Pan-cancer proteogenomics characterization of tumor immunity.

Despite the successes of immunotherapy in cancer treatment over recent decades, less than <10%-20% cancer cases have demonstrated durable responses from immune checkpoint blockade. To enhance the efficacy of immunotherapies, combination therapies suppressing multiple immune evasion mechanisms are increasingly contemplated. To better understand immune cell surveillance and diverse immune evasion responses in tumor tissues, we comprehensively characterized the immune landscape of more than 1,000 tumors across ten different cancers using CPTAC pan-cancer proteogenomic data. We identified seven distinct immune subtypes based on integrative learning of cell type compositions and pathway activities. We then thoroughly categorized unique genomic, epigenetic, transcriptomic, and proteomic changes associated with each subtype. Further leveraging the deep phosphoproteomic data, we studied kinase activities in different immune subtypes, which revealed potential subtype-specific therapeutic targets. Insights from this work will facilitate the development of future immunotherapy strategies and enhance precision targeting with existing agents.

Proteogenomic analysis of chemo-refractory high-grade serous ovarian cancer.

To improve the understanding of chemo-refractory high-grade serous ovarian cancers (HGSOCs), we characterized the proteogenomic landscape of 242 (refractory and sensitive) HGSOCs, representing one discovery and two validation cohorts across two biospecimen types (formalin-fixed paraffin-embedded and frozen). We identified a 64-protein signature that predicts with high specificity a subset of HGSOCs refractory to initial platinum-based therapy and is validated in two independent patient cohorts. We detected significant association between lack of Ch17 loss of heterozygosity (LOH) and chemo-refractoriness. Based on pathway protein expression, we identified 5 clusters of HGSOC, which validated across two independent patient cohorts and patient-derived xenograft (PDX) models. These clusters may represent different mechanisms of refractoriness and implicate putative therapeutic vulnerabilities.

Proteogenomic characterization of pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with poor patient survival. Toward understanding the underlying molecular alterations that drive PDAC oncogenesis, we conducted comprehensive proteogenomic analysis of 140 pancreatic cancers, 67 normal adjacent tissues, and 9 normal pancreatic ductal tissues. Proteomic, phosphoproteomic, and glycoproteomic analyses were used to characterize proteins and their modifications. In addition, whole-genome sequencing, whole-exome sequencing, methylation, RNA sequencing (RNA-seq), and microRNA sequencing (miRNA-seq) were performed on the same tissues to facilitate an integrated proteogenomic analysis and determine the impact of genomic alterations on protein expression, signaling pathways, and post-translational modifications. To ensure robust downstream analyses, tumor neoplastic cellularity was assessed via multiple orthogonal strategies using molecular features and verified via pathological estimation of tumor cellularity based on histological review. This integrated proteogenomic characterization of PDAC will serve as a valuable resource for the community, paving the way for early detection and identification of novel therapeutic targets.

The Landscape of Circular RNA in Cancer.

Circular RNAs (circRNAs) are an intriguing class of RNA due to their covalently closed structure, high stability, and implicated roles in gene regulation. Here, we used an exome capture RNA sequencing protocol to detect and characterize circRNAs across >2,000 cancer samples. When compared against Ribo-Zero and RNase R, capture sequencing significantly enhanced the enrichment of circRNAs and preserved accurate circular-to-linear ratios. Using capture sequencing, we built the most comprehensive catalog of circRNA species to date: MiOncoCirc, the first database to be composed primarily of circRNAs directly detected in tumor tissues. Using MiOncoCirc, we identified candidate circRNAs to serve as biomarkers for prostate cancer and were able to detect circRNAs in urine. We further detected a novel class of circular transcripts, termed read-through circRNAs, that involved exons originating from different genes. MiOncoCirc will serve as a valuable resource for the development of circRNAs as diagnostic or therapeutic targets across cancer types.

RAD51AP1 regulates ALT-HDR through chromatin-directed homeostasis of TERRA.

