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.

Proteogenomic Characterization of Endometrial Carcinoma.

We undertook a comprehensive proteogenomic characterization of 95 prospectively collected endometrial carcinomas, comprising 83 endometrioid and 12 serous tumors. This analysis revealed possible new consequences of perturbations to the p53 and Wnt/ß-catenin pathways, identified a potential role for circRNAs in the epithelial-mesenchymal transition, and provided new information about proteomic markers of clinical and genomic tumor subgroups, including relationships to known druggable pathways. An extensive genome-wide acetylation survey yielded insights into regulatory mechanisms linking Wnt signaling and histone acetylation. We also characterized aspects of the tumor immune landscape, including immunogenic alterations, neoantigens, common cancer/testis antigens, and the immune microenvironment, all of which can inform immunotherapy decisions. Collectively, our multi-omic analyses provide a valuable resource for researchers and clinicians, identify new molecular associations of potential mechanistic significance in the development of endometrial cancers, and suggest novel approaches for identifying potential therapeutic targets.

Proteogenomic Analysis of Human Colon Cancer Reveals New Therapeutic Opportunities.

We performed the first proteogenomic study on a prospectively collected colon cancer cohort. Comparative proteomic and phosphoproteomic analysis of paired tumor and normal adjacent tissues produced a catalog of colon cancer-associated proteins and phosphosites, including known and putative new biomarkers, drug targets, and cancer/testis antigens. Proteogenomic integration not only prioritized genomically inferred targets, such as copy-number drivers and mutation-derived neoantigens, but also yielded novel findings. Phosphoproteomics data associated Rb phosphorylation with increased proliferation and decreased apoptosis in colon cancer, which explains why this classical tumor suppressor is amplified in colon tumors and suggests a rationale for targeting Rb phosphorylation in colon cancer. Proteomics identified an association between decreased CD8 T cell infiltration and increased glycolysis in microsatellite instability-high (MSI-H) tumors, suggesting glycolysis as a potential target to overcome the resistance of MSI-H tumors to immune checkpoint blockade. Proteogenomics presents new avenues for biological discoveries and therapeutic development.

Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer.

To provide a detailed analysis of the molecular components and underlying mechanisms associated with ovarian cancer, we performed a comprehensive mass-spectrometry-based proteomic characterization of 174 ovarian tumors previously analyzed by The Cancer Genome Atlas (TCGA), of which 169 were high-grade serous carcinomas (HGSCs). Integrating our proteomic measurements with the genomic data yielded a number of insights into disease, such as how different copy-number alternations influence the proteome, the proteins associated with chromosomal instability, the sets of signaling pathways that diverse genome rearrangements converge on, and the ones most associated with short overall survival. Specific protein acetylations associated with homologous recombination deficiency suggest a potential means for stratifying patients for therapy. In addition to providing a valuable resource, these findings provide a view of how the somatic genome drives the cancer proteome and associations between protein and post-translational modification levels and clinical outcomes in HGSC. VIDEO ABSTRACT.

Psychosocial experiences are associated with human brain mitochondrial biology.

Psychosocial experiences affect brain health and aging trajectories, but the molecular pathways underlying these associations remain unclear. Normal brain function relies on energy transformation by mitochondria oxidative phosphorylation (OxPhos). Two main lines of evidence position mitochondria both as targets and drivers of psychosocial experiences. On the one hand, chronic stress exposure and mood states may alter multiple aspects of mitochondrial biology; on the other hand, functional variations in mitochondrial OxPhos capacity may alter social behavior, stress reactivity, and mood. But are psychosocial exposures and subjective experiences linked to mitochondrial biology in the human brain? By combining longitudinal antemortem assessments of psychosocial factors with postmortem brain (dorsolateral prefrontal cortex) proteomics in older adults, we find that higher well-being is linked to greater abundance of the mitochondrial OxPhos machinery, whereas higher negative mood is linked to lower OxPhos protein content. Combined, positive and negative psychosocial factors explained 18 to 25% of the variance in the abundance of OxPhos complex I, the primary biochemical entry point that energizes brain mitochondria. Moreover, interrogating mitochondrial psychobiological associations in specific neuronal and nonneuronal brain cells with single-nucleus RNA sequencing (RNA-seq) revealed strong cell-type-specific associations for positive psychosocial experiences and mitochondria in glia but opposite associations in neurons. As a result, these "mind-mitochondria" associations were masked in bulk RNA-seq, highlighting the likely underestimation of true psychobiological effect sizes in bulk brain tissues. Thus, self-reported psychosocial experiences are linked to human brain mitochondrial phenotypes.

