A predictive phosphorylation signature of lung cancer

Protein posttranslational modifications in health and diseases: Functions, regulatory mechanisms, and therapeutic implications

Protein posttranslational modifications (PTMs) refer to the breaking or generation of covalent bonds on the backbones or amino acid side chains of proteins and expand the diversity of proteins, which provides the basis for the emergence of organismal complexity. To date, more than 650 types of protein modifications, such as the most well-known phosphorylation, ubiquitination, glycosylation, methylation, SUMOylation, short-chain and long-chain acylation modifications, redox modifications, and irreversible modifications, have been described, and the inventory is still increasing. By changing the protein conformation, localization, activity, stability, charges, and interactions with other biomolecules, PTMs ultimately alter the phenotypes and biological processes of cells. The homeostasis of protein modifications is important to human health. Abnormal PTMs may cause changes in protein properties and loss of protein functions, which are closely related to the occurrence and development of various diseases. In this review, we systematically introduce the characteristics, regulatory mechanisms, and functions of various PTMs in health and diseases. In addition, the therapeutic prospects in various diseases by targeting PTMs and associated regulatory enzymes are also summarized. This work will deepen the understanding of protein modifications in health and diseases and promote the discovery of diagnostic and prognostic markers and drug targets for diseases.

SYNPRED: prediction of drug combination effects in cancer using different synergy metrics and ensemble learning

Background

In cancer research, high-throughput screening technologies produce large amounts of multiomics data from different populations and cell types. However, analysis of such data encounters difficulties due to disease heterogeneity, further exacerbated by human biological complexity and genomic variability. The specific profile of cancer as a disease (or, more realistically, a set of diseases) urges the development of approaches that maximize the effect while minimizing the dosage of drugs. Now is the time to redefine the approach to drug discovery, bringing an artificial intelligence (AI)–powered informational view that integrates the relevant scientific fields and explores new territories.

Results

Here, we show SYNPRED, an interdisciplinary approach that leverages specifically designed ensembles of AI algorithms, as well as links omics and biophysical traits to predict anticancer drug synergy. It uses 5 reference models (Bliss, Highest Single Agent, Loewe, Zero Interaction Potency, and Combination Sensitivity Score), which, coupled with AI algorithms, allowed us to attain the ones with the best predictive performance and pinpoint the most appropriate reference model for synergy prediction, often overlooked in similar studies. By using an independent test set, SYNPRED exhibits state-of-the-art performance metrics either in the classification (accuracy, 0.85; precision, 0.91; recall, 0.90; area under the receiver operating characteristic, 0.80; and F1-score, 0.91) or in the regression models, mainly when using the Combination Sensitivity Score synergy reference model (root mean square error, 11.07; mean squared error, 122.61; Pearson, 0.86; mean absolute error, 7.43; Spearman, 0.87). Moreover, data interpretability was achieved by deploying the most current and robust feature importance approaches. A simple web-based application was constructed, allowing easy access by nonexpert researchers.

Conclusions

The performance of SYNPRED rivals that of the existing methods that tackle the same problem, yielding unbiased results trained with one of the most comprehensive datasets available (NCI ALMANAC). The leveraging of different reference models allowed deeper insights into which of them can be more appropriately used for synergy prediction. The Combination Sensitivity Score clearly stood out with improved performance among the full scope of surveyed approaches and synergy reference models. Furthermore, SYNPRED takes a particular focus on data interpretability, which has been in the spotlight lately when using the most advanced AI techniques.

