Publications by authors named "Parag Mallick"

76 Publications

Multicompartment modeling of protein shedding kinetics during vascularized tumor growth.

Sci Rep 2020 10 7;10(1):16709. Epub 2020 Oct 7.

Department of Bioengineering, University of Louisville, Louisville, KY, USA.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41598-020-73866-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7542472PMC
October 2020

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

BMC Bioinformatics 2020 Aug 10;21(1):346. Epub 2020 Aug 10.

Canary Center at Stanford, Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12859-020-03702-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7430879PMC
August 2020

Geostatistical visualization of ecological interactions in tumors.

Proceedings (IEEE Int Conf Bioinformatics Biomed) 2019 Nov 6;2019:2741-2749. Epub 2020 Feb 6.

Canary Center at Stanford for Cancer Early Detection, Stanford University, Palo Alto, CA, USA.

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 -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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1109/bibm47256.2019.8983076DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7198084PMC
November 2019

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

Pac Symp Biocomput 2020 ;25:475-486

Dept. of Computer Science and Engineering, The Ohio State University, 2015 Neil Ave, Columbus, OH, USA,

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).
View Article and Find Full Text PDF

Download full-text PDF

Source
March 2021

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

Pac Symp Biocomput 2019 ;24:208-219

Computer Science and Engineering, The Ohio State University, 2015 Neil Ave Columbus, OH 43210, USA,

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6417805PMC
August 2019

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

J Am Soc Mass Spectrom 2019 Apr 22;30(4):669-684. Epub 2019 Jan 22.

Department of Genome Sciences, University of Washington, 3720 15th Ave. NE, Seattle, WA, USA.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s13361-018-2122-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6445824PMC
April 2019

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

Oncogene 2019 04 21;38(16):3003-3018. Epub 2018 Dec 21.

University of Southern California, Keck School of Medicine, Lawrence J. Ellison Institute for Transformative Medicine, Los Angeles, CA, USA.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41388-018-0638-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7523706PMC
April 2019

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

Biomed Inform Insights 2018 31;10:1178222618807481. Epub 2018 Oct 31.

Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1177/1178222618807481DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6236488PMC
October 2018

Proteogenomic Analysis of Surgically Resected Lung Adenocarcinoma.

J Thorac Oncol 2018 10 11;13(10):1519-1529. Epub 2018 Jul 11.

Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio. Electronic address:

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jtho.2018.06.025DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7135954PMC
October 2018

A Bayesian Active Learning Experimental Design for Inferring Signaling Networks.

J Comput Biol 2018 07 21;25(7):709-725. Epub 2018 Jun 21.

4 College of Science, College of Computer and Information Science, Northeastern University , Boston, Massachusetts.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1089/cmb.2017.0247DOI Listing
July 2018

A Temporal Examination of Platelet Counts as a Predictor of Prognosis in Lung, Prostate, and Colon Cancer Patients.

Sci Rep 2018 04 26;8(1):6564. Epub 2018 Apr 26.

Canary Center at Stanford, Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.

Platelets, components of hemostasis, when present in excess (>400 K/μL, thrombocytosis) have also been associated with worse outcomes in lung, ovarian, breast, renal, and colorectal cancer patients. Associations between thrombocytosis and cancer outcomes have been made mostly from single-time-point studies, often at the time of diagnosis. Using laboratory data from the Department of Veterans Affairs (VA), we examined the potential benefits of using longitudinal platelet counts in improving patient prognosis predictions. Ten features (summary statistics and engineered features) were derived to describe the platelet counts of 10,000+ VA lung, prostate, and colon cancer patients and incorporated into an age-adjusted LASSO regression analysis to determine feature importance, and predict overall or relapse-free survival, which was compared to the previously used approach of monitoring for thrombocytosis near diagnosis (Postdiag AG400 model). Temporal features describing acute platelet count increases/decreases were found to be important in cancer survival and relapse-survival that helped stratify good and bad outcomes of cancer patient groups. Predictions of overall and relapse-free survival were improved by up to 30% compared to the Postdiag AG400 model. Our study indicates the association of temporally derived platelet count features with a patients' prognosis predictions.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41598-018-25019-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5920102PMC
April 2018

Multi-lectin Affinity Chromatography and Quantitative Proteomic Analysis Reveal Differential Glycoform Levels between Prostate Cancer and Benign Prostatic Hyperplasia Sera.

