Proteomic Dynamics of Breast Cancer Cell Lines Identifies Potential Therapeutic Protein Targets

Treatment and relevant targets for breast cancer (BC) remain limited, especially for triple-negative BC (TNBC). We identified 6091 proteins of 76 human BC cell lines using data-independent acquisition (DIA). Integrating our proteomic findings with prior multi-omics datasets, we found that including proteomics data improved drug sensitivity predictions and provided insights into the mechanisms of action. We subsequently profiled the proteomic changes in nine cell lines (five TNBC and four non-TNBC) treated with EGFR/AKT/mTOR inhibitors. In TNBC, metabolism pathways were dysregulated after EGFR/mTOR inhibitor treatment, while RNA modification and cell cycle pathways were affected by AKT inhibitor. This systematic multi-omics and in-depth analysis of the proteome of BC cells can help prioritize potential therapeutic targets and provide insights into adaptive resistance in TNBC.

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.

R-Loop-Mediated ssDNA Breaks Accumulate Following Short-Term Exposure to the HDAC Inhibitor Romidepsin

Histone deacetylase inhibitors (HDACi) induce hyperacetylation of histones by blocking HDAC catalytic sites. Despite regulatory approvals in hematological malignancies, limited solid tumor clinical activity has constrained their potential, arguing for better understanding of mechanisms of action (MOA). Multiple activities of HDACis have been demonstrated, dependent on cell context, beyond the canonical induction of gene expression. Here, using a clinically relevant exposure duration, we established DNA damage as the dominant signature using the NCI-60 cell line database and then focused on the mechanism by which hyperacetylation induces DNA damage. We identified accumulation of DNA-RNA hybrids (R-loops) following romidepsin-induced histone hyperacetylation, with single-stranded DNA (ssDNA) breaks detected by single-cell electrophoresis. Our data suggest that transcription-coupled base excision repair (BER) is involved in resolving ssDNA breaks that, when overwhelmed, evolve to lethal dsDNA breaks. We show that inhibition of BER proteins such as PARP will increase dsDNA breaks in this context. These studies establish accumulation of R-loops as a consequence of romidepsin-mediated histone hyperacetylation. We believe that the insights provided will inform design of more effective combination therapy with HDACis for treatment of solid tumors. IMPLICATIONS: Key HDAC inhibitor mechanisms of action remain unknown; we identify accumulation of DNA-RNA hybrids (R-loops) due to chromatin hyperacetylation that provokes single-stranded DNA damage as a first step toward cell death.

Association of expression of epigenetic molecular factors with DNA methylation and sensitivity to chemotherapeutic agents in cancer cell lines

BACKGROUND

Altered DNA methylation patterns play important roles in cancer development and progression. We examined whether expression levels of genes directly or indirectly involved in DNA methylation and demethylation may be associated with response of cancer cell lines to chemotherapy treatment with a variety of antitumor agents.

RESULTS

We analyzed 72 genes encoding epigenetic factors directly or indirectly involved in DNA methylation and demethylation processes. We examined association of their pretreatment expression levels with methylation beta-values of individual DNA methylation probes, DNA methylation averaged within gene regions, and average epigenome-wide methylation levels. We analyzed data from 645 cancer cell lines and 23 cancer types from the Cancer Cell Line Encyclopedia and Genomics of Drug Sensitivity in Cancer datasets. We observed numerous correlations between expression of genes encoding epigenetic factors and response to chemotherapeutic agents. Expression of genes encoding a variety of epigenetic factors, including KDM2B, DNMT1, EHMT2, SETDB1, EZH2, APOBEC3G, and other genes, was correlated with response to multiple agents. DNA methylation of numerous target probes and gene regions was associated with expression of multiple genes encoding epigenetic factors, underscoring complex regulation of epigenome methylation by multiple intersecting molecular pathways. The genes whose expression was associated with methylation of multiple epigenome targets encode DNA methyltransferases, TET DNA methylcytosine dioxygenases, the methylated DNA-binding protein ZBTB38, KDM2B, SETDB1, and other molecular factors which are involved in diverse epigenetic processes affecting DNA methylation. While baseline DNA methylation of numerous epigenome targets was correlated with cell line response to antitumor agents, the complex relationships between the overlapping effects of each epigenetic factor on methylation of specific targets and the importance of such influences in tumor response to individual agents require further investigation.

CONCLUSIONS

Expression of multiple genes encoding epigenetic factors is associated with drug response and with DNA methylation of numerous epigenome targets that may affect response to therapeutic agents. Our findings suggest complex and interconnected pathways regulating DNA methylation in the epigenome, which may both directly and indirectly affect response to chemotherapy.

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.

SCLC-CellMiner: A Resource for Small Cell Lung Cancer Cell Line Genomics and Pharmacology Based on Genomic Signatures

CellMiner-SCLC (https://discover.nci.nih.gov/SclcCellMinerCDB/) integrates drug sensitivity and genomic data, including high-resolution methylome and transcriptome from 118 patient-derived small cell lung cancer (SCLC) cell lines, providing a resource for research into this "recalcitrant cancer." We demonstrate the reproducibility and stability of data from multiple sources and validate the SCLC consensus nomenclature on the basis of expression of master transcription factors NEUROD1, ASCL1, POU2F3, and YAP1. Our analyses reveal transcription networks linking SCLC subtypes with MYC and its paralogs and the NOTCH and HIPPO pathways. SCLC subsets express specific surface markers, providing potential opportunities for antibody-based targeted therapies. YAP1-driven SCLCs are notable for differential expression of the NOTCH pathway, epithelial-mesenchymal transition (EMT), and antigen-presenting machinery (APM) genes and sensitivity to mTOR and AKT inhibitors. These analyses provide insights into SCLC biology and a framework for future investigations into subtype-specific SCLC vulnerabilities.

Abstract 2090: Machine learning-derived gene signature predicts strong sensitivity of several solid tumors to the alkylating agent LP-184

A novel clinical agent, LP-184 is being developed in conjunction with a dedicated machine learning-guided response signature, to allow optimal benefit of LP-184 through genomics-guided therapy. To define correlates of tumor genomics with sensitivity to LP-184, we used RADR™ (Response Algorithm for Drug Positioning and Rescue), a proprietary artificial intelligence (AI)-driven platform, and CellMinerCDB (cross database)™, a systems biology platform integrating molecular and pharmacological datasets on cancer cell lines. The input for our correlational analyses, include LP-184 IC50 data on NCI-60 cell line panel representing drug response, and multi-omics data on these cell lines. CellMinerCDB™ based analysis of LP-184 using a Lasso regression model generated a 38 gene response signature that included expression of APP, NEK6, EGFR, SQSTM1, SLC25A42, SLC16A10, POLD1, SMARCC1, POLG2, CHEK1. In building the model, the signature demonstrates a sensitivity R2 = 0.98 between observed and predicted IC50 values. Additional omics data including methylation and protein levels validate the relevance of several signature genes: APP, NEK6, EGFR, SQSTM1. Concurrently, a RADR™ based analysis of LP-184 using an Artificial Neural Network (ANN) classifier model generated a 10 gene response signature with PTGR1 and PTPN14 as the top weighted genes by relative importance. To establish a signature with highest possible sensitivity, we integrated these 2 additional genes in the 38 gene signature, creating a modified 40 gene signature now having an R2 = 0.99. We next used laboratory experimental studies to measure the performance of this signature. Using an independent set of 18 cell lines not used in signature development, we obtained IC50 values and categorized cell lines as sensitive or resistant. Our in silico results matched in vitro experimental results for 13/18 cell lines (72 percent accuracy). We further applied this 40 gene signature to interrogate 1036 cell lines representing a spectrum of tumor types in the Cancer Cell Line Encyclopedia (CCLE) and obtain a signature-derived IC50. The predicted IC50 ranges from 2.7nM to 114.6uM. The least responsive tumors represent hematological cancers, reproducing the observations made in the NCI-60 panel. 92 of 116 (79%) hematologic cancers in the CCLE database showed a predicted IC50 above 1µM with a median of 3.4uM. The top 279 cell line records with a predicted IC50 below 100nM represented solid tumors with NSCLC, renal, CNS and head & neck cancers as the most sensitive. In conclusion our results demonstrate that the development of LP-184 guided by tumor gene expression patterns modeled using a combination of algorithms and signatures provides a valuable component to the armamentarium of drugs in diverse solid tumors.

