FBXL12 degrades FANCD2 to regulate replication recovery and promote cancer cell survival under conditions of replication stress.

Fanconi anemia (FA) signaling, a key genomic maintenance pathway, is activated in response to replication stress. Here, we report that phosphorylation of the pivotal pathway protein FANCD2 by CHK1 triggers its FBXL12-dependent proteasomal degradation, facilitating FANCD2 clearance at stalled replication forks. This promotes efficient DNA replication under conditions of CYCLIN E- and drug-induced replication stress. Reconstituting FANCD2-deficient fibroblasts with phosphodegron mutants failed to re-establish fork progression. In the absence of FBXL12, FANCD2 becomes trapped on chromatin, leading to replication stress and excessive DNA damage. In human cancers, FBXL12, CYCLIN E, and FA signaling are positively correlated, and FBXL12 upregulation is linked to reduced survival in patients with high CYCLIN E-expressing breast tumors. Finally, depletion of FBXL12 exacerbated oncogene-induced replication stress and sensitized cancer cells to drug-induced replication stress by WEE1 inhibition. Collectively, our results indicate that FBXL12 constitutes a vulnerability and a potential therapeutic target in CYCLIN E-overexpressing cancers.

SOX11 is a novel binding partner and endogenous inhibitor of SAMHD1 ara-CTPase activity in mantle cell lymphoma.

ABSTRACT: Sterile alpha motif and histidine-aspartate (HD) domain-containing protein 1 (SAMHD1) is a deoxynucleoside triphosphate triphosphohydrolase with ara-CTPase activity that confers cytarabine (ara-C) resistance in several hematological malignancies. Targeting SAMHD1's ara-CTPase activity has recently been demonstrated to enhance ara-C efficacy in acute myeloid leukemia. Here, we identify the transcription factor SRY-related HMG-box containing protein 11 (SOX11) as a novel direct binding partner and first known endogenous inhibitor of SAMHD1. SOX11 is aberrantly expressed not only in mantle cell lymphoma (MCL), but also in some Burkitt lymphomas. Coimmunoprecipitation of SOX11 followed by mass spectrometry in MCL cell lines identified SAMHD1 as the top SOX11 interaction partner, which was validated by proximity ligation assay. In vitro, SAMHD1 bound to the HMG box of SOX11 with low-micromolar affinity. In situ crosslinking studies further indicated that SOX11-SAMHD1 binding resulted in a reduced tetramerization of SAMHD1. Functionally, expression of SOX11 inhibited SAMHD1 ara-CTPase activity in a dose-dependent manner resulting in ara-C sensitization in cell lines and in a SOX11-inducible mouse model of MCL. In SOX11-negative MCL, SOX11-mediated ara-CTPase inhibition could be mimicked by adding the recently identified SAMHD1 inhibitor hydroxyurea. Taken together, our results identify SOX11 as a novel SAMHD1 interaction partner and its first known endogenous inhibitor with potentially important implications for clinical therapy stratification.

SOX11 is a novel binding partner and endogenous inhibitor of SAMHD1 ara-CTPase activity in mantle cell lymphoma.

The sterile alpha motif and histidine-aspartate (HD) domain containing protein 1 (SAMHD1) is a deoxynucleoside triphosphate triphosphohydrolase with ara-CTPase activity that confers cytarabine (ara-C) resistance in several haematological malignancies. Targeting SAMHD1's ara-CTPase activity has recently been demonstrated to enhance ara-C efficacy in acute myeloid leukemia. Here, we identify the transcription factor SRY-related HMG-box containing protein 11 (SOX11) as a novel direct binding partner and first known endogenous inhibitor of SAMHD1. SOX11 is aberrantly expressed not only in mantle cell lymphoma (MCL), but also in some Burkitt lymphomas. Co-immunoprecipitation of SOX11 followed by mass spectrometry in MCL cell lines identified SAMHD1 as the top SOX11 interaction partner which was validated by proximity ligation assay. In vitro, SAMHD1 bound to the HMG box of SOX11 with low-micromolar affinity. In situ crosslinking studies further indicated that SOX11-SAMHD1 binding resulted in a reduced tetramerization of SAMHD1. Functionally, expression of SOX11 inhibited SAMHD1 ara-CTPase activity in a dose-dependent manner resulting in ara-C sensitization in cell lines and in a SOX11-inducible mouse model of MCL. In SOX11-negative MCL, SOX11-mediated ara-CTPase inhibition could be mimicked by adding the recently identified SAMHD1 inhibitor hydroxyurea. Taken together, our results identify SOX11 as a novel SAMHD1 interaction partner and its first known endogenous inhibitor with potentially important implications for clinical therapy stratification.