Alternative lengthening of telomeres (ALT) is a homology-directed repair (HDR) mechanism of telomere elongation that controls proliferation in subsets of aggressive cancer. Recent studies have revealed that telomere repeat-containing RNA (TERRA) promotes ALT-associated HDR (ALT-HDR). Here, we report that RAD51AP1, a crucial ALT factor, interacts with TERRA and utilizes it to generate D- and R-loop HR intermediates. We also show that RAD51AP1 binds to and might stabilize TERRA-containing R-loops as RAD51AP1 depletion reduces R-loop formation at telomere DNA breaks. Proteomic analyses uncover a role for RAD51AP1-mediated TERRA R-loop homeostasis in a mechanism of chromatin-directed suppression of TERRA and prevention of transcription-replication collisions (TRCs) during ALT-HDR. Intriguingly, we find that both TERRA binding and this non-canonical function of RAD51AP1 require its intrinsic SUMO-SIM regulatory axis. These findings provide insights into the multi-contextual functions of RAD51AP1 within the ALT mechanism and regulation of TERRA.

Targeting SWI/SNF ATPases in enhancer-addicted prostate cancer.

The switch/sucrose non-fermentable (SWI/SNF) complex has a crucial role in chromatin remodelling1 and is altered in over 20% of cancers2,3. Here we developed a proteolysis-targeting chimera (PROTAC) degrader of the SWI/SNF ATPase subunits, SMARCA2 and SMARCA4, called AU-15330. Androgen receptor (AR)+ forkhead box A1 (FOXA1)+ prostate cancer cells are exquisitely sensitive to dual SMARCA2 and SMARCA4 degradation relative to normal and other cancer cell lines. SWI/SNF ATPase degradation rapidly compacts cis-regulatory elements bound by transcription factors that drive prostate cancer cell proliferation, namely AR, FOXA1, ERG and MYC, which dislodges them from chromatin, disables their core enhancer circuitry, and abolishes the downstream oncogenic gene programs. SWI/SNF ATPase degradation also disrupts super-enhancer and promoter looping interactions that wire supra-physiologic expression of the AR, FOXA1 and MYC oncogenes themselves. AU-15330 induces potent inhibition of tumour growth in xenograft models of prostate cancer and synergizes with the AR antagonist enzalutamide, even inducing disease remission in castration-resistant prostate cancer (CRPC) models without toxicity. Thus, impeding SWI/SNF-mediated enhancer accessibility represents a promising therapeutic approach for enhancer-addicted cancers.

Proteome-Scale Tissue Mapping Using Mass Spectrometry Based on Label-Free and Multiplexed Workflows.

Multiplexed bimolecular profiling of tissue microenvironment, or spatial omics, can provide deep insight into cellular compositions and interactions in healthy and diseased tissues. Proteome-scale tissue mapping, which aims to unbiasedly visualize all the proteins in a whole tissue section or region of interest, has attracted significant interest because it holds great potential to directly reveal diagnostic biomarkers and therapeutic targets. While many approaches are available, however, proteome mapping still exhibits significant technical challenges in both protein coverage and analytical throughput. Since many of these existing challenges are associated with mass spectrometry-based protein identification and quantification, we performed a detailed benchmarking study of three protein quantification methods for spatial proteome mapping, including label-free, TMT-MS2, and TMT-MS3. Our study indicates label-free method provided the deepest coverages of ∼3500 proteins at a spatial resolution of 50 µm and the highest quantification dynamic range, while TMT-MS2 method holds great benefit in mapping throughput at >125 pixels per day. The evaluation also indicates both label-free and TMT-MS2 provides robust protein quantifications in identifying differentially abundant proteins and spatially covariable clusters. In the study of pancreatic islet microenvironment, we demonstrated deep proteome mapping not only enables the identification of protein markers specific to different cell types, but more importantly, it also reveals unknown or hidden protein patterns by spatial coexpression analysis.

Are Internal Fragments Observable in Electron Based Top-Down Mass Spectrometry?

