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

CDKN2A copy number alteration in bladder cancer: Integrative analysis in patient-derived xenografts and cancer patients

Bladder cancer (BlCa) is an extensively heterogeneous disease that leads to great variability in tumor evolution scenarios and lifelong patient surveillance, emphasizing the need for modern, minimally invasive precision medicine. Here, we explored the clinical significance of copy number alterations (CNAs) in BlCa. CNA profiling was performed in 15 patient-derived xenografts (PDXs) and validated in The Cancer Genome Atlas BlCa (TCGA-BLCA; n = 408) and Lindgren et al. (n = 143) cohorts. CDKN2A copy number loss was identified as the most frequent CNA in bladder tumors, associated with reduced CDKN2A expression, tumors of a papillary phenotype, and prolonged PDX survival. The study's screening cohort consisted of 243 BlCa patients, and CDKN2A copy number was assessed in genomic DNA and cell-free DNA (cfDNA) from 217 tumors and 189 pre-treatment serum samples, respectively. CDKN2A copy number loss was correlated with superior disease-free and progression-free survival of non-muscle-invasive BlCa (NMIBC) patients. Moreover, a higher CDKN2A index (CDKN2A/LEP ratio) in pre-treatment cfDNA was associated with advanced tumor stage and grade and short-term NMIBC progression to invasive disease, while multivariate models fitted for CDKN2A index in pre-treatment cfDNA offered superior risk stratification of T1/high-grade and EORTC high-risk patients, enhancing prediction of treatment outcome. CDKN2A copy number status could serve as a minimally invasive tool to improve risk stratification and support personalized prognosis in BlCa.

CellMinerCDB: NCATS Is a Web-Based Portal Integrating Public Cancer Cell Line Databases for Pharmacogenomic Explorations

Major advances have been made in the field of precision medicine for treating cancer. However, many open questions remain that need to be answered to realize the goal of matching every patient with cancer to the most efficacious therapy. To facilitate these efforts, we have developed CellMinerCDB: National Center for Advancing Translational Sciences (NCATS; https://discover.nci.nih.gov/rsconnect/cellminercdb_ncats/), which makes available activity information for 2,675 drugs and compounds, including multiple nononcology drugs and 1,866 drugs and compounds unique to the NCATS. CellMinerCDB: NCATS comprises 183 cancer cell lines, with 72 unique to NCATS, including some from previously understudied tissues of origin. Multiple forms of data from different institutes are integrated, including single and combination drug activity, DNA copy number, methylation and mutation, transcriptome, protein levels, histone acetylation and methylation, metabolites, CRISPR, and miscellaneous signatures. Curation of cell lines and drug names enables cross-database (CDB) analyses. Comparison of the datasets is made possible by the overlap between cell lines and drugs across databases. Multiple univariate and multivariate analysis tools are built-in, including linear regression and LASSO. Examples have been presented here for the clinical topoisomerase I (TOP1) inhibitors topotecan and irinotecan/SN-38. This web application provides both substantial new data and significant pharmacogenomic integration, allowing exploration of interrelationships.

SIGNIFICANCE

CellMinerCDB: NCATS provides activity information for 2,675 drugs in 183 cancer cell lines and analysis tools to facilitate pharmacogenomic research and to identify determinants of response.

A Validation Framework for Somatic Copy Number Detection in Targeted Sequencing Panels

Somatic copy number alterations (SCNAs) in tumors are clinically significant diagnostic, prognostic, and predictive biomarkers. SCNA detection from targeted next-generation sequencing panels is increasingly common in clinical practice; however, detailed descriptions of optimization and validation of SCNA pipelines for small targeted panels are limited. This study describes the validation and implementation of a tumor-only SCNA pipeline using CNVkit, augmented with custom modules and optimized for clinical implementation by testing reference materials and clinical tumor samples with different classes of copy number variation (CNV; amplification, single copy loss, and biallelic loss). Using wet-bench and in silico methods, various parameters impacting CNV calling, including assay-intrinsic variables (establishment of normal reference and sequencing coverage), sample-intrinsic variables (tumor purity and sample quality), and CNV algorithm-intrinsic variables (bin size), were optimized. The pipeline was trained and tested on an optimization cohort and validated using an independent cohort with a sensitivity and specificity of 100% and 93%, respectively. Using custom modules, intragenic CNVs with breakpoints within tumor suppressor genes were uncovered. Using the validated pipeline, re-analysis of 28 pediatric solid tumors that had been previously profiled for mutations identified SCNAs in 86% (24/28) samples, with 46% (13/28) samples harboring findings of potential clinical relevance. Our report highlights the importance of rigorous establishment of performance characteristics of SCNA pipelines and presents a detailed validation framework for optimal SCNA detection in targeted sequencing panels.