Sci Rep 2018 04 25;8(1):6509. Epub 2018 Apr 25.

Canary Center at Stanford for Cancer Early Detection, Department of Radiology, Stanford University School of Medicine, Palo Alto, CA, 94304, USA.

Currently prostate-specific antigen is used for prostate cancer (PCa) screening, however it lacks the necessary specificity for differentiating PCa from other diseases of the prostate such as benign prostatic hyperplasia (BPH), presenting a clinical need to distinguish these cases at the molecular level. Protein glycosylation plays an important role in a number of cellular processes involved in neoplastic progression and is aberrant in PCa. In this study, we systematically interrogate the alterations in the circulating levels of hundreds of serum proteins and their glycoforms in PCa and BPH samples using multi-lectin affinity chromatography and quantitative mass spectrometry-based proteomics. Specific lectins (AAL, PHA-L and PHA-E) were used to target and chromatographically separate core-fucosylated and highly-branched protein glycoforms for analysis, as differential expression of these glycan types have been previously associated with PCa. Global levels of CD5L, CFP, C8A, BST1, and C7 were significantly increased in the PCa samples. Notable glycoform-specific alterations between BPH and PCa were identified among proteins CD163, C4A, and ATRN in the PHA-L/E fraction and among C4BPB and AZGP1 glycoforms in the AAL fraction. Despite these modest differences, substantial similarities in glycoproteomic profiles were observed between PCa and BPH sera.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41598-018-24270-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5916935PMC
April 2018

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

Cancer Biomark 2018 ;22(2):333-344

Molecular Imaging Program at Stanford, Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3233/CBM-171149DOI Listing
October 2018

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

Front Oncol 2018 21;8:78. Epub 2018 Mar 21.

Biomedical Engineering, School of Medicine, Oregon Health & Science University, Portland, OR, United States.

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 frameworks to leverage this high-dimensional data.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3389/fonc.2018.00078DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5871812PMC
March 2018

Longitudinal Multiplexed Measurement of Quantitative Proteomic Signatures in Mouse Lymphoma Models Using Magneto-Nanosensors.

Theranostics 2018 3;8(5):1389-1398. Epub 2018 Feb 3.

Department of Materials Science and Engineering, Stanford University, Stanford, California, USA.

Cancer proteomics is the manifestation of relevant biological processes in cancer development. Thus, it reflects the activities of tumor cells, host-tumor interactions, and systemic responses to cancer therapy. To understand the causal effects of tumorigenesis or therapeutic intervention, longitudinal studies are greatly needed. However, most of the conventional mouse experiments are unlikely to accommodate frequent collection of serum samples with a large enough volume for multiple protein assays towards single-object analysis. Here, we present a technique based on magneto-nanosensors to longitudinally monitor the protein profiles in individual mice of lymphoma models using a small volume of a sample for multiplex assays. Drug-sensitive and -resistant cancer cell lines were used to develop the mouse models that render different outcomes upon the drug treatment. Two groups of mice were inoculated with each cell line, and treated with either cyclophosphamide or vehicle solution. Serum samples taken longitudinally from each mouse in the groups were measured with 6-plex magneto-nanosensor cytokine assays. To find the origin of IL-6, experiments were performed using IL-6 knock-out mice. The differences in serum IL-6 and GCSF levels between the drug-treated and untreated groups were revealed by the magneto-nanosensor measurement on individual mice. Using the multiplex assays and mouse models, we found that IL-6 is secreted by the host in the presence of tumor cells upon the drug treatment. The multiplex magneto-nanosensor assays enable longitudinal proteomic studies on mouse tumor models to understand tumor development and therapy mechanisms more precisely within a single biological object.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.7150/thno.20706DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5835944PMC
January 2019

How many human proteoforms are there?

Nat Chem Biol 2018 02;14(3):206-214

Department of Chemistry, Princeton University, Princeton, New Jersey, USA.