Citation Format: Umesh Kathad, Aditya Kulkarni, Jean Philippe Richard, Terri Lehman, Rama Modali, Kishor Bhatia, Panna Sharma, Fathi Elloumi, Yves Pommier, William C. Reinhold. Machine learning-derived gene signature predicts strong sensitivity of several solid tumors to the alkylating agent LP-184 [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2090.

Epigenetic Regulation of DNA Repair Pathway Choice by MacroH2A1 Splice Variants Ensures Genome Stability

The inactive X chromosome (Xi) is inherently susceptible to genomic aberrations. Replication stress (RS) has been proposed as an underlying cause, but the mechanisms that protect from Xi instability remain unknown. Here, we show that macroH2A1.2, an RS-protective histone variant enriched on the Xi, is required for Xi integrity and female survival. Mechanistically, macroH2A1.2 counteracts its structurally distinct and equally Xi-enriched alternative splice variant, macroH2A1.1. Comparative proteomics identified a role for macroH2A1.1 in alternative end joining (alt-EJ), which accounts for Xi anaphase defects in the absence of macroH2A1.2. Genomic instability was rescued by simultaneous depletion of macroH2A1.1 or alt-EJ factors, and mice deficient for both macroH2A1 variants harbor no overt female defects. Notably, macroH2A1 splice variant imbalance affected alt-EJ capacity also in tumor cells. Together, these findings identify macroH2A1 splicing as a modulator of genome maintenance that ensures Xi integrity and may, more broadly, predict DNA repair outcome in malignant cells.

Candidate biomarker assessment for pharmacological response

Using the information from our CellMiner (https://discover.nci.nih.gov/cellminer/) and CellMinerCDB (https://discover.nci.nih.gov/cellminercdb/) web-based applications, we identified 3978 molecular events with significant links to pharmacological response for genes that are either targets, biomarkers, or have established causal linkage to drugs. Molecular events included DNA copy number, methylation and mutation; and transcript; and whole or phospho-protein expression for the NCI-60 human cancer cell lines. While all forms of molecular data were informative in some (gene-drug) pairings, the type of significantly linked molecular events was found to vary widely by drug. Some forms of molecular data were found to have more frequent significant correlation than others. Leading were phosphoproteins as measured by antibody (31%), followed by transcript as measured by microarray (16%), and total protein levels as measured by mass spectrometry or antibody (14%). All other measurements ranged between 5 and 11%. Data reliability was underscored by concordant results when using differing drugs with the same targets, as well as different measurements of the same molecular parameter. The significance of correlations of the various molecular parameters to the pharmacological responses provides functional indication of those parameters that are biologically relevant for each gene-drug pairing, as well as comparisons between measurement types.

Association of Independent Prognostic Factors and Treatment Modality With Survival and Recurrence Outcomes in Breast Cancer

Importance

It is not well understood whether prognostic factors in breast cancer are affected by specific treatment and vary by clinical outcome type compared with untreated patients.

Objective

To identify independent clinical and molecular measurements associated with overall survival (OS) and recurrence-free survival (RFS) by homogeneous treatment in women with breast cancer.

Design, Setting, and Participants

This prognostic study included 956 patients diagnosed with invasive breast cancer from hospital centers across 4 geographical regions of the United States who participated in the accreditation program of the Commission on Cancer of the American College of Surgeons from 1985 to 1997. The duration of follow-up ranged from 1 to 282 months. The study analysis was conducted from June 10, 2019, to March 18, 2020.

Main Outcomes and Measures

Analysis of OS and RFS in patients who underwent chemotherapy, radiotherapy, or endocrine therapy alone compared with no systemic or locoregional therapy. Cox proportional hazards regression models were used to estimate independent performance and 95% CI of age, tumor size, number of positive nodes (nodal status), tumor grades 2 and 3, p53 status, estrogen receptor (ER) status, and ERBB2 (formerly HER2) status.

Results

Among 956 participants, median age was 61 (range, 25-96) years. Age (adjusted hazard ratio [AHR], 2.24; 95% CI, 1.27-3.94; P = .01) and high grade (AHR, 2.05; 95% CI, 1.09-3.86; P = .02), in addition to nodal status and tumor size, were independently associated with OS and RFS, respectively, in untreated patients. p53 status (AHR, 2.11; 95% CI, 1.07-4.18; P = .03) and ER status (AHR, 0.46; 95% CI, 0.23-0.92; P = .03) were associated with higher and lower risks of death, respectively, whereas nodal status (AHR, 1.13; 95% CI, 1.06-1.20; P < .005), high grade (AHR, 4.01; 95% CI, 1.51-10.70; P = .01), and ERBB2 positivity (AHR, 2.67; 95% CI, 1.25-5.70; P = .01) were associated with the risk of recurrence after endocrine therapy. Tumor size (AHR for OS, 2.76 [95% CI, 1.79-4.31; P < .005]; AHR for RFS, 2.27 [95% CI, 1.23-4.18; P = .01]) and ERBB2 status (AHR for OS, 5.35 [95% CI, 1.31-21.98; P = .02]; AHR for RFS, 6.05 [95% CI, 1.48-24.78; P = .01]) were independently associated with radiotherapy outcomes, and nodal status was significantly associated with chemotherapy outcomes (AHR for OS, 1.06 [95% CI, 1.02-1.09; P < .005]; AHR for RFS, 1.05 [95% CI, 1.01-1.09; P = .01]).

Conclusions and Relevance

In this study, independent prognostic factors were associated with specific treatment and weighted by the outcome category with reference to untreated patients within biological and clinical contexts.

Epigenome-wide DNA methylation analysis of small cell lung cancer cell lines suggests potential chemotherapy targets

BACKGROUND

Small cell lung cancer (SCLC) is an aggressive neuroendocrine lung cancer. SCLC progression and treatment resistance involve epigenetic processes. However, links between SCLC DNA methylation and drug response remain unclear. We performed an epigenome-wide study of 66 human SCLC cell lines using the Illumina Infinium MethylationEPIC BeadChip array. Correlations of SCLC DNA methylation and gene expression with in vitro response to 526 antitumor agents were examined.

