Transcriptomic analysis of the NCI-60 cancer cell lines

Progress in construction of mouse models to investigate the pathogenesis and immune therapy of human hematological malignancy

Hematological malignancy is a disease arisen by complicate reasons that seriously endangers human health. The research on its pathogenesis and therapies depends on the usage of animal models. Conventional animal model cannot faithfully mirror some characteristics of human features due to the evolutionary divergence, whereas the mouse models hosting human hematological malignancy are more and more applied in basic as well as translational investigations in recent years. According to the construction methods, they can be divided into different types (e.g. cell-derived xenograft (CDX) and patient-derived xenograft model (PDX) model) that have diverse characteristics and application values. In addition, a variety of strategies have been developed to improve human hematological malignant cell engraftment and differentiation in vivo. Moreover, the humanized mouse model with both functional human immune system and autologous human hematological malignancy provides a unique tool for the evaluation of the efficacy of novel immunotherapeutic drugs/approaches. Herein, we first review the evolution of the mouse model of human hematological malignancy; Then, we analyze the characteristics of different types of models and summarize the ways to improve the models; Finally, the way and value of humanized mouse model of human immune system in the immunotherapy of human hematological malignancy are discussed.

Metabolic plasticity in blast crisis-chronic myeloid leukaemia cells under hypoxia reduces the cytotoxic potency of drugs targeting mitochondria

Metabolic reprogramming (MR) influences progression of chronic myeloid leukaemia (CML) to blast crisis (BC), but metabolic programs may change transiently in a second dimension (metabolic plasticity, MP), driven by environments as hypoxia, affecting cytotoxic potency (CPot) of drugs targeting mitochondria or mitochondria-related cell stress responses (MRCSR) such as mitophagy and mitochondrial biogenesis. We assessed mitochondrial membrane potential (MMP), mitochondrial mass (MM), apoptosis, glucose uptake (GU), and CPot of arsenic trioxide (ATO), CCCP, valproic acid (VPA), vincristine (VCR), Mdivi1, and dichloroacetic acid (DCA) in CML BC cells K562 (BC-K562) under hypoxia through flow cytometry, and gene expression from GEO database. About 60% of untreated cells were killed after 72 h under hypoxia, but paradoxically, all drugs but ATO rescued cells and increased survival rates to almost 90%. Blocking mitophagy either with VCR or Mdivi1, or increasing mitochondrial biogenesis with VPA enhanced cell-survival with increased MM. DCA increased MM and rescued cells in spite of its role in activating pyruvate dehydrogenase and Krebs cycle. Cells rescued by DCA, VPA and CCCP showed decreased GU. ATO showed equal CPot in hypoxia and normoxia. MP was evidenced by differential expression of genes (DEG) under hypoxia related to Krebs cycle, lipid synthesis, cholesterol homeostasis, mitophagy, and mitochondrial biogenesis (GSE144527). A 25-gene MP-signature of BC-K562 cells under hypoxia identified BC cases among 113 transcriptomes from CML patients (GSE4170). We concluded that hypoxic environment drove a MP change evidenced by DEG that was reflected in a paradoxical pro-survival, instead of cytotoxic, effect of drugs targeting mitochondria and MRCSR.

Pharmacogenomics of in vitro response of the NCI-60 cancer cell line panel to Indian natural products

BACKGROUND

Indian natural products have been anecdotally used for cancer treatment but with limited efficacy. To better understand their mechanism, we examined the publicly available data for the activity of Indian natural products in the NCI-60 cell line panel.

METHODS

We examined associations of molecular genomic features in the well-characterized NCI-60 cancer cell line panel with in vitro response to treatment with 75 compounds derived from Indian plant-based natural products. We analyzed expression measures for annotated transcripts, lncRNAs, and miRNAs, and protein-changing single nucleotide variants in cancer-related genes. We also examined the similarities between cancer cell line response to Indian natural products and response to reference anti-tumor compounds recorded in a U.S. National Cancer Institute (NCI) Developmental Therapeutics Program database.

RESULTS

Hierarchical clustering based on cell line response measures identified clustering of Phyllanthus and cucurbitacin products with known anti-tumor agents with anti-mitotic mechanisms of action. Curcumin and curcuminoids mostly clustered together. We found associations of response to Indian natural products with expression of multiple genes, notably including SLC7A11 involved in solute transport and ATAD3A and ATAD3B encoding mitochondrial ATPase proteins, as well as significant associations with functional single nucleotide variants, including BRAF V600E.

CONCLUSION

These findings suggest potential mechanisms of action and novel associations of in vitro response with gene expression and some cancer-related mutations that increase our understanding of these Indian natural products.

Opportunities and challenges of patient-derived models in cancer research: patient-derived xenografts, patient-derived organoid and patient-derived cells

Background

As reported, preclinical animal models differ greatly from the human body. The evaluation model may be the colossal obstacle for scientific research and anticancer drug development. Therefore, it is essential to propose efficient evaluation systems similar to clinical practice for cancer research.

