Phosphorylated H2AX (γ-H2AX) is a sensitive marker for DNA double-strand breaks (DSBs), but the variability of H2AX expression in different cell and tissue types makes it difficult to interpret the ...meaning of the γ-H2AX level. Furthermore, the assays commonly used for γ-H2AX detection utilize laborious and low-throughput microscopy-based methods. We describe here an ELISA assay that measures both phosphorylated H2AX and total H2AX absolute amounts to determine the percentage of γ-H2AX, providing a normalized value representative of the amount of DNA damage. We demonstrate the utility of the assay to measure DSBs introduced by either ionizing radiation or DNA-damaging agents in cultured cells and in xenograft models. Furthermore, utilizing the NCI-60 cancer cell line panel, we show a correlation between the basal fraction of γ-H2AX and cellular mutation levels. This additional application highlights the ability of the assay to measure γ-H2AX levels in many extracts at once, making it possible to correlate findings with other cellular characteristics. Overall, the γ-H2AX ELISA represents a novel approach to quantifying DNA damage, which may lead to a better understanding of mutagenic pathways in cancer and provide a useful biomarker for monitoring the effectiveness of DNA-damaging anticancer agents.
Abstract
CellMiner Cross-Database (CellMinerCDB, discover.nci.nih.gov/cellminercdb) allows integration and analysis of molecular and pharmacological data within and across cancer cell line datasets ...from the National Cancer Institute (NCI), Broad Institute, Sanger/MGH and MD Anderson Cancer Center (MDACC). We present CellMinerCDB 1.2 with updates to datasets from NCI-60, Broad Cancer Cell Line Encyclopedia and Sanger/MGH, and the addition of new datasets, including NCI-ALMANAC drug combination, MDACC Cell Line Project proteomic, NCI-SCLC DNA copy number and methylation data, and Broad methylation, genetic dependency and metabolomic datasets. CellMinerCDB (v1.2) includes several improvements over the previously published version: (i) new and updated datasets; (ii) support for pattern comparisons and multivariate analyses across data sources; (iii) updated annotations with drug mechanism of action information and biologically relevant multigene signatures; (iv) analysis speedups via caching; (v) a new dataset download feature; (vi) improved visualization of subsets of multiple tissue types; (vii) breakdown of univariate associations by tissue type; and (viii) enhanced help information. The curation and common annotations (e.g. tissues of origin and identifiers) provided here across pharmacogenomic datasets increase the utility of the individual datasets to address multiple researcher question types, including data reproducibility, biomarker discovery and multivariate analysis of drug activity.
High-throughput and high-content databases are increasingly important resources in molecular medicine, systems biology, and pharmacology. However, the information usually resides in unwieldy ...databases, limiting ready data analysis and integration. One resource that offers substantial potential for improvement in this regard is the NCI-60 cell line database compiled by the U.S. National Cancer Institute, which has been extensively characterized across numerous genomic and pharmacologic response platforms. In this report, we introduce a CellMiner (http://discover.nci.nih.gov/cellminer/) web application designed to improve the use of this extensive database. CellMiner tools allowed rapid data retrieval of transcripts for 22,379 genes and 360 microRNAs along with activity reports for 20,503 chemical compounds including 102 drugs approved by the U.S. Food and Drug Administration. Converting these differential levels into quantitative patterns across the NCI-60 clarified data organization and cross-comparisons using a novel pattern match tool. Data queries for potential relationships among parameters can be conducted in an iterative manner specific to user interests and expertise. Examples of the in silico discovery process afforded by CellMiner were provided for multidrug resistance analyses and doxorubicin activity; identification of colon-specific genes, microRNAs, and drugs; microRNAs related to the miR-17-92 cluster; and drug identification patterns matched to erlotinib, gefitinib, afatinib, and lapatinib. CellMiner greatly broadens applications of the extensive NCI-60 database for discovery by creating web-based processes that are rapid, flexible, and readily applied by users without bioinformatics expertise.
