Integrated stress response plasticity governs normal cell adaptation to chronic stress via the PP2A-TFE3-ATF4 pathway.

The integrated stress response (ISR) regulates cell fate during conditions of stress by leveraging the cell's capacity to endure sustainable and efficient adaptive stress responses. Protein phosphatase 2A (PP2A) activity modulation has been shown to be successful in achieving both therapeutic efficacy and safety across various cancer models; however, the molecular mechanisms driving its selective antitumor effects remain unclear. Here, we show for the first time that ISR plasticity relies on PP2A activation to regulate drug response and dictate cellular fate under conditions of chronic stress. We demonstrate that genetic and chemical modulation of the PP2A leads to chronic proteolytic stress and triggers an ISR to dictate cell fate. More specifically, we uncovered that the PP2A-TFE3-ATF4 pathway governs ISR cell plasticity during endoplasmic reticular and cellular stress independent of the unfolded protein response. We further show that normal cells reprogram their genetic signatures to undergo ISR-mediated adaptation and homeostatic recovery thereby successfully avoiding toxicity following PP2A-mediated stress. Conversely, oncogenic specific cytotoxicity induced by chemical modulation of PP2A is achieved by activating chronic and irreversible ISR in cancer cells. Our findings propose that a differential response to chemical modulation of PP2A is determined by intrinsic ISR plasticity, providing a novel biological vulnerability to selectively induce cancer cell death and improve targeted therapeutic efficacy.

The Intrinsically Disordered N Terminus in Atg12 from Yeast Is Necessary for the Functional Structure of the Protein.

The Atg12 protein in yeast is an indispensable polypeptide in the highly conserved ubiquitin-like conjugation system operating in the macroautophagy/autophagy pathway. Atg12 is covalently conjugated to Atg5 through the action of Atg7 and Atg10; the Atg12-Atg5 conjugate binds Atg16 to form an E3 ligase that functions in a separate conjugation pathway involving Atg8. Atg12 is comprised of a ubiquitin-like (UBL) domain preceded at the N terminus by an intrinsically disordered protein region (IDPR), a domain that comprises a major portion of the protein but remains elusive in its conformation and function. Here, we show that the IDPR in unconjugated Atg12 is positioned in proximity to the UBL domain, a configuration that is important for the functional structure of the protein. A major deletion in the IDPR disrupts intactness of the UBL domain at the unconjugated C terminus, and a mutation in the predicted α0 helix in the IDPR prevents Atg12 from binding to Atg7 and Atg10, which ultimately affects the protein function in the ubiquitin-like conjugation cascade. These findings provide evidence that the IDPR is an indispensable part of the Atg12 protein from yeast.

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 data and resources for pan-cancer analysis.

The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) investigates tumors from a proteogenomic perspective, creating rich multi-omics datasets connecting genomic aberrations to cancer phenotypes. To facilitate pan-cancer investigations, we have generated harmonized genomic, transcriptomic, proteomic, and clinical data for >1000 tumors in 10 cohorts to create a cohesive and powerful dataset for scientific discovery. We outline efforts by the CPTAC pan-cancer working group in data harmonization, data dissemination, and computational resources for aiding biological discoveries. We also discuss challenges for multi-omics data integration and analysis, specifically the unique challenges of working with both nucleotide sequencing and mass spectrometry proteomics data.

Unraveling the glycosylated immunopeptidome with HLA-Glyco.

Recent interest in targeted therapies has been sparked by the study of MHC-associated peptides (MAPs) that undergo post-translational modifications (PTMs), particularly glycosylation. In this study, we introduce a fast computational workflow that merges the MSFragger-Glyco search algorithm with a false discovery rate control for glycopeptide analysis from mass spectrometry-based immunopeptidome data. By analyzing eight large-scale publicly available studies, we find that glycosylated MAPs are predominantly presented by MHC class II. Here, we present HLA-Glyco, a comprehensive resource containing over 3,400 human leukocyte antigen (HLA) class II N-glycopeptides from 1,049 distinct protein glycosylation sites. This resource provides valuable insights, including high levels of truncated glycans, conserved HLA-binding cores, and differences in glycosylation positional specificity between HLA allele groups. We integrate the workflow within the FragPipe computational platform and provide HLA-Glyco as a free web resource. Overall, our work provides a valuable tool and resource to aid the nascent field of glyco-immunopeptidomics.