Reprogrammed transsulfuration promotes basal-like breast tumor progression via realigning cellular cysteine persulfidation.

Basal-like breast cancer (BLBC) is the most aggressive subtype of breast tumors with poor prognosis and limited molecular-targeted therapy options. We show that BLBC cells have a high Cys demand and reprogrammed Cys metabolism. Patient-derived BLBC tumors from four different cohorts exhibited elevated expression of the transsulfuration enzyme cystathione ß-synthetase (CBS). CBS silencing (shCBS) made BLBC cells less invasive, proliferate slower, more vulnerable to oxidative stress and cystine (CySSCy) deprivation, prone to ferroptosis, and less responsive to HIF1-α activation under hypoxia. shCBS xenograft tumors grew slower than controls and exhibited impaired angiogenesis and larger necrotic areas. Sulfur metabolite profiling suggested that realigned sulfide/persulfide-inducing functions of CBS are important in BLBC tumor progression. Supporting this, the exclusion of serine, a substrate of CBS for producing Cys but not for producing sulfide/persulfide, did not exacerbate CySSCy deprivation-induced ferroptosis in shCBS BLBC cells. Impaired Tyr phosphorylation was detected in shCBS cells and xenografts, likely due to persulfidation-inhibited phosphatase functions. Overexpression of cystathione γ-lyase (CSE), which can also contribute to cellular sulfide/persulfide production, compensated for the loss of CBS activities, and treatment of shCBS xenografts with a CSE inhibitor further blocked tumor growth. Glutathione and protein-Cys levels were not diminished in shCBS cells or xenografts, but levels of Cys persulfidation and the persulfide-catabolizing enzyme ETHE1 were suppressed. Finally, expression of enzymes of the oxidizing Cys catabolism pathway was diminished, but expression of the persulfide-producing CARS2 was elevated in human BLBC tumors. Hence, the persulfide-producing pathways are major targetable determinants of BLBC pathology that could be therapeutically exploited.

DEqMS: A Method for Accurate Variance Estimation in Differential Protein Expression Analysis.

Quantitative proteomics by mass spectrometry is widely used in biomarker research and basic biology research for investigation of phenotype level cellular events. Despite the wide application, the methodology for statistical analysis of differentially expressed proteins has not been unified. Various methods such as t test, linear model and mixed effect models are used to define changes in proteomics experiments. However, none of these methods consider the specific structure of MS-data. Choices between methods, often originally developed for other types of data, are based on compromises between features such as statistical power, general applicability and user friendliness. Furthermore, whether to include proteins identified with one peptide in statistical analysis of differential protein expression varies between studies. Here we present DEqMS, a robust statistical method developed specifically for differential protein expression analysis in mass spectrometry data. In all data sets investigated there is a clear dependence of variance on the number of PSMs or peptides used for protein quantification. DEqMS takes this feature into account when assessing differential protein expression. This allows for a more accurate data-dependent estimation of protein variance and inclusion of single peptide identifications without increasing false discoveries. The method was tested in several data sets including E. coli proteome spike-in data, using both label-free and TMT-labeled quantification. Compared with previous statistical methods used in quantitative proteomics, DEqMS showed consistently better accuracy in detecting altered protein levels compared with other statistical methods in both label-free and labeled quantitative proteomics data. DEqMS is available as an R package in Bioconductor.

Immediate Adaptation Analysis Implicates BCL6 as an EGFR-TKI Combination Therapy Target in NSCLC.