Proteogenomic characterization of difficult-to-treat breast cancer with tumor cells enriched through laser microdissection.

BACKGROUND: Breast cancer (BC) is the most commonly diagnosed cancer and the leading cause of cancer death among women globally. Despite advances, there is considerable variation in clinical outcomes for patients with non-luminal A tumors, classified as difficult-to-treat breast cancers (DTBC). This study aims to delineate the proteogenomic landscape of DTBC tumors compared to luminal A (LumA) tumors. METHODS: We retrospectively collected a total of 117 untreated primary breast tumor specimens, focusing on DTBC subtypes. Breast tumors were processed by laser microdissection (LMD) to enrich tumor cells. DNA, RNA, and protein were simultaneously extracted from each tumor preparation, followed by whole genome sequencing, paired-end RNA sequencing, global proteomics and phosphoproteomics. Differential feature analysis, pathway analysis and survival analysis were performed to better understand DTBC and investigate biomarkers. RESULTS: We observed distinct variations in gene mutations, structural variations, and chromosomal alterations between DTBC and LumA breast tumors. DTBC tumors predominantly had more mutations in TP53, PLXNB3, Zinc finger genes, and fewer mutations in SDC2, CDH1, PIK3CA, SVIL, and PTEN. Notably, Cytoband 1q21, which contains numerous cell proliferation-related genes, was significantly amplified in the DTBC tumors. LMD successfully minimized stromal components and increased RNA-protein concordance, as evidenced by stromal score comparisons and proteomic analysis. Distinct DTBC and LumA-enriched clusters were observed by proteomic and phosphoproteomic clustering analysis, some with survival differences. Phosphoproteomics identified two distinct phosphoproteomic profiles for high relapse-risk and low relapse-risk basal-like tumors, involving several genes known to be associated with breast cancer oncogenesis and progression, including KIAA1522, DCK, FOXO3, MYO9B, ARID1A, EPRS, ZC3HAV1, and RBM14. Lastly, an integrated pathway analysis of multi-omics data highlighted a robust enrichment of proliferation pathways in DTBC tumors. CONCLUSIONS: This study provides an integrated proteogenomic characterization of DTBC vs LumA with tumor cells enriched through laser microdissection. We identified many common features of DTBC tumors and the phosphopeptides that could serve as potential biomarkers for high/low relapse-risk basal-like BC and possibly guide treatment selections.

Associations of VGF with Neuropathologies and Cognitive Health in Older Adults.

OBJECTIVE: VGF is proposed as a potential therapeutic target for Alzheimer's (AD) and other neurodegenerative conditions. The cell-type specific and, separately, peptide specific associations of VGF with pathologic and cognitive outcomes remain largely unknown. We leveraged gene expression and protein data from the human neocortex and investigated the VGF associations with common neuropathologies and late-life cognitive decline. METHODS: Community-dwelling older adults were followed every year, died, and underwent brain autopsy. Cognitive decline was captured via annual cognitive testing. Common neurodegenerative and cerebrovascular conditions were assessed during neuropathologic evaluations. Bulk brain RNASeq and targeted proteomics analyses were conducted using frozen tissues from dorsolateral prefrontal cortex of 1,020 individuals. Cell-type specific gene expressions were quantified in a subsample (N = 424) following single nuclei RNASeq analysis from the same cortex. RESULTS: The bulk brain VGF gene expression was primarily associated with AD and Lewy bodies. The VGF gene association with cognitive decline was in part accounted for by neuropathologies. Similar associations were observed for the VGF protein. Cell-type specific analyses revealed that, while VGF was differentially expressed in most major cell types in the cortex, its association with neuropathologies and cognitive decline was restricted to the neuronal cells. Further, the peptide fragments across the VGF polypeptide resembled each other in relation to neuropathologies and cognitive decline. INTERPRETATION: Multiple pathways link VGF to cognitive health in older age, including neurodegeneration. The VGF gene functions primarily in neuronal cells and its protein associations with pathologic and cognitive outcomes do not map to a specific peptide. ANN NEUROL 2023;94:232-244.

TopPICR: A Companion R Package for Top-Down Proteomics Data Analysis.