Autoinhibited kinesin-1 adopts a hierarchical folding pattern.

Conventional kinesin-1 is the primary anterograde motor in cells for transporting cellular cargo. While there is a consensus that the C-terminal tail of kinesin-1 inhibits motility, the molecular architecture of a full-length autoinhibited kinesin-1 remains unknown. Here, we combine cross-linking mass spectrometry (XL-MS), electron microscopy (EM), and AlphaFold structure prediction to determine the architecture of the full-length autoinhibited kinesin-1 homodimer [kinesin-1 heavy chain (KHC)] and kinesin-1 heterotetramer [KHC bound to kinesin light chain 1 (KLC1)]. Our integrative analysis shows that kinesin-1 forms a compact, bent conformation through a break in coiled coil 3. Moreover, our XL-MS analysis demonstrates that kinesin light chains stabilize the folded inhibited state rather than inducing a new structural state. Using our structural model, we show that disruption of multiple interactions between the motor, stalk, and tail domains is required to activate the full-length kinesin-1. Our work offers a conceptual framework for understanding how cargo adaptors and microtubule-associated proteins relieve autoinhibition to promote activation.

Post-translational modifications reshape the antigenic landscape of the MHC I immunopeptidome in tumors.

Post-translational modification (PTM) of antigens provides an additional source of specificities targeted by immune responses to tumors or pathogens, but identifying antigen PTMs and assessing their role in shaping the immunopeptidome is challenging. Here we describe the Protein Modification Integrated Search Engine (PROMISE), an antigen discovery pipeline that enables the analysis of 29 different PTM combinations from multiple clinical cohorts and cell lines. We expanded the antigen landscape, uncovering human leukocyte antigen class I binding motifs defined by specific PTMs with haplotype-specific binding preferences and revealing disease-specific modified targets, including thousands of new cancer-specific antigens that can be shared between patients and across cancer types. Furthermore, we uncovered a subset of modified peptides that are specific to cancer tissue and driven by post-translational changes that occurred in the tumor proteome. Our findings highlight principles of PTM-driven antigenicity, which may have broad implications for T cell-mediated therapies in cancer and beyond.

Identification of secreted proteins by comparison of protein abundance in conditioned media and cell lysates.

Analysis of the full spectrum of secreted proteins in cell culture is complicated by leakage of intracellular proteins from damaged cells. To address this issue, we compared the abundance of individual proteins between the cell lysate and the conditioned medium, reasoning that secreted proteins should be relatively more abundant in the conditioned medium. Marked enrichment for signal-peptide-bearing proteins with increasing conditioned media to cell lysate ratio, as well loss of this signal following brefeldin A treatment, confirmed the sensitivity and specificity of this approach. The subset of proteins demonstrating increased conditioned media to cell lysate ratio in the presence of Brefeldin A identified candidates for unconventional secretion via a pathway independent of ER to Golgi trafficking.

GRASP55 regulates the unconventional secretion and aggregation of mutant huntingtin.

Recent studies demonstrated that the Golgi reassembly stacking proteins (GRASPs), especially GRASP55, regulate Golgi-independent unconventional secretion of certain cytosolic and transmembrane cargoes; however, the underlying mechanism remains unknown. Here, we surveyed several neurodegenerative disease-related proteins, including mutant huntingtin (Htt-Q74), superoxide dismutase 1 (SOD1), tau, and TAR DNA-binding protein 43 (TDP-43), for unconventional secretion; our results show that Htt-Q74 is most robustly secreted in a GRASP55-dependent manner. Using Htt-Q74 as a model system, we demonstrate that unconventional secretion of Htt is GRASP55 and autophagy dependent and is enhanced under stress conditions such as starvation and endoplasmic reticulum stress. Mechanistically, we show that GRASP55 facilitates Htt secretion by tethering autophagosomes to lysosomes to promote autophagosome maturation and subsequent lysosome secretion and by stabilizing p23/TMED10, a channel for translocation of cytoplasmic proteins into the lumen of the endoplasmic reticulum-Golgi intermediate compartment. Moreover, we found that GRASP55 levels are upregulated by various stresses to facilitate unconventional secretion, whereas inhibition of Htt-Q74 secretion by GRASP55 KO enhances Htt aggregation and toxicity. Finally, comprehensive secretomic analysis identified novel cytosolic cargoes secreted by the same unconventional pathway, including transgelin (TAGLN), multifunctional protein ADE2 (PAICS), and peroxiredoxin-1 (PRDX1). In conclusion, this study defines the pathway of GRASP55-mediated unconventional protein secretion and provides important insights into the progression of Huntington's disease.