Drug resistance is a major obstacle to curative cancer therapies, and increased understanding of the molecular events contributing to resistance would enable better prediction of therapy response, as well as contribute to new targets for combination therapy. Here we have analyzed the early molecular response to epidermal growth factor receptor (EGFR) inhibition using RNA sequencing data covering 13,486 genes and mass spectrometry data covering 10,138 proteins. This analysis revealed a massive response to EGFR inhibition already within the first 24 h, including significant regulation of hundreds of genes known to control downstream signaling, such as transcription factors, kinases, phosphatases and ubiquitin E3-ligases. Importantly, this response included upregulation of key genes in multiple oncogenic signaling pathways that promote proliferation and survival, such as ERBB3, FGFR2, JAK3, and BCL6, indicating an early adaptive response to EGFR inhibition. Using a library of more than 500 approved and experimental compounds in a combination therapy screen, we could show that several kinase inhibitors with targets including JAK3 and FGFR2 increased the response to EGFR inhibitors. Further, we investigated the functional impact of BCL6 upregulation in response to EGFR inhibition using siRNA-based silencing of BCL6. Proteomics profiling revealed that BCL6 inhibited transcription of multiple target genes including p53, resulting in reduced apoptosis which implicates BCL6 upregulation as a new EGFR inhibitor treatment escape mechanism. Finally, we demonstrate that combined treatment targeting both EGFR and BCL6 act synergistically in killing lung cancer cells. In conclusion, or data indicates that multiple different adaptive mechanisms may act in concert to blunt the cellular impact of EGFR inhibition, and we suggest BCL6 as a potential target for EGFR inhibitor-based combination therapy.

Mutation-independent Proteomic Signatures of Pathological Progression in Murine Models of Duchenne Muscular Dystrophy.

The absence of the dystrophin protein in Duchenne muscular dystrophy (DMD) results in myofiber fragility and a plethora of downstream secondary pathologies. Although a variety of experimental therapies are in development, achieving effective treatments for DMD remains exceptionally challenging, not least because the pathological consequences of dystrophin loss are incompletely understood. Here we have performed proteome profiling in tibialis anterior muscles from two murine DMD models (mdx and mdx52) at three ages (8, 16, and 80 weeks of age), all n = 3. High-resolution isoelectric focusing liquid chromatography-tandem MS (HiRIEF-LC-MS/MS) was used to quantify the expression of 4974 proteins across all 27 samples. The two dystrophic models were found to be highly similar, whereas multiple proteins were differentially expressed relative to WT (C57BL/6) controls at each age. Furthermore, 1795 proteins were differentially expressed when samples were pooled across ages and dystrophic strains. These included numerous proteins associated with the extracellular matrix and muscle function that have not been reported previously. Pathway analysis revealed multiple perturbed pathways and predicted upstream regulators, which together are indicative of cross-talk between inflammatory, metabolic, and muscle growth pathways (e.g. TNF, INFγ, NF-κB, SIRT1, AMPK, PGC-1α, PPARs, ILK, and AKT/PI3K). Upregulation of CAV3, MVP and PAK1 protein expression was validated in dystrophic muscle by Western blot. Furthermore, MVP was upregulated during, but not required for, the differentiation of C2C12 myoblasts suggesting that this protein may affect muscle regeneration. This study provides novel insights into mutation-independent proteomic signatures characteristic of the dystrophic phenotype and its progression with aging.

Time-resolved transcriptome and proteome landscape of human regulatory T cell (Treg) differentiation reveals novel regulators of FOXP3.

BACKGROUND: Regulatory T cells (Tregs) expressing the transcription factor FOXP3 are crucial mediators of self-tolerance, preventing autoimmune diseases but possibly hampering tumor rejection. Clinical manipulation of Tregs is of great interest, and first-in-man trials of Treg transfer have achieved promising outcomes. Yet, the mechanisms governing induced Treg (iTreg) differentiation and the regulation of FOXP3 are incompletely understood. RESULTS: To gain a comprehensive and unbiased molecular understanding of FOXP3 induction, we performed time-series RNA sequencing (RNA-Seq) and proteomics profiling on the same samples during human iTreg differentiation. To enable the broad analysis of universal FOXP3-inducing pathways, we used five differentiation protocols in parallel. Integrative analysis of the transcriptome and proteome confirmed involvement of specific molecular processes, as well as overlap of a novel iTreg subnetwork with known Treg regulators and autoimmunity-associated genes. Importantly, we propose 37 novel molecules putatively involved in iTreg differentiation. Their relevance was validated by a targeted shRNA screen confirming a functional role in FOXP3 induction, discriminant analyses classifying iTregs accordingly, and comparable expression in an independent novel iTreg RNA-Seq dataset. CONCLUSION: The data generated by this novel approach facilitates understanding of the molecular mechanisms underlying iTreg generation as well as of the concomitant changes in the transcriptome and proteome. Our results provide a reference map exploitable for future discovery of markers and drug candidates governing control of Tregs, which has important implications for the treatment of cancer, autoimmune, and inflammatory diseases.