Comprehensive Analysis of Cancer-Proteogenome to Identify Biomarkers for the Early Diagnosis and Prognosis of Cancer

During the past century, our understanding of cancer diagnosis and treatment has been based on a monogenic approach, and as a consequence our knowledge of the clinical genetic underpinnings of cancer is incomplete. Since the completion of the human genome in 2003, it has steered us into therapeutic target discovery, enabling us to mine the genome using cutting edge proteogenomics tools. A number of novel and promising cancer targets have emerged from the genome project for diagnostics, therapeutics, and prognostic markers, which are being used to monitor response to cancer treatment. The heterogeneous nature of cancer has hindered progress in understanding the underlying mechanisms that lead to abnormal cellular growth. Since, the start of The Cancer Genome Atlas (TCGA), and the International Genome consortium projects, there has been tremendous progress in genome sequencing and immense numbers of cancer genomes have been completed, and this approach has transformed our understanding of the diagnosis and treatment of different types of cancers. By employing Genomics and proteomics technologies, an immense amount of genomic data is being generated on clinical tumors, which has transformed the cancer landscape and has the potential to transform cancer diagnosis and prognosis. A complete molecular view of the cancer landscape is necessary for understanding the underlying mechanisms of cancer initiation to improve diagnosis and prognosis, which ultimately will lead to personalized treatment. Interestingly, cancer proteome analysis has also allowed us to identify biomarkers to monitor drug and radiation resistance in patients undergoing cancer treatment. Further, TCGA-funded studies have allowed for the genomic and transcriptomic characterization of targeted cancers, this analysis aiding the development of targeted therapies for highly lethal malignancy. High-throughput technologies, such as complete proteome, epigenome, protein-protein interaction, and pharmacogenomics data, are indispensable to glean into the cancer genome and proteome and these approaches have generated multidimensional universal studies of genes and proteins (OMICS) data which has the potential to facilitate precision medicine. However, due to slow progress in computational technologies, the translation of big omics data into their clinical aspects have been slow. In this review, attempts have been made to describe the role of high-throughput genomic and proteomic technologies in identifying a panel of biomarkers which could be used for the early diagnosis and prognosis of cancer.

Differential Effects of Tyrosine Kinase Inhibitors on Normal and Oncogenic EGFR Signaling and Downstream Effectors

Constitutive activation of EGFR due to overexpression or mutation in tumor cells leads to dysregulated downstream cellular signaling pathways. Therefore, EGFR as well as its downstream effectors have been identified as important therapeutic targets. The FDA-approved small-molecule inhibitors of EGFR, gefitinib (Iressa) and erlotinib (Tarceva), are clinically effective in a subset of patients with non-small cell lung cancer (NSCLC) whose tumors harbor activating mutations within the kinase domain of EGFR. The current study examined effects of these drugs in 32D cells expressing native (WT) or oncogenic (L858R) EGFR as well as in cancer cell lines A431 and H3255. Distinct patterns for gefitinib and erlotinib inhibition of EGFR autophosphorylation at individual tyrosines were revealed for wild-type (WT) and L858R EGFR. Phosphorylation of Y845 has been shown to be important in cancer cells and Y1045 phosphorylation is linked to Cbl-mediated ubiquitination and degradation. Dramatic differences were observed by greater potency of these drugs for inhibiting downstream effectors for L858R EGFR including Cbl and STAT5. Selective targeting of Cbl may play a role in oncogene addiction and effects on STAT5 identify features of signaling circuitry for L858R EGFR that contribute to drug sensitivity and clinical efficacy. These data provide new understanding of the EGFR signaling environment and suggest useful paradigms for predicting patient response to EGFR-targeted therapy as well as combination treatments.

IMPLICATIONS

This study offers fundamental insights for understanding molecular mechanisms of drug sensitivity on oncogenic forms of EGFR and downstream signaling components as well as considerations for further drug optimization and design of combination therapy.

Prenatal tobacco smoke exposure is associated with childhood DNA CpG methylation

Background

Smoking while pregnant is associated with a myriad of negative health outcomes in the child. Some of the detrimental effects may be due to epigenetic modifications, although few studies have investigated this hypothesis in detail.

Objectives

To characterize site-specific epigenetic modifications conferred by prenatal smoking exposure within asthmatic children.