Despite decades of accumulated knowledge about proteins and their post-translational modifications (PTMs), numerous questions remain regarding their molecular composition and biological function. One of the most fundamental queries is the extent to which the combinations of DNA-, RNA- and PTM-level variations explode the complexity of the human proteome. Here, we outline what we know from current databases and measurement strategies including mass spectrometry-based proteomics. In doing so, we examine prevailing notions about the number of modifications displayed on human proteins and how they combine to generate the protein diversity underlying health and disease. We frame central issues regarding determination of protein-level variation and PTMs, including some paradoxes present in the field today. We use this framework to assess existing data and to ask the question, "How many distinct primary structures of proteins (proteoforms) are created from the 20,300 human genes?" We also explore prospects for improving measurements to better regularize protein-level biology and efficiently associate PTMs to function and phenotype.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/nchembio.2576DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5837046PMC
February 2018

Global Transcriptome Analysis of RNA Abundance Regulation by ADAR in Lung Adenocarcinoma.

EBioMedicine 2018 Jan 6;27:167-175. Epub 2017 Dec 6.

Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States; Current Address: Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, United States. Electronic address:

Despite tremendous advances in targeted therapies against lung adenocarcinoma, the majority of patients do not benefit from personalized treatments. A deeper understanding of potential therapeutic targets is crucial to increase the survival of patients. One promising target, ADAR, is amplified in 13% of lung adenocarcinomas and in-vitro studies have demonstrated the potential of its therapeutic inhibition to inhibit tumor growth. ADAR edits millions of adenosines to inosines within the transcriptome, and while previous studies of ADAR in cancer have solely focused on protein-coding edits, >99% of edits occur in non-protein coding regions. Here, we develop a pipeline to discover the regulatory potential of RNA editing sites across the entire transcriptome and apply it to lung adenocarcinoma tumors from The Cancer Genome Atlas. This method predicts that 1413 genes contain regulatory edits, predominantly in non-coding regions. Genes with the largest numbers of regulatory edits are enriched in both apoptotic and innate immune pathways, providing a link between these known functions of ADAR and its role in cancer. We further show that despite a positive association between ADAR RNA expression and apoptotic and immune pathways, ADAR copy number is negatively associated with apoptosis and several immune cell types' signatures.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.ebiom.2017.12.005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5828651PMC
January 2018

Building trans-omics evidence: using imaging and 'omics' to characterize cancer profiles.

Pac Symp Biocomput 2018 ;23:377-387

Department of Computer Science and Engineering, The Ohio State University, 2015 Neil Avenue, Columbus, OH 43210, USA,

Utilization of single modality data to build predictive models in cancer results in a rather narrow view of most patient profiles. Some clinical facet s relate strongly to histology image features, e.g. tumor stages, whereas others are associated with genomic and proteomic variations (e.g. cancer subtypes and disease aggression biomarkers). We hypothesize that there are coherent "trans-omics" features that characterize varied clinical cohorts across multiple sources of data leading to more descriptive and robust disease characterization. In this work, for l 05 breast cancer patients from the TCGA (The Cancer Genome Atlas), we consider four clinical attributes (AJCC Stage, Tumor Stage, ER-Status and PAM50 mRNA Subtypes), and build predictive models using three different modalities of data (histopathological images, transcriptomics and proteomics). Following which, we identify critical multi-level features that drive successful classification of patients for the various different cohorts. To build predictors for each data type, we employ widely used "best practice" techniques including CNN-based (convolutional neural network) classifiers for histopathological images and regression models for proteogenomic data. While, as expected, histology images outperformed molecular features while predicting cancer stages, and transcriptomics held superior discriminatory power for ER-Status and PAM50 subtypes, there exist a few cases where all data modalities exhibited comparable performance. Further, we also identified sets of key genes and proteins whose expression and abundance correlate across each clinical cohort including (i) tumor severity and progression (incl. GABARAP), (ii) ER-status (incl.ESRl) and (iii) disease subtypes (incl. FOXCl). Thus, we quantitatively assess the efficacy of different data types to predict critical breast cancer patient attributes and improve disease characterization.
View Article and Find Full Text PDF

Download full-text PDF

Source
August 2018

Platelet count as a predictor of metastasis and venous thromboembolism in patients with cancer.

Converg Sci Phys Oncol 2017 Jun 17;3(2). Epub 2017 May 17.