Landscape of allele-specific transcription factor binding in the human genome

Sequence variants in gene regulatory regions alter gene expression and contribute to phenotypes of individual cells and the whole organism, including disease susceptibility and progression. Single-nucleotide variants in enhancers or promoters may affect gene transcription by altering transcription factor binding sites. Differential transcription factor binding in heterozygous genomic loci provides a natural source of information on such regulatory variants. We present a novel approach to call the allele-specific transcription factor binding events at single-nucleotide variants in ChIP-Seq data, taking into account the joint contribution of aneuploidy and local copy number variation, that is estimated directly from variant calls. We have conducted a meta-analysis of more than 7 thousand ChIP-Seq experiments and assembled the database of allele-specific binding events listing more than half a million entries at nearly 270 thousand single-nucleotide polymorphisms for several hundred human transcription factors and cell types. These polymorphisms are enriched for associations with phenotypes of medical relevance and often overlap eQTLs, making candidates for causality by linking variants with molecular mechanisms. Specifically, there is a special class of switching sites, where different transcription factors preferably bind alternative alleles, thus revealing allele-specific rewiring of molecular circuitry.

Integrative pan cancer analysis reveals epigenomic variation in cancer type and cell specific chromatin domains

Epigenetic mechanisms contribute to the initiation and development of cancer, and epigenetic variation promotes dynamic gene expression patterns that facilitate tumor evolution and adaptation. While the NCI-60 panel represents a diverse set of human cancer cell lines that has been used to screen chemical compounds, a comprehensive epigenomic atlas of these cells has been lacking. Here, we report an integrative analysis of 60 human cancer epigenomes, representing a catalog of activating and repressive histone modifications. We identify genome-wide maps of canonical sharp and broad H3K4me3 domains at promoter regions of tumor suppressors, H3K27ac-marked conventional enhancers and super enhancers, and widespread inter-cancer and intra-cancer specific variability in H3K9me3 and H4K20me3-marked heterochromatin domains. Furthermore, we identify features of chromatin states, including chromatin state switching along chromosomes, correlation of histone modification density with genetic mutations, DNA methylation, enrichment of DNA binding motifs in regulatory regions, and gene activity and inactivity. These findings underscore the importance of integrating epigenomic maps with gene expression and genetic variation data to understand the molecular basis of human cancer. Our findings provide a resource for mining epigenomic maps of human cancer cells and for identifying epigenetic therapeutic targets.

CellMiner Cross-Database (CellMinerCDB) version 1.2: Exploration of patient-derived cancer cell line pharmacogenomics

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.

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.

A multiple genomic data fused SF2 prediction model, signature identification, and gene regulatory network inference for personalized radiotherapy

Radiotherapy is one of the most important cancer treatments, but its response varies greatly among individual patients. Therefore, the prediction of radiosensitivity, identification of potential signature genes, and inference of their regulatory networks are important for clinical and oncological reasons. Here, we proposed a novel multiple genomic fused partial least squares deep regression method to simultaneously analyze multi-genomic data. Using 60 National Cancer Institute cell lines as examples, we aimed to identify signature genes by optimizing the radiosensitivity prediction model and uncovering regulatory relationships. A total of 113 signature genes were selected from more than 20,000 genes. The root mean square error of the model was only 0.0025, which was much lower than previously published results, suggesting that our method can predict radiosensitivity with the highest accuracy. Additionally, our regulatory network analysis identified 24 highly important ‘hub’ genes. The data analysis workflow we propose provides a unified and computational framework to harness the full potential of large-scale integrated cancer genomic data for integrative signature discovery. Furthermore, the regression model, signature genes, and their regulatory network should provide a reliable quantitative reference for optimizing personalized treatment options, and may aid our understanding of cancer progress mechanisms.