RESULTS

We found multiple significant correlations between DNA methylation and chemosensitivity. A potentially important association was observed for TREX1, which encodes the 3' exonuclease I that serves as a STING antagonist in the regulation of a cytosolic DNA-sensing pathway. Increased methylation and low expression of TREX1 were associated with the sensitivity to Aurora kinase inhibitors AZD-1152, SCH-1473759, SNS-314, and TAK-901; the CDK inhibitor R-547; the Vertex ATR inhibitor Cpd 45; and the mitotic spindle disruptor vinorelbine. Compared with cell lines of other cancer types, TREX1 had low mRNA expression and increased upstream region methylation in SCLC, suggesting a possible relationship with SCLC sensitivity to Aurora kinase inhibitors. We also identified multiple additional correlations indicative of potential mechanisms of chemosensitivity. Methylation of the 3'UTR of CEP350 and MLPH, involved in centrosome machinery and microtubule tracking, respectively, was associated with response to Aurora kinase inhibitors and other agents. EPAS1 methylation was associated with response to Aurora kinase inhibitors, a PLK-1 inhibitor and a Bcl-2 inhibitor. KDM1A methylation was associated with PLK-1 inhibitors and a KSP inhibitor. Increased promoter methylation of SLFN11 was correlated with resistance to DNA damaging agents, as a result of low or no SLFN11 expression. The 5' UTR of the epigenetic modifier EZH2 was associated with response to Aurora kinase inhibitors and a FGFR inhibitor. Methylation and expression of YAP1 were correlated with response to an mTOR inhibitor. Among non-neuroendocrine markers, EPHA2 was associated with response to Aurora kinase inhibitors and a PLK-1 inhibitor and CD151 with Bcl-2 inhibitors.

CONCLUSIONS

Multiple associations indicate potential epigenetic mechanisms affecting SCLC response to chemotherapy and suggest targets for combination therapies. While many correlations were not specific to SCLC lineages, several lineage markers were associated with specific agents.

Abstract B013: Associations of epigenome-wide DNA methylation patterns with chemosensitivity and chemoresistance of small cell lung cancer cell lines

Small cell lung cancer (SCLC), an aggressive neuroendocrine type of lung cancer, rapidly acquires resistance to treatment. SCLC progression, lineage differentiation, and resistance to therapy have been suggested to involve epigenetic processes. To date, epigenetic links connecting SCLC DNA methylation patterns to drug response and the ways in which these links are mediated by gene expression remain unclear. In order to understand how DNA methylation may affect SCLC response to chemotherapy, we performed an epigenome-wide association study of 66 SCLC cell lines. We used Illumina Infinium MethylationEPIC BeadChip to measure methylation of 866,091 probes. We examined how methylation of probes and gene regions was associated with SCLC in vitro response to 526 antitumor agents. We also identified associations of epigenetic variation with drug response which may be mediated by regulation of gene expression. A potentially important strong association was observed for TREX1, which encodes the 3’ exonuclease I (DNase III) that is involved in resolution of chromatin bridges and has a potential role in chromothripsis. Increased methylation and low expression of TREX1 were associated with SCLC cell line sensitivity to multiple Aurora kinase inhibitors AZD-1152, SCH-1473759, SNS-314, and TAK-901, as well as to the CDK inhibitor R-547, Vertex ATR inhibitor Cpd 45, and the mitotic spindle disruptor vinorelbine. TREX1 upregulation has been previously associated with resistance of other cancers to DNA damaging agents and with DNA repair or DNA degradation after drug exposure. In our analysis, when compared to other cancer categories, TREX1 in SCLC cell lines had low mRNA expression and increased DNA methylation upstream of its transcription start site, which may provide a possible molecular mechanism for SCLC sensitivity to Aurora kinase inhibitors. CEP350 and MLPH, which are involved in centrosome machinery and microtubule tracking, were associated with several Aurora kinase inhibitors and other agents. Among other examples, EPAS1 (HIF2A) was associated with several Aurora kinase inhibitors, the PLK1 inhibitor GSK-461364, and the Bcl-2 inhibitor ABT-737. Methylation of KDM1A, encoding the histone modifier lysine demethylase 1A (LSD1), was associated with PLK1 inhibitors and the KSP inhibitor SB-743921. IGFBP5, which is expressed in the tuft cell-like SCLC subtype, was associated with the mTOR inhibitor INK-128. Upstream regions of MDM2 and DLL3, a Notch pathway regulator overexpressed in ASCL1-high SCLC tumors, were associated with Bcl-2 inhibitors. Methylation and expression of YAP1, a SCLC lineage driver regulating the Hippo pathway, were correlated with the MTOR inhibitor rapamycin. Among non-neuroendocrine lineage markers, EPHA2 was associated with Aurora kinase inhibitors and a PLK1 inhibitor, and CD151 with Bcl-2 inhibitors. Increased methylation upstream of SLFN11 was correlated with resistance to DNA damaging agents, which is likely mediated by SLFN11 expression. The 5’ UTR region of the epigenetic modifier EZH2 was associated with Aurora kinase inhibitors and the FGFR inhibitor BGJ-398. These and multiple other associations identified in this study provide a novel understanding of epigenetic mechanisms which may modulate SCLC response to chemotherapy, and suggest potential molecular targets for combination therapies. This research was supported in part with federal funds from the National Cancer Institute, NIH, under contract HHSN261200800001E.

Citation Format: Julia Krushkal, Thomas Silvers, Dmitriy Sonkin, Suleyman Vural, John Connelly, Sudhir Varma, Paul S. Meltzer, William C. Reinhold, Annamaria Rapisarda, David Evans, Yves Pommier, Beverly A. Teicher. Associations of epigenome-wide DNA methylation patterns with chemosensitivity and chemoresistance of small cell lung cancer cell lines [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr B013. doi:10.1158/1535-7163.TARG-19-B013

Quantitative Proteome Landscape of the NCI-60 Cancer Cell Lines

Here we describe a proteomic data resource for the NCI-60 cell lines generated by pressure cycling technology and SWATH mass spectrometry. We developed the DIA-expert software to curate and visualize the SWATH data, leading to reproducible detection of over 3,100 SwissProt proteotypic proteins and systematic quantification of pathway activities. Stoichiometric relationships of interacting proteins for DNA replication, repair, the chromatin remodeling NuRD complex, β-catenin, RNA metabolism, and prefoldins are more evident than that at the mRNA level. The data are available in CellMiner (discover.nci.nih.gov/cellminercdb and discover.nci.nih.gov/cellminer), allowing casual users to test hypotheses and perform integrative, cross-database analyses of multi-omic drug response correlations for over 20,000 drugs. We demonstrate the value of proteome data in predicting drug response for over 240 clinically relevant chemotherapeutic and targeted therapies. In summary, we present a novel proteome resource for the NCI-60, together with relevant software tools, and demonstrate the benefit of proteome analyses.

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High-quality NCI-60 proteotypes created using pressure cycling technology and SWATH-MS

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Proteotypes improve drug response prediction in multi-omics regression analysis

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∼3000 measured proteins allow investigation into protein complex stoichiometry

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CellMinerCDB (discover.nci.nih.gov/cellminercdb) portal allows dataset exploration

A novel assay to screen siRNA libraries identifies protein kinases required for chromosome transmission

One of the hallmarks of cancer is chromosome instability (CIN), which leads to aneuploidy, translocations, and other chromosome aberrations. However, in the vast majority of human tumors the molecular basis of CIN remains unknown, partly because not all genes controlling chromosome transmission have yet been identified. To address this question, we developed an experimental high-throughput imaging (HTI) siRNA assay that allows the identification of novel CIN genes. Our method uses a human artificial chromosome (HAC) expressing the GFP transgene. When this assay was applied to screen an siRNA library of protein kinases, we identified PINK1, TRIO, IRAK1, PNCK, and TAOK1 as potential novel genes whose knockdown induces various mitotic abnormalities and results in chromosome loss. The HAC-based assay can be applied for screening different siRNA libraries (cell cycle regulation, DNA damage response, epigenetics, and transcription factors) to identify additional genes involved in CIN. Identification of the complete spectrum of CIN genes will reveal new insights into mechanisms of chromosome segregation and may expedite the development of novel therapeutic strategies to target the CIN phenotype in cancer cells.