Main body

While it has emerged for decades, the development of patient-derived xenografts, patient-derived organoid and patient-derived cell used to be limited. As the requirements for anticancer drug evaluation increases, patient-derived models developed rapidly recently, which is widely applied in basic research, drug development, and clinical application and achieved remarkable progress. However, there still lack systematic comparison and summarize reports for patient-derived models. In the current review, the development, applications, strengths, and challenges of patient-derived models in cancer research were characterized.

Conclusion

Patient-derived models are an indispensable approach for cancer research and human health.

Molecular genomic features associated with in vitro response of the NCI-60 cancer cell line panel to natural products

Natural products remain a significant source of anticancer chemotherapeutics. The search for targeted drugs for cancer treatment includes consideration of natural products, which may provide new opportunities for antitumor cytotoxicity as single agents or in combination therapy. We examined the association of molecular genomic features in the well-characterized NCI-60 cancer cell line panel with in vitro response to treatment with 1302 small molecules which included natural products, semisynthetic natural product derivatives, and synthetic compounds based on a natural product pharmacophore from the Developmental Therapeutics Program of the US National Cancer Institute's database. These compounds were obtained from a variety of plant, marine, and microbial species. Molecular information utilized for the analysis included expression measures for 23059 annotated transcripts, lncRNAs, and miRNAs, and data on protein-changing single nucleotide variants in 211 cancer-related genes. We found associations of expression of multiple genes including SLFN11, CYP2J2, EPHX1, GPC1, ELF3, and MGMT involved in DNA damage repair, NOTCH family members, ABC and SLC transporters, and both mutations in tyrosine kinases and BRAF V600E with NCI-60 responses to specific categories of natural products. Hierarchical clustering identified groups of natural products, which correlated with a specific mechanism of action. Specifically, several natural product clusters were associated with SLFN11 gene expression, suggesting that potential action of these compounds may involve DNA damage. The associations between gene expression or genome alterations of functionally relevant genes with the response of cancer cells to natural products provide new information about potential mechanisms of action of these identified clusters of compounds with potentially similar biological effects. This information will assist in future drug discovery and in design of new targeted cancer chemotherapy agents.

miR-10a as a therapeutic target and predictive biomarker for MDM2 inhibition in acute myeloid leukemia

Pharmacological inhibition of MDM2/4, which activates the critical tumor suppressor p53, has been gaining increasing interest as a strategy for the treatment of acute myeloid leukemia (AML). While clinical trials of MDM2 inhibitors have shown promise, responses have been confined to largely molecularly undefined patients, indicating that new biomarkers and optimized treatment strategies are needed. We previously reported that the microRNA miR-10a is strongly overexpressed in some AML, and demonstrate here that it modulates several key members of the p53/Rb network, including p53 regulator MDM4, Rb regulator RB1CC1, p21 regulator TFAP2C, and p53 itself. The expression of both miR-10a and its downstream targets were strongly predictive of MDM2 inhibitor sensitivity in cell lines, primary AML specimens, and correlated to response in patients treated with both MDM2 inhibitors and cytarabine. Furthermore, miR-10a inhibition induced synergy between MDM2 inhibitor Nutlin-3a and cytarabine in both in vitro and in vivo AML models. Mechanistically this synergism primarily occurs via the p53-mediated activation of cytotoxic apoptosis at the expense of cytoprotective autophagy. Together these findings demonstrate that miR-10a may be useful as both a biomarker to identify patients most likely to respond to cytarabine+MDM2 inhibition and also a druggable target to increase their efficacy.

Systematic Transcriptional Profiling of Responses to STAT1- and STAT3-Activating Cytokines in Different Cancer Types

Cytokines orchestrate responses to pathogens and in inflammatory processes, but they also play an important role in cancer by shaping the expression levels of cytokine response genes. Here, we conducted a large profiling study comparing miRNome and mRNA transcriptome data generated following different cytokine stimulations. Transcriptomic responses to STAT1- (IFNγ, IL-27) and STAT3-activating cytokines (IL6, OSM) were systematically compared in nine cancerous and non-neoplastic cell lines of different tissue origins (skin, liver and colon). The largest variation in our datasets was seen between cell lines of the three different tissues rather than stimuli. Notably, the variability in miRNome datasets was a lot more pronounced than in mRNA data. Our data also revealed that cells of skin, liver and colon tissues respond very differently to cytokines and that the cell signaling networks activated or silenced in response to STAT1- or STAT3-activating cytokines are specific to the tissue and the type of cytokine. However, globally, STAT1-activating cytokines had stronger effects than STAT3-inducing cytokines with most significant responses in liver cells, showing more genes upregulated and with higher fold change. A more detailed analysis of gene regulations upon cytokine stimulation in these cells provided insights into STAT1- versus STAT3-driven processes in hepatocarcinogenesis. Finally, independent component analysis revealed interconnected transcriptional networks distinct between cancer cells and their healthy counterparts.