A profile of 8-oxo-dGTPase activities in the NCI-60 human cancer panel: Meta-analytic insight into the regulation and role of MTH1 (NUDT1) gene expression in carcinogenesis

We measured the specific 8-oxo-dGTPase activity profile of the NCI-60 panel of malignant cell lines, and MTH1 protein levels in a subset of 16 lines. Their 8-oxo-dGTPase activity was compared to twelve publicly accessible MTH1 mRNA expression data bases and their cross-consistency was analyzed. 8-oxo-dGTPase and MTH1 protein levels in these cell lines are generally, but not always, mainly determined by MTH1 mRNA expression levels. The aneuploidy number of MTH1 gene copies only slightly affects its mRNA expression levels. By using the data mining platforms Compare and CellMiner, our 8-oxo-dGTPase profile was compared to five global gene expression datasets to identify genes whose expression levels are directly or inversely associated with 8-oxo-dGTPase. We analyzed effects of SNP within MTH1 on MTH1 mRNA level and enzyme activity. Similar association analysis was performed for five microRNA expression datasets. We identified several proteins and microRNA which might be involved in the regulation of MTH1 expression and we discuss potential mechanisms. Comparison of chemical and natural products sensitivities of the NCI-60 panel suggests seven compounds which are directly or inversely associated with 8-oxo-dGTPase. We provide an integrated picture of MTH1 expression combined from eleven consistent MTH1 mRNA and our 8-oxo-dGTPase activity NCI-60 profiles.

The landscape of genomic copy number alterations in colorectal cancer and their consequences on gene expression levels and disease outcome

Aneuploidy, the unbalanced state of the chromosome content, represents a hallmark of most solid tumors, including colorectal cancer. Such aneuploidies result in tumor specific genomic imbalances, which emerge in premalignant precursor lesions. Moreover, increasing levels of chromosomal instability have been observed in adenocarcinomas and are maintained in distant metastases. A number of studies have systematically integrated copy number alterations with gene expression changes in primary carcinomas, cell lines, and experimental models of aneuploidy. In fact, chromosomal aneuploidies target a number of genes conferring a selective advantage for the metabolism of the cancer cell. Copy number alterations not only have a positive correlation with expression changes of the majority of genes on the altered genomic segment, but also have effects on the transcriptional levels of genes genome-wide. Finally, copy number alterations have been associated with disease outcome; nevertheless, the translational applicability in clinical practice requires further studies. Here, we (i) review the spectrum of genetic alterations that lead to colorectal cancer, (ii) describe the most frequent copy number alterations at different stages of colorectal carcinogenesis, (iii) exemplify their positive correlation with gene expression levels, and (iv) discuss copy number alterations that are potentially involved in disease outcome of individual patients.

RNA Sequencing of the NCI-60: Integration into CellMiner and CellMiner CDB

CellMiner (http://discover.nci.nih.gov/cellminer) and CellMinerCDB (https://discover.nci.nih.gov/cellminercdb/) are web-based applications for mining publicly available genomic, molecular, and pharmacologic datasets of human cancer cell lines including the NCI-60, Cancer Cell Line Encyclopedia, Genomics of Drug Sensitivity in Cancer, Cancer Therapeutics Response Portal, NCI/DTP small cell lung cancer, and NCI Almanac cell line sets. Here, we introduce our RNA sequencing (RNA-seq) data for the NCI-60 and their access and integration with the other databases. Correlation to transcript microarray expression levels for identical genes and identical cell lines across CellMinerCDB demonstrates the high quality of these new RNA-seq data. We provide composite and isoform transcript expression data and demonstrate diversity in isoform composition for individual cancer- and pharmacologically relevant genes, including HRAS, PTEN, EGFR, RAD51, ALKBH2, BRCA1, ERBB2, TP53, FGFR2, and CTNND1. We reveal cell-specific differences in the overall levels of isoforms and show their linkage to expression of RNA processing and splicing genes as well as resultant alterations in cancer and pharmacologic gene sets. Gene-drug pairings linked by pathways or functions show specific correlations to isoforms compared with composite gene expression, including ALKBH2-benzaldehyde, AKT3-vandetanib, BCR-imatinib, CDK1 and 20-palbociclib, CASP1-imexon, and FGFR3-pazopanib. Loss of MUC1 20 amino acid variable number tandem repeats, which is used to elicit immune response, and the presence of the androgen receptor AR-V4 and -V7 isoforms in all NCI-60 tissue of origin types demonstrate translational relevance. In summary, we introduce RNA-seq data to our CellMiner and CellMinerCDB web applications, allowing their exploration for both research and translational purposes. SIGNIFICANCE: The current study provides RNA sequencing data for the NCI-60 cell lines made accessible through both CellMiner and CellMinerCDB and is an important pharmacogenomics resource for the field.

