AZGP1 deficiency promotes angiogenesis in prostate cancer.

BACKGROUND: Loss of AZGP1 expression is a biomarker associated with progression to castration resistance, development of metastasis, and poor disease-specific survival in prostate cancer. However, high expression of AZGP1 cells in prostate cancer has been reported to increase proliferation and invasion. The exact role of AZGP1 in prostate cancer progression remains elusive. METHOD: AZGP1 knockout and overexpressing prostate cancer cells were generated using a lentiviral system. The effects of AZGP1 under- or over-expression in prostate cancer cells were evaluated by in vitro cell proliferation, migration, and invasion assays. Heterozygous AZGP1± mice were obtained from European Mouse Mutant Archive (EMMA), and prostate tissues from homozygous knockout male mice were collected at 2, 6 and 10 months for histological analysis. In vivo xenografts generated from AZGP1 under- or over-expressing prostate cancer cells were used to determine the role of AZGP1 in prostate cancer tumor growth, and subsequent proteomics analysis was conducted to elucidate the mechanisms of AZGP1 action in prostate cancer progression. AZGP1 expression and microvessel density were measured in human prostate cancer samples on a tissue microarray of 215 independent patient samples. RESULT: Neither the knockout nor overexpression of AZGP1 exhibited significant effects on prostate cancer cell proliferation, clonal growth, migration, or invasion in vitro. The prostates of AZGP1-/- mice initially appeared to have grossly normal morphology; however, we observed fibrosis in the periglandular stroma and higher blood vessel density in the mouse prostate by 6 months. In PC3 and DU145 mouse xenografts, over-expression of AZGP1 did not affect tumor growth. Instead, these tumors displayed decreased microvessel density compared to xenografts derived from PC3 and DU145 control cells, suggesting that AZGP1 functions to inhibit angiogenesis in prostate cancer. Proteomics profiling further indicated that, compared to control xenografts, AZGP1 overexpressing PC3 xenografts are enriched with angiogenesis pathway proteins, including YWHAZ, EPHA2, SERPINE1, and PDCD6, MMP9, GPX1, HSPB1, COL18A1, RNH1, and ANXA1. In vitro functional studies show that AZGP1 inhibits human umbilical vein endothelial cell proliferation, migration, tubular formation and branching. Additionally, tumor microarray analysis shows that AZGP1 expression is negatively correlated with blood vessel density in human prostate cancer tissues. CONCLUSION: AZGP1 is a negative regulator of angiogenesis, such that loss of AZGP1 promotes angiogenesis in prostate cancer. AZGP1 likely exerts heterotypical effects on cells in the tumor microenvironment, such as stromal and endothelial cells. This study sheds light on the anti-angiogenic characteristics of AZGP1 in the prostate and provides a rationale to target AZGP1 to inhibit prostate cancer progression.

An Inflection Point in Cancer Protein Biomarkers: What was and What's Next.

Biomarkers remain the highest value proposition in cancer medicine today-especially protein biomarkers. Despite decades of evolving regulatory frameworks to facilitate the review of emerging technologies, biomarkers have been mostly about promise with very little to show for improvements in human health. Cancer is an emergent property of a complex system, and deconvoluting the integrative and dynamic nature of the overall system through biomarkers is a daunting proposition. The last 2 decades have seen an explosion of multiomics profiling and a range of advanced technologies for precision medicine, including the emergence of liquid biopsy, exciting advances in single-cell analysis, artificial intelligence (machine and deep learning) for data analysis, and many other advanced technologies that promise to transform biomarker discovery. Combining multiple omics modalities to acquire a more comprehensive landscape of the disease state, we are increasingly developing biomarkers to support therapy selection and patient monitoring. Furthering precision medicine, especially in oncology, necessitates moving away from the lens of reductionist thinking toward viewing and understanding that complex diseases are, in fact, complex adaptive systems. As such, we believe it is necessary to redefine biomarkers as representations of biological system states at different hierarchical levels of biological order. This definition could include traditional molecular, histologic, radiographic, or physiological characteristics, as well as emerging classes of digital markers and complex algorithms. To succeed in the future, we must move past purely observational individual studies and instead start building a mechanistic framework to enable integrative analysis of new studies within the context of prior studies. Identifying information in complex systems and applying theoretical constructs, such as information theory, to study cancer as a disease of dysregulated communication could prove to be "game changing" for the clinical outcome of cancer patients.