EPS8 Facilitates Uncoating of Influenza A Virus

All viruses balance interactions between cellular machinery co-opted to support replication and host factors deployed to halt the infection. We use gene correlation analysis to perform an unbiased screen for host factors involved in influenza A virus (FLUAV) infection. Our screen identifies the cellular factor epidermal growth factor receptor pathway substrate 8 (EPS8) as the highest confidence pro-viral candidate. Knockout and overexpression of EPS8 confirm its importance in enhancing FLUAV infection and titers. Loss of EPS8 does not affect virion attachment, uptake, or fusion. Rather, our data show that EPS8 specifically functions during virion uncoating. EPS8 physically associates with incoming virion components, and subsequent nuclear import of released ribonucleoprotein complexes is significantly delayed in the absence of EPS8. Our study identifies EPS8 as a host factor important for uncoating, a crucial step of FLUAV infection during which the interface between the virus and host is still being discovered.

An Interactive Resource to Probe Genetic Diversity and Estimated Ancestry in Cancer Cell Lines

Recent work points to a lack of diversity in genomics studies from genome-wide association studies to somatic (tumor) genome analyses. Yet, population-specific genetic variation has been shown to contribute to health disparities in cancer risk and outcomes. Immortalized cancer cell lines are widely used in cancer research, from mechanistic studies to drug screening. Larger collections of cancer cell lines better represent the genomic heterogeneity found in primary tumors. Yet, the genetic ancestral origin of cancer cell lines is rarely acknowledged and often unknown. Using genome-wide genotyping data from 1,393 cancer cell lines from the Catalogue of Somatic Mutations in Cancer (COSMIC) and Cancer Cell Line Encyclopedia (CCLE), we estimated the genetic ancestral origin for each cell line. Our data indicate that cancer cell line collections are not representative of the diverse ancestry and admixture characterizing human populations. We discuss the implications of genetic ancestry and diversity of cellular models for cancer research and present an interactive tool, Estimated Cell Line Ancestry (ECLA), where ancestry can be visualized with reference populations of the 1000 Genomes Project. Cancer researchers can use this resource to identify cell line models for their studies by taking ancestral origins into consideration.

Embracing an integromic approach to tissue biomarker research in cancer: Perspectives and lessons learned

Modern approaches to biomedical research and diagnostics targeted towards precision medicine are generating 'big data' across a range of high-throughput experimental and analytical platforms. Integrative analysis of this rich clinical, pathological, molecular and imaging data represents one of the greatest bottlenecks in biomarker discovery research in cancer and other diseases. Following on from the publication of our successful framework for multimodal data amalgamation and integrative analysis, Pathology Integromics in Cancer (PICan), this article will explore the essential elements of assembling an integromics framework from a more detailed perspective. PICan, built around a relational database storing curated multimodal data, is the research tool sitting at the heart of our interdisciplinary efforts to streamline biomarker discovery and validation. While recognizing that every institution has a unique set of priorities and challenges, we will use our experiences with PICan as a case study and starting point, rationalizing the design choices we made within the context of our local infrastructure and specific needs, but also highlighting alternative approaches that may better suit other programmes of research and discovery. Along the way, we stress that integromics is not just a set of tools, but rather a cohesive paradigm for how modern bioinformatics can be enhanced. Successful implementation of an integromics framework is a collaborative team effort that is built with an eye to the future and greatly accelerates the processes of biomarker discovery, validation and translation into clinical practice.

In vitro human cell line models to predict clinical response to anticancer drugs

In vitro human cell line models have been widely used for cancer pharmacogenomic studies to predict clinical response, to help generate pharmacogenomic hypothesis for further testing, and to help identify novel mechanisms associated with variation in drug response. Among cell line model systems, immortalized cell lines such as Epstein-Barr virus (EBV)-transformed lymphoblastoid cell lines (LCLs) have been used most often to test the effect of germline genetic variation on drug efficacy and toxicity. Another model, especially in cancer research, uses cancer cell lines such as the NCI-60 panel. These models have been used mainly to determine the effect of somatic alterations on response to anticancer therapy. Even though these cell line model systems are very useful for initial screening, results from integrated analyses of multiple omics data and drug response phenotypes using cell line model systems still need to be confirmed by functional validation and mechanistic studies, as well as validation studies using clinical samples. Future models might include the use of patient-specific inducible pluripotent stem cells and the incorporation of 3D culture which could further optimize in vitro cell line models to improve their predictive validity.