Biomedical Engineering, School of Medicine, Oregon Health and Science University, Portland, OR.

Platelets are anucleate cells in the blood at concentrations of 150,000 to 400,000 cells/µL and play a key role in hemostasis. Several studies have suggested that platelets contribute to cancer progression and cancer-associated thrombosis. In this review, we provide an overview of the biochemical and biophysical mechanisms by which platelets interact with cancer cells and review the evidence supporting a role for platelet-enhanced metastasis of cancer, and venous thromboembolism (VTE) in patients with cancer. We discuss the potential for and limitations of platelet counts to discriminate cancer disease burden and prognosis. Lastly, we consider more advanced diagnostic approaches to improve studies on the interaction between the hemostatic system and cancer cells.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1088/2057-1739/aa6c05DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5658139PMC
June 2017

Assessing biological and technological variability in protein levels measured in pre-diagnostic plasma samples of women with breast cancer.

Biomark Res 2017 17;5:30. Epub 2017 Oct 17.

Department of Radiology, Canary Center at Stanford for Cancer Early Detection, Stanford University School of Medicine, Palo Alto, CA 94304 USA.

Background: Quantitative proteomics allows for the discovery and functional investigation of blood-based pre-diagnostic biomarkers for early cancer detection. However, a major limitation of proteomic investigations in biomarker studies remains the biological and technical variability in the analysis of complex clinical samples. Moreover, unlike 'omics analogues such as genomics and transcriptomics, proteomics has yet to achieve reproducibility and long-term stability on a unified technological platform. Few studies have thoroughly investigated protein variability in pre-diagnostic samples of cancer patients across multiple platforms.

Methods: We obtained ten blood plasma "case" samples collected up to 2 years prior to breast cancer diagnosis. Each case sample was paired with a matched control plasma from a full biological sister without breast cancer. We measured protein levels using both mass-spectrometry and antibody-based technologies to: (1) assess the technical considerations in different protein assays when analyzing limited clinical samples, and (2) evaluate the statistical power of potential diagnostic analytes.

Results: Although we found inherent technical variation in the three assays used, we detected protein dependent biological signal from the limited samples. The three assay types yielded 32 proteins with statistically significantly ( < 1E-01) altered expression levels between cases and controls, with no proteins retaining statistical significance after false discovery correction.

Conclusions: Technical, practical, and study design considerations are essential to maximize information obtained in limited pre-diagnostic samples of cancer patients. This study provides a framework that estimates biological effect sizes critical for consideration in designing studies for pre-diagnostic blood-based biomarker detection.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s40364-017-0110-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5645980PMC
October 2017

Longitudinal Monitoring of Antibody Responses against Tumor Cells Using Magneto-nanosensors with a Nanoliter of Blood.

Nano Lett 2017 11 20;17(11):6644-6652. Epub 2017 Oct 20.

Department of Materials Science and Engineering, Stanford University , Stanford, California 94305, United States.

Each immunoglobulin isotype has unique immune effector functions. The contribution of these functions in the elimination of pathogens and tumors can be determined by monitoring quantitative temporal changes in isotype levels. Here, we developed a novel technique using magneto-nanosensors based on the effect of giant magnetoresistance (GMR) for longitudinal monitoring of total and antigen-specific isotype levels with high precision, using as little as 1 nL of serum. Combining in vitro serologic measurements with in vivo imaging techniques, we investigated the role of the antibody response in the regression of firefly luciferase (FL)-labeled lymphoma cells in spleen, kidney, and lymph nodes in a syngeneic Burkitt's lymphoma mouse model. Regression status was determined by whole body bioluminescent imaging (BLI). The magneto-nanosensors revealed that anti-FL IgG2a and total IgG2a were elevated and sustained in regression mice compared to non-regression mice (p < 0.05). This platform shows promise for monitoring immunotherapy, vaccination, and autoimmunity.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1021/acs.nanolett.7b02591DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5851288PMC
November 2017

JUN-Mediated Downregulation of EGFR Signaling Is Associated with Resistance to Gefitinib in EGFR-mutant NSCLC Cell Lines.

Mol Cancer Ther 2017 08 31;16(8):1645-1657. Epub 2017 May 31.

Lawrence J. Ellison Institute for Transformative Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California.