Deep learning-based automated pipeline for blood vessel detection and distribution analysis in multiplexed prostate cancer images.

Introduction: Prostate cancer is a highly heterogeneous disease, presenting varying levels of aggressiveness and response to treatment. Angiogenesis is one of the hallmarks of cancer, providing oxygen and nutrient supply to tumors. Micro vessel density has previously been correlated with higher Gleason score and poor prognosis. Manual segmentation of blood vessels (BVs) In microscopy images is challenging, time consuming and may be prone to inter-rater variabilities. In this study, an automated pipeline is presented for BV detection and distribution analysis in multiplexed prostate cancer images. Methods: A deep learning model was trained to segment BVs by combining CD31, CD34 and collagen IV images. In addition, the trained model was used to analyze the size and distribution patterns of BVs in relation to disease progression in a cohort of prostate cancer patients (N = 215). Results: The model was capable of accurately detecting and segmenting BVs, as compared to ground truth annotations provided by two reviewers. The precision (P), recall (R) and dice similarity coefficient (DSC) were equal to 0.93 (SD 0.04), 0.97 (SD 0.02) and 0.71 (SD 0.07) with respect to reviewer 1, and 0.95 (SD 0.05), 0.94 (SD 0.07) and 0.70 (SD 0.08) with respect to reviewer 2, respectively. BV count was significantly associated with 5-year recurrence (adjusted p = 0.0042), while both count and area of blood vessel were significantly associated with Gleason grade (adjusted p = 0.032 and 0.003 respectively). Discussion: The proposed methodology is anticipated to streamline and standardize BV analysis, offering additional insights into the biology of prostate cancer, with broad applicability to other cancers.

A flexible electronic strain sensor for the real-time monitoring of tumor regression.

Assessing the efficacy of cancer therapeutics in mouse models is a critical step in treatment development. However, low-resolution measurement tools and small sample sizes make determining drug efficacy in vivo a difficult and time-intensive task. Here, we present a commercially scalable wearable electronic strain sensor that automates the in vivo testing of cancer therapeutics by continuously monitoring the micrometer-scale progression or regression of subcutaneously implanted tumors at the minute time scale. In two in vivo cancer mouse models, our sensor discerned differences in tumor volume dynamics between drug- and vehicle-treated tumors within 5 hours following therapy initiation. These short-term regression measurements were validated through histology, and caliper and bioluminescence measurements taken over weeklong treatment periods demonstrated the correlation with longer-term treatment response. We anticipate that real-time tumor regression datasets could help expedite and automate the process of screening cancer therapies in vivo.

Nano-omics: nanotechnology-based multidimensional harvesting of the blood-circulating cancerome.

Over the past decade, the development of 'simple' blood tests that enable cancer screening, diagnosis or monitoring and facilitate the design of personalized therapies without the need for invasive tumour biopsy sampling has been a core ambition in cancer research. Data emerging from ongoing biomarker development efforts indicate that multiple markers, used individually or as part of a multimodal panel, are required to enhance the sensitivity and specificity of assays for early stage cancer detection. The discovery of cancer-associated molecular alterations that are reflected in blood at multiple dimensions (genome, epigenome, transcriptome, proteome and metabolome) and integration of the resultant multi-omics data have the potential to uncover novel biomarkers as well as to further elucidate the underlying molecular pathways. Herein, we review key advances in multi-omics liquid biopsy approaches and introduce the 'nano-omics' paradigm: the development and utilization of nanotechnology tools for the enrichment and subsequent omics analysis of the blood-circulating cancerome.

Modular fluorescent nanoparticle DNA probes for detection of peptides and proteins.