Protein tandem mass spectrometry (MS/MS) often generates sequence-informative fragments from backbone bond cleavages near the termini. This lack of fragmentation in the protein interior is particularly apparent in native top-down mass spectrometry (MS). Improved sequence coverage, critical for reliable annotation of posttranslational modifications and sequence variants, may be obtained from internal fragments generated by multiple backbone cleavage events. However, internal fragment assignments can be error prone due to isomeric/isobaric fragments from different parts of a protein sequence. Also, internal fragment generation propensity depends on the chosen MS/MS activation strategy. Here, we examine internal fragment formation in electron capture dissociation (ECD) and electron transfer dissociation (ETD) following native and denaturing MS, as well as LC/MS of several proteins. Experiments were undertaken on multiple instruments, including quadrupole time-of-flight, Orbitrap, and high-field Fourier-transform ion cyclotron resonance (FT-ICR) across four laboratories. ECD was performed at both ultrahigh vacuum and at similar pressure to ETD conditions. Two complementary software packages were used for data analysis. When feasible, ETD-higher energy collision dissociation MS3 was performed to validate/refute potential internal fragment assignments, including differentiating MS3 fragmentation behavior of radical versus even-electron primary fragments. We show that, under typical operating conditions, internal fragments cannot be confidently assigned in ECD or ETD. On the other hand, such fragments, along with some b-type terminal fragments (not typically observed in ECD/ETD spectra) appear at atypical ECD operating conditions, suggesting they originate from a separate ion-electron activation process. Furthermore, atypical fragment ion types, e.g., x ions, are observed at such conditions as well as upon EThcD, presumably due to vibrational activation of radical z-type ions.

Mass Spectrometric Profiling of HLA-B44 Peptidomes Provides Evidence for Tapasin-Mediated Tryptophan Editing.

The extreme polymorphisms of HLA class I proteins result in structural variations in their peptide binding sites to achieve diversity in Ag presentation. External factors could independently constrict or alter HLA class I peptide repertoires. Such effects of the assembly factor tapasin were assessed for HLA-B*44:05 (Y116) and a close variant, HLA-B*44:02 (D116), which have low and high tapasin dependence, respectively, for their cell surface expression. Analyses of the HLA-B*44:05 peptidomes in the presence and absence of tapasin reveal that peptides with C-terminal tryptophans and higher predicted affinities are preferentially selected by tapasin, coincident with reduced frequencies of peptides with other C-terminal amino acids, including leucine. Comparisons of the HLA-B*44:05 and HLA-B*44:02 peptidomes indicate the expected structure-based alterations near the peptide C termini, but also C-terminal amino acid frequency and predicted affinity changes among the unique and shared peptide groups for B*44:02 and B*44:05. Overall, these findings indicate that the presence of tapasin and the tapasin dependence of assembly alter HLA class I peptide-binding preferences at the peptide C terminus. The particular C-terminal amino acid preferences that are altered by tapasin are expected to be determined by the intrinsic peptide-binding specificities of HLA class I allotypes. Additionally, the findings suggest that tapasin deficiency and reduced tapasin dependence expand the permissive affinities of HLA class I-bound peptides, consistent with prior findings that HLA class I allotypes with low tapasin dependence have increased breadth of CD8+ T cell epitope presentation and are more protective in HIV infections.

MSFragger-Labile: A Flexible Method to Improve Labile PTM Analysis in Proteomics.

Posttranslational modifications of proteins play essential roles in defining and regulating the functions of the proteins they decorate, making identification of these modifications critical to understanding biology and disease. Methods for enriching and analyzing a wide variety of biological and chemical modifications of proteins have been developed using mass spectrometry-based proteomics, largely relying on traditional database search methods to identify the resulting mass spectra of modified peptides. These database search methods treat modifications as static attachments of a mass to particular position in the peptide sequence, but many modifications undergo fragmentation in tandem mass spectrometry experiments alongside, or instead of, the peptide backbone. While this fragmentation can confound traditional search methods, it also offers unique opportunities for improved searches that incorporate modification-specific fragment ions. Here, we present a new labile mode in the MSFragger search engine that provides the flexibility to tailor modification-centric searches to the fragmentation observed. We show that labile mode can dramatically improve spectrum identification rates of phosphopeptides, RNA-crosslinked peptides, and ADP-ribosylated peptides. Each of these modifications presents distinct fragmentation characteristics, showcasing the flexibility of MSFragger labile mode to improve search for a wide variety of biological and chemical modifications.

Analysis and Visualization of Quantitative Proteomics Data Using FragPipe-Analyst.