Methods

Using Illumina HumanMethylation27 microarrays, we estimated the degree of methylation at 27,578 distinct DNA sequences located primarily in gene promoters using whole blood DNA samples from the Childhood Asthma Management Program (CAMP) subset of Asthma BRIDGE childhood asthmatics (n = 527) ages 5–12 with prenatal smoking exposure data available. Using beta-regression, we screened loci for differential methylation related to prenatal smoke exposure, adjusting for gender, age and clinical site, and accounting for multiple comparisons by FDR.

Results

Of 27,578 loci evaluated, 22,131 (80%) passed quality control assessment and were analyzed. Sixty-five children (12%) had a history of prenatal smoke exposure. At an FDR of 0.05, we identified 19 CpG loci significantly associated with prenatal smoke, of which two replicated in two independent populations. Exposure was associated with a 2% increase in mean CpG methylation in FRMD4A (p = 0.01) and Cllorf52 (p = 0.001) compared to no exposure. Four additional genes, XPNPEP1, PPEF2, SMPD3 and CRYGN, were nominally associated in at least one replication group.

Conclusions

These data suggest that prenatal exposure to tobacco smoke is associated with reproducible epigenetic changes that persist well into childhood. However, the biological significance of these altered loci remains unknown.

Correlating phosphoproteomic signaling with castration resistant prostate cancer survival through regression analysis

Prostate cancer most commonly presents as initially castration dependent, however in a minority of patients the disease will progress to a state of castration resistance. Here, approaches for correlating alterations in the phosphoproteome with androgen independent cell survival in the LNCaP, PC3, and MDa-PCa-2b cell lines are discussed. The performance of the regression techniques multiple linear, ridge, principal component, and partial least squares regression is compared. The predictive performance of these algorithms over randomized data sets and using the Akaike Information Criterion is explored, and principal component and partial least squares regression are found to outperform other regression approaches. The effect of altering the number of features versus observations on the R(2) value and predictive performance is also examined using the partial least squares regression model. Utilizing these approaches "drivers" of castration resistant disease can be identified whose modulation alters phenotypic outcomes. These data provide an empirical comparison of the various considerations when statistically analyzing phosphorylation data with the aim of correlating with phenotypic outcomes.

Role of ABL family kinases in cancer: from leukaemia to solid tumours

The Abelson (ABL) family of nonreceptor tyrosine kinases, ABL1 and ABL2, transduces diverse extracellular signals to protein networks that control proliferation, survival, migration and invasion. ABL1 was first identified as an oncogene required for the development of leukaemias initiated by retroviruses or chromosome translocations. The demonstration that small-molecule ABL kinase inhibitors could effectively treat chronic myeloid leukaemia opened the door to the era of targeted cancer therapies. Recent reports have uncovered roles for ABL kinases in solid tumours. Enhanced ABL expression and activation in some solid tumours, together with altered cell polarity, invasion or growth induced by activated ABL kinases, suggest that drugs targeting these kinases may be useful for treating selected solid tumours.

Proteomic analysis of podocyte exosome-enriched fraction from normal human urine

Urine results from a coordinated activity of glomerular and tubular compartments of the kidney. As a footprint of these cellular functional processes, urinary exosomes, and 40-80 nm membrane vesicles released after fusion with the plasma membrane into the extracellular environment by renal epithelial cells, are a source for identification of proteins and investigation of their role in the kidney. The aim of the present study was the identification of podocyte exosome proteins based on urine immunoabsorption using podocyte-specific CR1-immunocoated beads followed by proteomic analysis using LC MS/MS techniques. This methodology allowed the identification of 1195 proteins. By using a bioinformatic approach, 27 brain-expressed proteins were identified, in which 14 out of them were newly demonstrated to be expressed in the kidney at a mRNA level, and, one of them, the COMT protein, was demonstrated to be expressed in podocytes at a protein level. These results, attesting the reliability of the methodology to identify podocyte proteins, need now to be completed by further experiments to analyze more precisely their biological function(s) in the podocytes.