Top-down proteomics is the analysis of proteins in their intact form without proteolysis, thus preserving valuable information about post-translational modifications, isoforms, and proteolytic processing. However, it is still a developing field due to limitations in the instrumentation, difficulties with the interpretation of complex mass spectra, and a lack of well-established quantification approaches. TopPIC is one of the popular tools for proteoform identification. We extended its capabilities into label-free proteoform quantification by developing a companion R package (TopPICR). Key steps in the TopPICR pipeline include filtering identifications, inferring a minimal set of protein accessions explaining the observed sequences, aligning retention times, recalibrating measured masses, clustering features across data sets, and finally compiling feature intensities using the match-between-runs approach. The output of the pipeline is an MSnSet object which makes downstream data analysis seamlessly compatible with packages from the Bioconductor project. It also provides the capability for visualizing proteoforms within the context of the parent protein sequence. The functionality of TopPICR is demonstrated on top-down LC-MS/MS data sets of 10 human-in-mouse xenografts of luminal and basal breast tumor samples.

Plasma p-tau181 and p-tau217 in discriminating PART, AD and other key neuropathologies in older adults.

We examined whether plasma p-tau181 and p-tau217 are specific biomarkers of pathologically confirmed Alzheimer's disease (AD). In particular, we investigated the utility of plasma p-tau for differentiating AD from primary age-related tauopathy (PART), as well as AD with mixed pathologies. Data came from 269 older adults who participated in the Religious Orders Study or the Rush Memory and Aging Project. Blood samples were collected during annual clinical evaluations. Participants died and underwent brain autopsy. P-tau181 and p-tau217 were quantified in the plasma samples proximate to death (average interval before death: 1.4 years) using Lilly-developed MSD immunoassays. Uniform neuropathologic evaluations assessed AD, PART, and other common degenerative and cerebrovascular conditions. Plasma p-tau217 was more strongly correlated with brain ß-amyloid and paired helical filament tau (PHFtau) tangles than p-tau181. Both p-tau markers were associated with greater odds of AD, but p-tau217 had higher accuracy (area under the ROC curve (AUC): 0.83) than p-tau181 (AUC: 0.76). Plasma p-tau markers were almost exclusively associated with AD pathologic indices with the exception of cerebral amyloid angiopathy. Compared to p-tau181, p-tau217 showed a higher AUC (0.82 versus 0.74) in differentiating AD from PART. For either p-tau, we did not observe a level difference between individuals with AD alone and those with mixed AD pathologies. In summary, plasma p-tau181and p-tau217 were specifically associated with AD pathological changes. Further, our data provide initial evidence that p-tau217 may be able to differentiate between AD and PART in individuals with comparable burdens of tau tangle pathology. These results demonstrate the specificity of p-tau217 for AD, supporting its use to identify patients suitable for anti-AD therapies including ß-amyloid immunotherapies.

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.

A numerical approach for detecting switch-like bistability in mass action chemical reaction networks with conservation laws.

BACKGROUND: Theoretical analysis of signaling pathways can provide a substantial amount of insight into their function. One particular area of research considers signaling pathways capable of assuming two or more stable states given the same amount of signaling ligand. This phenomenon of bistability can give rise to switch-like behavior, a mechanism that governs cellular decision making. Investigation of whether or not a signaling pathway can confer bistability and switch-like behavior, without knowledge of specific kinetic rate constant values, is a mathematically challenging problem. Recently a technique based on optimization has been introduced, which is capable of finding example parameter values that confer switch-like behavior for a given pathway. Although this approach has made it possible to analyze moderately sized pathways, it is limited to reaction networks that presume a uniterminal structure. It is this limited structure we address by developing a general technique that applies to any mass action reaction network with conservation laws. RESULTS: In this paper we developed a generalized method for detecting switch-like bistable behavior in any mass action reaction network with conservation laws. The method involves (1) construction of a constrained optimization problem using the determinant of the Jacobian of the underlying rate equations, (2) minimization of the objective function to search for conditions resulting in a zero eigenvalue, (3) computation of a confidence level that describes if the global minimum has been found and (4) evaluation of optimization values, using either numerical continuation or directly simulating the ODE system, to verify that a bistability region exists. The generalized method has been tested on three motifs known to be capable of bistability. CONCLUSIONS: We have developed a variation of an optimization-based method for the discovery of bistability, which is not limited to uniterminal chemical reaction networks. Successful completion of the method provides an S-shaped bifurcation diagram, which indicates that the network acts as a bistable switch for the given optimization parameters.