Fluorescently labeled antibody and aptamer probes are used in biological studies to characterize binding interactions, measure concentrations of analytes, and sort cells. Fluorescent nanoparticle labels offer an excellent alternative to standard fluorescent labeling strategies due to their enhanced brightness, stability and multivalency; however, challenges in functionalization and characterization have impeded their use. This work introduces a straightforward approach for preparation of fluorescent nanoparticle probes using commercially available reagents and common laboratory equipment. Fluorescent polystyrene nanoparticles, Thermo Fisher Scientific FluoSpheres, were used in these proof-of-principle studies. Particle passivation was achieved by covalent attachment of amine-PEG-azide to carboxylated particles, neutralizing the surface charge from - 43 to - 15 mV. A conjugation-annealing handle and DNA aptamer probe were attached to the azide-PEG nanoparticle surface either through reaction of pre-annealed handle and probe or through a stepwise reaction of the nanoparticles with the handle followed by aptamer annealing. Nanoparticles functionalized with DNA aptamers targeting histidine tags and VEGF protein had high affinity (EC50s ranging from 3 to 12 nM) and specificity, and were more stable than conventional labels. This protocol for preparation of nanoparticle probes relies solely on commercially available reagents and common equipment, breaking down the barriers to use nanoparticles in biological experiments.

Epidemiological Profile and Clinicopathological, Therapeutic, and Prognostic Characteristics of Nasopharyngeal Carcinoma in Northern Morocco.

BACKGROUND: Nasopharyngeal carcinoma is a multifactorial disease mainly affecting the Asian and North African populations including Morocco. This study aimed to determine the epidemiological profile of nasopharyngeal carcinoma in Northern Morocco as well as its clinicopathological, therapeutic, and prognostic characteristics. METHODS: 129 patients with nasopharyngeal carcinoma followed at the regional center of oncology of Tangier in the period between April 2017 and July 2019, and diagnosed elsewhere from March 2000 to February 2019, were included in this study. Statistical analysis of the data was realized using Statistical Package for the Social Sciences (SPSS) software. RESULTS: Nasopharyngeal carcinoma (NPC) represented 5% of all cases with a median age of 50. The most affected age group was 40-54 years (41.1%). Of all patients, 65.9% were men and 34.1% were women with a sex ratio of 1.93 (Male/Female). Undifferentiated nasopharyngeal carcinomas were the most common histological type affecting 96.12% of patients. At diagnosis, the majority of patients (82.2%) had an advanced stage of NPC (III, VIa, b, c) including 5.4% of metastatic cases (IVc). Most cases (86%) had lymph node involvement with cervical mass being the most common clinical presentation. 81.4% of patients received radiotherapy combined with chemotherapy. Among these patients, 54.3% had concurrent radiochemotherapy preceded by induction chemotherapy. The 5-year overall survival (OS) was 86.8% for all patients. It represented 91.3% for early stages, 87.9% for locally advanced stages, and 57.1% for the metastatic stage significantly. The disease-free survival (DFS) at 5 years was 87.6% knowing that relapse occurred in 16 cases. CONCLUSIONS: Nasopharyngeal carcinoma is a particular disease with a late declaration. It is common in Morocco as is the case in other endemic areas with a high prevalence. Patients' survival is significantly influenced by disease staging.

Transcriptome and genome evolution during HER2-amplified breast neoplasia.

BACKGROUND: The acquisition of oncogenic drivers is a critical feature of cancer progression. For some carcinomas, it is clear that certain genetic drivers occur early in neoplasia and others late. Why these drivers are selected and how these changes alter the neoplasia's fitness is less understood. METHODS: Here we use spatially oriented genomic approaches to identify transcriptomic and genetic changes at the single-duct level within precursor neoplasia associated with invasive breast cancer. We study HER2 amplification in ductal carcinoma in situ (DCIS) as an event that can be both quantified and spatially located via fluorescence in situ hybridization (FISH) and immunohistochemistry on fixed paraffin-embedded tissue. RESULTS: By combining the HER2-FISH with the laser capture microdissection (LCM) Smart-3SEQ method, we found that HER2 amplification in DCIS alters the transcriptomic profiles and increases diversity of copy number variations (CNVs). Particularly, interferon signaling pathway is activated by HER2 amplification in DCIS, which may provide a prolonged interferon signaling activation in HER2-positive breast cancer. Multiple subclones of HER2-amplified DCIS with distinct CNV profiles are observed, suggesting that multiple events occurred for the acquisition of HER2 amplification. Notably, DCIS acquires key transcriptomic changes and CNV events prior to HER2 amplification, suggesting that pre-amplified DCIS may create a cellular state primed to gain HER2 amplification for growth advantage. CONCLUSION: By using genomic methods that are spatially oriented, this study identifies several features that appear to generate insights into neoplastic progression in precancer lesions at a single-duct level.