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.

Synthesis and Biological Evaluation of New Madecassic Acid Derivatives Targeting ERK Cascade Signaling

In the present study, a series of novel madecassic acid derivatives was synthesized and screened against the National Cancer Institute's 60 human cancer cell line panel. Among them, compounds 5, 12, and 17 displayed potent and highly differential antiproliferative activity against 80% of the tumor cells harboring the B-Raf^V600E mutation within the nanomolar range. Structure-activity analysis revealed that a 5-membered A ring containing an α,β-unsaturated aldehyde substituted at C-23 with a 2-furoyl group seems to be crucial to produce this particular growth inhibition signature. In silico analysis of the cytotoxicity pattern of these compounds identified two highly correlated clinically approved drugs with known B-Raf^V600E inhibitory activity. Follow-up analysis revealed inhibition of the ERK signaling pathway through the reduction of cellular Raf protein levels is a key mechanism of action of these compounds. In particular, 17 was the most potent compound in suppressing tumor growth of B-Raf^V600E-mutant cell lines and displayed the highest reduction of Raf protein levels among the tested compounds. Taken together, this study revealed that modifications of madecassic acid structure can provide molecules with potent anticancer activity against cell lines harboring the clinically relevant B-Raf^V600E mutation, with compound 17 identified as a promising lead for the development of new anticancer drugs.

A pan-cancer study of the transcriptional regulation of uricogenesis in human tumours: pathological and pharmacological correlates

Uric acid (UA) is the end product of the catabolism of purines, and its serum levels are commonly increased in cancer patients. We aimed to explore the transcriptional regulation of tumour uricogenesis in human tumours, and relate uricogenesis with tumour pathological and pharmacological findings. Using data from The Cancer Genome Atlas (TCGA), we analysed the expression levels of xanthine dehydrogenase (XDH) and adenine phosphoribosyltransferase (APRT), two key enzymes in UA production and the purine salvage pathway, respectively. We found large differences between tumour types and individual tumours in their expression of XDH and APRT. Variations in locus-specific DNA methylation and gene copy number correlated with the expression levels of XDH and APRT in human tumours respectively. We explored the consequences of this differential regulation of uricogenesis. Tumours with high levels of XDH mRNA were characterised by higher expression of several genes encoding pro-inflammatory and immune cytokines, and increased levels of tumour infiltration with immune cells. Finally, we studied cancer drug sensitivity using data from the National Cancer Institute-60 (NCI-60) database. A specific correlation was found between the expression levels of APRT and cell sensitivity to the chemotherapeutic agent 5-fluorouracil (5-FU). Our findings underline the existence of great differences in uricogenesis between different types of human tumours. The study of uricogenesis offers promising perspectives for the identification of clinically relevant molecular biomarkers and for tumour stratification in the therapeutic context.

Overcoming Resistance to DNA-Targeted Agents by Epigenetic Activation of Schlafen 11 (SLFN11) Expression with Class I Histone Deacetylase Inhibitors