Tyrosyl-DNA Phosphodiesterase 1 and Topoisomerase I Activities as Predictive Indicators for Glioblastoma Susceptibility to Genotoxic Agents

Glioblastoma (GBM) patients have an estimated survival of ~15 months with treatment, and the standard of care only modestly enhances patient survival. Identifying biomarkers representing vulnerabilities may allow for the selection of efficacious chemotherapy options to address personalized variations in GBM tumors. Irinotecan targets topoisomerase I (TOP1) by forming a ternary DNA-TOP1 cleavage complex (TOP1cc), inducing apoptosis. Tyrosyl-DNA phosphodiesterase 1 (TDP1) is a crucial repair enzyme that may reduce the effectiveness of irinotecan. We treated GBM cell lines with increasing concentrations of irinotecan and compared the IC₅₀ values. We found that the TDP1/TOP1 activity ratio had the strongest correlation (Pearson correlation coefficient R = 0.972, based on the average from three sets of experiments) with IC₅₀ values following irinotecan treatment. Increasing the TDP1/TOP1 activity ratio by the ectopic expression of wild-type TDP1 increased in irinotecan IC₅₀, while the expression of the TDP1 catalytic-null mutant did not alter the susceptibility to irinotecan. The TDP1/TOP1 activity ratio may be a new predictive indicator for GBM vulnerability to irinotecan, allowing for the selection of individual patients for irinotecan treatment based on risk-benefit. Moreover, TDP1 inhibitors may be a novel combination treatment with irinotecan to improve GBM patient responsiveness to genotoxic chemotherapies.

Abstract 2488: CellMinerCDB and CellMiner web-applications for genomics and pharmacogenomics analyses of cancer cell lines

The CellMiner (http://discover.nci.nih.gov/cellminer)and CellMinerCDB (https://discover.nci.nih.gov/cellminercdb/) web-applications provide complimentary functionality and datatsets that may be used for comparisons of genomic, molecular and pharmacological data within the NCI-60 cancerous cell lines, Cancer Cell Line Encyclopedia (CCLE), Genomics of Drug Sensitivity in Cancer (GDSC), Cancer Therapeutics Response Portal (CTRP), NCI/DTP small cell lung cancer (SCLC), and NCI Almanac cell line sets. CellMiner contains data for the NCI-60, including the most extensive sets of molecular and drug activity data (generated by the NCI Developmental Therapeutics Program https://dtp.cancer.gov),found for any of the databases. CellMinerCDB contains all the above mentioned cell line sets, including the substantially increased cell line numbers and tissue of origin types found in the CCLE, GDSC, and CTRP. The two web-applications have separate but complimentary functionalities. Each cell line set has some variable number of data types, some of which measure the same parameters, and some that do not. The number and make up of cell lines also varies, from 60 for the NCI-60, 69 for the SCLC, and ~1000 for the CCLE, GDSC, and CTRP. As there are partial overlaps of cell lines between many of these cell line sets, one may fill in some data type gaps by merging data from two sources, as well as do quality control by comparisons of the same data from multiple institutions. This rich set of data and functions facilitates the exploration of the relationships between and among molecular alterations and pharmacological responses in cancer cell lines from the omic perspective.

Citation Format: William C. Reinhold, Sudir Varma, Yang-Hsin Wang, Fathi Elloumi, Yves Pommier. CellMinerCDB and CellMiner web-applications for genomics and pharmacogenomics analyses of cancer cell lines [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2488.

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.

CellMinerCDB for Integrative Cross-Database Genomics and Pharmacogenomics Analyses of Cancer Cell Lines

CellMinerCDB provides a web-based resource (https://discover.nci.nih.gov/cellminercdb/) for integrating multiple forms of pharmacological and genomic analyses, and unifying the richest cancer cell line datasets (the NCI-60, NCI-SCLC, Sanger/MGH GDSC, and Broad CCLE/CTRP). CellMinerCDB enables data queries for genomics and gene regulatory network analyses, and exploration of pharmacogenomic determinants and drug signatures. It leverages overlaps of cell lines and drugs across databases to examine reproducibility and expand pathway analyses. We illustrate the value of CellMinerCDB for elucidating gene expression determinants, such as DNA methylation and copy number variations, and highlight complexities in assessing mutational burden. We demonstrate the value of CellMinerCDB in selecting drugs with reproducible activity, expand on the dominant role of SLFN11 for drug response, and present novel response determinants and genomic signatures for topoisomerase inhibitors and schweinfurthins. We also introduce LIX1L as a gene associated with mesenchymal signature and regulation of cellular migration and invasiveness.

Macrophilones from the Marine Hydroid Macrorhynchia philippina Can Inhibit ERK Cascade Signaling

Six new macrophilone-type pyrroloiminoquines were isolated and identified from an extract of the marine hydroid Macrorhynchia philippina. The proton-deficient and heteroatom-rich structures of macrophilones B-G (2-7) were elucidated by spectroscopic analysis and comparison of their data with those of the previously reported metabolite macrophilone A (1). Compounds 1-7 are the first pyrroloiminoquines to be reported from a hydroid. The macrophilones were shown to inhibit the enzymatic conjugation of SUMO to peptide substrates, and macrophilones A (1) and C (3) exhibit potent and selective cytotoxic properties in the NCI-60 anticancer screen. Bioinformatic analysis revealed a close association of the cytotoxicity profiles of 1 and 3 with two known B-Raf kinase inhibitory drugs. While compounds 1 and 3 showed no kinase inhibitory activity, they resulted in a dramatic decrease in cellular protein levels of selected components of the ERK signal cascade. As such, the chemical scaffold of the macrophilones could provide small-molecule therapeutic leads that target the ERK signal transduction pathway.

DNA-Targeted Precision Medicine; Have we Been Caught Sleeping?

In the current drive to incorporate molecular markers into treatment-selection for precision medicine, there has been a significant and we believe ill-advised omission of the large and routinely used group of drugs whose mechanism of action is DNA damage.

ONC201 kills breast cancer cells in vitro by targeting mitochondria

We report a novel mechanism of action of ONC201 as a mitochondria-targeting drug in cancer cells. ONC201 was originally identified as a small molecule that induces transcription of TNF-related apoptosis-inducing ligand (TRAIL) and subsequently kills cancer cells by activating TRAIL death receptors. In this study, we examined ONC201 toxicity on multiple human breast and endometrial cancer cell lines. ONC201 attenuated cell viability in all cancer cell lines tested. Unexpectedly, ONC201 toxicity was not dependent on either TRAIL receptors nor caspases. Time-lapse live cell imaging revealed that ONC201 induces cell membrane ballooning followed by rupture, distinct from the morphology of cells undergoing apoptosis. Further investigation found that ONC201 induces phosphorylation of AMP-dependent kinase and ATP loss. Cytotoxicity and ATP depletion were significantly enhanced in the absence of glucose, suggesting that ONC201 targets mitochondrial respiration. Further analysis indicated that ONC201 indirectly inhibits mitochondrial respiration. Confocal and electron microscopic analysis demonstrated that ONC201 triggers mitochondrial structural damage and functional impairment. Moreover, ONC201 decreased mitochondrial DNA (mtDNA). RNAseq analysis revealed that ONC201 suppresses expression of multiple mtDNA-encoded genes and nuclear-encoded mitochondrial genes involved in oxidative phosphorylation and other mitochondrial functions. Importantly, fumarate hydratase deficient cancer cells and multiple cancer cell lines with reduced amounts of mtDNA were resistant to ONC201. These results indicate that cells not dependent on mitochondrial respiration are ONC201-resistant. Our data demonstrate that ONC201 kills cancer cells by disrupting mitochondrial function and further suggests that cancer cells that are dependent on glycolysis will be resistant to ONC201.