Translational genomics in cancer research: converting profiles into personalized cancer medicine

Cancer genomics is a rapidly growing discipline in which the genetic molecular basis of malignancy is studied at the scale of whole genomes. While the discipline has been successful with respect to identifying specific oncogenes and tumor suppressors involved in oncogenesis, it is also challenging our approach to managing patients suffering from this deadly disease. Specifically cancer genomics is driving clinical oncology to take a more molecular approach to diagnosis, prognostication, and treatment selection. We review here recent work undertaken in cancer genomics with an emphasis on translation of genomic findings. Finally, we discuss scientific challenges and research opportunities emerging from findings derived through analysis of tumors with high-depth sequencing.

High resolution copy number variation data in the NCI-60 cancer cell lines from whole genome microarrays accessible through CellMiner

Array-based comparative genomic hybridization (aCGH) is a powerful technique for detecting gene copy number variation. It is generally considered to be robust and convenient since it measures DNA rather than RNA. In the current study, we combine copy number estimates from four different platforms (Agilent 44 K, NimbleGen 385 K, Affymetrix 500 K and Illumina Human1Mv1_C) to compute a reliable, high-resolution, easy to understand output for the measure of copy number changes in the 60 cancer cells of the NCI-DTP (the NCI-60). We then relate the results to gene expression. We explain how to access that database using our CellMiner web-tool and provide an example of the ease of comparison with transcript expression, whole exome sequencing, microRNA expression and response to 20,000 drugs and other chemical compounds. We then demonstrate how the data can be analyzed integratively with transcript expression data for the whole genome (26,065 genes). Comparison of copy number and expression levels shows an overall medium high correlation (median r = 0.247), with significantly higher correlations (median r = 0.408) for the known tumor suppressor genes. That observation is consistent with the hypothesis that gene loss is an important mechanism for tumor suppressor inactivation. An integrated analysis of concurrent DNA copy number and gene expression change is presented. Limiting attention to focal DNA gains or losses, we identify and reveal novel candidate tumor suppressors with matching alterations in transcript level.

Integrated analysis of transcriptomics and metabolomics profiles

BACKGROUND

Integrated analysis of transcriptomics and metabolomics data has the potential greatly to increase our understanding of metabolic networks and biological systems leading to various potential clinical applications.

OBJECTIVE

The aim is to present different applications as well as analysis tools utilized for the parallel study of gene and metabolite expressions.

METHODS

Publications dealing with integrated analysis of gene and metabolite expression data as well as publications describing tools that can be used for integrated analysis are reviewed.

RESULTS/CONCLUSION

The full benefit of integrated analysis can be achieved only if data from all utilized methods are treated equally by multidisciplinary teams. This approach can lead to advances in functional genomics with possible clinical developments in diagnostics and improved drug target selection.

Entry tropism of BK and Merkel cell polyomaviruses in cell culture

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.

Permissivity of the NCI-60 cancer cell lines to oncolytic Vaccinia Virus GLV-1h68

Background

Oncolytic viral therapy represents an alternative therapeutic strategy for the treatment of cancer. We previously described GLV-1h68, a modified Vaccinia Virus with exclusive tropism for tumor cells, and we observed a cell line-specific relationship between the ability of GLV-1h68 to replicate in vitro and its ability to colonize and eliminate tumor in vivo.

Methods

In the current study we surveyed the in vitro permissivity to GLV-1h68 replication of the NCI-60 panel of cell lines. Selected cell lines were also tested for permissivity to another Vaccinia Virus and a vesicular stomatitis virus (VSV) strain. In order to identify correlates of permissity to viral infection, we measured transcriptional profiles of the cell lines prior infection.

Results

We observed highly heterogeneous permissivity to VACV infection amongst the cell lines. The heterogeneity of permissivity was independent of tissue with the exception of B cell derivation. Cell lines were also tested for permissivity to another Vaccinia Virus and a vesicular stomatitis virus (VSV) strain and a significant correlation was found suggesting a common permissive phenotype. While no clear transcriptional pattern could be identified as predictor of permissivity to infection, some associations were observed suggesting multifactorial basis permissivity to viral infection.

Conclusions

Our findings have implications for the design of oncolytic therapies for cancer and offer insights into the nature of permissivity of tumor cells to viral infection.

G4 motifs correlate with promoter-proximal transcriptional pausing in human genes

The RNA Pol II transcription complex pauses just downstream of the promoter in a significant fraction of human genes. The local features of genomic structure that contribute to pausing have not been defined. Here, we show that genes that pause are more G-rich within the region flanking the transcription start site (TSS) than RefSeq genes or non-paused genes. We show that enrichment of binding motifs for common transcription factors, such as SP1, may account for G-richness upstream but not downstream of the TSS. We further show that pausing correlates with the presence of a GrIn1 element, an element bearing one or more G4 motifs at the 5′-end of the first intron, on the non-template DNA strand. These results suggest potential roles for dynamic G4 DNA and G4 RNA structures in cis-regulation of pausing, and thus genome-wide regulation of gene expression, in human cells.