Purpose: Schlafen 11 (SLFN11), a putative DNA/RNA helicase is a dominant genomic determinant of response to DNA-damaging agents and is frequently not expressed in cancer cells. Whether histone deacetylase (HDAC) inhibitors can be used to release SLFN11 and sensitize SLFN11-inactivated cancers to DNA-targeted agents is tested here.Experimental Design:SLFN11 expression was examined in The Cancer Genome Atlas (TCGA), in cancer cell line databases and in patients treated with romidepsin. Isogenic cells overexpressing or genetically inactivated for SLFN11 were used to investigate the effect of HDAC inhibitors on SLFN11 expression and sensitivity to DNA-damaging agents.Results:SLFN11 expression is suppressed in a broad fraction of common cancers and cancer cell lines. In cancer cells not expressing SLFN11, transfection of SLFN11 sensitized the cells to camptothecin, topotecan, hydroxyurea, and cisplatin but not to paclitaxel. SLFN11 mRNA and protein levels were strongly induced by class I (romidepsin, entinostat), but not class II (roclinostat) HDAC inhibitors in a broad panel of cancer cells. SLFN11 expression was also enhanced in peripheral blood mononuclear cells of patients with circulating cutaneous T-cell lymphoma treated with romidepsin. Consistent with the epigenetic regulation of SLFN11, camptothecin and class I HDAC inhibitors were synergistic in many of the cell lines tested.Conclusions: This study reports the prevalent epigenetic regulation of SLFN11 and the dominant stimulatory effect of HDAC inhibitors on SLFN11 expression. Our results provide a rationale for combining class I HDAC inhibitors and DNA-damaging agents to overcome epigenetic inactivation of SLFN11-mediated resistance to DNA-targeted agents. Clin Cancer Res; 24(8); 1944-53. ©2018 AACR.

Modernizing Human Cancer Risk Assessment of Therapeutics

Cancer risk assessment of therapeutics is plagued by poor translatability of rodent models of carcinogenesis. In order to overcome this fundamental limitation, new approaches are needed that enable us to evaluate cancer risk directly in humans and human-based cellular models. Our enhanced understanding of the mechanisms of carcinogenesis and the influence of human genome sequence variation on cancer risk motivates us to re-evaluate how we assess the carcinogenic risk of therapeutics. This review will highlight new opportunities for applying this knowledge to the development of a battery of human-based in vitro models and biomarkers for assessing cancer risk of novel therapeutics.

microRNAs and Acute Myeloid Leukemia Chemoresistance: A Mechanistic Overview

Up until the early 2000s, a functional role for microRNAs (miRNAs) was yet to be elucidated. With the advent of increasingly high-throughput and precise RNA-sequencing techniques within the last two decades, it has become well established that miRNAs can regulate almost all cellular processes through their ability to post-transcriptionally regulate a majority of protein-coding genes and countless other non-coding genes. In cancer, miRNAs have been demonstrated to play critical roles by modifying or controlling all major hallmarks including cell division, self-renewal, invasion, and DNA damage among others. Before the introduction of anthracyclines and cytarabine in the 1960s, acute myeloid leukemia (AML) was considered a fatal disease. In decades since, prognosis has improved substantially; however, long-term survival with AML remains poor. Resistance to chemotherapy, whether it is present at diagnosis or induced during treatment is a major therapeutic challenge in the treatment of this disease. Certain mechanisms such as DNA damage response and drug targeting, cell cycling, cell death, and drug trafficking pathways have been shown to be further dysregulated in treatment resistant cancers. miRNAs playing key roles in the emergence of these drug resistance phenotypes have recently emerged and replacement or inhibition of these miRNAs may be a viable treatment option. Herein, we describe the roles miRNAs can play in drug resistant AML and we describe miRNA-transcript interactions found within other cancer states which may be present within drug resistant AML. We describe the mechanisms of action of these miRNAs and how they can contribute to a poor overall survival and outcome as well. With the precision of miRNA mimic- or antagomir-based therapies, miRNAs provide an avenue for exquisite targeting in the therapy of drug resistant cancers.

Targeting DNA repair and replication stress in the treatment of ovarian cancer

Approximately half of high-grade serous epithelial ovarian cancers incur alterations in genes of homologous recombination (BRCA1, BRCA2, RAD51C, Fanconi anemia genes), and the rest incur alterations in other DNA repair pathways at high frequencies. Such cancer-specific gene alterations can confer selective sensitivity to DNA damaging agents such as cisplatin and carboplatin, topotecan, etoposide, doxorubicin, and gemcitabine. Originally presumed to inhibit DNA repair, PARP inhibitors that have recently been approved by the FDA for the treatment of advanced ovarian cancer also act as DNA damaging agents by inducing PARP-DNA complexes. These DNA damaging agents induce different types of DNA lesions that require various DNA repair genes for the repair, but commonly induce replication fork slowing or stalling, also referred to as replication stress. Replication stress activates DNA repair checkpoint proteins (ATR, CHK1), which prevent further DNA damage. Hence, targeting DNA repair genes or DNA repair checkpoint genes augments the anti-tumor activity of DNA damaging agents. This review describes the rational basis for using DNA repair and DNA repair checkpoint inhibitors as single agents. The review also presents the strategies combining these inhibitors with DNA damaging agents for ovarian cancer therapy based on specific gene alterations.

