Data Discovery and Anomaly Detection Using Atypicality: Theory

A central question in the era of 'big data' is what to do with the enormous amount of information. One possibility is to characterize it through statistics, e.g., averages, or classify it using machine learning, in order to understand the general structure of the overall data. The perspective in this paper is the opposite, namely that most of the value in the information in some applications is in the parts that deviate from the average, that are unusual, atypical. We define what we mean by 'atypical' in an axiomatic way as data that can be encoded with fewer bits in itself rather than using the code for the typical data. We show that this definition has good theoretical properties. We then develop an implementation based on universal source coding, and apply this to a number of real world data sets.Comment: 40 page

Medulloblastoma Exosome Proteomics Yield Functional Roles for Extracellular Vesicles

<div><p>Medulloblastomas are the most prevalent malignant pediatric brain tumors. Survival for these patients has remained largely the same for approximately 20 years, and our therapies for these cancers cause significant health, cognitive, behavioral and developmental sequelae for those who survive the tumor and their treatments. We obviously need a better understanding of the biology of these tumors, particularly with regard to their migratory/invasive behaviors, their proliferative propensity, and their abilities to deflect immune responses. Exosomes, virus-sized membrane vesicles released extracellularly from cells after formation in, and transit thru, the endosomal pathway, may play roles in medulloblastoma pathogenesis but are as yet unstudied in this disease. Here we characterized exosomes from a medulloblastoma cell line with biochemical and proteomic analyses, and included characterization of patient serum exosomes. Further scrutiny of the proteomic data suggested functional properties of the exosomes that are relevant to medulloblastoma tumor biology, including their roles as proliferation stimulants, their activities as attractants for tumor cell migration, and their immune modulatory impacts on lymphocytes. Aspects of this held true for exosomes from other medulloblastoma cell lines as well. Additionally, pathway analyses suggested a possible role for the transcription factor hepatocyte nuclear factor 4 alpha (HNF4A); however, inhibition of the protein’s activity actually increased D283MED cell proliferation/clonogenecity, suggesting that HNF4A may act as a tumor suppressor in this cell line. Our work demonstrates that relevant functional properties of exosomes may be derived from appropriate proteomic analyses, which translate into mechanisms of tumor pathophysiology harbored in these extracellular vesicles.</p> </div

Interactomes of the Top Networks/Associated Functions from IPA “Core Analysis”.

<p>Proteins clustered within the Top Networks/Associated Functions as derived from IPA algorithms are shown as members of “interactomes”. Proteins identified during this work are labeled in larger bold font, with the protein symbol in gold fill. Direct connections between/among proteins are shown in solid lines; indirect interactions are shown as dashed lines (also called “edges”). Connections between proteins identified in this proteomic screen are shown in dark blue; interactions between proteins we identified and proteins not identified in our proteomics are shown in turquoise. Protein shapes are indicative of function and that legend is shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042064#pone-0042064-g005" target="_blank"><b>Figure 5</b></a><b> A,B</b>. Each network’s score (Fisher’s exact test, -log [p values] shown; all networks were highly significant) and number of focus molecules (those which are “seeds” for generation of focal points within the network) are shown. The top 2 network terminologies are: (<b>A</b>) “Cell Morphology, Post-Translational Modification, Protein Folding”; (<b>B</b>) “Genetic Disorder, Hematological Disease, Renal and Urological Disease”;</p

Inhibition of hepatocyte nuclear factor 4α (HNF4A) actually increases D283MED cell growth.

<p>IPA Networks 3 and 5 in combination (<b>A</b>) reveal that HNF4A (circled in red) sits at a node of interaction with nearly a dozen other proteins in networks tied to cancer cell metabolism (boxed terms and scores for each work are show at right). Networks are represented as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042064#pone-0042064-g005" target="_blank"><b>Figures 5</b></a><b>, </b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042064#pone-0042064-g006" target="_blank"><b>6</b></a><b>, </b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042064#pone-0042064-g007" target="_blank"><b>7</b></a><b>and </b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042064#pone-0042064-g008" target="_blank"><b>8</b></a>. (<b>B</b>) D283MED cells were treated with MEDICA16 (125 µM) to inhibit HNF4A; cells were also treated with D283MED exosomes (25 or 100 µg/ml) with or without MEDICA16. Cells were grown in a clonogenic assay for 8 days and were quantified following the various treatments. Differences between groups were statistically evaluated by ANOVA; significant differences (p<0.05) between groups are indicated by different “star cluster” numbers (eg, *, **, ***). Control cell growth (D283MED cells with no drug or exosome treatments) was defined as 100%.</p

Exogenous exosomes are attractants for tumor cell migration.

<p>IPA Networks 2 and 4 in combination (<b>A</b>) suggested that exosomes may provide impetus for tumor cell migration (boxed terms and scores for each work are show at right). Networks are represented as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042064#pone-0042064-g005" target="_blank"><b>Figures 5</b></a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042064#pone-0042064-g006" target="_blank"><b>6</b></a>. (<b>B</b>) depicts the set-up for a Boyden chamber type of migration assay (<b>top</b>) and results (<b>bottom</b>). D283MED cells were placed in the upper chambers and attractants (10% fetal bovine serum [FBS] as positive control, media only [no FBS] as a negative control, or increasing concentrations of D283MED exosomes) were added to the lower chambers. Cells were separated from the lower chamber by a polycarbonate (8 µm pore size) filter. After 48 hrs, cells that migrated thru the insert were stained and counted in 3 microscope fields (average per field +/− standard deviation shown). The same assays were performed using UW228 and DAOY medulloblastoma cell lines and exosomes (<b>C</b>), <b>left</b> and <b>right</b>, respectively. Differences between groups were statistically evaluated by ANOVA; significant differences (p<0.05) between groups are indicated by different “star cluster” numbers (eg, *, **, ***).</p

Western blot and FACS analyses of D283MED exosomes.