Exon array analyses across the NCI-60 reveal potential regulation of TOP1 by transcription pausing at guanosine quartets in the first intron

Because topoisomerase 1 (TOP1) is critical for the relaxation of DNA supercoils and because it is the target for the anticancer activity of camptothecins, we assessed TOP1 transcript levels in the 60 cell line panel (the NCI-60) of the National Cancer Institute's anticancer drug screen. TOP1 expression levels varied over a 5.7-fold range across the NCI-60. HCT116 colon and MCF-7 breast cancer cells were the highest expressers; SK-MEL-28 melanoma and HS578T breast carcinoma cells were the lowest. TOP1 mRNA expression was highly correlated with Top1 protein levels, indicating that TOP1 transcripts could be conveniently used to monitor Top1 protein levels and activity in tissues. Assessment of the TOP1 locus by array comparative genomic hybridization across the NCI-60 showed copy numbers ranging from 1.71 to 4.13 and a statistically significant correlation with TOP1 transcript levels (P < 0.01). Further analyses of TOP1 expression on an exon-specific basis revealed that exon 1 expression was generally higher and less variable than expression of the other exons, suggesting some form of transcriptional pausing regulation between exons 1 and 2. Accordingly, we found the presence of multiple evolutionarily conserved potential G-quadruplex-forming sequences in the first TOP1 intron. Physicochemical tests for actual quadruplex formation by several of those sequences yielded quadruplex formation for two of them and duplex formation for one. The observations reported here suggest the hypothesis that there is a conserved negative transcription regulator within intron 1 of the TOP1 gene associated with a quadruplex-prone region.

Pharmacogenomics of platinum-based chemotherapy in NSCLC

NSCLC is the leading cause of cancer-related death in the US. Patients with NSCLC are mostly treated with platinum-based chemotherapy, often in combination with radiation therapy. However, the development of chemo-resistance is a major hurdle limiting treatment success. In this review, we summarize the current understanding of the genetic factors modulating chemoresistance to platinum chemotherapeutics and their association with clinical outcomes for NSCLC patients. We focus on candidate pathways responsible for drug influx and efflux, metabolism and detoxification, DNA damage repair, and other downstream cellular processes that modulate the effect of platinum-based therapy. We also discuss the application of pathway-based polygenic and genome-wide approaches in identifying genetic factors involved in NSCLC clinical outcomes. Overall, current studies have shown that the effects of each individual polymorphism on clinical outcomes are modest suggesting that a more comprehensive approach that incorporates polygenetic, phenotypic, epidemiologic and clinical variables will be necessary to predict prognosis for NSCLC patients receiving platinum-based chemotherapeutics.

Pharmacogenomics: a systems approach

Pharmacogenetics and pharmacogenomics involve the study of the role of inheritance in individual variation in drug response, a phenotype that varies from potentially life-threatening adverse drug reactions to equally serious lack of therapeutic efficacy. Pharmacogenetics-pharmacogenomics represents a major component of the movement to 'individualized medicine'. Pharmacogenetic studies originally focused on monogenic traits, often involving genetic variation in drug metabolism. However, contemporary studies increasingly involve entire 'pathways' that include both pharmacokinetics (PKs)--factors that influence the concentration of a drug reaching its target(s)--and pharmacodynamics (PDs), factors associated with the drug target(s), as well as genome-wide approaches. The convergence of advances in pharmacogenetics with rapid developments in human genomics has resulted in the evolution of pharmacogenetics into pharmacogenomics. At the same time, studies of drug response are expanding beyond genomics to encompass pharmacotranscriptomics and pharmacometabolomics to become a systems-based discipline. This discipline is also increasingly moving across the 'translational interface' into the clinic and is being incorporated into the drug development process and governmental regulation of that process. The article will provide an overview of the development of pharmacogenetics-pharmacogenomics, the scientific advances that have contributed to the continuing evolution of this discipline, the incorporation of transcriptomic and metabolomic data into attempts to understand and predict variation in drug response phenotypes as well as challenges associated with the 'translation' of this important aspect of biomedical science into the clinic.

Therapeutically targeting ErbB3: a key node in ligand-induced activation of the ErbB receptor-PI3K axis

The signaling network downstream of the ErbB family of receptors has been extensively targeted by cancer therapeutics; however, understanding the relative importance of the different components of the ErbB network is nontrivial. To explore the optimal way to therapeutically inhibit combinatorial, ligand-induced activation of the ErbB-phosphatidylinositol 3-kinase (PI3K) axis, we built a computational model of the ErbB signaling network that describes the most effective ErbB ligands, as well as known and previously unidentified ErbB inhibitors. Sensitivity analysis identified ErbB3 as the key node in response to ligands that can bind either ErbB3 or EGFR (epidermal growth factor receptor). We describe MM-121, a human monoclonal antibody that halts the growth of tumor xenografts in mice and, consistent with model-simulated inhibitor data, potently inhibits ErbB3 phosphorylation in a manner distinct from that of other ErbB-targeted therapies. MM-121, a previously unidentified anticancer therapeutic designed using a systems approach, promises to benefit patients with combinatorial, ligand-induced activation of the ErbB signaling network that are not effectively treated by current therapies targeting overexpressed or mutated oncogenes.