Multicompartment modeling of protein shedding kinetics during vascularized tumor growth.

Identification of protein biomarkers for cancer diagnosis and prognosis remains a critical unmet clinical need. A major reason is that the dynamic relationship between proliferating and necrotic cell populations during vascularized tumor growth, and the associated extra- and intra-cellular protein outflux from these populations into blood circulation remains poorly understood. Complementary to experimental efforts, mathematical approaches have been employed to effectively simulate the kinetics of detectable surface proteins (e.g., CA-125) shed into the bloodstream. However, existing models can be difficult to tune and may be unable to capture the dynamics of non-extracellular proteins, such as those shed from necrotic and apoptosing cells. The models may also fail to account for intra-tumoral spatial and microenvironmental heterogeneity. We present a new multi-compartment model to simulate heterogeneously vascularized growing tumors and the corresponding protein outflux. Model parameters can be tuned from histology data, including relative vascular volume, mean vessel diameter, and distance from vasculature to necrotic tissue. The model enables evaluating the difference in shedding rates between extra- and non-extracellular proteins from viable and necrosing cells as a function of heterogeneous vascularization. Simulation results indicate that under certain conditions it is possible for non-extracellular proteins to have superior outflux relative to extracellular proteins. This work contributes towards the goal of cancer biomarker identification by enabling simulation of protein shedding kinetics based on tumor tissue-specific characteristics. Ultimately, we anticipate that models like the one introduced herein will enable examining origins and circulating dynamics of candidate biomarkers, thus facilitating marker selection for validation studies.

ImmunoGlobe: enabling systems immunology with a manually curated intercellular immune interaction network.

BACKGROUND: While technological advances have made it possible to profile the immune system at high resolution, translating high-throughput data into knowledge of immune mechanisms has been challenged by the complexity of the interactions underlying immune processes. Tools to explore the immune network are critical for better understanding the multi-layered processes that underlie immune function and dysfunction, but require a standardized network map of immune interactions. To facilitate this we have developed ImmunoGlobe, a manually curated intercellular immune interaction network extracted from Janeway's Immunobiology textbook. RESULTS: ImmunoGlobe is the first graphical representation of the immune interactome, and is comprised of 253 immune system components and 1112 unique immune interactions with detailed functional and characteristic annotations. Analysis of this network shows that it recapitulates known features of the human immune system and can be used uncover novel multi-step immune pathways, examine species-specific differences in immune processes, and predict the response of immune cells to stimuli. ImmunoGlobe is publicly available through a user-friendly interface at www.immunoglobe.org and can be downloaded as a computable graph and network table. CONCLUSION: While the fields of proteomics and genomics have long benefited from network analysis tools, no such tool yet exists for immunology. ImmunoGlobe provides a ground truth immune interaction network upon which such tools can be built. These tools will allow us to predict the outcome of complex immune interactions, providing mechanistic insight that allows us to precisely modulate immune responses in health and disease.

PTR Explorer: An approach to identify and explore Post Transcriptional Regulatory mechanisms using proteogenomics.