A validated analysis pipeline for mass spectrometry-based vitreous proteomics: new insights into proliferative diabetic retinopathy.

BACKGROUND: Vitreous is an accessible, information-rich biofluid that has recently been studied as a source of retinal disease-related proteins and pathways. However, the number of samples required to confidently identify perturbed pathways remains unknown. In order to confidently identify these pathways, power analysis must be performed to determine the number of samples required, and sample preparation and analysis must be rigorously defined. METHODS: Control (n = 27) and proliferative diabetic retinopathy (n = 23) vitreous samples were treated as biologically distinct individuals or pooled together and aliquoted into technical replicates. Quantitative mass spectrometry with tandem mass tag labeling was used to identify proteins in individual or pooled control samples to determine technical and biological variability. To determine effect size and perform power analysis, control and proliferative diabetic retinopathy samples were analyzed across four 10-plexes. Pooled samples were used to normalize the data across plexes and generate a single data matrix for downstream analysis. RESULTS: The total number of unique proteins identified was 1152 in experiment 1, 989 of which were measured in all samples. In experiment 2, 1191 proteins were identified, 727 of which were measured across all samples in all plexes. Data are available via ProteomeXchange with identifier PXD025986. Spearman correlations of protein abundance estimations revealed minimal technical (0.99-1.00) and biological (0.94-0.98) variability. Each plex contained two unique pooled samples: one for normalizing across each 10-plex, and one to internally validate the normalization algorithm. Spearman correlation of the validation pool following normalization was 0.86-0.90. Principal component analysis revealed stratification of samples by disease and not by plex. Subsequent differential expression and pathway analyses demonstrated significant activation of metabolic pathways and inhibition of neuroprotective pathways in proliferative diabetic retinopathy samples relative to controls. CONCLUSIONS: This study demonstrates a feasible, rigorous, and scalable method that can be applied to future proteomic studies of vitreous and identifies previously unrecognized metabolic pathways that advance understanding of diabetic retinopathy.

Deep-Learning-Derived Evaluation Metrics Enable Effective Benchmarking of Computational Tools for Phosphopeptide Identification.

Tandem mass spectrometry (MS/MS)-based phosphoproteomics is a powerful technology for global phosphorylation analysis. However, applying four computational pipelines to a typical mass spectrometry (MS)-based phosphoproteomic dataset from a human cancer study, we observed a large discrepancy among the reported phosphopeptide identification and phosphosite localization results, underscoring a critical need for benchmarking. While efforts have been made to compare performance of computational pipelines using data from synthetic phosphopeptides, evaluations involving real application data have been largely limited to comparing the numbers of phosphopeptide identifications due to the lack of appropriate evaluation metrics. We investigated three deep-learning-derived features as potential evaluation metrics: phosphosite probability, Delta RT, and spectral similarity. Predicted phosphosite probability is computed by MusiteDeep, which provides high accuracy as previously reported; Delta RT is defined as the absolute retention time (RT) difference between RTs observed and predicted by AutoRT; and spectral similarity is defined as the Pearson's correlation coefficient between spectra observed and predicted by pDeep2. Using a synthetic peptide dataset, we found that both Delta RT and spectral similarity provided excellent discrimination between correct and incorrect peptide-spectrum matches (PSMs) both when incorrect PSMs involved wrong peptide sequences and even when incorrect PSMs were caused by only incorrect phosphosite localization. Based on these results, we used all the three deep-learning-derived features as evaluation metrics to compare different computational pipelines on diverse set of phosphoproteomic datasets and showed their utility in benchmarking performance of the pipelines. The benchmark metrics demonstrated in this study will enable users to select computational pipelines and parameters for routine analysis of phosphoproteomics data and will offer guidance for developers to improve computational methods.