Mutations or deletions in exons 18-21 in the ) are present in approximately 15% of tumors in patients with non-small cell lung cancer (NSCLC). They lead to activation of the EGFR kinase domain and sensitivity to molecularly targeted therapeutics aimed at this domain (gefitinib or erlotinib). These drugs have demonstrated objective clinical response in many of these patients; however, invariably, all patients acquire resistance. To examine the molecular origins of resistance, we derived a set of gefitinib-resistant cells by exposing lung adenocarcinoma cell line, HCC827, with an activating mutation in the EGFR tyrosine kinase domain, to increasing gefitinib concentrations. Gefitinib-resistant cells acquired an increased expression and activation of JUN, a known oncogene involved in cancer progression. Ectopic overexpression of JUN in HCC827 cells increased gefitinib IC from 49 nmol/L to 8 μmol/L ( < 0.001). Downregulation of JUN expression through shRNA resensitized HCC827 cells to gefitinib (IC from 49 nmol/L to 2 nmol/L; < 0.01). Inhibitors targeting JUN were 3-fold more effective in the gefitinib-resistant cells than in the parental cell line ( < 0.01). Analysis of gene expression in patient tumors with EGFR-activating mutations and poor response to erlotinib revealed a similar pattern as the top 260 differentially expressed genes in the gefitinib-resistant cells (Spearman correlation coefficient of 0.78, < 0.01). These findings suggest that increased JUN expression and activity may contribute to gefitinib resistance in NSCLC and that JUN pathway therapeutics merit investigation as an alternate treatment strategy. .
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1158/1535-7163.MCT-16-0564DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5558834PMC
August 2017

Data Conversion with ProteoWizard msConvert.

Methods Mol Biol 2017 ;1550:339-368

Canary Center at Stanford for Cancer Early Detection, Department of Radiology, Stanford University, Stanford, CA, USA.

Recent advances in proteome informatics have led to an explosion in tools to analyze mass spectrometry data. These tools operate across the analysis pipeline doing everything from assessing quality control to matching peptides to spectra to quantification. Unfortunately, the vast majority of these tools are not able to operate directly on the proprietary formats generated by the diverse mass spectrometers. Consequently, the first step in many protocols is the conversion of data from vendor-specific binary files to open-format files. This protocol details the use of ProteoWizard's msConvert and msConvertGUI software for this conversion, taking format features, coding options, and vendor particularities into account. We specifically describe the various options available when doing conversions and the implications of each option.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/978-1-4939-6747-6_23DOI Listing
February 2018

A Robust Protocol for Protein Extraction and Digestion.

Methods Mol Biol 2017 ;1550:1-10

Canary Center at Stanford for Cancer Early Detection, Stanford University, 3155 Porter Drive, Palo Alto, CA, 94304, USA.

Proteins play a key role in all aspects of cellular homeostasis. Proteomics, the large-scale study of proteins, provides in-depth data on protein properties, including abundances and post-translational modification states, and as such provides a rich avenue for the investigation of biological and disease processes. While proteomic tools such as mass spectrometry have enabled exquisitely sensitive sample analysis, sample preparation remains a critical unstandardized variable that can have a significant impact on downstream data readouts. Consistency in sample preparation and handling is therefore paramount in the collection and analysis of proteomic data.Here we describe methods for performing protein extraction from cell culture or tissues, digesting the isolated protein into peptides via in-solution enzymatic digest, and peptide cleanup with final preparations for analysis via liquid chromatography-mass spectrometry. These protocols have been optimized and standardized for maximum consistency and maintenance of sample integrity.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/978-1-4939-6747-6_1DOI Listing
February 2018

Single cell dynamic phenotyping.

Sci Rep 2016 10 6;6:34785. Epub 2016 Oct 6.

Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, California, USA.

