CD63, MHC class 1, and CD47 identify subsets of extracellular vesicles containing distinct populations of noncoding RNAs.

Extracellular vesicles (EVs) mediate the intercellular transfer of RNAs, which alter gene expression in target cells. EV heterogeneity has limited progress towards defining their physiological functions and utility as disease-specific biomarkers. CD63 and MHC1 are widely used as markers to purify EVs. CD47 is also present on EVs and alters their effects on target cells, suggesting that specific surface markers define functionally distinct EVs. This hypothesis was addressed by comparing Jurkat T cell EVs captured using CD47, CD63, and MHC1 antibodies. These EV subsets have similar sizes but divergent RNA contents. Apart from differences in numbers of nonannotated transcripts, CD63+, MHC1+, and CD47+ EVs have similar overall contents of most noncoding RNA classes, but the relative enrichment of specific RNAs differs. The enrichment of micro-RNAs is highly divergent, and some including miR320a are selectively concentrated in CD47+ EVs. Small nucleolar RNAs including SNORD116@ and SNHG10 are also selectively enriched in CD47+ EVs, whereas no small nuclear RNAs are enriched in CD47+ EVs. Conversely, MHC1+ EVs are selectively enriched in a subset of tRNAs including TRE-CTC and TRR-CCG. This heterogeneity in RNA composition suggests multiple sorting mechanisms that direct specific RNAs into subsets of EVs that express specific surface markers.

Systematic Protein Level Regulation via Degradation Machinery Induced by Genotoxic Drugs.

In this study we monitored protein dynamics in response to cisplatin, 5-fluorouracil, and irinotecan with different concentrations and administration modes using "reverse-phase" protein arrays (RPPAs) in order to gain comprehensive insight into the protein dynamics induced by genotoxic drugs. Among 666 protein time-courses, 38% exhibited an increasing trend, 32% exhibited a steady decrease, and 30% fluctuated within 24 h after drug exposure. We analyzed almost 12,000 time-course pairs of protein levels based on the geometrical similarity by correlation distance (dCor). Twenty-two percent of the pairs showed dCor > 0.8, which indicates that each protein of the pair had similar dynamics. These trends were disrupted by a proteasome inhibitor, MG132, suggesting that the protein degradation system was activated in response to the drugs. Among the pairs with high dCor, the average dCor of pairs with apoptosis-related protein was significantly higher than those without, indicating that regulation of protein levels was induced by the drugs. These results suggest that the levels of numerous functionally distinct proteins may be regulated by common degradation machinery induced by genotoxic drugs.

Predicting cisplatin and trabectedin drug sensitivity in ovarian and colon cancers.

Molecular profiling of markers involved in the activity of chemotherapeutic agents can shed light on the successes and failures of treatment in patients and can also provide a basis for individualization of therapy. Toward those ends, we have used reverse-phase protein lysate microarrays to evaluate expression of protein components of the nucleotide excision repair (NER) pathways. Those pathways strongly influence the anticancer activities of numerous drugs, including those that are the focus here, cisplatin and ecteinascidin 743 (Et-743; Yondelis, Trabectedin). Cisplatin is generally more active in cell types deficient in NER, whereas Et-743 tends to be less active in those cells. We measured protein expression and sensitivity to those drugs in 17 human ovarian and colon cancer cell lines (13 of them from the NCI-60 panel) and five xeroderma pigmentosum (XP) patient cell types, each containing a different NER defect. Of the NER proteins giving reliable signals, XPF and XPG showed the highest correlations of protein expression with drug activity across all three tissue-of-origin groups. When we compared protein expression data with mRNA expression data from Affymetrix U133A chips, we found no consistent correlation between the two across the cell lines studied, which reinforces the conclusion that protein measurements can give more interpretable mechanistic information than can transcript measurements. The work reported here provides motivation for larger proteomic studies with more cell types focused on potential biomarkers in additional pharmacologically pertinent pathways.

Quantitative protein network monitoring in response to DNA damage.

Conventional molecular biology techniques have identified a large number of cell signaling pathways; however, the importance of these pathways often varies, depending on factors such as treatment type, dose, time after treatment, and cell type. Here, we describe a technique using "reverse-phase" protein lysate microarrays (RPAs) to acquire multiple dimensions of information on protein dynamics in response to DNA damage. Whole-cell lysates from three cellular stress treatments (IR, UV, and ADR) were collected at four doses per treatment, and each, in turn, at 10 time points, resulting in a single-slide RPA consisting of 10,240 features, including replicates. The dynamic molecular profile of 18 unique protein species was compared to phenotypic fate by FACS analysis for corresponding stress conditions. Our initial quantitative results in this new platform confirmed that (1) there is clear stress dose-response effect in p53 protein and (2) a comparison of the rates of increase of p21 and Cyclin D3/p53-Ser15 in response to DNA damage may be associated with the pattern of DNA content. This method, offering a quantitative time-course monitoring of protein expression levels, can provide an experimental reference for developing mathematical models of cell signaling dynamics. Although the present study focuses on the DNA damage-repair pathway, the technique is generally useful to the study of protein signaling.