Brain expression of the vascular endothelial growth factor gene family in cognitive aging and alzheimer's disease.

Vascular endothelial growth factor (VEGF) is associated with the clinical manifestation of Alzheimer's disease (AD). However, the role of the VEGF gene family in neuroprotection is complex due to the number of biological pathways they regulate. This study explored associations between brain expression of VEGF genes with cognitive performance and AD pathology. Genetic, cognitive, and neuropathology data were acquired from the Religious Orders Study and Rush Memory and Aging Project. Expression of ten VEGF ligand and receptor genes was quantified using RNA sequencing of prefrontal cortex tissue. Global cognitive composite scores were calculated from 17 neuropsychological tests. ß-amyloid and tau burden were measured at autopsy. Participants (n = 531) included individuals with normal cognition (n = 180), mild cognitive impairment (n = 148), or AD dementia (n = 203). Mean age at death was 89 years and 37% were male. Higher prefrontal cortex expression of VEGFB, FLT4, FLT1, and PGF was associated with worse cognitive trajectories (p ≤ 0.01). Increased expression of VEGFB and FLT4 was also associated with lower cognition scores at the last visit before death (p ≤ 0.01). VEGFB, FLT4, and FLT1 were upregulated among AD dementia compared with normal cognition participants (p ≤ 0.03). All four genes associated with cognition related to elevated ß-amyloid (p ≤ 0.01) and/or tau burden (p ≤ 0.03). VEGF ligand and receptor genes, specifically genes relevant to FLT4 and FLT1 receptor signaling, are associated with cognition, longitudinal cognitive decline, and AD neuropathology. Future work should confirm these observations at the protein level to better understand how changes in VEGF transcription and translation relate to neurodegenerative disease.

Proteomic Profiling of the Substantia Nigra to Identify Determinants of Lewy Body Pathology and Dopaminergic Neuronal Loss.

Proteinaceous aggregates containing α-synuclein protein called Lewy bodies in the substantia nigra is a hallmark of Parkinson's disease. The molecular mechanisms of Lewy body formation and associated neuronal loss remain largely unknown. To gain insights into proteins and pathways associated with Lewy body pathology, we performed quantitative profiling of the proteome. We analyzed substantia nigra tissue from 51 subjects arranged into three groups: cases with Lewy body pathology, Lewy body-negative controls with matching neuronal loss, and controls with no neuronal loss. Using a label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS) approach, we characterized the proteome both in terms of protein abundances and peptide modifications. Statistical testing for differential abundance of the most abundant 2963 proteins, followed by pathway enrichment and Bayesian learning of the causal network structure, was performed to identify likely drivers of Lewy body formation and dopaminergic neuronal loss. The identified pathways include (1) Arp2/3 complex-mediated actin nucleation; (2) synaptic function; (3) poly(A) RNA binding; (4) basement membrane and endothelium; and (5) hydrogen peroxide metabolic process. According to the data, the endothelial/basement membrane pathway is tightly connected with both pathologies and likely to be one of the drivers of neuronal loss. The poly(A) RNA-binding proteins, including the ones relevant to other neurodegenerative disorders (e.g., TDP-43 and FUS), have a strong inverse correlation with Lewy bodies and may reflect an alternative mechanism of nigral neurodegeneration.

Enhancing Top-Down Proteomics of Brain Tissue with FAIMS.

Proteomic investigations of Alzheimer's and Parkinson's disease have provided valuable insights into neurodegenerative disorders. Thus far, these investigations have largely been restricted to bottom-up approaches, hindering the degree to which one can characterize a protein's "intact" state. Top-down proteomics (TDP) overcomes this limitation; however, it is typically limited to observing only the most abundant proteoforms and of a relatively small size. Therefore, fractionation techniques are commonly used to reduce sample complexity. Here, we investigate gas-phase fractionation through high-field asymmetric waveform ion mobility spectrometry (FAIMS) within TDP. Utilizing a high complexity sample derived from Alzheimer's disease (AD) brain tissue, we describe how the addition of FAIMS to TDP can robustly improve the depth of proteome coverage. For example, implementation of FAIMS with external compensation voltage (CV) stepping at -50, -40, and -30 CV could more than double the mean number of non-redundant proteoforms, genes, and proteome sequence coverage compared to without FAIMS. We also found that FAIMS can influence the transmission of proteoforms and their charge envelopes based on their size. Importantly, FAIMS enabled the identification of intact amyloid beta (Aß) proteoforms, including the aggregation-prone Aß1-42 variant which is strongly linked to AD. Raw data and associated files have been deposited to the ProteomeXchange Consortium via the MassIVE data repository with data set identifier PXD023607.