Clinical and pathological staging of the cancer at the nanoscale

Clinical staging model at the nanoscale (CSMN) has been performed on adenocarcinoma of the colon from five patients ranging in age from 57 to 76 years based on determining their malignant size, estimating their doubling time through imaging techniques, and thus by measuring the average of the tumor nanoparticle doubling time their histologic grade has been identified at the nanoscale. Another two pathologic staging models at the nanoscale PSM [H-3] N and PSM [C-14] N for evaluating the histologic grade have been performed on those tumors based on the in vitro measuring of cell proliferating of tumor slices by either of the [H-3] tritiated and [C-14] thymidine incorporation hypothesizing in PSM [H-3] N that the malignant fraction of the detected tumor is the unlabeled fraction of the tumor by the [H-3] tritiated thymidine, while positing in PSM [C-14] N that the percentage increase of the tumor nanoparticle doubling time than that of the normal tissue at the Natural Background Radiation is equivalent to the percentage deficit of [C-14] incorporation in tumor cells. The consistency of results of the three staging models has been analyzed using ANOVA. Identical histologic grades have been identified by the three staging models for tumors of early stages (p < 0.0001). While for those of advanced disease, evaluation of the histologic grade was identical by CSMN and PSM [H-3] N only (p < 0.0001), whereas was invalid by PSM [C-14] N.

Prestroke proteomic changes in cerebral microvessels in stroke-prone, transgenic[hCETP]-Hyperlipidemic, Dahl salt-sensitive hypertensive rats

Stroke is the third leading cause of death in the United States with high rates of morbidity among survivors. The search to fill the unequivocal need for new therapeutic approaches would benefit from unbiased proteomic analyses of animal models of spontaneous stroke in the prestroke stage. Since brain microvessels play key roles in neurovascular coupling, we investigated prestroke microvascular proteome changes. Proteomic analysis of cerebral cortical microvessels (cMVs) was done by tandem mass spectrometry comparing two prestroke time points. Metaprotein-pathway analyses of proteomic spectral count data were done to identify risk factor-induced changes, followed by QSPEC-analyses of individual protein changes associated with increased stroke susceptibility. We report 26 cMV proteome profiles from male and female stroke-prone and non-stroke-prone rats at 2 months and 4.5 months of age prior to overt stroke events. We identified 1,934 proteins by two or more peptides. Metaprotein pathway analysis detected age-associated changes in energy metabolism and cell-to-microenvironment interactions, as well as sex-specific changes in energy metabolism and endothelial leukocyte transmigration pathways. Stroke susceptibility was associated independently with multiple protein changes associated with ischemia, angiogenesis or involved in blood brain barrier (BBB) integrity. Immunohistochemical analysis confirmed aquaporin-4 and laminin-α1 induction in cMVs, representative of proteomic changes with >65 Bayes factor (BF), associated with stroke susceptibility. Altogether, proteomic analysis demonstrates significant molecular changes in ischemic cerebral microvasculature in the prestroke stage, which could contribute to the observed model phenotype of microhemorrhages and postischemic hemorrhagic transformation. These pathways comprise putative targets for translational research of much needed novel diagnostic and therapeutic approaches for stroke.

Strategies for quantitation of phosphoproteomic data

Recent developments in phosphoproteomic sample-preparation techniques and sensitive mass spectrometry instrumentation have led to large-scale identifications of phosphoproteins and phosphorylation sites from highly complex samples. This has facilitated the implementation of different quantitation strategies in order to study the biological role of protein phosphorylation during disease progression, differentiation or during external stimulation of a cellular system. In this article, a brief summary of the most popular strategies for phosphoproteomic studies is given; however, the main focus will be on different quantitation strategies. Methods for metabolic labeling, chemical modification and label-free quantitation and their applicability or inapplicability in phosphoproteomic studies are discussed.