Quantitative assessment of the p53-Mdm2 feedback loop using protein lysate microarrays.

Mathematical simulations of the p53-Mdm2 feedback loop suggest that both proteins will exhibit impulsive expression characteristics in response to high cellular stress levels. However, little quantitative experimental evaluation has been done, particularly of the phosphorylated forms. To evaluate the mathematical models experimentally, we used lysate microarrays from an isogenic pair of gamma-ray-irradiated cell lysates from HCT116 (p53(+/+) and p53(-/-)). Both p53 and Mdm2 proteins showed expected pulses in the wild type, whereas no pulses were seen in the knockout. Based on experimental observations, we determined model parameters and generated an in silico "knockout," reflecting the experimental data, including phosphorylated proteins.

Global analysis of IL-2 target genes: identification of chromosomal clusters of expressed genes.

T lymphocytes play a central role in controlling adaptive immune responses. IL-2 critically regulates both T cell growth and death and is involved in maintaining peripheral tolerance, but the molecules involved in these and other IL-2 actions are only partially known. We now provide a comprehensive compendium of the genes expressed in T cells and of those regulated by IL-2 based on a combination of DNA microarrays and serial analysis of gene expression (SAGE). The newly identified IL-2 target genes include many genes previously linked to apoptosis in other cellular systems that may contribute to IL-2-dependent survival functions. We also studied the mRNA expression of known regulators of signaling pathways for their induction in response to IL-2 in order to identify potential novel positive and/or negative feedback regulators of IL-2 signaling. We show that IL-2 regulates only a limited number of these genes. These include suppressors of cytokine signaling (SOCS) 1, SOCS2, dual-specificity phosphatase (DUSP) 5, DUSP6 and non-receptor type phosphatase-7 (PTPN7). Additionally, we provide evidence that many genes expressed in T cells locate in chromosomal clusters, and that select IL-2-regulated genes are located in at least two clusters, one at 5q31, a known cytokine gene cluster, and the other at 6p21.3, a region that contains genes encoding the tumor necrosis factor (TNF) superfamily members TNF, LT-alpha and LT-beta.

Mapping molecular networks using proteomics: a vision for patient-tailored combination therapy.

Mapping tumor cell protein networks in vivo will be critical for realizing the promise of patient-tailored molecular therapy. Cancer can be defined as a dysregulation or hyperactivity in the network of intracellular and extracellular signaling cascades. These protein signaling circuits are the ultimate targets of molecular therapy. Each patient's tumor may be driven by a distinct series of molecular pathogenic defects. Thus, for any single molecular targeted therapy, only a subset of cancer patients may respond. Individualization of therapy, which tailors a therapeutic regimen to a tumor molecular portrait, may be the solution to this dilemma. Until recently, the field lacked the technology for molecular profiling at the genomic and proteomic level. Emerging proteomic technology, used concomitantly with genomic analysis, promises to meet this need and bring to reality the clinical adoption of molecular stratification. The activation state of kinase-driven signal networks contains important information relative to cancer pathogenesis and therapeutic target selection. Proteomic technology offers a means to quantify the state of kinase pathways, and provides post-translational phosphorylation data not obtainable by gene arrays. Case studies using clinical research specimens are provided to show the feasibility of generating the critical information needed to individualize therapy. Such technology can reveal potential new pathway interconnections, including differences between primary and metastatic lesions. We provide a vision for individualized combinatorial therapy based on proteomic mapping of phosphorylation end points in clinical tissue material.

Synthetic lethality of retinoblastoma mutant cells in the Drosophila eye by mutation of a novel peptidyl prolyl isomerase gene.

Mutations that inactivate the retinoblastoma (Rb) pathway are common in human tumors. Such mutations promote tumor growth by deregulating the G1 cell cycle checkpoint. However, uncontrolled cell cycle progression can also produce new liabilities for cell survival. To uncover such liabilities in Rb mutant cells, we performed a clonal screen in the Drosophila eye to identify second-site mutations that eliminate Rbf(-) cells, but allow Rbf(+) cells to survive. Here we report the identification of a mutation in a novel highly conserved peptidyl prolyl isomerase (PPIase) that selectively eliminates Rbf(-) cells from the Drosophila eye.

Distinct effects on gene expression of chemical and genetic manipulation of the cancer epigenome revealed by a multimodality approach.

We tested the hypothesis that the effects on gene expression of altered DNA methylation by 5-aza-2'-deoxycytidine (5-aza-CdR) and genetic (DNMT knockout) manipulation of DNA are similar, and distinct from Trichostatin A (TSA)-induced chromatin decondensation. Surprisingly, the effects of 5-aza-CdR were more similar to those of TSA than to DNMT1, DNMT3B, or double DNMT somatic cell knockout. Furthermore, the effects of 5-aza-CdR were similar at one and five days exposure, suggesting active demethylation or direct influence of both drugs on the stability of methylation and/or chromatin marks. Agents that induce gene activation through hypomethylation may have unintended consequences, since nearly as many genes were downregulated as upregulated after demethylation. In addition, a 75 kb cluster of metallothionein genes was coordinately regulated.