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.

A proteogenomic portrait of lung squamous cell carcinoma.

Lung squamous cell carcinoma (LSCC) remains a leading cause of cancer death with few therapeutic options. We characterized the proteogenomic landscape of LSCC, providing a deeper exposition of LSCC biology with potential therapeutic implications. We identify NSD3 as an alternative driver in FGFR1-amplified tumors and low-p63 tumors overexpressing the therapeutic target survivin. SOX2 is considered undruggable, but our analyses provide rationale for exploring chromatin modifiers such as LSD1 and EZH2 to target SOX2-overexpressing tumors. Our data support complex regulation of metabolic pathways by crosstalk between post-translational modifications including ubiquitylation. Numerous immune-related proteogenomic observations suggest directions for further investigation. Proteogenomic dissection of CDKN2A mutations argue for more nuanced assessment of RB1 protein expression and phosphorylation before declaring CDK4/6 inhibition unsuccessful. Finally, triangulation between LSCC, LUAD, and HNSCC identified both unique and common therapeutic vulnerabilities. These observations and proteogenomics data resources may guide research into the biology and treatment of LSCC.

Differences in Extracellular Vesicle Protein Cargo Are Dependent on Head and Neck Squamous Cell Carcinoma Cell of Origin and Human Papillomavirus Status.

To identify potential extracellular vesicle (EV) biomarkers in head and neck squamous cell carcinoma (HNSCC), we evaluated EV protein cargo and whole cell lysates (WCL) from HPV-positive and -negative HNSCC cell lines, as well as normal oral keratinocytes and HPV16-transformed cells. EVs were isolated from serum-depleted, conditioned cell culture media by polyethylene glycol (PEG) precipitation/ultracentrifugation. EV and WCL preparations were analyzed by LC-MS/MS. Candidate proteins detected at significantly higher levels in EV compared with WCL, or compared with EV from normal oral keratinocytes, were identified and confirmed by Wes Simple Western protein analysis. Our findings suggest that these proteins may be potential HNSCC EV markers as proteins that may be (1) selectively included in EV cargo for export from the cell as a strategy for metastasis, tumor cell survival, or modification of tumor microenvironment, or (2) representative of originating cell composition, which may be developed for diagnostic or prognostic use in clinical liquid biopsy applications. This work demonstrates that our method can be used to reliably detect EV proteins from HNSCC, normal keratinocyte, and transformed cell lines. Furthermore, this work has identified HNSCC EV protein candidates for continued evaluation, specifically tenascin-C, HLA-A, E-cadherin, EGFR, EPHA2, and cytokeratin 19.

Proteomic Analyses of Vitreous in Proliferative Diabetic Retinopathy: Prior Studies and Future Outlook.

Vitreous fluid is becoming an increasingly popular medium for the study of retinal disease. Numerous studies have demonstrated that proteomic analysis of the vitreous from patients with proliferative diabetic retinopathy yields valuable molecular information regarding known and novel proteins and pathways involved in this disease. However, there is no standardized methodology for vitreous proteomic studies. Here, we share a suggested protocol for such studies and outline the various experimental and analytic methods that are currently available. We also review prior mass spectrometry-based proteomic studies of the vitreous from patients with proliferative diabetic retinopathy, discuss common pitfalls of these studies, and propose next steps for moving the field forward.

Proteogenomic insights into the biology and treatment of HPV-negative head and neck squamous cell carcinoma.