The FragPipe computational proteomics platform is gaining widespread popularity among the proteomics research community because of its fast processing speed and user-friendly graphical interface. Although FragPipe produces well-formatted output tables that are ready for analysis, there is still a need for an easy-to-use and user-friendly downstream statistical analysis and visualization tool. FragPipe-Analyst addresses this need by providing an R shiny web server to assist FragPipe users in conducting downstream analyses of the resulting quantitative proteomics data. It supports major quantification workflows, including label-free quantification, tandem mass tags, and data-independent acquisition. FragPipe-Analyst offers a range of useful functionalities, such as various missing value imputation options, data quality control, unsupervised clustering, differential expression (DE) analysis using Limma, and gene ontology and pathway enrichment analysis using Enrichr. To support advanced analysis and customized visualizations, we also developed FragPipeAnalystR, an R package encompassing all FragPipe-Analyst functionalities that is extended to support site-specific analysis of post-translational modifications (PTMs). FragPipe-Analyst and FragPipeAnalystR are both open-source and freely available.

Sulfoproteomics Workflow with Precursor Ion Accurate Mass Shift Analysis Reveals Novel Tyrosine Sulfoproteins in the Golgi.

Tyrosine sulfation in the Golgi of secreted and membrane proteins is an important post-translational modification (PTM). However, its labile nature has limited analysis by mass spectrometry (MS), a major reason why no sulfoproteome studies have been previously reported. Here, we show that a phosphoproteomics experimental workflow, which includes serial enrichment followed by high resolution, high mass accuracy MS, and tandem MS (MS/MS) analysis, enables sulfopeptide coenrichment and identification via accurate precursor ion mass shift open MSFragger database search. This approach, supported by manual validation, allows the confident identification of sulfotyrosine-containing peptides in the presence of high levels of phosphorylated peptides, thus enabling these two sterically and ionically similar isobaric PTMs to be distinguished and annotated in a single proteomic analysis. We applied this approach to isolated interphase and mitotic rat liver Golgi membranes and identified 67 tyrosine sulfopeptides, corresponding to 26 different proteins. This work discovered 23 new sulfoproteins with functions related to, for example, Ca2+-binding, glycan biosynthesis, and exocytosis. In addition, we report the first preliminary evidence for crosstalk between sulfation and phosphorylation in the Golgi, with implications for functional control.

Quantitative proteome-wide O-glycoproteomics analysis with FragPipe.

Identification of O-glycopeptides from tandem mass spectrometry data is complicated by the near complete dissociation of O-glycans from the peptide during collisional activation and by the combinatorial explosion of possible glycoforms when glycans are retained intact in electron-based activation. The recent O-Pair search method provides an elegant solution to these problems, using a collisional activation scan to identify the peptide sequence and total glycan mass, and a follow-up electron-based activation scan to localize the glycosite(s) using a graph-based algorithm in a reduced search space. Our previous O-glycoproteomics methods with MSFragger-Glyco allowed for extremely fast and sensitive identification of O-glycopeptides from collisional activation data but had limited support for site localization of glycans and quantification of glycopeptides. Here, we report an improved pipeline for O-glycoproteomics analysis that provides proteome-wide, site-specific, quantitative results by incorporating the O-Pair method as a module within FragPipe. In addition to improved search speed and sensitivity, we add flexible options for oxonium ion-based filtering of glycans and support for a variety of MS acquisition methods and provide a comparison between all software tools currently capable of O-glycosite localization in proteome-wide searches.

Multiattribute Glycan Identification and FDR Control for Glycoproteomics.

Rapidly improving methods for glycoproteomics have enabled increasingly large-scale analyses of complex glycopeptide samples, but annotating the resulting mass spectrometry data with high confidence remains a major bottleneck. We recently introduced a fast and sensitive glycoproteomics search method in our MSFragger search engine, which reports glycopeptides as a combination of a peptide sequence and the mass of the attached glycan. In samples with complex glycosylation patterns, converting this mass to a specific glycan composition is not straightforward; however, as many glycans have similar or identical masses. Here, we have developed a new method for determining the glycan composition of N-linked glycopeptides fragmented by collisional or hybrid activation that uses multiple sources of information from the spectrum, including observed glycan B-type (oxonium) and Y-type ions and mass and precursor monoisotopic selection errors to discriminate between possible glycan candidates. Combined with false discovery rate estimation for the glycan assignment, we show that this method is capable of specifically and sensitively identifying glycans in complex glycopeptide analyses and effectively controls the rate of false glycan assignments. The new method has been incorporated into the PTM-Shepherd modification analysis tool to work directly with the MSFragger glyco search in the FragPipe graphical user interface, providing a complete computational pipeline for annotation of N-glycopeptide spectra with false discovery rate control of both peptide and glycan components that is both sensitive and robust against false identifications.