Proteomic profiling of the NCI-60 cancer cell lines using new high-density reverse-phase lysate microarrays.

Because most potential molecular markers and targets are proteins, proteomic profiling is expected to yield more direct answers to functional and pharmacological questions than does transcriptional profiling. To aid in such studies, we have developed a protocol for making reverse-phase protein lysate microarrays with larger numbers of spots than previously feasible. Our first application of these arrays was to profiling of the 60 human cancer cell lines (NCI-60) used by the National Cancer Institute to screen compounds for anticancer activity. Each glass slide microarray included 648 lysate spots representing the NCI-60 cell lines plus controls, each at 10 two-fold serial dilutions to provide a wide dynamic range. Mouse monoclonal antibodies and the catalyzed signal amplification system were used for immunoquantitation. The signal levels from the >30,000 data points for our first 52 antibodies were analyzed by using p-scan and a quantitative dose interpolation method. Clustered image maps revealed biologically interpretable patterns of protein expression. Among the principal early findings from these arrays were two promising pathological markers for distinguishing colon from ovarian adenocarcinomas. When we compared the patterns of protein expression with those we had obtained for the same genes at the mRNA level by using both cDNA and oligonucleotide arrays, a striking regularity appeared: cell-structure-related proteins almost invariably showed a high correlation between mRNA and protein levels across the NCI-60 cell lines, whereas non-cell-structure-related proteins showed poor correlation.

Diagnostic markers that distinguish colon and ovarian adenocarcinomas: identification by genomic, proteomic, and tissue array profiling.

Colon and ovarian cancers can be difficult to distinguish in the abdomen, and the distinction is important because it determines which drugs will be used for therapy. To identify molecular markers for that differential diagnosis, we developed a multistep protocol starting with the 60 human cancer cell lines used by the National Cancer Institute to screen for new anticancer agents. The steps included: (a) identification of candidate markers using cDNA microarrays; (b) verification of clone identities by resequencing; (c) corroboration of transcript levels using Affymetrix oligonucleotide chips; (d) quantitation of protein expression by "reverse-phase" protein microarray; and (e) prospective validation of candidate markers on clinical tumor sections in tissue microarrays. The two best candidates identified were villin for colon cancer cells and moesin for ovarian cancer cells. Because moesin stained stromal elements in both types of cancer, it would probably not have been identified as a marker if we had started with mRNA or protein profiling of bulk tumors. Villin appears at least as useful as the currently used colon cancer marker cytokeratin 20, and moesin also appears to have utility. The multistep process introduced here has the potential to produce additional markers for cancer diagnosis, prognosis, and therapy.

Impact of p53 knockout and topotecan treatment on gene expression profiles in human colon carcinoma cells: a pharmacogenomic study.

To uncover transcriptional stress responses related to p53, we used cDNA microarrays (National Cancer Institute Oncochips comprising 6500 different genes) to characterize the gene expression profiles of wild-type p53 HCT-116 cells and an isogenic p53 knockout counterpart after treatment with topotecan, a specific topoisomerase I inhibitor. The use of the p53 knockout cells had the advantage over p53-overexpressing systems in that p53 activation is mediated physiologically. RNA was extracted after low (0.1 microM)- and high (1 microM)-dose topotecan at multiple time points within the first 6 h of treatment. To facilitate simultaneous study of the p53 status and pharmacological effects on gene expression, we developed a novel "cross-referenced network" experimental design and used multiple linear least squares fitting to optimize estimates of relative transcript levels in the network of experimental conditions. Approximately 10% of the transcripts were up- or down-regulated in response to topotecan in the p53+/+ cells, whereas only 1% of the transcripts changed in the p53-/- cells, indicating that p53 has a broad effect on the transcriptional response to this stress. Individual transcripts and their relationships were analyzed using clustered image maps and by a novel two-dimensional analysis/visualization, gene expression map, in which each gene expression level is represented as a function of both the genotypic/phenotypic difference (i.e., p53 status) and the treatment effect (i.e., of topotecan dose and time of exposure). Overall, drug-induced p53 activation was associated with a coherent genetic program leading to cell cycle arrest and apoptosis. We identified novel p53-induced and DNA damage-induced genes (the proapoptotic SIVA gene and a set of transforming growth factor beta-related genes). Genes induced independently of p53 included the antiapoptotic cFLIP gene and known stress genes related to the mitogen-activated protein kinase pathway and the Fos/Jun pathway. Genes that were negatively regulated by p53 included members of the antiapoptotic protein chaperone heat shock protein 70 family. Finally, among the p53-dependent genes whose expression was independent of drug treatment was S100A4, a small Ca(2+)-binding protein that has recently been implicated in p53 binding and regulation. The new experimental design and gene expression map analysis introduced here are applicable to a wide range of studies that encompass both treatment effects and genotypic or phenotypic differences.

Protein microarrays: meeting analytical challenges for clinical applications.

Protein microarrays, one emerging class of proteomic technologies, have broad applications for discovery and quantitative analysis. A rapidly expanding use of this technology is the acquisition of information about the posttranslational modifications of proteins reflecting the activity state of signal pathways and networks, and is now employed for the analysis of biopsy samples in clinical trial research.