Targeted inhibition of STAT/TET1 axis as a therapeutic strategy for acute myeloid leukemia

Effective therapy of acute myeloid leukemia (AML) remains an unmet need. DNA methylcytosine dioxygenase Ten-eleven translocation 1 (TET1) is a critical oncoprotein in AML. Through a series of data analysis and drug screening, we identified two compounds (i.e., NSC-311068 and NSC-370284) that selectively suppress TET1 transcription and 5-hydroxymethylcytosine (5hmC) modification, and effectively inhibit cell viability in AML with high expression of TET1 (i.e., TET1-high AML), including AML carrying t(11q23)/MLL-rearrangements and t(8;21) AML. NSC-311068 and especially NSC-370284 significantly repressed TET1-high AML progression in vivo. UC-514321, a structural analog of NSC-370284, exhibited a more potent therapeutic effect and prolonged the median survival of TET1-high AML mice over three fold. NSC-370284 and UC-514321 both directly target STAT3/5, transcriptional activators of TET1, and thus repress TET1 expression. They also exhibit strong synergistic effects with standard chemotherapy. Our results highlight the therapeutic potential of targeting the STAT/TET1 axis by selective inhibitors in AML treatment.

Ten-eleven translocation 1 (TET1) is a critical oncoprotein in AML. Here, the authors identify 2 compounds that target the binding of STAT3/5 specifically to the TET1 promoter, inhibiting its expression and AML cell viability.

Abstract 2586: CellMinerCDB: Enabling cross-database exploration of molecular pharmacology data and response determinant discovery in cancer cell lines

Cancer cell line panels are widely used for evaluating drug response across diverse tissue types. A growing set of molecular profiling data complements measurements of chemosensitivity, providing novel avenues for response determinant discovery and clinical translation. Accessing and inter-relating data from different sources is essential for evaluating such determinants, but remains challenging. To enable wider access to cell line pharmacogenomic data, we have developed CellMinerCDB (CellMiner Cross-Database, discover.nci.nih.gov/cellminercdb), a web application integrating data from several widely studied cancer cell line panels, including the NCI-60 (NIH), GDSC (Sanger/MGH), and CCLE/CTRP (Broad). All together, our database spans over 1300 distinct cell lines, 400 clinically relevant cancer drugs, 20,000 experimental compounds, and molecular profiling data, such as gene/protein expression, DNA copy, methylation, and mutational status. Cell line and tested drug overlaps allow cross-database validation of genomic and drug data, and CellMinerCDB simplifies this by transparently matching differently named entities between sources. Data exploration can be additionally restricted to particular tissue types, with individual cell lines annotated to the OncoTree ontology for consistent treatment across sources.

A range of analysis tools support interactive data exploration, from 2D plots of drug response and molecular profiling features to exhaustive correlation analyses and multivariate predictive models. We illustrate the power and utility of CellMinerCDB with examples of response determinant discovery and predictive modeling for Top1 and PARP inhibitors. Beginning with established individual determinants, such as SLFN11 mRNA expression, we show how both unbiased and biological knowledge network-based feature selection methods enable iterative refinement of a multivariate genomic signature of drug response. For Top 1 inhibitors, additional predictive features include expression of chromatin remodeling factors and genes modulating apoptosis capacity, while complementary PARP inhibitor response determinants include PARP1 and drug efflux pump expression. Pathway and process-based gene annotations allow biological interpretation of response predictive features. CellMinerCDB also includes ongoing algorithmic work to improve the construction of multivariate predictive models using constraints from biological networks. These approaches bridge a limiting gap in existing methods, which either ignore biological knowledge altogether or are limited to exploration within known pathways and processes.

Citation Format: Vinodh N. Rajapakse, Augustin Luna, Chris Sander, William C. Reinhold, Yves Pommier. CellMinerCDB: Enabling cross-database exploration of molecular pharmacology data and response determinant discovery in cancer cell lines [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2586. doi:10.1158/1538-7445.AM2017-2586

Temozolomide in the Era of Precision Medicine

In the January 1, 2017, issue of Cancer Research, Nagel and colleagues demonstrate the value of assays that determine the DNA repair capacity of cancers in predicting response to temozolomide. Using a fluorescence-based multiplex flow cytometric host cell reactivation assay that provides simultaneous readout of DNA repair capacity across multiple pathways, they show that the multivariate drug response models derived from cell line data were applicable to patient-derived xenograft models of glioblastoma. In this commentary, we first outline the mechanism of activity and current clinical application of temozolomide, which, until now, has been largely limited to glioblastoma. Given the challenges of clinical application of functional assays, we argue that functional readouts be approximated by genomic signatures. In this context, a combination of MGMT activity and mismatch repair (MMR) status of the tumor are important parameters that determine sensitivity to temozolomide. More reliable methods are needed to determine MGMT activity as DNA methylation, the current standard, does not accurately reflect the expression of MGMT. Also, genomics for MMR are warranted. Furthermore, based on patterns of MGMT expression across different solid tumors, we make a case for revisiting temozolomide use in a broader spectrum of cancers based on our current understanding of its molecular basis of activity. Cancer Res; 77(4); 823-6. ©2017 AACR.

Epigenetic inactivation of the putative DNA/RNA helicase SLFN11 in human cancer confers resistance to platinum drugs

Platinum-derived drugs such as cisplatin and carboplatin are among the most commonly used cancer chemotherapy drugs, but very few specific molecular and cellular markers predicting differential sensitivity to these agents in a given tumor type have been clearly identified. Epigenetic gene silencing is increasingly being recognized as a factor conferring distinct tumoral drug sensitivity, so we have used a comprehensive DNA methylation microarray platform to interrogate the widely characterized NCI60 panel of human cancer cell lines with respect to CpG methylation status and cisplatin/carboplatin sensitivity. Using this approach, we have found promoter CpG island hypermethylation-associated silencing of the putative DNA/RNA helicase Schlafen-11 (SLFN11) to be associated with increased resistance to platinum compounds. We have also experimentally validated these findings in vitro. In this setting, we also identified the BRCA1 interacting DHX9 RNA helicase (also known as RHA) as a protein partner for SLFN11, suggesting a mechanistic pathway for the observed chemoresistance effect. Most importantly, we have been able to extend these findings clinically, following the observation that those patients with ovarian and non-small cell lung cancer carrying SLFN11 hypermethylation had a poor response to both cisplatin and carboplatin treatments. Overall, these results identify SLFN11 epigenetic inactivation as a predictor of resistance to platinum drugs in human cancer.

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.