Integration of transcriptomic and proteomic data should reveal multi-layered regulatory processes governing cancer cell behaviors. Traditional correlation-based analyses have demonstrated limited ability to identify the post-transcriptional regulatory (PTR) processes that drive the non-linear relationship between transcript and protein abundances. In this work, we ideate an integrative approach to explore the variety of post-transcriptional mechanisms that dictate relationships between genes and corresponding proteins. The proposed workflow utilizes the intuitive technique of scatterplot diagnostics or scagnostics, to characterize and examine the diverse scatterplots built from transcript and protein abundances in a proteogenomic experiment. The workflow includes representing gene-protein relationships as scatterplots, clustering on geometric scagnostic features of these scatterplots, and finally identifying and grouping the potential gene-protein relationships according to their disposition to various PTR mechanisms. Our study verifies the efficacy of the implemented approach to excavate possible regulatory mechanisms by utilizing comprehensive tests on a synthetic dataset. We also propose a variety of 2D pattern-specific downstream analyses methodologies such as mixture modeling, and mapping miRNA post-transcriptional effects to explore each mechanism further. This work suggests that the proposed methodology has the potential for discovering and categorizing post-transcriptional regulatory mechanisms, manifesting in proteogenomic trends. These trends subsequently provide evidence for cancer specificity, miRNA targeting, and identification of regulation impacted by biological functionality and different types of degradation. (Supplementary Material - https://github.com/arunima2/PTRE_PSB_2020).

Semantic workflows for benchmark challenges: Enhancing comparability, reusability and reproducibility.

Benchmark challenges, such as the Critical Assessment of Structure Prediction (CASP) and Dialogue for Reverse Engineering Assessments and Methods (DREAM) have been instrumental in driving the development of bioinformatics methods. Typically, challenges are posted, and then competitors perform a prediction based upon blinded test data. Challengers then submit their answers to a central server where they are scored. Recent efforts to automate these challenges have been enabled by systems in which challengers submit Docker containers, a unit of software that packages up code and all of its dependencies, to be run on the cloud. Despite their incredible value for providing an unbiased test-bed for the bioinformatics community, there remain opportunities to further enhance the potential impact of benchmark challenges. Specifically, current approaches only evaluate end-to-end performance; it is nearly impossible to directly compare methodologies or parameters. Furthermore, the scientific community cannot easily reuse challengers' approaches, due to lack of specifics, ambiguity in tools and parameters as well as problems in sharing and maintenance. Lastly, the intuition behind why particular steps are used is not captured, as the proposed workflows are not explicitly defined, making it cumbersome to understand the flow and utilization of data. Here we introduce an approach to overcome these limitations based upon the WINGS semantic workflow system. Specifically, WINGS enables researchers to submit complete semantic workflows as challenge submissions. By submitting entries as workflows, it then becomes possible to compare not just the results and performance of a challenger, but also the methodology employed. This is particularly important when dozens of challenge entries may use nearly identical tools, but with only subtle changes in parameters (and radical differences in results). WINGS uses a component driven workflow design and offers intelligent parameter and data selection by reasoning about data characteristics. This proves to be especially critical in bioinformatics workflows where using default or incorrect parameter values is prone to drastically altering results. Different challenge entries may be readily compared through the use of abstract workflows, which also facilitate reuse. WINGS is housed on a cloud based setup, which stores data, dependencies and workflows for easy sharing and utility. It also has the ability to scale workflow executions using distributed computing through the Pegasus workflow execution system. We demonstrate the application of this architecture to the DREAM proteogenomic challenge.

Improving Precursor Selectivity in Data-Independent Acquisition Using Overlapping Windows.

A major goal of proteomics research is the accurate and sensitive identification and quantification of a broad range of proteins within a sample. Data-independent acquisition (DIA) approaches that acquire MS/MS spectra independently of precursor information have been developed to overcome the reproducibility challenges of data-dependent acquisition and the limited breadth of targeted proteomics strategies. Typical DIA implementations use wide MS/MS isolation windows to acquire comprehensive fragment ion data. However, wide isolation windows produce highly chimeric spectra, limiting the achievable sensitivity and accuracy of quantification and identification. Here, we present a DIA strategy in which spectra are collected with overlapping (rather than adjacent or random) windows and then computationally demultiplexed. This approach improves precursor selectivity by nearly a factor of 2, without incurring any loss in mass range, mass resolution, chromatographic resolution, scan speed, or other key acquisition parameters. We demonstrate a 64% improvement in sensitivity and a 17% improvement in peptides detected in a 6-protein bovine mix spiked into a yeast background. To confirm the method's applicability to a realistic biological experiment, we also analyze the regulation of the proteasome in yeast grown in rapamycin and show that DIA experiments with overlapping windows can help elucidate its adaptation toward the degradation of oxidatively damaged proteins. Our integrated computational and experimental DIA strategy is compatible with any DIA-capable instrument. The computational demultiplexing algorithm required to analyze the data has been made available as part of the open-source proteomics software tools Skyline and msconvert (Proteowizard), making it easy to apply as part of standard proteomics workflows. Graphical Abstract.