CellMiner: a relational database and query tool for the NCI-60 cancer cell lines

Background

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.

Description

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.

Conclusion

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 .

Pharmacogenomics: candidate gene identification, functional validation and mechanisms

Pharmacogenetics is the study of the role of inheritance in variation in drug response phenotypes. Those phenotypes can range from life-threatening adverse drugs reactions at one end of the spectrum to equally serious lack of therapeutic efficacy at the other. Over the past half century, pharmacogenetics has--like all of medical genetics--evolved from a discipline with a focus on monogenetic traits to become pharmacogenomics, with a genome-wide perspective. This article will briefly review recent examples of the application of genome-wide techniques to clinical pharmacogenomic studies and to pharmacogenomic model systems that vary from cell line-based model systems to yeast gene deletion libraries. Functional validation of candidate genes and the use of genome-wide techniques to gain mechanistic insights will be emphasized for the establishment of biological plausibility and as essential follow-up steps after the identification of candidate genes.

Somatic pharmacogenomics in cancer

Many of the initial examples of the clinical utility of pharmacogenetics were elucidated in the field of oncology. Those examples were largely based on the existence of germline genetic variation that influences the metabolism of cytotoxic drugs. However, with the development of kinase inhibitors, drugs designed to preferentially target altered proteins driving oncogenesis, pharmacogenetics in cancer has shifted to understanding the somatic differences that determine response to these targeted agents. It is becoming increasingly clear that understanding the molecular genetics of cancer will lead to the further development of targeted therapeutics. Therefore, it is imperative that pharmacogenomics researchers understand the motivations and challenges of developing targeted therapies to treat cancer as a paradigm for personalized medicine. However, much of the discussion in the pharmacogenomics community in cancer is still largely focused on the germline variants as predictors of drug toxicity. In light of that fact, this review presents a detailed discussion of the development of commonly used targeted therapies for the treatment of hematological and solid tumors, the somatic mutations that determine response to those therapies, and the mechanisms of drug resistance.

MicroRNA expression profiles for the NCI-60 cancer cell panel

Advances in the understanding of cancer cell biology and response to drug treatment have benefited from new molecular technologies and methods for integrating information from multiple sources. The NCI-60, a panel of 60 diverse human cancer cell lines, has been used by the National Cancer Institute to screen >100,000 chemical compounds and natural product extracts for anticancer activity. The NCI-60 has also been profiled for mRNA and protein expression, mutational status, chromosomal aberrations, and DNA copy number, generating an unparalleled public resource for integrated chemogenomic studies. Recently, microRNAs have been shown to target particular sets of mRNAs, thereby preventing translation or accelerating mRNA turnover. To complement the existing NCI-60 data sets, we have measured expression levels of microRNAs in the NCI-60 and incorporated the resulting data into the CellMiner program package for integrative analysis. Cell line groupings based on microRNA expression were generally consistent with tissue type and with cell line clustering based on mRNA expression. However, mRNA expression seemed to be somewhat more informative for discriminating among tissue types than was microRNA expression. In addition, we found that there does not seem to be a significant correlation between microRNA expression patterns and those of known target transcripts. Comparison of microRNA expression patterns and compound potency patterns showed significant correlations, suggesting that microRNAs may play a role in chemoresistance. Combined with gene expression and other biological data using multivariate analysis, microRNA expression profiles may provide a critical link for understanding mechanisms involved in chemosensitivity and chemoresistance.

Comparative DNA methylation analysis in normal and tumour tissues and in cancer cell lines using differential methylation hybridisation

Immortalized human cancer cell lines are widely used as tools and model systems in cancer research but their authenticity with regard to primary tissues remains a matter of debate. We have used differential methylation hybridisation to obtain comparative methylation profiles from normal and tumour tissues of lung and colon, and permanent cancer cell lines originally derived from these tissues. Average methylation differences only larger than 25% between sample groups were considered for the profiles and with this criterion approximately 1000 probesets, around 2% of the sites represented on the array, indicated differential methylation between normal lung and primary lung cancer tissue, and approximately 700 probesets between normal colon and primary colon cancer tissue. Both hyper- and hypomethylation was found to differentiate normal tissue from cancer tissue. The profiles obtained from these tissue comparisons were found to correspond largely to those from the corresponding cancer cell lines, indicating that the cell lines represent the methylation pattern of the primary tissue rather well. Moreover, the cancer specific profiles were found to be very similar for the two tumour types studied. Tissue specific differential methylation between lung and colon tissues, in contrast, was found to be preserved to a larger extent only in the malignant tissue, but was not preserved well in the cancer cell lines studied. Overall, our data therefore provide further evidence that permanent cell lines are good model systems for cancer specific methylation patterns, but deviate with regard to tissue-specific methylation.