We present a proteogenomic study of 108 human papilloma virus (HPV)-negative head and neck squamous cell carcinomas (HNSCCs). Proteomic analysis systematically catalogs HNSCC-associated proteins and phosphosites, prioritizes copy number drivers, and highlights an oncogenic role for RNA processing genes. Proteomic investigation of mutual exclusivity between FAT1 truncating mutations and 11q13.3 amplifications reveals dysregulated actin dynamics as a common functional consequence. Phosphoproteomics characterizes two modes of EGFR activation, suggesting a new strategy to stratify HNSCCs based on EGFR ligand abundance for effective treatment with inhibitory EGFR monoclonal antibodies. Widespread deletion of immune modulatory genes accounts for low immune infiltration in immune-cold tumors, whereas concordant upregulation of multiple immune checkpoint proteins may underlie resistance to anti-programmed cell death protein 1 monotherapy in immune-hot tumors. Multi-omic analysis identifies three molecular subtypes with high potential for treatment with CDK inhibitors, anti-EGFR antibody therapy, and immunotherapy, respectively. Altogether, proteogenomics provides a systematic framework to inform HNSCC biology and treatment.

Integrated Proteogenomic Characterization across Major Histological Types of Pediatric Brain Cancer.

We report a comprehensive proteogenomics analysis, including whole-genome sequencing, RNA sequencing, and proteomics and phosphoproteomics profiling, of 218 tumors across 7 histological types of childhood brain cancer: low-grade glioma (n = 93), ependymoma (32), high-grade glioma (25), medulloblastoma (22), ganglioglioma (18), craniopharyngioma (16), and atypical teratoid rhabdoid tumor (12). Proteomics data identify common biological themes that span histological boundaries, suggesting that treatments used for one histological type may be applied effectively to other tumors sharing similar proteomics features. Immune landscape characterization reveals diverse tumor microenvironments across and within diagnoses. Proteomics data further reveal functional effects of somatic mutations and copy number variations (CNVs) not evident in transcriptomics data. Kinase-substrate association and co-expression network analysis identify important biological mechanisms of tumorigenesis. This is the first large-scale proteogenomics analysis across traditional histological boundaries to uncover foundational pediatric brain tumor biology and inform rational treatment selection.

Regulation of ALT-associated homology-directed repair by polyADP-ribosylation.

The synthesis of poly(ADP-ribose) (PAR) reconfigures the local chromatin environment and recruits DNA-repair complexes to damaged chromatin. PAR degradation by poly(ADP-ribose) glycohydrolase (PARG) is essential for progression and completion of DNA repair. Here, we show that inhibition of PARG disrupts homology-directed repair (HDR) mechanisms that underpin alternative lengthening of telomeres (ALT). Proteomic analyses uncover a new role for poly(ADP-ribosyl)ation (PARylation) in regulating the chromatin-assembly factor HIRA in ALT cancer cells. We show that HIRA is enriched at telomeres during the G2 phase and is required for histone H3.3 deposition and telomere DNA synthesis. Depletion of HIRA elicits systemic death of ALT cancer cells that is mitigated by re-expression of ATRX, a protein that is frequently inactivated in ALT tumors. We propose that PARylation enables HIRA to fulfill its essential role in the adaptive response to ATRX deficiency that pervades ALT cancers.

Proteogenomic Characterization Reveals Therapeutic Vulnerabilities in Lung Adenocarcinoma.

To explore the biology of lung adenocarcinoma (LUAD) and identify new therapeutic opportunities, we performed comprehensive proteogenomic characterization of 110 tumors and 101 matched normal adjacent tissues (NATs) incorporating genomics, epigenomics, deep-scale proteomics, phosphoproteomics, and acetylproteomics. Multi-omics clustering revealed four subgroups defined by key driver mutations, country, and gender. Proteomic and phosphoproteomic data illuminated biology downstream of copy number aberrations, somatic mutations, and fusions and identified therapeutic vulnerabilities associated with driver events involving KRAS, EGFR, and ALK. Immune subtyping revealed a complex landscape, reinforced the association of STK11 with immune-cold behavior, and underscored a potential immunosuppressive role of neutrophil degranulation. Smoking-associated LUADs showed correlation with other environmental exposure signatures and a field effect in NATs. Matched NATs allowed identification of differentially expressed proteins with potential diagnostic and therapeutic utility. This proteogenomics dataset represents a unique public resource for researchers and clinicians seeking to better understand and treat lung adenocarcinomas.