Loss of ER retention motif of AGR2 can impact mTORC signaling and promote cancer metastasis.

Anterior gradient 2 (AGR2) is a member of the protein disulfide isomerase (PDI) family, which plays a role in the regulation of protein homeostasis and the unfolded protein response pathway (UPR). AGR2 has also been characterized as a proto-oncogene and a potential cancer biomarker. Cellular localization of AGR2 is emerging as a key component for understanding the role of AGR2 as a proto-oncogene. Here, we provide evidence that extracellular AGR2 (eAGR2) promotes tumor metastasis in various in vivo models. To further characterize the role of the intracellular-resident versus extracellular protein, we performed a comprehensive protein-protein interaction screen. Based on these results, we identify AGR2 as an interacting partner of the mTORC2 pathway. Importantly, our data indicates that eAGR2 promotes increased phosphorylation of RICTOR (T1135), while intracellular AGR2 (iAGR2) antagonizes its levels and phosphorylation. Localization of AGR2 also has opposing effects on the Hippo pathway, spheroid formation, and response to chemotherapy in vitro. Collectively, our results identify disparate phenotypes predicated on AGR2 localization. Our findings also provide credence for screening of eAGR2 to guide therapeutic decisions.

Geostatistical visualization of ecological interactions in tumors.

Recent advances in our understanding of cancer progression have highlighted the roles played by molecular heterogeneity and by the tumor microenvironment in driving drug resistance and metastasis. The coupling of single-cell measurement technologies with algorithms, such as t-sne and SPADE, have enabled deep investigation of tumor heterogeneity. However, such techniques only capture molecular heterogeneity and do not enable the quantification nor visualization of intercellular interactions. They additionally do not allow the visualization of ecological niches that are critical to understanding tumor behavior. Novel computational tools to quantify and visualize spatial patterns in the tumor microenvironment are critically needed. Here, we take a tumor ecology perspective to examine how predation, mutualism, commensalism, and parasitism may impact tumor development and spatial patterning. We additionally quantify local spatial heterogeneity and the emergent global spatial behavior of the models using geostatistics. By visualizing emergent spatial patterns we demonstrate the potential utility of a geostatistical analysis in differentiating amongst cell-cell interactions in the tumor microenvironment. These studies introduce both an ecological framework for characterizing intercellular interactions in cancer and a novel way of quantifying and visualizing spatial patterns in cancer.

Imitating Pathologist Based Assessment With Interpretable and Context Based Neural Network Modeling of Histology Images.

Convolutional neural networks (CNNs) have gained steady popularity as a tool to perform automatic classification of whole slide histology images. While CNNs have proven to be powerful classifiers in this context, they fail to explain this classification, as the network engineered features used for modeling and classification are ONLY interpretable by the CNNs themselves. This work aims at enhancing a traditional neural network model to perform histology image modeling, patient classification, and interpretation of the distinctive features identified by the network within the histology whole slide images (WSIs). We synthesize a workflow which (a) intelligently samples the training data by automatically selecting only image areas that display visible disease-relevant tissue state and (b) isolates regions most pertinent to the trained CNN prediction and translates them to observable and qualitative features such as color, intensity, cell and tissue morphology and texture. We use the Cancer Genome Atlas's Breast Invasive Carcinoma (TCGA-BRCA) histology dataset to build a model predicting patient attributes (disease stage and node status) and the tumor proliferation challenge (TUPAC 2016) breast cancer histology image repository to help identify disease-relevant tissue state (mitotic activity). We find that our enhanced CNN based workflow both increased patient attribute predictive accuracy (~2% increase for disease stage and ~10% increase for node status) and experimentally proved that a data-driven CNN histology model predicting breast invasive carcinoma stages is highly sensitive to features such as color, cell size, and shape, granularity, and uniformity. This work summarizes the need for understanding the widely trusted models built using deep learning and adds a layer of biological context to a technique that functioned as a classification only approach till now.