Transcript and protein expression profiles of the NCI-60 cancer cell panel: an integromic microarray study

To evaluate the utility of transcript profiling for prediction of protein expression levels, we compared profiles across the NCI-60 cancer cell panel, which represents nine tissues of origin. For that analysis, we present here two new NCI-60 transcript profile data sets (A based on Affymetrix HG-U95 and HG-U133A chips; Affymetrix, Santa Clara, CA) and one new protein profile data set (based on reverse-phase protein lysate arrays). The data sets are available online at http://discover.nci.nih.gov in the CellMiner program package. Using the new transcript data in combination with our previously published cDNA array and Affymetrix HU6800 data sets, we first developed a "consensus set" of transcript profiles based on the four different microarray platforms. Using that set, we found that 65% of the genes showed statistically significant transcript-protein correlation, and the correlations were generally higher than those reported previously for panels of mammalian cells. Using the predictive analysis of microarray nearest shrunken centroid algorithm for functional prediction of tissue of origin, we then found that (a) the consensus mRNA set did better than did data from any of the individual mRNA platforms and (b) the protein data seemed to do somewhat better (P = 0.027) on a gene-for-gene basis in this particular study than did the consensus mRNA data, but both did well. Analysis based on the Gene Ontology showed protein levels of structure-related genes to be well predicted by mRNA levels (mean r = 0.71). Because the transcript-based technologies are more mature and are currently able to assess larger numbers of genes at one time, they continue to be useful, even when the ultimate aim is information about proteins.

AbMiner: a bioinformatic resource on available monoclonal antibodies and corresponding gene identifiers for genomic, proteomic, and immunologic studies

Background

Monoclonal antibodies are used extensively throughout the biomedical sciences for detection of antigens, either in vitro or in vivo. We, for example, have used them for quantitation of proteins on "reverse-phase" protein lysate arrays. For those studies, we quality-controlled > 600 available monoclonal antibodies and also needed to develop precise information on the genes that encode their antigens. Translation among the various protein and gene identifier types proved non-trivial because of one-to-many and many-to-one relationships. To organize the antibody, protein, and gene information, we initially developed a relational database in Filemaker for our own use. When it became apparent that the information would be useful to many other researchers faced with the need to choose or characterize antibodies, we developed it further as AbMiner, a fully relational web-based database under MySQL, programmed in Java.

Description

AbMiner is a user-friendly, web-based relational database of information on > 600 commercially available antibodies that we validated by Western blot for protein microarray studies. It includes many types of information on the antibody, the immunogen, the vendor, the antigen, and the antigen's gene. Multiple gene and protein identifier types provide links to corresponding entries in a variety of other public databases, including resources for phosphorylation-specific antibodies. AbMiner also includes our quality-control data against a pool of 60 diverse cancer cell types (the NCI-60) and also protein expression levels for the NCI-60 cells measured using our high-density "reverse-phase" protein lysate microarrays for a selection of the listed antibodies. Some other available database resources give information on antibody specificity for one or a couple of cell types. In contrast, the data in AbMiner indicate specificity with respect to the antigens in a pool of 60 diverse cell types from nine different tissues of origin.

Conclusion

AbMiner is a relational database that provides extensive information from our own laboratory and other sources on more than 600 available antibodies and the genes that encode the antibodies' antigens. The data will be made freely available at

Gene expression profiling in the discovery, optimization and development of novel drugs: one universal screening platform

With recent advances in robotics and high-content screening and analysis methods, transcriptional profiling can now be utilized as a comprehensive forward chemical genomics platform for drug discovery, lead selection and lead optimization. It can be used to define the state of a cell on the basis of gene networks, and to search for drugs that can shift cellular states in a manner predicted at the genome level to be therapeutically beneficial. The treatment of cells with compounds produces transcriptional 'fingerprints' that reveal mechanism-of-action, and enable discrimination between individual compounds based on drug behaviors important to all phases of drug discovery and development.