Phosphorylated fraction of H2AX as a measurement for DNA damage in cancer cells and potential applications of a novel assay

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.

The NCI-60 Methylome and Its Integration into CellMiner

A unique resource for systems pharmacology and genomic studies is the NCI-60 cancer cell line panel, which provides data for the largest publicly available library of compounds with cytotoxic activity (∼21,000 compounds), including 108 FDA-approved and 70 clinical trial drugs as well as genomic data, including whole-exome sequencing, gene and miRNA transcripts, DNA copy number, and protein levels. Here, we provide the first readily usable genome-wide DNA methylation database for the NCI-60, including 485,577 probes from the Infinium HumanMethylation450k BeadChip array, which yielded DNA methylation signatures for 17,559 genes integrated into our open access CellMiner version 2.0 (https://discover.nci.nih.gov/cellminer). Among new insights, transcript versus DNA methylation correlations revealed the epithelial/mesenchymal gene functional category as being influenced most heavily by methylation. DNA methylation and copy number integration with transcript levels yielded an assessment of their relative influence for 15,798 genes, including tumor suppressor, mitochondrial, and mismatch repair genes. Four forms of molecular data were combined, providing rationale for microsatellite instability for 8 of the 9 cell lines in which it occurred. Individual cell line analyses showed global methylome patterns with overall methylation levels ranging from 17% to 84%. A six-gene model, including PARP1, EP300, KDM5C, SMARCB1, and UHRF1 matched this pattern. In addition, promoter methylation of two translationally relevant genes, Schlafen 11 (SLFN11) and methylguanine methyltransferase (MGMT), served as indicators of therapeutic resistance or susceptibility, respectively. Overall, our database provides a resource of pharmacologic data that can reinforce known therapeutic strategies and identify novel drugs and drug targets across multiple cancer types. Cancer Res; 77(3); 601-12. ©2016 AACR.

A map of mobile DNA insertions in the NCI-60 human cancer cell panel

Background

The National Cancer Institute-60 (NCI-60) cell lines are among the most widely used models of human cancer. They provide a platform to integrate DNA sequence information, epigenetic data, RNA and protein expression, and pharmacologic susceptibilities in studies of cancer cell biology. Genome-wide studies of the complete panel have included exome sequencing, karyotyping, and copy number analyses but have not targeted repetitive sequences. Interspersed repeats derived from mobile DNAs are a significant source of heritable genetic variation, and insertions of active elements can occur somatically in malignancy.

Method

We used Transposon Insertion Profiling by microarray (TIP-chip) to map Long INterspersed Element-1 (LINE-1, L1) and Alu Short INterspersed Element (SINE) insertions in cancer genes in NCI-60 cells. We focused this discovery effort on annotated Cancer Gene Index loci.

Results

We catalogued a total of 749 and 2,100 loci corresponding to candidate LINE-1 and Alu insertion sites, respectively. As expected, these numbers encompass previously known insertions, polymorphisms shared in unrelated tumor cell lines, as well as unique, potentially tumor-specific insertions. We also conducted association analyses relating individual insertions to a variety of cellular phenotypes.

Conclusions

These data provide a resource for investigators with interests in specific cancer gene loci or mobile element insertion effects more broadly. Our data underscore that significant genetic variation in cancer genomes is owed to LINE-1 and Alu retrotransposons. Our findings also indicate that as large numbers of cancer genomes become available, it will be possible to associate individual transposable element insertion variants with molecular and phenotypic features of these malignancies.

Electronic supplementary material

The online version of this article (doi:10.1186/s13100-016-0078-4) contains supplementary material, which is available to authorized users.