Proteogenomic Analysis of Surgically Resected Lung Adenocarcinoma.

INTRODUCTION: Despite apparently complete surgical resection, approximately half of resected early-stage lung cancer patients relapse and die of their disease. Adjuvant chemotherapy reduces this risk by only 5% to 8%. Thus, there is a need for better identifying who benefits from adjuvant therapy, the drivers of relapse, and novel targets in this setting. METHODS: RNA sequencing and liquid chromatography/liquid chromatography-mass spectrometry proteomics data were generated from 51 surgically resected non-small cell lung tumors with known recurrence status. RESULTS: We present a rationale and framework for the incorporation of high-content RNA and protein measurements into integrative biomarkers and show the potential of this approach for predicting risk of recurrence in a group of lung adenocarcinomas. In addition, we characterize the relationship between mRNA and protein measurements in lung adenocarcinoma and show that it is outcome specific. CONCLUSIONS: Our results suggest that mRNA and protein data possess independent biological and clinical importance, which can be leveraged to create higher-powered expression biomarkers.

A Bayesian Active Learning Experimental Design for Inferring Signaling Networks.

Machine learning methods for learning network structure are applied to quantitative proteomics experiments and reverse-engineer intracellular signal transduction networks. They provide insight into the rewiring of signaling within the context of a disease or a phenotype. To learn the causal patterns of influence between proteins in the network, the methods require experiments that include targeted interventions that fix the activity of specific proteins. However, the interventions are costly and add experimental complexity. We describe an active learning strategy for selecting optimal interventions. Our approach takes as inputs pathway databases and historic data sets, expresses them in form of prior probability distributions on network structures, and selects interventions that maximize their expected contribution to structure learning. Evaluations on simulated and real data show that the strategy reduces the detection error of validated edges as compared with an unguided choice of interventions and avoids redundant interventions, thereby increasing the effectiveness of the experiment.

A blood biomarker for monitoring response to anti-EGFR therapy.

BACKGROUND AND OBJECTIVE: To monitor therapies targeted to epidermal growth factor receptors (EGFR) in non-small cell lung cancer (NSCLC), we investigated Peroxiredoxin 6 (PRDX6) as a biomarker of response to anti-EGFR agents. METHODS: We studied cells that are sensitive (H3255, HCC827) or resistant (H1975, H460) to gefitinib. PRDX6 was examined with either gefitinib or vehicle treatment using enzyme-linked immunosorbent assays. We created xenograft models from one sensitive (HCC827) and one resistant cell line (H1975) and monitored serum PRDX6 levels during treatment. RESULTS: PRDX6 levels in cell media from sensitive cell lines increased significantly after gefitinib treatment vs. vehicle, whereas there was no significant difference for resistant lines. PRDX6 accumulation over time correlated positively with gefitinib sensitivity. Serum PRDX6 levels in gefitinib-sensitive xenograft models increased markedly during the first 24 hours of treatment and then decreased dramatically during the following 48 hours. Differences in serum PRDX6 levels between vehicle and gefitinib-treated animals could not be explained by differences in tumor burden. CONCLUSIONS: Our results show that changes in serum PRDX6 during the course of gefitinib treatment of xenograft models provide insight into tumor response and such an approach offers several advantages over imaging-based strategies for monitoring response to anti-EGFR agents.

The Predictive Value of Inflammation-Related Peripheral Blood Measurements in Cancer Staging and Prognosis.

In this review, we discuss the interaction between cancer and markers of inflammation (such as levels of inflammatory cells and proteins) in the circulation, and the potential benefits of routinely monitoring these markers in peripheral blood measurement assays. Next, we discuss the prognostic value and limitations of using inflammatory markers such as neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios and C-reactive protein measurements. Furthermore, the review discusses the benefits of combining multiple types of measurements and longitudinal tracking to improve staging and prognosis prediction of patients with cancer, and the ability of novel in silico frameworks to leverage this high-dimensional data.