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

NLGN4x expression in selected breast cancer cell lines.

<p>A) NLGN4X protein expression in two different cell lines MCF-7 and MDA-MB-231, as determined by measuring mean fluorescence intensity by flow cytometry. B) The fluorescence curves of secondary antibody intracellular staining and NLGN4X mAB intracellular staining for the cell lines stated above.</p

NLGN4X is expressed in breast cancer tissues and metastasis.

<p>A) Oncomine box plot RNA expression data for NLGN4X in metastatic versus non-metastatic are shown within the TCGA data set. B) Representative image of breast cancer microarrays showing NLGN4X expression in normal, invasive ductal carcinoma and lymph node of invasive ductal carcinoma.</p

Effect of NLGN4X knockdown on apoptosis and apoptotic markers.

<p>A) The fluorescence curves of secondary antibody intracellular staining, then apoptosis markers caspase 3, 7 and cleaved PAPRP mAB intracellular staining in siRNA-treated vs scrambled-siRNA treated cells. B.) Apoptotic activity was analyzed on a flow cytometer with quadrants designating Early Apoptosis, Late Apoptosis, Necrosis, or Viable Cells in relation to NLGN4X siRNA and scrambled sirNA treated cells.</p

Phage selection and sequence similarity to NLGN4X.

<p>A) Flow chart depicting the selection of phages from the landscape phage library against cancer cells. B) Amino acid sequences of phages selected on human breast cancer cells (red) matched to the sequence of human Neuroligin 4X.</p

NLGN4X is highly expressed in breast cancer cell lines, particularly TNBC.

<p>The figure depicts Mean Robust Analysis of NLGN4X mRNA expression across multiple cell lines in the CCLE database.</p

Cellular localization of NLGN4X.

<p>A) Cell surface staining of NLGN4X in non-permeabilized and permeabilized MDA-MB-231 by measuring mean fluorescence intensity as determined by flow cytometry. B) The fluorescence curves of secondary antibody intracellular staining and NLGN4X mAB intracellular staining for the non-permeabilized and permeabilized MDA-MB-231; C) Representative images of NLGN4X staining in MDA-MB-231 as visualized by Confocal IF Control denotes secondary antibody staining without NLGN4X primary antibody.</p

AR negative triple negative or “quadruple negative” breast cancers in African American women have an enriched basal and immune signature

<div><p>There is increasing evidence that Androgen Receptor (AR) expression has prognostic usefulness in Triple negative breast cancer (TNBC), where tumors that lack AR expression are considered “Quadruple negative” Breast Cancers (“QNBC”). However, a comprehensive analysis of AR expression within all breast cancer subtypes or stratified by race has not been reported. We assessed AR mRNA expression in 925 tumors from The Cancer Genome Atlas (TCGA), and 136 tumors in 2 confirmation sets. AR protein expression was determined by immunohistochemistry in 197 tumors from a multi-institutional cohort, for a total of 1258 patients analyzed. Cox hazard ratios were used to determine correlations to PAM50 breast cancer subtypes, and TNBC subtypes. Overall, AR-negative patients are diagnosed at a younger age compared to AR-positive patients, with the average age of AA AR-negative patients being, 49. AA breast tumors express AR at lower rates compared to Whites, independent of ER and PR expression (p<0.0001). AR-negative patients have a (66.60; 95% CI, 32–146) odds ratio of being basal-like compared to other PAM50 subtypes, and this is associated with an increased time to progression and decreased overall survival. AA “QNBC” patients predominately demonstrated BL1, BL2 and IM subtypes, with differential expression of E<i>2F1</i>, <i>NFKBIL2</i>, <i>CCL2</i>, <i>TGFB3</i>, <i>CEBPB</i>, <i>PDK1</i>, <i>IL12RB2</i>, <i>IL2RA</i>, and <i>SOS1</i> genes compared to white patients. Immune checkpoint inhibitors PD-1, PD-L1, and CTLA-4 were significantly upregulated in both overall “QNBC” and AA “QNBC” patients as well. Thus, AR could be used as a prognostic marker for breast cancer, particularly in AA “QNBC” patients.</p></div

AR-associated genes.

<p>A. Genes most highly associated <i>(bivariate cutoff 1</i>.<i>0E-07)</i> with AR expression across the TCGA dataset were used to determined novel gene expression signatures associated with AR tumor status. Distinct subgroups of genes with shared expression trends were identified using K-means cluster analysis and separated into 5 nodes of genes with expression trends that are either upregulated or downregulated in the AR-negative tumors. B. A subset of genes related to the Immunomodulatory TNBC subtype display statistically significant differences in expression between AA vs White patients when comparing expression in AR-high and AR-low categories.</p

Panepoxydone inhibited NF-kB:

<p>(<b>A</b>) Immunoblotting of IkB and pIkBα showed accumulation of IkBα (inactive) and down-regulation of pIkBα (active) in PP treated breast cancer cells. This indicates the potential of PP in keeping NF-kB in the active state. (<b>B</b>) Bar diagram indicate the increased IkB/pIkB ratio in all breast cancer cells after PP treatment. * indicates statistically significant difference between PP treated and untreated cells at p<0.05 (*), p<0.01(**), and, p<.001(***) levels by student's t-test. (<b>C</b>) Localization of NF-κB was done on MCF-7, MDAMB-231, MDAMB-468 and MDAMB-453 breast cancer cells that were fixed, permeabilized and labeled with anti-p65 subunit of NF-κB then nuclei were stained with DAPI. Controls cells were compared to cells treated with the top dose of PP (D3). Controls cells showed increased expression of NF- κB in the nucleus, whereas following treatment with PP, NF-κB accumulated in the cytoplasm, indicating decreased activity.</p