Live cell imaging has improved our ability to measure phenotypic heterogeneity. However, bottlenecks in imaging and image processing often make it difficult to differentiate interesting biological behavior from technical artifact. Thus there is a need for new methods that improve data quality without sacrificing throughput. Here we present a 3-step workflow to improve dynamic phenotype measurements of heterogeneous cell populations. We provide guidelines for image acquisition, phenotype tracking, and data filtering to remove erroneous cell tracks using the novel Tracking Aberration Measure (TrAM). Our workflow is broadly applicable across imaging platforms and analysis software. By applying this workflow to cancer cell assays, we reduced aberrant cell track prevalence from 17% to 2%. The cost of this improvement was removing 15% of the well-tracked cells. This enabled detection of significant motility differences between cell lines. Similarly, we avoided detecting a false change in translocation kinetics by eliminating the true cause: varied proportions of unresponsive cells. Finally, by systematically seeking heterogeneous behaviors, we detected subpopulations that otherwise could have been missed, including early apoptotic events and pre-mitotic cells. We provide optimized protocols for specific applications and step-by-step guidelines for adapting them to a variety of biological systems.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/srep34785DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5052535PMC
October 2016

Dual transcript and protein quantification in a massive single cell array.

Lab Chip 2016 10 22;16(19):3682-8. Epub 2016 Aug 22.

Department of Bioengineering, University of California, Berkeley, California, USA.

Recently, single-cell molecular analysis has been leveraged to achieve unprecedented levels of biological investigation. However, a lack of simple, high-throughput single-cell methods has hindered in-depth population-wide studies with single-cell resolution. We report a microwell-based cytometric method for simultaneous measurements of gene and protein expression dynamics in thousands of single cells. We quantified the regulatory effects of transcriptional and translational inhibitors on cMET mRNA and cMET protein in cell populations. We studied the dynamic responses of individual cells to drug treatments, by measuring cMET overexpression levels in individual non-small cell lung cancer (NSCLC) cells with induced drug resistance. Across NSCLC cell lines with a given protein expression, distinct patterns of transcript-protein correlation emerged. We believe this platform is applicable for interrogating the dynamics of gene expression, protein expression, and translational kinetics at the single-cell level - a paradigm shift in life science and medicine toward discovering vital cell regulatory mechanisms.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1039/c6lc00762gDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5221609PMC
October 2016

A high-content image-based method for quantitatively studying context-dependent cell population dynamics.

Sci Rep 2016 07 25;6:29752. Epub 2016 Jul 25.

Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, California, USA.

Tumor progression results from a complex interplay between cellular heterogeneity, treatment response, microenvironment and heterocellular interactions. Existing approaches to characterize this interplay suffer from an inability to distinguish between multiple cell types, often lack environmental context, and are unable to perform multiplex phenotypic profiling of cell populations. Here we present a high-throughput platform for characterizing, with single-cell resolution, the dynamic phenotypic responses (i.e. morphology changes, proliferation, apoptosis) of heterogeneous cell populations both during standard growth and in response to multiple, co-occurring selective pressures. The speed of this platform enables a thorough investigation of the impacts of diverse selective pressures including genetic alterations, therapeutic interventions, heterocellular components and microenvironmental factors. The platform has been applied to both 2D and 3D culture systems and readily distinguishes between (1) cytotoxic versus cytostatic cellular responses; and (2) changes in morphological features over time and in response to perturbation. These important features can directly influence tumor evolution and clinical outcome. Our image-based approach provides a deeper insight into the cellular dynamics and heterogeneity of tumors (or other complex systems), with reduced reagents and time, offering advantages over traditional biological assays.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/srep29752DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4958988PMC
July 2016

Epigenetic changes mediated by polycomb repressive complex 2 and E2a are associated with drug resistance in a mouse model of lymphoma.

Genome Med 2016 05 4;8(1):54. Epub 2016 May 4.

Department of Molecular, Cellular and Developmental Biology, University of California, Los Angeles, CA, 90095, USA.

Background: The genetic origins of chemotherapy resistance are well established; however, the role of epigenetics in drug resistance is less well understood. To investigate mechanisms of drug resistance, we performed systematic genetic, epigenetic, and transcriptomic analyses of an alkylating agent-sensitive murine lymphoma cell line and a series of resistant lines derived by drug dose escalation.

Methods: Dose escalation of the alkylating agent mafosfamide was used to create a series of increasingly drug-resistant mouse Burkitt's lymphoma cell lines. Whole genome sequencing, DNA microarrays, reduced representation bisulfite sequencing, and chromatin immunoprecipitation sequencing were used to identify alterations in DNA sequence, mRNA expression, CpG methylation, and H3K27me3 occupancy, respectively, that were associated with increased resistance.