Cytidine Deaminase Deficiency Reveals New Therapeutic Opportunities against Cancer

Purpose: One of the main challenges in cancer therapy is the identification of molecular mechanisms mediating resistance or sensitivity to treatment. Cytidine deaminase (CDA) was reported to be downregulated in cells derived from patients with Bloom syndrome, a genetic disease associated with a strong predisposition to a wide range of cancers. The purpose of this study was to determine whether CDA deficiency could be associated with tumors from the general population and could constitute a predictive marker of susceptibility to antitumor drugs.Experimental Design: We analyzed CDA expression in silico, in large datasets for cancer cell lines and tumors and in various cancer cell lines and primary tumor tissues using IHC, PDXs, qRT-PCR, and Western blotting. We also studied the mechanism underlying CDA silencing and searched for molecules that might target specifically CDA-deficient tumor cells using in silico analysis coupled to classical cellular experimental approaches.Results: We found that CDA expression is downregulated in about 60% of cancer cells and tissues. We demonstrate that DNA methylation is a prevalent mechanism of CDA silencing in tumors. Finally, we show that CDA-deficient tumor cells can be specifically targeted with epigenetic treatments and with the anticancer drug aminoflavone.Conclusions: CDA expression status identifies new subgroups of cancers, and CDA deficiency appears to be a novel and relevant predictive marker of susceptibility to antitumor drugs, opening up new possibilities for treating cancer. Clin Cancer Res; 23(8); 2116-26. ©2016 AACR.

Abstract 75: CellMiner, a systems pharmacological web-application for the NCI-60 cancerous cell-lines: Updates, data integration, and translationally relevant results

Understanding the influences of molecular alterations on pharmacological responses in the omic sense is at the fore of the effort to make oncology more effective and specific. At present, however, this remains a field in its infancy. The NCI-60 cancerous cell lines provide a premier set of databases for systems molecular pharmacological studies. This is due to the availability of both the robust, high-quality activity profiles generated by the Developmental Therapeutics Program (https://dtp.cancer.gov), and the panoply of molecular and phenotypic information available. The CellMiner web-application (http://discover.nci.nih.gov/cellminer) provides the user access to both of these. Activity data is available for 20,861 compounds, including 159 Food and Drug Administration (FDA)-approved, 85 clinical trial, and 443 known mechanism-of-action drugs. The molecular data includes transcript expression for 25,772 genes, genetic variants for 16,568 genes, transcript expression for 360 microRNAs, protein levels for 94 genes, DNA copy number (from aCGH) for 23,413 genes, and soon DNA methylation levels for 17,559 genes. “Cell line signatures” are provided for each of these data types, facilitating their comparison. It also provides “Pattern comparisons” for any input pattern of interest to 87,419 activity, molecular and phenotypic patterns (For CellMiner 1.7).

Here we present our CellMiner updates, examples of data integration, and translationally relevant results.

Citation Format: William C. Reinhold, Margot Sunshine, Sudir Varma, James Doroshow, Yves Pommier. CellMiner, a systems pharmacological web-application for the NCI-60 cancerous cell-lines: Updates, data integration, and translationally relevant results. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 75.

A replicator-specific binding protein essential for site-specific initiation of DNA replication in mammalian cells

Mammalian chromosome replication starts from distinct sites; however, the principles governing initiation site selection are unclear because proteins essential for DNA replication do not exhibit sequence-specific DNA binding. Here we identify a replication-initiation determinant (RepID) protein that binds a subset of replication-initiation sites. A large fraction of RepID-binding sites share a common G-rich motif and exhibit elevated replication initiation. RepID is required for initiation of DNA replication from RepID-bound replication origins, including the origin at the human beta-globin (HBB) locus. At HBB, RepID is involved in an interaction between the replication origin (Rep-P) and the locus control region. RepID-depleted murine embryonic fibroblasts exhibit abnormal replication fork progression and fewer replication-initiation events. These observations are consistent with a model, suggesting that RepID facilitates replication initiation at a distinct group of human replication origins.

Origins of mammalian DNA replication are poorly characterised because they lack an Identifiable consensus sequence. Here the authors identify RepID, a protein that binds to a subset of G-rich replication origins and facilitates initiation from those origins.

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.

Abstract 3749: Uses and update for CellMiner, a tool for access to and comparison of molecular data and pharmacological response for the NCI-60

Integration and understanding of genomic and proteomic data and their relationships with and influences on pharmacology have become key components of cancer research and its translation to therapeutics. Nonetheless, multiple significant obstacles need to be overcome for both their integration and understanding. The NCI-60 cancerous cell lines provide a premier opportunity in this area due to the scope and quality of the available data. CellMiner (http://discover.nci.nih.gov/cellminer) is a web-application that allows the user to rapidly access data for relative levels of transcript expression for 25,772 genes, genetic variants for 16,568 genes, transcript expression for 360 microRNAs, protein levels for 94 genes, and DNA copy number (from aCGH) for 23,413 genes. In addition, it provides concentration-response activities for 20,002 compounds including 114 Food and Drug Administration (FDA)-approved, 55 clinical trial, and 349 known mechanism-of-action drugs. Here we present our latest CellMiner update along with examples of data integration that provide enhanced understanding in the systems biological sense. Included in the tool updates are i) the upcoming “Protein mean value” cell line signature tool, ii) the recently introduced “Gene DNA copy number” tool, iii) introduction of the “Cross-correlations of transcripts, microRNAs, and drugs” tool, iv) the provision of additional forms of data in the standard output for the “Pattern comparisons” tool, and v) upgrades of the “Genetic variant versus drug visualization” tool allowing the user to query for all drugs significantly correlated to genomic variants for the whole human genome (or visa versa). Examples of data integration will include SLFN11 expression versus the activities of DNA-damaging drugs, RAS activation considering three forms of molecular data, and PTEN knockdown and pharmacological connection (also using three forms of molecular data). The data and tools are or shortly will be publicly available at CellMiner.

Citation Format: William C. Reinhold, Margot Sunshine, Sudir Varma, Vinodh Rajapakse, James Doroshow, Joel Morris, Yves Pmmier. Uses and update for CellMiner, a tool for access to and comparison of molecular data and pharmacological response for the NCI-60. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3749. doi:10.1158/1538-7445.AM2015-3749

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.

Usp7 protects genomic stability by regulating Bub3

USP7 (Ubiquitin Specific processing Protease-7) is a deubiquitinase which, over the past decade emerged as a critical regulator of cellular processes. Deregulation of USP7 activity has been linked to cancer, making USP7 inhibition an appealing anti-cancer strategy. The identification of novel USP7 substrates and additional USP7-dependent cellular activities will broaden our knowledge towards potential clinical application of USP7 inhibitors. Results presented in this study uncover a novel and pivotal function of USP7 in the maintenance of genomic stability. Upon USP7 depletion we observed prolonged mitosis and mitotic abnormalities including micronuclei accumulation, lagging chromosomes and karyotype instability. Inhibition of USP7 with small molecule inhibitors stabilizes cyclin B and causes mitotic abnormalities. Our results suggest that these USP7-dependent effects are mediated by decreased levels of spindle assembly checkpoint (SAC) component Bub3, which we characterized as an interacting partner and substrate of USP7. In silico analysis across the NCI-60 panels of cell lines supports our results where lower levels of USP7 strongly correlate with genomic instability. In conclusion, we identified a novel role of USP7 as regulator of the SAC component Bub3 and genomic stability.