SP3-Enabled Rapid and High Coverage Chemoproteomic Identification of Cell-State-Dependent Redox-Sensitive Cysteines.

Proteinaceous cysteine residues act as privileged sensors of oxidative stress. As reactive oxygen and nitrogen species have been implicated in numerous pathophysiological processes, deciphering which cysteines are sensitive to oxidative modification and the specific nature of these modifications is essential to understanding protein and cellular function in health and disease. While established mass spectrometry-based proteomic platforms have improved our understanding of the redox proteome, the widespread adoption of these methods is often hindered by complex sample preparation workflows, prohibitive cost of isotopic labeling reagents, and requirements for custom data analysis workflows. Here, we present the SP3-Rox redox proteomics method that combines tailored low cost isotopically labeled capture reagents with SP3 sample cleanup to achieve high throughput and high coverage proteome-wide identification of redox-sensitive cysteines. By implementing a customized workflow in the free FragPipe computational pipeline, we achieve accurate MS1-based quantitation, including for peptides containing multiple cysteine residues. Application of the SP3-Rox method to cellular proteomes identified cysteines sensitive to the oxidative stressor GSNO and cysteine oxidation state changes that occur during T cell activation.

Implementing the MSFragger Search Engine as a Node in Proteome Discoverer.

Here, we describe the implementation of the fast proteomics search engine MSFragger as a processing node in the widely used Proteome Discoverer (PD) software platform. PeptideProphet (via the Philosopher tool kit) is also implemented as an additional PD node to allow validation of MSFragger open (mass-tolerant) search results. These two nodes, along with the existing Percolator validation module, allow users to employ different search strategies and conveniently inspect search results through PD. Our results have demonstrated the improved numbers of PSMs, peptides, and proteins identified by MSFragger coupled with Percolator and significantly faster search speed compared to the conventional SEQUEST/Percolator PD workflows. The MSFragger-PD node is available at https://github.com/nesvilab/PD-Nodes/releases/.

Identification of LMAN1- and SURF4-Dependent Secretory Cargoes.

Most proteins secreted into the extracellular space are first recruited from the endoplasmic reticulum into coat protein complex II (COPII)-coated vesicles or tubules that facilitate their transport to the Golgi apparatus. Although several secreted proteins have been shown to be actively recruited into COPII vesicles and tubules by the cargo receptors LMAN1 and SURF4, the full cargo repertoire of these receptors is unknown. We now report mass spectrometry analysis of conditioned media and cell lysates from HuH7 cells CRISPR targeted to inactivate the LMAN1 or SURF4 gene. We found that LMAN1 has limited clients in HuH7 cells, whereas SURF4 traffics a broad range of cargoes. Analysis of putative SURF4 cargoes suggests that cargo recognition is governed by complex mechanisms rather than interaction with a universal binding motif..

Statistical Detection of Differentially Abundant Proteins in Experiments with Repeated Measures Designs and Isobaric Labeling.

Repeated measures experimental designs, which quantify proteins in biological subjects repeatedly over multiple experimental conditions or times, are commonly used in mass spectrometry-based proteomics. Such designs distinguish the biological variation within and between the subjects and increase the statistical power of detecting within-subject changes in protein abundance. Meanwhile, proteomics experiments increasingly incorporate tandem mass tag (TMT) labeling, a multiplexing strategy that gains both relative protein quantification accuracy and sample throughput. However, combining repeated measures and TMT multiplexing in a large-scale investigation presents statistical challenges due to unique interplays of between-mixture, within-mixture, between-subject, and within-subject variation. This manuscript proposes a family of linear mixed-effects models for differential analysis of proteomics experiments with repeated measures and TMT multiplexing. These models decompose the variation in the data into the contributions from its sources as appropriate for the specifics of each experiment, enable statistical inference of differential protein abundance, and recognize a difference in the uncertainty of between-subject versus within-subject comparisons. The proposed family of models is implemented in the R/Bioconductor package MSstatsTMT v2.2.0. Evaluations of four simulated datasets and four investigations answering diverse biological questions demonstrated the value of this approach as compared to the existing general-purpose approaches and implementations.