Results: Our data suggest that acquired resistance cannot be explained by genetic alterations. Based on integration of transcriptional profiles with transcription factor binding data, we hypothesize that resistance is driven by epigenetic plasticity. We observed that the resistant cells had H3K27me3 and DNA methylation profiles distinct from those of the parental lines. Moreover, we observed DNA methylation changes in the promoters of genes regulated by E2a and members of the polycomb repressor complex 2 (PRC2) and differentially expressed genes were enriched for targets of E2a. The integrative analysis considering H3K27me3 further supported a role for PRC2 in mediating resistance. By integrating our results with data from the Immunological Genome Project (Immgen.org), we showed that these transcriptional changes track the B-cell maturation axis.

Conclusions: Our data suggest a novel mechanism of drug resistance in which E2a and PRC2 drive changes in the B-cell epigenome; these alterations attenuate alkylating agent treatment-induced apoptosis.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s13073-016-0305-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4857420PMC
May 2016

Protein biomarkers on tissue as imaged via MALDI mass spectrometry: A systematic approach to study the limits of detection.

Proteomics 2016 06 11;16(11-12):1660-9. Epub 2016 May 11.

Canary Center at Stanford, Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.

MALDI mass spectrometry imaging (MSI) is emerging as a tool for protein and peptide imaging across tissue sections. Despite extensive study, there does not yet exist a baseline study evaluating the potential capabilities for this technique to detect diverse proteins in tissue sections. In this study, we developed a systematic approach for characterizing MALDI-MSI workflows in terms of limits of detection, coefficients of variation, spatial resolution, and the identification of endogenous tissue proteins. Our goal was to quantify these figures of merit for a number of different proteins and peptides, in order to gain more insight in the feasibility of protein biomarker discovery efforts using this technique. Control proteins and peptides were deposited in serial dilutions on thinly sectioned mouse xenograft tissue. Using our experimental setup, coefficients of variation were <30% on tissue sections and spatial resolution was 200 μm (or greater). Limits of detection for proteins and peptides on tissue were in the micromolar to millimolar range. Protein identification was only possible for proteins present in high abundance in the tissue. These results provide a baseline for the application of MALDI-MSI towards the discovery of new candidate biomarkers and a new benchmarking strategy that can be used for comparing diverse MALDI-MSI workflows.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/pmic.201500515DOI Listing
June 2016

Probabilistic Segmentation of Mass Spectrometry (MS) Images Helps Select Important Ions and Characterize Confidence in the Resulting Segments.

Mol Cell Proteomics 2016 05 21;15(5):1761-72. Epub 2016 Jan 21.

**College of Computer and Information Science, Northeastern University, Boston, MA 02115

Mass spectrometry imaging is a powerful tool for investigating the spatial distribution of chemical compounds in a biological sample such as tissue. Two common goals of these experiments are unsupervised segmentation of images into newly discovered homogeneous segments and supervised classification of images into predefined classes. In both cases, the important secondary goals are to characterize the uncertainty associated with the segmentation and with the classification and to characterize the spectral features that define each segment or class. Recent analysis methods have focused on the spatial structure of the data to improve results. However, they either do not address these secondary goals or do this with separate post hoc procedures.We introduce spatial shrunken centroids, a statistical model-based framework for both supervised classification and unsupervised segmentation. It takes as input sets of previously detected, aligned, quantified, and normalized spectral features and expresses both spatial and multivariate nature of the data using probabilistic modeling. It selects informative subsets of spectral features that define each unsupervised segment or supervised class and quantifies and visualizes the uncertainty in spatial segmentations and in tissue classification. In the unsupervised setting, it also guides the choice of an appropriate number of segments. We demonstrate the usefulness of this framework in a supervised human renal cell carcinoma experimental dataset and several unsupervised experimental datasets, including a pig fetus cross-section, three rodent brains, and a controlled image with known ground truth. This framework is available for use within the open-source R package Cardinal as part of a full pipeline for the processing, visualization, and statistical analysis of mass spectrometry imaging experiments.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1074/mcp.O115.053918DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4858953PMC
May 2016