The 15kDa selenoprotein and thioredoxin reductase 1 promote colon cancer by different pathways

Selenoproteins mediate much of the cancer-preventive properties of the essential nutrient selenium, but some of these proteins have been shown to also have cancer-promoting effects. We examined the contributions of the 15kDa selenoprotein (Sep15) and thioredoxin reductase 1 (TR1) to cancer development. Targeted down-regulation of either gene inhibited anchorage-dependent and anchorage-independent growth and formation of experimental metastases of mouse colon carcinoma CT26 cells. Surprisingly, combined deficiency of Sep15 and TR1 reversed the anti-cancer effects observed with down-regulation of each single gene. We found that inflammation-related genes regulated by Stat-1, especially interferon-γ-regulated guanylate-binding proteins, were highly elevated in Sep15-deficient, but not in TR1-deficient cells. Interestingly, components of the Wnt/β-catenin signaling pathway were up-regulated in cells lacking both TR1 and Sep15. These results suggest that Sep15 and TR1 participate in interfering regulatory pathways in colon cancer cells. Considering the variable expression levels of Sep15 and TR1 found within the human population, our results provide insights into new roles of selenoproteins in cancer.

Alterations of DNA repair genes in the NCI-60 cell lines and their predictive value for anticancer drug activity

Loss of function of DNA repair (DNAR) genes is associated with genomic instability and cancer predisposition; it also makes cancer cells reliant on a reduced set of DNAR pathways to resist DNA-targeted therapy, which remains the core of the anticancer armamentarium. Because the landscape of DNAR defects across numerous types of cancers and its relation with drug activity have not been systematically examined, we took advantage of the unique drug and genomic databases of the US National Cancer Institute cancer cell lines (the NCI-60) to characterize 260 DNAR genes with respect to deleterious mutations and expression down-regulation; 169 genes exhibited a total of 549 function-affecting alterations, with 39 of them scoring as putative knockouts across 31 cell lines. Those mutations were compared to tumor samples from 12 studies of The Cancer Genome Atlas (TCGA) and The Cancer Cell Line Encyclopedia (CCLE). Based on this compendium of alterations, we determined which DNAR genomic alterations predicted drug response for 20,195 compounds present in the NCI-60 drug database. Among 242 DNA damaging agents, 202 showed associations with at least one DNAR genomic signature. In addition to SLFN11, the Fanconi anemia-scaffolding gene SLX4 (FANCP/BTBD12) stood out among the genes most significantly related with DNA synthesis and topoisomerase inhibitors. Depletion and complementation experiments validated the causal relationship between SLX4 defects and sensitivity to raltitrexed and cytarabine in addition to camptothecin. Therefore, we propose new rational uses for existing anticancer drugs based on a comprehensive analysis of DNAR genomic parameters.

Abstract 1366: Discovery of biomarkers for antitumor drug resistance using 450K methylation data of NCI60

The National Cancer Institute Antitumor Cell line panel NCI60 is a very frequently used collection of cancer cell lines that has been largely characterized in different biological aspects. Data from proteomic and RNA expression analyses of this panel is already available to the scientific community. Also, the IC50 data for more than 100,000 different compound treatments is accessible in the developmental therapeutic program of NCI (dtp). However, very little has been explored about the epigenetic component of this panel. We have performed a thorough correlational study combining the available molecular information of NCI60, the data of our 450K NCI60 dataset and the IC50 data from the dtp to discover candidate genes for new biomarkers of resistance to antitumor agents. Finding new biomarkers to determine the most probable response to the therapy seems to be a key stage in the development of personalized medicine. Due to its stability and how easy it is to detect its level of methylation, DNA methylation changes appear to be a perfect candidate mark for the construction of this new generation of biomarkers. In this study we obtained multiples candidate genes whose methylation correlated to resistance to different drugs. In order to perform validation analyses we chose a gene that showed a strong positive correlation with the resistance to several DNA damage agents (DDA).Using overexpression and silencing assays we were able to demonstrate that the expression of this gene has a profound influence on the cell sensitivity to the DDA. Furthermore, through gene expression validation assays, such as 5-azacytidine treatments, we demonstrated that the promotor methylation of this gene is associated to its non-expression. In addition, we observed that this gene methylation correlates with a lower overall survival in an ovarian cancer cohort treated with platinum agents. Little is known about our candidate gene but it seems to be related to the DNA repair machine and also to have a function in the cell cycle control. These features could give an explanation to the importance of its presence in the response to DDA treatment. Our studies show that the analysis of DNA methylation changes in cancer cells in combination with other molecular alterations can be used in correlational studies to find trustworthy biomarkers for antitumor drug resistance.

Citation Format: Vanesa Nogales, Catia Moutinho, Anna Martinez-Cardús, Sudhir Varma, J. Keith Killian, William C. Reinhold, Paul S. Meltzer, Yves Pommier, Manel Esteller. Discovery of biomarkers for antitumor drug resistance using 450K methylation data of NCI60. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1366. doi:10.1158/1538-7445.AM2014-1366

Abstract 3788: Drug mechanisms of action: Triangulating with cultured cancer cells

The clinical trial is irreplaceable as a scientific test bed for cancer therapeutics, animal models are becoming ever more sophisticated for preclinical testing, and 3D cell cultures can mimic some aspects of tumor structure and microenvironment. So what are the proper roles of 2D cell cultures in research on mechanisms of drug action? A partial answer is historical: most of our information about mechanisms of drug sensitivity and resistance has come from such cultures. A second is that cells in culture can be engineered readily by selective pressure or genetic manipulation to express or silence any chosen drug target or modulator of drug activity. A third is provided by the emergence of cancer cell panels for use in large-scale drug discovery programs, at the same time generating massive amounts of data that can be used to support or refute hypotheses about molecular mechanism. Cell line assays are not in general predictive of clinical efficacy, but patterns of response among lines and under different conditions can certainly predict mechanism of action, often quite incisively (see, e.g., Weinstein, Science 258;447, 1992).

The first large-scale cell-line screen used to illuminate mechanisms of action was the NCI-60, a diverse set of 60 human lines used to test &gt;100,000 compounds and natural products since 1988 (Shoemaker, Prog. Clin. Biol. Res. 2:361, 1988). Because we and others have profiled the same lines at the DNA, RNA, protein, chromosomal, and epigenomic levels (see, e.g., Weinstein, Science 258;447, 1997), it has been possible to correlate molecular aberrations in the cells with their sensitivity to the agents tested. More recently, the Cancer Cell Line Encyclopedia (CCLE) (Barretina, Nature 483;603, 2012) and Cancer Genome Project (CGP) (Garnett, Nature 483;570, 2012) were published, describing molecular profiles and drug response assays for 1,036 cell lines and 24 drugs, and 727 cell lines and 138 drugs, respectively. The three cell panel assays complement each other: the NCI-60 provides a relatively small number of cell types but many tested agents; the CCLE and CGP provide many more cell types but a much smaller collection of tested compounds. Because the assays and experimental conditions being used in the three systems differ (Weinstein and Lorenzi, Nature, doi: 10.1038/nature12839, 2013), we are able to use the similarities and differences in response to ‘triangulate’ for more incisive and extensive information on the robustness of molecular mechanisms suggested by each separately. Even if single pharmacological measurements for a given cell/drug pair show considerable variability across different assay types (Haibe-Kains et al. Nature doi: 10.1038/nature12831, 2013), we find that integrated analysis of the patterns of response across the 60 cell lines and the hundreds of cell lines are often pathognomonic for particular genomic drivers or particular functional pathways.