Multi-level omics analysis in a murine model of dystrophin loss and therapeutic restoration.

Duchenne muscular dystrophy (DMD) is a classical monogenic disorder, a model disease for genomic studies and a priority candidate for regenerative medicine and gene therapy. Although the genetic cause of DMD is well known, the molecular pathogenesis of disease and the response to therapy are incompletely understood. Here, we describe analyses of protein, mRNA and microRNA expression in the tibialis anterior of the mdx mouse model of DMD. Notably, 3272 proteins were quantifiable and 525 identified as differentially expressed in mdx muscle (P < 0.01). Therapeutic restoration of dystrophin by exon skipping induced widespread shifts in protein and mRNA expression towards wild-type expression levels, whereas the miRNome was largely unaffected. Comparison analyses between datasets showed that protein and mRNA ratios were only weakly correlated (r = 0.405), and identified a multitude of differentially affected cellular pathways, upstream regulators and predicted miRNA-target interactions. This study provides fundamental new insights into gene expression and regulation in dystrophic muscle.

HiRIEF LC-MS enables deep proteome coverage and unbiased proteogenomics.

We present a liquid chromatography-mass spectrometry (LC-MS)-based method permitting unbiased (gene prediction-independent) genome-wide discovery of protein-coding loci in higher eukaryotes. Using high-resolution isoelectric focusing (HiRIEF) at the peptide level in the 3.7-5.0 pH range and accurate peptide isoelectric point (pI) prediction, we probed the six-reading-frame translation of the human and mouse genomes and identified 98 and 52 previously undiscovered protein-coding loci, respectively. The method also enabled deep proteome coverage, identifying 13,078 human and 10,637 mouse proteins.

Comprehensive Proteomic Analysis of Mesenchymal Stem Cell Exosomes Reveals Modulation of Angiogenesis via Nuclear Factor-KappaB Signaling.

Mesenchymal stem cells (MSC) are known to facilitate healing of ischemic tissue related diseases through proangiogenic secretory proteins. Recent studies further show that MSC derived exosomes function as paracrine effectors of angiogenesis, however, the identity of which components of the exosome proteome responsible for this effect remains elusive. To address this we used high-resolution isoelectric focusing coupled liquid chromatography tandem mass spectrometry, an unbiased high throughput proteomics approach to comprehensively characterize the proteinaceous contents of MSCs and MSC derived exosomes. We probed the proteome of MSCs and MSC derived exosomes from cells cultured under expansion conditions and under ischemic tissue simulated conditions to elucidate key angiogenic paracrine effectors present and potentially differentially expressed in these conditions. In total, 6,342 proteins were identified in MSCs and 1,927 proteins in MSC derived exosomes, representing to our knowledge the first time these proteomes have been probed comprehensively. Multilayered analyses identified several putative paracrine effectors of angiogenesis present in MSC exosomes and increased in expression in MSCs exposed to ischemic tissue-simulated conditions; these include platelet derived growth factor, epidermal growth factor, fibroblast growth factor, and most notably nuclear factor-kappaB (NFkB) signaling pathway proteins. NFkB signaling was identified as a key mediator of MSC exosome induced angiogenesis in endothelial cells by functional in vitro validation using a specific inhibitor. Collectively, the results of our proteomic analysis show that MSC derived exosomes contain a robust profile of angiogenic paracrine effectors, which have potential for the treatment of ischemic tissue-related diseases.

Proteome screening of pleural effusions identifies galectin 1 as a diagnostic biomarker and highlights several prognostic biomarkers for malignant mesothelioma.