Using gene expression data to enhance our knowledge of control networks relevant to cancer biology and therapy is a challenging but urgent task. Based on the premise that genes that are expressed ...together in a variety of cell types are likely to functions together, we derived mutually correlated genes that function together in various processes in epithelial-like tumor cells. Expression-correlated genes were derived from data for the NCI-60 human tumor cell lines, as well as data from the Broad Institute's CCLE cell lines. NCI-60 cell lines that selectively expressed a mutually correlated subset of tight junction genes served as a signature for epithelial-like cancer cells. Those signature cell lines served as a seed to derive other correlated genes, many of which had various other epithelial-related functions. Literature survey yielded molecular interaction and function information about those genes, from which molecular interaction maps were assembled. Many of the genes had epithelial functions unrelated to tight junctions, demonstrating that new function categories were elicited. The most highly correlated genes were implicated in the following epithelial functions: interactions at tight junctions (CLDN7, CLDN4, CLDN3, MARVELD3, MARVELD2, TJP3, CGN, CRB3, LLGL2, EPCAM, LNX1); interactions at adherens junctions (CDH1, ADAP1, CAMSAP3); interactions at desmosomes (PPL, PKP3, JUP); transcription regulation of cell-cell junction complexes (GRHL1 and 2); epithelial RNA splicing regulators (ESRP1 and 2); epithelial vesicle traffic (RAB25, EPN3, GRHL2, EHF, ADAP1, MYO5B); epithelial Ca(+2) signaling (ATP2C2, S100A14, BSPRY); terminal differentiation of epithelial cells (OVOL1 and 2, ST14, PRSS8, SPINT1 and 2); maintenance of apico-basal polarity (RAB25, LLGL2, EPN3). The findings provide a foundation for future studies to elucidate the functions of regulatory networks specific to epithelial-like cancer cells and to probe for anti-cancer drug targets.
Advances in the high-throughput omic technologies have made it possible to profile cells in a large number of ways at the DNA, RNA, protein, chromosomal, functional, and pharmacological levels. A ...persistent problem is that some classes of molecular data are labeled with gene identifiers, others with transcript or protein identifiers, and still others with chromosomal locations. What has lagged behind is the ability to integrate the resulting data to uncover complex relationships and patterns. Those issues are reflected in full form by molecular profile data on the panel of 60 diverse human cancer cell lines (the NCI-60) used since 1990 by the U.S. National Cancer Institute to screen compounds for anticancer activity. To our knowledge, CellMiner is the first online database resource for integration of the diverse molecular types of NCI-60 and related meta data.
CellMiner enables scientists to perform advanced querying of molecular information on NCI-60 (and additional types) through a single web interface. CellMiner is a freely available tool that organizes and stores raw and normalized data that represent multiple types of molecular characterizations at the DNA, RNA, protein, and pharmacological levels. Annotations for each project, along with associated metadata on the samples and datasets, are stored in a MySQL database and linked to the molecular profile data. Data can be queried and downloaded along with comprehensive information on experimental and analytic methods for each data set. A Data Intersection tool allows selection of a list of genes (proteins) in common between two or more data sets and outputs the data for those genes (proteins) in the respective sets. In addition to its role as an integrative resource for the NCI-60, the CellMiner package also serves as a shell for incorporation of molecular profile data on other cell or tissue sample types.
CellMiner is a relational database tool for storing, querying, integrating, and downloading molecular profile data on the NCI-60 and other cancer cell types. More broadly, it provides a template to use in providing such functionality for other molecular profile data generated by academic institutions, public projects, or the private sector. CellMiner is available online at (http://discover.nci.nih.gov/cellminer/).
In the January 1, 2017, issue of
, Nagel and colleagues demonstrate the value of assays that determine the DNA repair capacity of cancers in predicting response to temozolomide. Using a ...fluorescence-based multiplex flow cytometric host cell reactivation assay that provides simultaneous readout of DNA repair capacity across multiple pathways, they show that the multivariate drug response models derived from cell line data were applicable to patient-derived xenograft models of glioblastoma. In this commentary, we first outline the mechanism of activity and current clinical application of temozolomide, which, until now, has been largely limited to glioblastoma. Given the challenges of clinical application of functional assays, we argue that functional readouts be approximated by genomic signatures. In this context, a combination of MGMT activity and mismatch repair (MMR) status of the tumor are important parameters that determine sensitivity to temozolomide. More reliable methods are needed to determine MGMT activity as DNA methylation, the current standard, does not accurately reflect the expression of MGMT. Also, genomics for MMR are warranted. Furthermore, based on patterns of MGMT expression across different solid tumors, we make a case for revisiting temozolomide use in a broader spectrum of cancers based on our current understanding of its molecular basis of activity.
.
DNA-damaging agents (DDAs) constitute the backbone of treatment for most human tumors. Here we used the National Cancer Institute Antitumor Cell Line Panel (the NCI-60) to identify predictors of ...cancer cell response to topoisomerase I (Top1) inhibitors, a widely used class of DDAs. We assessed the NCI-60 transcriptome using Affymetrix Human Exon 1.0 ST microarrays and correlated the in vitro activity of four Top1 inhibitors with gene expression in the 60 cell lines. A single gene, Schlafen-11 (SLFN11), showed an extremely significant positive correlation with the response not only to Top1 inhibitors, but also to Top2 inhibitors, alkylating agents, and DNA synthesis inhibitors. Using cells with endogenously high and low SLFN11 expression and siRNA-mediated silencing, we show that SLFN11 is causative in determining cell death and cell cycle arrest in response to DDAs in cancer cells from different tissues of origin. We next analyzed SLFN11 expression in ovarian and colorectal cancers and normal corresponding tissues from The Cancer Genome Atlas database and observed that SLFN11 has a wide expression range. We also observed that high SLFN11 expression independently predicts overall survival in a group of ovarian cancer patients treated with cisplatin-containing regimens. We conclude that SLFN11 expression is causally associated with the activity of DDAs in cancer cells, has a broad expression range in colon and ovarian adenocarcinomas, and may behave as a biomarker for prediction of response to DDAs in the clinical setting.