Author Correction: Informed-Proteomics: open-source software package for top-down proteomics.

In the version of this article initially published, the authors erroneously reported the search mode that was used for ProSightPC 3.0 in the Online Methods and in Supplementary Table 3.

CRNT4SBML: a Python package for the detection of bistability in biochemical reaction networks.

MOTIVATION: Signaling pathways capable of switching between two states are ubiquitous within living organisms. They provide the cells with the means to produce reversible or irreversible decisions. Switch-like behavior of biological systems is realized through biochemical reaction networks capable of having two or more distinct steady states, which are dependent on initial conditions. Investigation of whether a certain signaling pathway can confer bistability involves a substantial amount of hypothesis testing. The cost of direct experimental testing can be prohibitive. Therefore, constraining the hypothesis space is highly beneficial. One such methodology is based on chemical reaction network theory (CRNT), which uses computational techniques to rule out pathways that are not capable of bistability regardless of kinetic constant values and molecule concentrations. Although useful, these methods are complicated from both pure and computational mathematics perspectives. Thus, their adoption is very limited amongst biologists. RESULTS: We brought CRNT approaches closer to experimental biologists by automating all the necessary steps in CRNT4SMBL. The input is based on systems biology markup language (SBML) format, which is the community standard for biological pathway communication. The tool parses SBML and derives C-graph representations of the biological pathway with mass action kinetics. Next steps involve an efficient search for potential saddle-node bifurcation points using an optimization technique. This type of bifurcation is important as it has the potential of acting as a switching point between two steady states. Finally, if any bifurcation points are present, continuation analysis with respect to a user-defined parameter extends the steady state branches and generates a bifurcation diagram. Presence of an S-shaped bifurcation diagram indicates that the pathway acts as a bistable switch for the given optimization parameters. AVAILABILITY AND IMPLEMENTATION: CRNT4SBML is available via the Python Package Index. The documentation can be found at https://crnt4sbml.readthedocs.io. CRNT4SBML is licensed under the Apache Software License 2.0.

Proteomic identification of select protein variants of the SNARE interactome associated with cognitive reserve in a large community sample.

Age-related neuropathologies progressively impair cognitive abilities by damaging synaptic function. We aimed to identify key components within the presynaptic SNARE (soluble N-ethylmaleimide-sensitive factor attachment protein receptor) machinery associated with cognitive performance and estimate their potential contribution to brain reserve in old age. We used targeted SRM proteomics to quantify amounts of 60 peptides, encoded in 30 different genes, in postmortem specimens of the prefrontal cortex from 1209 participants of two aging studies, with available antemortem cognitive evaluations and postmortem neuropathologic assessments. We found that select (but not all) proteoforms are strongly associated with cognitive function and the burden of Alzheimer's disease (AD) pathology. Specifically, greater abundance of STX1A (but not other syntaxins), SYT12, full-length SNAP25, and the GABAergic STXBP1 variant were robustly associated with better cognitive performance. By contrast, greater abundance of other presynaptic proteins (e.g., STXBP5 or tomosyn, STX7, or SYN2) showed a negative influence on cognition. Regression models adjusting for demographic and pathologic variables showed that altered levels of these protein species explained 7.7% additional between-subject variance in cognition (more than any individual age-related neuropathology in the model), suggesting that these molecules constitute key elements of brain reserve. Network analyses indicated that those peptides associated with brain reserve, and closest to the SNARE fusogenic activity, showed greater centrality measures and were better connected in the network. Validation assays confirmed the selective loss of the STX1A (but not STX1B) isoform in cognitively impaired cases. In rodent and human brains, STX1A was selectively located at glutamatergic terminals. However, in AD brains, STX1A was redistributed adjacent to neuritic pathology, and markedly expressed in astrocytes. Our study provides strong evidence, indicating that select presynaptic proteins are key in maintaining brain reserve. Compromised ability to sustain expression levels of these proteins may trigger synaptic dysfunction and concomitant cognitive impairment.

An Integrative Analysis of Tumor Proteomic and Phosphoproteomic Profiles to Examine the Relationships Between Kinase Activity and Phosphorylation.