Malignant mesothelioma is an aggressive asbestos-induced cancer, and affected patients have a median survival of approximately one year after diagnosis. It is often difficult to reach a conclusive diagnosis, and ancillary measurements of soluble biomarkers could increase diagnostic accuracy. Unfortunately, few soluble mesothelioma biomarkers are suitable for clinical application. Here we screened the effusion proteomes of mesothelioma and lung adenocarcinoma patients to identify novel soluble mesothelioma biomarkers. We performed quantitative mass-spectrometry-based proteomics using isobaric tags for quantification and used narrow-range immobilized pH gradient/high-resolution isoelectric focusing (pH 4-4.25) prior to analysis by means of nano liquid chromatography coupled to MS/MS. More than 1,300 proteins were identified in pleural effusions from patients with malignant mesothelioma (n = 6), lung adenocarcinoma (n = 6), or benign mesotheliosis (n = 7). Data are available via ProteomeXchange with identifier PXD000531. The identified proteins included a set of known mesothelioma markers and proteins that regulate hallmarks of cancer such as invasion, angiogenesis, and immune evasion, plus several new candidate proteins. Seven candidates (aldo-keto reductase 1B10, apolipoprotein C-I, galectin 1, myosin-VIIb, superoxide dismutase 2, tenascin C, and thrombospondin 1) were validated by enzyme-linked immunosorbent assays in a larger group of patients with mesothelioma (n = 37) or metastatic carcinomas (n = 25) and in effusions from patients with benign, reactive conditions (n = 16). Galectin 1 was identified as overexpressed in effusions from lung adenocarcinoma relative to mesothelioma and was validated as an excellent predictor for metastatic carcinomas against malignant mesothelioma. Galectin 1, aldo-keto reductase 1B10, and apolipoprotein C-I were all identified as potential prognostic biomarkers for malignant mesothelioma. This analysis of the effusion proteome furthers our understanding of malignant mesothelioma, identified galectin 1 as a potential diagnostic biomarker, and highlighted several possible prognostic biomarkers of this disease.

Proteomics profiling identify CAPS as a potential predictive marker of tamoxifen resistance in estrogen receptor positive breast cancer.

BACKGROUND: Despite the success of tamoxifen since its introduction, about one-third of patients with estrogen (ER) and/or progesterone receptor (PgR) - positive breast cancer (BC) do not benefit from therapy. Here, we aim to identify molecular mechanisms and protein biomarkers involved in tamoxifen resistance. RESULTS: Using iTRAQ and Immobilized pH gradient-isoelectric focusing (IPG-IEF) mass spectrometry based proteomics we compared tumors from 12 patients with early relapses (<2 years) and 12 responsive to therapy (relapse-free > 7 years). A panel of 13 proteins (TCEAL4, AZGP1, S100A10, ALDH6A1, AHNAK, FBP1, S100A4, HSP90AB1, PDXK, GFPT1, RAB21, MX1, CAPS) from the 3101 identified proteins, potentially separate relapse from non-relapse BC patients. The proteins in the panel are involved in processes such as calcium (Ca(2+)) signaling, metabolism, epithelial mesenchymal transition (EMT), metastasis and invasion. Validation of the highest expressed proteins in the relapse group identify high tumor levels of CAPS as predictive of tamoxifen response in a patient cohort receiving tamoxifen as only adjuvant therapy. CONCLUSIONS: This data implicate CAPS in tamoxifen resistance and as a potential predictive marker.

Defining, comparing, and improving iTRAQ quantification in mass spectrometry proteomics data.

The purpose of this study was to generate a basis for the decision of what protein quantities are reliable and find a way for accurate and precise protein quantification. To investigate this we have used thousands of peptide measurements to estimate variance and bias for quantification by iTRAQ (isobaric tags for relative and absolute quantification) mass spectrometry in complex human samples. A549 cell lysate was mixed in the proportions 2:2:1:1:2:2:1:1, fractionated by high resolution isoelectric focusing and liquid chromatography and analyzed by three mass spectrometry platforms; LTQ Orbitrap Velos, 4800 MALDI-TOF/TOF and 6530 Q-TOF. We have investigated how variance and bias in the iTRAQ reporter ions data are affected by common experimental variables such as sample amount, sample fractionation, fragmentation energy, and instrument platform. Based on this, we have suggested a concept for experimental design and a methodology for protein quantification. By using duplicate samples in each run, each experiment is validated based on its internal experimental variation. The duplicates are used for calculating peptide weights, unique to the experiment, which is used in the protein quantification. By weighting the peptides depending on reporter ion intensity, we can decrease the relative error in quantification at the protein level and assign a total weight to each protein that reflects the protein quantitation confidence. We also demonstrate the usability of this methodology in a cancer cell line experiment as well as in a clinical data set of lung cancer tissue samples. In conclusion, we have in this study developed a methodology for improved protein quantification in shotgun proteomics and introduced a way to assess quantification for proteins with few peptides. The experimental design and developed algorithms decreased the relative protein quantification error in the analysis of complex biological samples.