One of the hallmarks of cancer is
hromosome
stability (CIN), which leads to aneuploidy, translocations, and other chromosome aberrations. However, in the vast majority of human tumors the molecular ...basis of CIN remains unknown, partly because not all genes controlling chromosome transmission have yet been identified. To address this question, we developed an experimental high-throughput imaging (HTI) siRNA assay that allows the identification of novel CIN genes. Our method uses a human artificial chromosome (HAC) expressing the
transgene. When this assay was applied to screen an siRNA library of protein kinases, we identified
,
,
,
, and
as potential novel genes whose knockdown induces various mitotic abnormalities and results in chromosome loss. The HAC-based assay can be applied for screening different siRNA libraries (cell cycle regulation, DNA damage response, epigenetics, and transcription factors) to identify additional genes involved in CIN. Identification of the complete spectrum of CIN genes will reveal new insights into mechanisms of chromosome segregation and may expedite the development of novel therapeutic strategies to target the CIN phenotype in cancer cells.
CellMiner-SCLC (https://discover.nci.nih.gov/SclcCellMinerCDB/) integrates drug sensitivity and genomic data, including high-resolution methylome and transcriptome from 118 patient-derived small cell ...lung cancer (SCLC) cell lines, providing a resource for research into this “recalcitrant cancer.” We demonstrate the reproducibility and stability of data from multiple sources and validate the SCLC consensus nomenclature on the basis of expression of master transcription factors NEUROD1, ASCL1, POU2F3, and YAP1. Our analyses reveal transcription networks linking SCLC subtypes with MYC and its paralogs and the NOTCH and HIPPO pathways. SCLC subsets express specific surface markers, providing potential opportunities for antibody-based targeted therapies. YAP1-driven SCLCs are notable for differential expression of the NOTCH pathway, epithelial-mesenchymal transition (EMT), and antigen-presenting machinery (APM) genes and sensitivity to mTOR and AKT inhibitors. These analyses provide insights into SCLC biology and a framework for future investigations into subtype-specific SCLC vulnerabilities.
Display omitted
•SCLC-CellMiner is an extensive cell line genomic and pharmacology resource•SCLC cell lines show a methylome consistent with their plasticity and lineage•Transcriptome analyses reveal lineage transcriptional networks and drug predictions•SCLC-Y cells differ from other subgroups by transcriptome and potential therapeutics
Tlemsani et al. provide a unique resource, SCLC-CellMiner, integrating drug sensitivity and multi-omics data from 118 small cell lung cancer (SCLC) cell lines. They demonstrate that SCLCs have differential transcriptional networks driven by lineage-specific transcription factors (NEUROD1, ASCL1, POU2F3, and YAP1). Furthermore, YAP1-driven SCLCs have distinct drug sensitivity profiles.
Merkel Cell Polyomavirus (MCV or MCPyV) was recently discovered in an aggressive form of skin cancer known as Merkel cell carcinoma (MCC). Integration of MCV DNA into the host genome likely ...contributes to the development of MCC in humans. MCV infection is common and many healthy people shed MCV virions from the surface of their skin. MCV DNA has also been detected in samples from a variety of other tissues. Although MCC tumors serve as a record that MCV can infect the Merkel cell lineage, the true tissue tropism and natural reservoirs of MCV infection in the host are not known. In an effort to gain insight into the tissue tropism of MCV, and to possibly identify cellular factors responsible for mediating infectious entry of the virus, the infection potential of human cells derived from a variety of tissues was evaluated. MCV gene transfer vectors (pseudoviruses) carrying reporter plasmid DNA encoding GFP or luciferase genes were used to transduce keratinocytes and melanocytes, as well as lines derived from MCC tumors and the NCI-60 panel of human tumor cell lines. MCV transduction was compared to transduction with pseudoviruses based on the better-studied human BK polyomavirus (BKV). The efficiency of MCV and BKV transduction of various cell types occasionally overlapped, but often differed greatly, and no clear tissue type preference emerged. Application of native MCV virions to a subset of highly transducible cell types suggested that the lines do not support robust replication of MCV, consistent with recent proposals that the MCV late phase may be governed by cellular differentiation in vivo. The availability of carefully curated gene expression data for the NCI-60 panel should make the MCV and BKV transduction data for these lines a useful reference for future studies aimed at elucidation of the infectious entry pathways of these viruses.
PDF