Correlation of PIK3Ca mutations with gene expression and drug sensitivity in NCI-60 cell lines

The gene that encodes the alpha-isoform of phosphatidylinositol 3-kinase (PIK3Ca) is frequently mutated in human cancers. We profiled the mutation status of the PIK3Ca gene in the National Cancer Institute (NCI)-60 panel of human cancer cell lines maintained by the Developmental Therapeutics Program of the NCI. Mutation hotspots on the gene were PCR amplified and sequenced, and the trace data were analyzed with software designed to detect mutations. Seven of the cell lines tested have PIK3Ca mutations: two lines derived from breast cancer, two from colon cancer, two from ovarian cancer, and one from lung cancer. BRAF and EGFR genes were normal in the PIK3Ca mutant lines. Two of the cell lines with mutant PIK3Ca also have a mutant version of the KRAS gene. The mutation status was correlated with array-based gene expression that is publicly available for the NCI-60 cell lines. We found increased expression levels for estrogen receptor (ER) and ERBB2 in PIK3Ca mutant lines. The PIK3Ca mutation status was also correlated with compound screening data for the cell lines. PIK3Ca-mutant cell lines were relatively more sensitive than PIK3Ca-normal cell lines to the ER inhibitor tamoxifen and the AKT inhibitor triciribine, among other compounds. The results provide insights into the role of mutant PIK3Ca in oncogenic signaling and allow preliminary identification of novel targets for therapeutic intervention in cancers harboring PIK3Ca mutations.

HLA class I and II genotype of the NCI-60 cell lines

Sixty cancer cell lines have been extensively characterized and used by the National Cancer Institute's Developmental Therapeutics Program (NCI-60) since the early 90's as screening tools for anti-cancer drug development. An extensive database has been accumulated that could be used to select individual cells lines for specific experimental designs based on their global genetic and biological profile. However, information on the human leukocyte antigen (HLA) genotype of these cell lines is scant and mostly antiquated since it was derived from serological typing. We, therefore, re-typed the NCI-60 panel of cell lines by high-resolution sequence-based typing. This information may be used to: 1) identify and verify the identity of the same cell lines at various institutions; 2) check for possible contaminant cell lines in culture; 3) adopt individual cell lines for experiments in which knowledge of HLA molecule expression is relevant. Since genome-based typing does not guarantee actual surface protein expression, further characterization of relevant cell lines should be entertained to verify surface expression in experiments requiring correct antigen presentation.

Workshop on Cancer Biometrics: Identifying Biomarkers and Surrogates of Cancer in Patients

The current excitement about molecular targeted therapies has driven much of the recent dialog in cancer diagnosis and treatment. Particularly in the biologic therapy of cancer, identifiable antigenic T-cell targets restricted by MHC molecules and the related novel stress molecules such as MICA/B and Letal allow a degree of precision previously unknown in cancer therapy. We have previously held workshops on immunologic monitoring and angiogenesis monitoring. This workshop was designed to discuss the state of the art in identification of biomarkers and surrogates of tumor in patients with cancer, with particular emphasis on assays within the blood and tumor. We distinguish this from immunologic monitoring in the sense that it is primarily a measure of the tumor burden as opposed to the immune response to it. Recommendations for intensive investigation and targeted funding to enable such strategies were developed in seven areas: genomic analysis; detection of molecular markers in peripheral blood and lymph node by tumor capture and RT-PCR; serum, plasma, and tumor proteomics; immune polymorphisms; high content screening using flow and imaging cytometry; immunohistochemistry and tissue microarrays; and assessment of immune infiltrate and necrosis in tumors. Concrete recommendations for current application and enabling further development in cancer biometrics are summarized. This will allow a more informed, rapid, and accurate assessment of novel cancer therapies.

Using microarrays to predict resistance to chemotherapy in cancer patients

Chemotherapy resistance remains a major obstacle to successful treatment and better outcome in cancer patients. The advent of whole genome experimental strategies, such as DNA microarrays, has transformed the way researchers approach cancer research. There is considerable hope that microarray technology will lead to the identification of new targets for therapeutic intervention, a better understanding of the disease process, and, ultimately, to higher survival rates and more personalized medicine. The question at hand is what is the best approach to apply these new technologies to the study of anticancer drug resistance, and how can the results obtained in the laboratory be quickly moved to a clinical setting? This review offers an overview of the microarray technology, including its recently associated strategies, such as array comparative genomic hybridization and promoter arrays. It also highlights some recent examples of microarray studies, which represent a first step toward a better understanding of drug resistance in cancer and, ultimately, personalized medicine.

Foreword: from the TRANSCRIPTOME conferences to the SYSTEMOSCOPE international consortium

This thematic issue issue of the Comptes rendus Biologies contains review articles, original papers and conference reports presented at the first two TRANSCRIPTOME conferences From Functional Genomics to Systems Biology and IMAGE Consortium Invitational workshops (Paris, November 2000 and Seattle, March 2002), and discussed during the inaugural meetings of the SYSTEMOSCOPE International Consortium (Paris, June 2003). We describe the founding principles, missions, working plan and policy for partnership and industrial development of SYSTEMOSCOPE to promote the study of the complexity of biological systems by integrating scientific, medical, ethical and economic issues in implementation of interdisciplinary projects for human health.