Using Pharmacogenomic Databases for Discovering Patient-Target Genes and Small Molecule Candidates to Cancer Therapy

With multiple omics strategies being applied to several cancer genomics projects, researchers have the opportunity to develop a rational planning of targeted cancer therapy. The investigation of such numerous and diverse pharmacogenomic datasets is a complex task. It requires biological knowledge and skills on a set of tools to accurately predict signaling network and clinical outcomes. Herein, we describe Web-based in silico approaches user friendly for exploring integrative studies on cancer biology and pharmacogenomics. We briefly explain how to submit a query to cancer genome databases to predict which genes are significantly altered across several types of cancers using CBioPortal. Moreover, we describe how to identify clinically available drugs and potential small molecules for gene targeting using CellMiner. We also show how to generate a gene signature and compare gene expression profiles to investigate the complex biology behind drug response using Connectivity Map. Furthermore, we discuss on-going challenges, limitations and new directions to integrate molecular, biological and epidemiological information from oncogenomics platforms to create hypothesis-driven projects. Finally, we discuss the use of Patient-Derived Xenografts models (PDXs) for drug profiling in vivo assay. These platforms and approaches are a rational way to predict patient-targeted therapy response and to develop clinically relevant small molecules drugs.

Understanding the Genetic Mechanisms of Cancer Drug Resistance Using Genomic Approaches

A major obstacle in precision cancer medicine is the inevitable resistance to targeted therapies. Tremendous effort and progress has been made over the past few years to understand the biochemical and genetic mechanisms underlying drug resistance, with the goal to eventually overcome such daunting challenges. Diverse mechanisms, such as secondary mutations, oncogene bypass, and epigenetic alterations, can all lead to drug resistance, and the number of known involved genes is growing rapidly, thus providing many possibilities to overcome resistance. The finding of these mechanisms and genes invariably requires the application of genomic and functional genomic approaches to tumors or cancer models. In this review, we briefly highlight the major drug-resistance mechanisms known today, and then focus primarily on the technological approaches leading to the advancement of this field.

Current dichotomy between traditional molecular biological and omic research in cancer biology and pharmacology

There is currently a split within the cancer research community between traditional molecular biological hypothesis-driven and the more recent “omic” forms or research. While the molecular biological approach employs the tried and true single alteration-single response formulations of experimentation, the omic employs broad-based assay or sample collection approaches that generate large volumes of data. How to integrate the benefits of these two approaches in an efficient and productive fashion remains an outstanding issue. Ideally, one would merge the understandability, exactness, simplicity, and testability of the molecular biological approach, with the larger amounts of data, simultaneous consideration of multiple alterations, consideration of genes both of known interest along with the novel, cross-sample comparisons among cell lines and patient samples, and consideration of directed questions while simultaneously gaining exposure to the novel provided by the omic approach. While at the current time integration of the two disciplines remains problematic, attempts to do so are ongoing, and will be necessary for the understanding of the large cell line screens including the Developmental Therapeutics Program’s NCI-60, the Broad Institute’s Cancer Cell Line Encyclopedia, and the Wellcome Trust Sanger Institute’s Cancer Genome Project, as well as the the Cancer Genome Atlas clinical samples project. Going forward there is significant benefit to be had from the integration of the molecular biological and the omic forms or research, with the desired goal being improved translational understanding and application.

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.

Public data and open source tools for multi-assay genomic investigation of disease

Molecular interrogation of a biological sample through DNA sequencing, RNA and microRNA profiling, proteomics and other assays, has the potential to provide a systems level approach to predicting treatment response and disease progression, and to developing precision therapies. Large publicly funded projects have generated extensive and freely available multi-assay data resources; however, bioinformatic and statistical methods for the analysis of such experiments are still nascent. We review multi-assay genomic data resources in the areas of clinical oncology, pharmacogenomics and other perturbation experiments, population genomics and regulatory genomics and other areas, and tools for data acquisition. Finally, we review bioinformatic tools that are explicitly geared toward integrative genomic data visualization and analysis. This review provides starting points for accessing publicly available data and tools to support development of needed integrative methods.

Improved large-scale prediction of growth inhibition patterns using the NCI60 cancer cell line panel

MOTIVATION

Recent large-scale omics initiatives have catalogued the somatic alterations of cancer cell line panels along with their pharmacological response to hundreds of compounds. In this study, we have explored these data to advance computational approaches that enable more effective and targeted use of current and future anticancer therapeutics.