Phosphorylation of proteins is a key way cells regulate function, both at the individual protein level and at the level of signaling pathways. Kinases are responsible for phosphorylation of substrates, generally on serine, threonine, or tyrosine residues. Though particular sequence patterns can be identified that dictate whether a residue will be phosphorylated by a specific kinase, these patterns are not highly predictive of phosphorylation. The availability of large scale proteomic and phosphoproteomic data sets generated using mass-spectrometry-based approaches provides an opportunity to study the important relationship between kinase activity, substrate specificity, and phosphorylation. In this study, we analyze relationships between protein abundance and phosphopeptide abundance across more than 150 tumor samples and show that phosphorylation at specific phosphosites is not well correlated with overall kinase abundance. However, individual kinases show a clear and statistically significant difference in correlation among known phosphosite targets for that kinase and randomly selected phosphosites. We further investigate relationships between phosphorylation of known activating or inhibitory sites on kinases and phosphorylation of their target phosphosites. Combined with motif-based analysis, this approach can predict novel kinase targets and show which subsets of a kinase's target repertoire are specifically active in one condition versus another.

Phosphoproteome Analysis Reveals Estrogen-ER Pathway as a Modulator of mTOR Activity Via DEPTOR.

ER-positive breast tumors represent ∼70% of all breast cancer cases. Although their treatment with endocrine therapies is effective in the adjuvant or recurrent settings, the development of resistance compromises their effectiveness. The binding of estrogen to ERα, a transcription factor, triggers the regulation of the target genes (genomic pathway). Additionally, a cytoplasmic fraction of estrogen-bound ERα activates oncogenic signaling pathways such as PI3K/AKT/mTOR (nongenomic pathway). The upregulation of the estrogenic and the PI3K/AKT/mTOR signaling pathways are frequently associated with a poor outcome. To better characterize the connection between these two pathways, we performed a phosphoproteome analysis of ER-positive MCF7 breast cancer cells treated with estrogen or estrogen and the mTORC1 inhibitor rapamycin. Many proteins were identified as estrogen-regulated mTORC1 targets and among them, DEPTOR was selected for further characterization. DEPTOR binds to mTOR and inhibits the kinase activity of both mTOR complexes mTORC1 and mTORC2, but mitogen-activated mTOR promotes phosphorylation-mediated DEPTOR degradation. Although estrogen enhances the phosphorylation of DEPTOR by mTORC1, DEPTOR levels increase in estrogen-stimulated cells. We demonstrated that DEPTOR accumulation is the result of estrogen-ERα-mediated transcriptional upregulation of DEPTOR expression. Consequently, the elevated levels of DEPTOR partially counterbalance the estrogen-induced activation of mTORC1 and mTORC2. These results underscore the critical role of estrogen-ERα as a modulator of the PI3K/AKT/mTOR signaling pathway in ER-positive breast cancer cells. Additionally, these studies provide evidence supporting the use of dual PI3K/mTOR or dual mTORC1/2 inhibitors in combination with endocrine therapies as a first-line treatment option for the patients with ER-positive advanced breast cancer.

Block Design with Common Reference Samples Enables Robust Large-Scale Label-Free Quantitative Proteome Profiling.

Label-free quantitative proteomics has become an increasingly popular tool for profiling global protein abundances. However, one major limitation is the potential performance drift of the LC-MS platform over time, which, in turn, limits its utility for analyzing large-scale sample sets. To address this, we introduce an experimental and data analysis scheme based on a block design with common references within each block for enabling large-scale label-free quantification. In this scheme, a large number of samples (e.g., >100 samples) are analyzed in smaller and more manageable blocks, minimizing instrument drift and variability within individual blocks. Each designated block also contains common reference samples (e.g., controls) for normalization across all blocks. We demonstrated the robustness of this approach by profiling the proteome response of human macrophage THP-1 cells to 11 engineered nanomaterials at two different doses. A total of 116 samples were analyzed in six blocks, yielding an average coverage of 4500 proteins per sample. Following a common reference-based correction, 2537 proteins were quantified with high reproducibility without any imputation of missing values from 116 data sets. The data revealed the consistent quantification of proteins across all six blocks, as illustrated by the highly consistent abundances of house-keeping proteins in all samples and the high levels of correlation among samples from different blocks. The data also demonstrated that label-free quantification is robust and accurate enough to quantify even very subtle abundance changes as well as large fold-changes. Our streamlined workflow is easy to implement and can be readily adapted to other large cohort studies for reproducible label-free proteome quantification.