<p>Exosomes harvested from the spent medium of D283MED cells, and the cells themselves, were lysed and proteins separated on SDS-PAGE gels followed by electrotransfer for Western blotting and probing with the antibodies listed. (<b>A</b>) shows blots probed for chaperone proteins, a potential tumor transcription factor, and known brain tumor antigens. Results of probes for heat shock proteins (HSPs) 90, 70, 27, and 60 (and heat shock cognate 70–HSC70), as well as protein disulfide isomerase (PDI) and hemopexin (HPX), hepatocyte nuclear factor alpha (HNF4A), and tumor antigens glycoprotein non-metastatic B (GPNMB) and Her2/Neu (Erb B2 : ERBB2) are shown (<b>B</b>) shows blots probed for proteins typically found in exosomes such as alpha-1 antitrypsin (α-1AT), glyceraldehyde 3-phosphate dehydrogenase (GAPDH), and the exosome marker CD9. A20 (murine leukemia/lymphoma cell line) lysate is a positive control for CD9. (<b>C, D</b>) Control blots of exosomes and lysates listed were probed with anti-mouse and anti-rabbit secondary antibodies only (respectively). Molecular weight markers are indicated at the sides of the blots. Exosome surface HSP90 was identified by fluorescence activated cell sorting (FACS) analysis of exosomes bound to latex beads and treated as if they were cells in FACS (<b>E</b>). Gray fill indicates fluorescence of exosome-coated beads probed with a fluorescently-labeled isotype control antibody, and the red line shows fluorescence intensity of the exosome/bead complex with the fluorescently-labeled anti-HSP90 antibody.</p

Western blots and TEM of serum exosomes from medulloblastoma patients and healthy donors.

<p>Exosomes from sera from patients with varying medulloblastoma subtypes (denoted as MEDxxx) and 3 healthy donors (HDxx) were precipitated using ExoQuick solution. (<b>A</b>) Vesicles were lysed according to the manufacturer’s protocol and proteins separated on SDS-PAGE gels followed by electrotransfer for Western blotting and probing with the antibodies listed. Note that ERBB2 and heterogeneous (glycol)forms of GPNMB seem to show specificity for exosomes from patients only. Arrowhead in the GPNMB blot shows either the predicted GPNMB core protein or else a non-specific band also found in the healthy donor lanes. (<b>B</b>) Transmission Electron Microscopy (TEM) micrographs of medulloblastoma serum exosomes precipitated with ExoQuick.</p

Exogenous exosomes promote tumor cell proliferation.

<p>IPA Networks 7 and 8 in combination (<b>A</b>) suggested that exosomes may provide cell growth stimulation (boxed terms and scores for each work are show at right). Networks are represented as described for <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042064#pone-0042064-g005" target="_blank"><b>Figure 5</b></a>, with overlapping connections shown in orange. Proteins identified in our studies are shown in green fill here to stand out against the orange lines. Exosome-driven increases in proliferation (<b>B</b>) were measured by MTS assay (<b>left</b>) and an ATP assay (<b>right</b>), where increasing quantities of exosomes were incubated with D283MED cells in tissue culture resulting in dose-dependent increases in proliferation at 24 (<b>left</b>) and 48 hrs (<b>right</b>) (see also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042064#pone-0042064-g009" target="_blank"><b>Figure 9B</b></a> for a clonogenic analysis of increased proliferation following exosome stimulation). (<b>C</b>) shows quantified clonogenic outgrowths of UW228 and DAOY cells exposed to cognate exosomes. Differences between groups were statistically evaluated by ANOVA; significant differences (p<0.05) between groups are indicated by different “star cluster” numbers (eg, *, **, ***). Control cell proliferation (no exosomes) was set at 100%. For the UW228 experiment, (/*) means that the 50 µg/ml value differed significantly from the 500 µg/ml, but not control (0 µg/ml) or 100 µg/ml value. For the DAOY experiment, (/*) means that means that the 50 µg/ml value differed significantly only from the control (0 µg/ml).</p

D283MED exosomes affect interferon-gamma output from activated PBMCs.

<p>IPA Networks 3 and 8 in combination (<b>A</b>) suggested that exosomes may be involved in immune cell cytokine release (boxed terms and scores for each work are show at right). Networks are represented as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042064#pone-0042064-g005" target="_blank"><b>Figures 5</b></a><b>, </b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042064#pone-0042064-g006" target="_blank"><b>6</b></a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042064#pone-0042064-g007" target="_blank"><b>7</b></a>. Immune-related cytokines are also noted in larger font and with gray fill. (<b>B</b>) shows exosome-induced changes in PHA-activated PBMCs; healthy donor PBMCs were stimulated with PHA (5 µg/ml) for 48 hrs and D283MED exosomes at the concentrations listed were added as well. Interferon-γ release was measured by ELISA. Differences between groups were statistically evaluated by ANOVA; significant differences (p<0.05) between groups are indicated by different “star cluster” numbers (eg, *, **, ***).</p

Categorization of D283MED exosomal proteins by subcellular localization and by function.

<p>The proteins listed in Table S1 were categorized as percentages of the total number of proteins identified using Ingenuity Pathway Analysis descriptions and literature searches. Proteins are classified by subcellular (or extracellular) localization (<b>A</b>) or by function (<b>B</b>).</p