental or clinical reports, the remaining 19 pathways have been understudied in breast cancer. Among the anticancer drugs, 35 drugs matched uniquely with the clonal proteome dataset, with only 7 of them already approved in breast cancer. The number of target and drug interactions with non-anticancer drugs (such as agents targeting the cardiovascular system, metabolism, the musculoskeletal or the nervous systems) was higher in the clonal proteome dataset (540 interactions) compared to TCGA (83 interactions), BC360 (419 interactions), or CDx (172 interactions). Many of the protein targets identified and drugs screened were clinically relevant to breast cancer and are in clinical trials. Thus, we described the non-redundant knowledge brought by this clone-tailored approach compared to TCGA or transcriptomic panels, the targetable proteins identified in the clonal proteome dataset, and the potential of this approach for drug discovery and repurposing through drug interactions with antineoplastic agents and non-anticancer drugs.

Surfaceome Proteomic of Glioblastoma Revealed Potential Targets for Immunotherapy

Glioblastoma (GBM) is the most common and devastating malignant brain tumor in adults. The mortality rate is very high despite different treatments. New therapeutic targets are therefore highly needed. Cell-surface proteins represent attractive targets due to their accessibility, their involvement in essential signaling pathways, and their dysregulated expression in cancer. Moreover, they are potential targets for CAR-based immunotherapy or mRNA vaccine strategies. In this context, we investigated the GBM-associated surfaceome by comparing it to astrocytes cell line surfaceome to identify new specific targets for GBM. For this purpose, biotinylation of cell surface proteins has been carried out in GBM and astrocytes cell lines. Biotinylated proteins were purified on streptavidin beads and analyzed by shotgun proteomics. Cell surface proteins were identified with Cell Surface Proteins Atlas (CSPA) and Gene Ontology enrichment. Among all the surface proteins identified in the different cell lines we have confirmed the expression of 66 of these in patient’s glioblastoma using spatial proteomic guided by MALDI-mass spectrometry. Moreover, 87 surface proteins overexpressed or exclusive in GBM cell lines have been identified. Among these, we found 11 specific potential targets for GBM including 5 mutated proteins such as RELL1, CYBA, EGFR, and MHC I proteins. Matching with drugs and clinical trials databases revealed that 7 proteins were druggable and under evaluation, 3 proteins have no known drug interaction yet and none of them are the mutated form of the identified proteins. Taken together, we discovered potential targets for immune therapy strategies in GBM.

Reinspection of a Clinical Proteomics Tumor Analysis Consortium (CPTAC) Dataset with Cloud Computing Reveals Abundant Post-Translational Modifications and Protein Sequence Variants

The Clinical Proteomic Tumor Analysis Consortium (CPTAC) has provided some of the most in-depth analyses of the phenotypes of human tumors ever constructed. Today, the majority of proteomic data analysis is still performed using software housed on desktop computers which limits the number of sequence variants and post-translational modifications that can be considered. The original CPTAC studies limited the search for PTMs to only samples that were chemically enriched for those modified peptides. Similarly, the only sequence variants considered were those with strong evidence at the exon or transcript level. In this multi-institutional collaborative reanalysis, we utilized unbiased protein databases containing millions of human sequence variants in conjunction with hundreds of common post-translational modifications. Using these tools, we identified tens of thousands of high-confidence PTMs and sequence variants. We identified 4132 phosphorylated peptides in nonenriched samples, 93% of which were confirmed in the samples which were chemically enriched for phosphopeptides. In addition, our results also cover 90% of the high-confidence variants reported by the original proteogenomics study, without the need for sample specific next-generation sequencing. Finally, we report fivefold more somatic and germline variants that have an independent evidence at the peptide level, including mutations in ERRB2 and BCAS1. In this reanalysis of CPTAC proteomic data with cloud computing, we present an openly available and searchable web resource of the highest-coverage proteomic profiling of human tumors described to date.

Cloud Computing Based Immunopeptidomics Utilizing Community Curated Variant Libraries Simplifies and Improves Neo-Antigen Discovery in Metastatic Melanoma

Unique peptide neo-antigens presented on the cell surface are attractive targets for researchers in nearly all areas of personalized medicine. Cells presenting peptides with mutated or other non-canonical sequences can be utilized for both targeted therapies and diagnostics. Today's state-of-the-art pipelines utilize complementary proteogenomic approaches where RNA or ribosomal sequencing data helps to create libraries from which tandem mass spectrometry data can be compared. In this study, we present an alternative approach whereby cloud computing is utilized to power neo-antigen searches against community curated databases containing more than 7 million human sequence variants. Using these expansive databases of high-quality sequences as a reference, we reanalyze the original data from two previously reported studies to identify neo-antigen targets in metastatic melanoma. Using our approach, we identify 79 percent of the non-canonical peptides reported by previous genomic analyses of these files. Furthermore, we report 18-fold more non-canonical peptides than previously reported. The novel neo-antigens we report herein can be corroborated by secondary analyses such as high predicted binding affinity, when analyzed by well-established tools such as NetMHC. Finally, we report 738 non-canonical peptides shared by at least five patient samples, and 3258 shared across the two studies. This illustrates the depth of data that is present, but typically missed by lower statistical power proteogenomic approaches. This large list of shared peptides across the two studies, their annotation, non-canonical origin, as well as MS/MS spectra from the two studies are made available on a web portal for community analysis.

In-depth proteomics analysis of sentinel lymph nodes from individuals with endometrial cancer

Endometrial cancer (EC) is one of the most common gynecological cancers worldwide. Sentinel lymph node (SLN) status could be a major prognostic factor in evaluation of EC, but several prospective studies need to be performed. Here we report an in-depth proteomics analysis showing significant variations in the SLN protein landscape in EC. We show that SLNs are correlated to each tumor grade, which strengthens evidence of SLN involvement in EC. A few proteins are overexpressed specifically at each EC tumor grade and in the corresponding SLN. These proteins, which are significantly variable in both locations, should be considered potential markers of overall survival. Five major proteins for EC and SLN (PRSS3, PTX3, ASS1, ALDH2, and ANXA1) were identified in large-scale proteomics and validated by immunohistochemistry. This study improves stratification and diagnosis of individuals with EC as a result of proteomics profiling of SLNs.

Personalized Proteome: Comparing Proteogenomics and Open Variant Search Approaches for Single Amino Acid Variant Detection

Discovery of variant peptides such as a single amino acid variant (SAAV) in shotgun proteomics data is essential for personalized proteomics. Both the resolution of shotgun proteomics methods and the search engines have improved dramatically, allowing for confident identification of SAAV peptides. However, it is not yet known if these methods are truly successful in accurately identifying SAAV peptides without prior genomic information in the search database. We studied this in unprecedented detail by exploiting publicly available long-read RNA sequences and shotgun proteomics data from the gold standard reference cell line NA12878. Searching spectra from this cell line with the state-of-the-art open modification search engine ionbot against carefully curated search databases resulted in 96.7% false-positive SAAVs and an 85% lower true positive rate than searching with peptide search databases that incorporate prior genetic information. While adding genetic variants to the search database remains indispensable for correct peptide identification, inclusion of long-read RNA sequences in the search database contributes only 0.3% new peptide identifications. These findings reveal the differences in SAAV detection that result from various approaches, providing guidance to researchers studying SAAV peptides and developers of peptide spectrum identification tools.