RESULTS

We modelled the 50% growth inhibition bioassay end-point (GI50) of 17,142 compounds screened against 59 cancer cell lines from the NCI60 panel (941,831 data-points, matrix 93.08% complete) by integrating the chemical and biological (cell line) information. We determine that the protein, gene transcript and miRNA abundance provide the highest predictive signal when modelling the GI50 endpoint, which significantly outperformed the DNA copy-number variation or exome sequencing data (Tukey's Honestly Significant Difference, P <0.05). We demonstrate that, within the limits of the data, our approach exhibits the ability to both interpolate and extrapolate compound bioactivities to new cell lines and tissues and, although to a lesser extent, to dissimilar compounds. Moreover, our approach outperforms previous models generated on the GDSC dataset. Finally, we determine that in the cases investigated in more detail, the predicted drug-pathway associations and growth inhibition patterns are mostly consistent with the experimental data, which also suggests the possibility of identifying genomic markers of drug sensitivity for novel compounds on novel cell lines.

CONTACT

terez@pasteur.fr; ab454@ac.cam.uk

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Using CellMiner 1.6 for Systems Pharmacology and Genomic Analysis of the NCI-60

The NCI-60 cancer cell line panel provides a premier model for data integration, and systems pharmacology being the largest publicly available database of anticancer drug activity, genomic, molecular, and phenotypic data. It comprises gene expression (25,722 transcripts), microRNAs (360 miRNAs), whole-genome DNA copy number (23,413 genes), whole-exome sequencing (variants for 16,568 genes), protein levels (94 genes), and cytotoxic activity (20,861 compounds). Included are 158 FDA-approved drugs and 79 that are in clinical trials. To improve data accessibility to bioinformaticists and non-bioinformaticists alike, we have developed the CellMiner web-based tools. Here, we describe the newest CellMiner version, including integration of novel databases and tools associated with whole-exome sequencing and protein expression, and review the tools. Included are (i) "Cell line signature" for DNA, RNA, protein, and drugs; (ii) "Cross correlations" for up to 150 input genes, microRNAs, and compounds in a single query; (iii) "Pattern comparison" to identify connections among drugs, gene expression, genomic variants, microRNA, and protein expressions; (iv) "Genetic variation versus drug visualization" to identify potential new drug:gene DNA variant relationships; and (v) "Genetic variant summation" designed to provide a synopsis of mutational burden on any pathway or gene group for up to 150 genes. Together, these tools allow users to flexibly query the NCI-60 data for potential relationships between genomic, molecular, and pharmacologic parameters in a manner specific to the user's area of expertise. Examples for both gain- (RAS) and loss-of-function (PTEN) alterations are provided.

NCI-60 whole exome sequencing and pharmacological CellMiner analyses

Exome sequencing provides unprecedented insights into cancer biology and pharmacological response. Here we assess these two parameters for the NCI-60, which is among the richest genomic and pharmacological publicly available cancer cell line databases. Homozygous genetic variants that putatively affect protein function were identified in 1,199 genes (approximately 6% of all genes). Variants that are either enriched or depleted compared to non-cancerous genomes, and thus may be influential in cancer progression and differential drug response were identified for 2,546 genes. Potential gene knockouts are made available. Assessment of cell line response to 19,940 compounds, including 110 FDA-approved drugs, reveals ≈80-fold range in resistance versus sensitivity response across cell lines. 103,422 gene variants were significantly correlated with at least one compound (at p<0.0002). These include genes of known pharmacological importance such as IGF1R, BRAF, RAD52, MTOR, STAT2 and TSC2 as well as a large number of candidate genes such as NOM1, TLL2, and XDH. We introduce two new web-based CellMiner applications that enable exploration of variant-to-compound relationships for a broad range of researchers, especially those without bioinformatics support. The first tool, “Genetic variant versus drug visualization”, provides a visualization of significant correlations between drug activity-gene variant combinations. Examples are given for the known vemurafenib-BRAF, and novel ifosfamide-RAD52 pairings. The second, “Genetic variant summation” allows an assessment of cumulative genetic variations for up to 150 combined genes together; and is designed to identify the variant burden for molecular pathways or functional grouping of genes. An example of its use is provided for the EGFR-ERBB2 pathway gene variant data and the identification of correlated EGFR, ERBB2, MTOR, BRAF, MEK and ERK inhibitors. The new tools are implemented as an updated web-based CellMiner version, for which the present publication serves as a compendium.