Ultrafiltration with size-exclusion liquid chromatography for high yield isolation of extracellular vesicles preserving intact biophysical and functional properties.

Extracellular vesicles (EVs) are natural nanoparticles that mediate intercellular transfer of RNA and proteins and are of great medical interest; serving as novel biomarkers and potential therapeutic agents. However, there is little consensus on the most appropriate method to isolate high-yield and high-purity EVs from various biological fluids. Here, we describe a systematic comparison between two protocols for EV purification: ultrafiltration with subsequent liquid chromatography (UF-LC) and differential ultracentrifugation (UC). A significantly higher EV yield resulted from UF-LC as compared to UC, without affecting vesicle protein composition. Importantly, we provide novel evidence that, in contrast to UC-purified EVs, the biophysical properties of UF-LC-purified EVs are preserved, leading to a different in vivo biodistribution, with less accumulation in lungs. Finally, we show that UF-LC is scalable and adaptable for EV isolation from complex media types such as stem cell media, which is of huge significance for future clinical applications involving EVs. FROM THE CLINICAL EDITOR: Recent evidence suggests extracellular vesicles (EVs) as another route of cellular communication. These EVs may be utilized for future therapeutics. In this article, the authors compared ultrafiltration with size-exclusion liquid chromatography (UF-LC) and ultra-centrifugation (UC) for EV recovery.

Investigating the applicability of antibodies generated within the human protein atlas as capture agents in immunoenrichment coupled to mass spectrometry.

For identification and characterization of proteins in complex samples, immunoenrichment coupled to mass spectrometry is a good alternative due to the sensitivity of the affinity enrichment and the specificity of mass spectrometry analysis. Antibodies are commonly used affinity agents; however, for high-throughput analysis, antibody availability is usually a bottleneck. Here we present a protocol for immunoenrichment coupled to mass spectrometry in a high-throughput setup, where all steps from bead coupling to mass spectrometry sample preparation are performed in parallel in a 96-well format. Antibodies generated within the Human Protein Atlas project were tested for applicability as capture agents. The antibodies were covalently attached to protein A beads, making it possible to reuse the coupled beads at least three times without destroying the antibody binding efficiency. Target proteins were captured from a U251 MG cell lysate, eluted, digested, and analyzed using mass spectrometry. Of 30 investigated antibodies, around 50% could successfully capture the corresponding native target protein, making the available library of more than 21 000 antibodies a valuable resource for immunoenrichment assays. Due to the diversity of different antibodies regarding affinity and specificity, analyzing antibodies in a high-throughput format is challenging. Even though protocol optimization for individual antibodies can be advantageous for future studies, our method enables a fast screening strategy to determine the usefulness of antibodies in immunoenrichment setups. In addition, we show that the specificity of the antibodies can be investigated by using label-free quantification.

Longitudinal molecular profiling elucidates immunometabolism dynamics in breast cancer.

Although metabolic reprogramming within tumor cells and tumor microenvironment (TME) is well described in breast cancer, little is known about how the interplay of immune state and cancer metabolism evolves during treatment. Here, we characterize the immunometabolic profiles of tumor tissue samples longitudinally collected from individuals with breast cancer before, during and after neoadjuvant chemotherapy (NAC) using proteomics, genomics and histopathology. We show that the pre-, on-treatment and dynamic changes of the immune state, tumor metabolic proteins and tumor cell gene expression profiling-based metabolic phenotype are associated with treatment response. Single-cell/nucleus RNA sequencing revealed distinct tumor and immune cell states in metabolism between cold and hot tumors. Potential drivers of NAC based on above analyses were validated in vitro. In summary, the study shows that the interaction of tumor-intrinsic metabolic states and TME is associated with treatment outcome, supporting the concept of targeting tumor metabolism for immunoregulation.

Enhanced information output from shotgun proteomics data by protein quantification and peptide quality control (PQPQ).

We present a tool to improve quantitative accuracy and precision in mass spectrometry based on shotgun proteomics: protein quantification by peptide quality control, PQPQ. The method is based on the assumption that the quantitative pattern of peptides derived from one protein will correlate over several samples. Dissonant patterns arise either from outlier peptides or because of the presence of different protein species. By correlation analysis, protein quantification by peptide quality control identifies and excludes outliers and detects the existence of different protein species. Alternative protein species are then quantified separately. By validating the algorithm on seven data sets related to different cancer studies we show that data processing by protein quantification by peptide quality control improves the information output from shotgun proteomics. Data from two labeling procedures and three different instrumental platforms was included in the evaluation. With this unique method using both peptide sequence data and quantitative data we can improve the quantitative accuracy and precision on the protein level and detect different protein species.

Design of a peptide-based vector, PepFect6, for efficient delivery of siRNA in cell culture and systemically in vivo.

While small interfering RNAs (siRNAs) have been rapidly appreciated to silence genes, efficient and non-toxic vectors for primary cells and for systemic in vivo delivery are lacking. Several siRNA-delivery vehicles, including cell-penetrating peptides (CPPs), have been developed but their utility is often restricted by entrapment following endocytosis. Hence, developing CPPs that promote endosomal escape is a prerequisite for successful siRNA implementation. We here present a novel CPP, PepFect 6 (PF6), comprising the previously reported stearyl-TP10 peptide, having pH titratable trifluoromethylquinoline moieties covalently incorporated to facilitate endosomal release. Stable PF6/siRNA nanoparticles enter entire cell populations and rapidly promote endosomal escape, resulting in robust RNAi responses in various cell types (including primary cells), with minimal associated transcriptomic or proteomic changes. Furthermore, PF6-mediated delivery is independent of cell confluence and, in most cases, not significantly hampered by serum proteins. Finally, these nanoparticles promote strong RNAi responses in different organs following systemic delivery in mice without any associated toxicity. Strikingly, similar knockdown in liver is achieved by PF6/siRNA nanoparticles and siRNA injected by hydrodynamic infusion, a golden standard technique for liver transfection. These results imply that the peptide, in addition to having utility for RNAi screens in vitro, displays therapeutic potential.

Proteome Analysis of Pancreatic Tumors Implicates Extracellular Matrix in Patient Outcome.

Pancreatic cancer remains a disease with unmet clinical needs and inadequate diagnostic, prognostic, and predictive biomarkers. In-depth characterization of the disease proteome is limited. This study thus aims to define and describe protein networks underlying pancreatic cancer and identify protein centric subtypes with clinical relevance. Mass spectrometry-based proteomics was used to identify and quantify the proteome in tumor tissue, tumor-adjacent tissue, and patient-derived xenografts (PDX)-derived cell lines from patients with pancreatic cancer, and tissues from patients with chronic pancreatitis. We identified, quantified, and characterized 11,634 proteins from 72 pancreatic tissue samples. Network focused analysis of the proteomics data led to identification of a tumor epithelium-specific module and an extracellular matrix (ECM)-associated module that discriminated pancreatic tumor tissue from both tumor adjacent tissue and pancreatitis tissue. On the basis of the ECM module, we defined an ECM-high and an ECM-low subgroup, where the ECM-high subgroup was associated with poor prognosis (median survival months: 15.3 vs. 22.9 months; log-rank test, P = 0.02). The ECM-high tumors were characterized by elevated epithelial-mesenchymal transition and glycolytic activities, and low oxidative phosphorylation, E2F, and DNA repair pathway activities. This study offers novel insights into the protein network underlying pancreatic cancer opening up for proteome precision medicine development. Significance: Pancreatic cancer lacks reliable biomarkers for prognostication and treatment of patients. We analyzed the proteome of pancreatic tumors, nonmalignant tissues of the pancreas and PDX-derived cell lines, and identified proteins that discriminate between patients with good and poor survival. The proteomics data also unraveled potential novel drug targets.