Citation Format: John N. Weinstein, Bo Peng, William C. Reinhold, Yves Pommier, Philip L. Lorenzi. Drug mechanisms of action: Triangulating with cultured cancer cells. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3788. doi:10.1158/1538-7445.AM2014-3788

Abstract 5327: Using CellMiner for gene expression, DNA copy number, microRNA transcript levels, variant status, drug activity, and their integration for systems pharmacology for the NCI-60

Molecular biology and pharmacology are being transformed by high-throughput data. Access, and integration of this data, however, remains difficult. The NCI-60 cancerous cell lines are arguably the premier model for data integration and systems pharmacology. To make this data accessible for both bioinformaticists and non-bioinformaticists, we have developed the CellMiner set of web-based tools (http://discover.nci.nih.gov/cellminer). These tools currently or in the near future will provide “Cell line signatures” for gene and microRNA transcript expression, DNA copy number variation, genetic variation, and drug or compound activity. This data includes relative levels of transcript expression for 26,065 genes, 360 microRNAs, activities of 19,940 compounds including 110 Food and Drug Administration (FDA)-approved and 53 in clinical trial drugs, and percent conversions of 16,381 genetic variants (both germline and somatic). Current or upcoming tools facilitating the combination of this data include i) “Pattern comparison”, which provides correlations to a users input pattern to transcript and microRNA expression and compound/drug activity, ii) “Cross correlation”, which provides all correlations between up to 150 input genes, microRNAs, and compounds or drugs, iii) “Genetic variation versus drug visualization”, which provides a rapid visualization of potential drug: gene DNA variant relationships, and iv) “Genetic variant summation”, which provides a synopsis of mutational burden on any pathway of interest for up to 150 genes. These tools allow the user to flexibly query the data for potential relationships between molecular and pharmacological parameters, in a manner specific to that users area of expertise and interest.

Citation Format: William C. Reinhold, Margot Sunshine, Sudir Varma, Joel Morris, Kurt Kohn, James Doroshow, Yves Pommier. Using CellMiner for gene expression, DNA copy number, microRNA transcript levels, variant status, drug activity, and their integration for systems pharmacology for the NCI-60. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 5327. doi:10.1158/1538-7445.AM2014-5327

Using drug response data to identify molecular effectors, and molecular "omic" data to identify candidate drugs in cancer

The current convergence of molecular and pharmacological data provides unprecedented opportunities to gain insights into the relationships between the two types of data. Multiple forms of large-scale molecular data, including but not limited to gene and microRNA transcript expression, DNA somatic and germline variations from next-generation DNA and RNA sequencing, and DNA copy number from array comparative genomic hybridization are all potentially informative when one attempts to recognize the panoply of potentially influential events both for cancer progression and therapeutic outcome. Concurrently, there has also been a substantial expansion of the pharmacological data being accrued in a systematic fashion. For cancer cell lines, the National Cancer Institute cell line panel (NCI-60), the Cancer Cell Line Encyclopedia (CCLE), and the collaborative Genomics of Drug Sensitivity in Cancer (GDSC) databases all provide subsets of these forms of data. For the patient-derived data, The Cancer Genome Atlas (TCGA) provides analogous forms of genomic information along with treatment histories. Integration of these data in turn relies on the fields of statistics and statistical learning. Multiple algorithmic approaches may be chosen, depending on the data being considered, and the nature of the question being asked. Combining these algorithms with prior biological knowledge, the results of molecular biological studies, and the consideration of genes as pathways or functional groups provides both the challenge and the potential of the field. The ultimate goal is to provide a paradigm shift in the way that drugs are selected to provide a more targeted and efficacious outcome for the patient.

Gene expression correlations in human cancer cell lines define molecular interaction networks for epithelial phenotype

Using gene expression data to enhance our knowledge of control networks relevant to cancer biology and therapy is a challenging but urgent task. Based on the premise that genes that are expressed together in a variety of cell types are likely to functions together, we derived mutually correlated genes that function together in various processes in epithelial-like tumor cells. Expression-correlated genes were derived from data for the NCI-60 human tumor cell lines, as well as data from the Broad Institute's CCLE cell lines. NCI-60 cell lines that selectively expressed a mutually correlated subset of tight junction genes served as a signature for epithelial-like cancer cells. Those signature cell lines served as a seed to derive other correlated genes, many of which had various other epithelial-related functions. Literature survey yielded molecular interaction and function information about those genes, from which molecular interaction maps were assembled. Many of the genes had epithelial functions unrelated to tight junctions, demonstrating that new function categories were elicited. The most highly correlated genes were implicated in the following epithelial functions: interactions at tight junctions (CLDN7, CLDN4, CLDN3, MARVELD3, MARVELD2, TJP3, CGN, CRB3, LLGL2, EPCAM, LNX1); interactions at adherens junctions (CDH1, ADAP1, CAMSAP3); interactions at desmosomes (PPL, PKP3, JUP); transcription regulation of cell-cell junction complexes (GRHL1 and 2); epithelial RNA splicing regulators (ESRP1 and 2); epithelial vesicle traffic (RAB25, EPN3, GRHL2, EHF, ADAP1, MYO5B); epithelial Ca(+2) signaling (ATP2C2, S100A14, BSPRY); terminal differentiation of epithelial cells (OVOL1 and 2, ST14, PRSS8, SPINT1 and 2); maintenance of apico-basal polarity (RAB25, LLGL2, EPN3). The findings provide a foundation for future studies to elucidate the functions of regulatory networks specific to epithelial-like cancer cells and to probe for anti-cancer drug targets.

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.

The exomes of the NCI-60 panel: a genomic resource for cancer biology and systems pharmacology

The NCI-60 cell lines are the most frequently studied human tumor cell lines in cancer research. This panel has generated the most extensive cancer pharmacology database worldwide. In addition, these cell lines have been intensely investigated, providing a unique platform for hypothesis-driven research focused on enhancing our understanding of tumor biology. Here, we report a comprehensive analysis of coding variants in the NCI-60 panel of cell lines identified by whole exome sequencing, providing a list of possible cancer specific variants for the community. Furthermore, we identify pharmacogenomic correlations between specific variants in genes such as TP53, BRAF, ERBBs, and ATAD5 and anticancer agents such as nutlin, vemurafenib, erlotinib, and bleomycin showing one of many ways the data could be used to validate and generate novel hypotheses for further investigation. As new cancer genes are identified through large-scale sequencing studies, the data presented here for the NCI-60 will be an invaluable resource for identifying cell lines with mutations in such genes for hypothesis-driven research. To enhance the utility of the data for the greater research community, the genomic variants are freely available in different formats and from multiple sources including the CellMiner and Ingenuity websites.

Abstract 5219: Molecular phenotype of an epithelial-like subset of the NCI-60 human tumor cell lines and relevance to gene expression patterns in TCGA normal breast and breast cancer tissue samples

Better cancer therapy is likely to come from molecular characterization of individual patients’ tumors that will permit selection and development of tailored therapies. To that end, many bioinformatic studies have been carried out on human tumor cell lines and tissue samples. Large gene expression databases have become available for the National Cancer institute's 60 human tumor cell lines (NCI-60) and for normal breast and breast cancer tissue samples from the Cancer Genome Atlas (TCGA) Research Network. We aimed to infer functional relationships among expression-correlated genes based on these data and on published information about molecular interactions. In addition, we asked how or to what extent inferences based on cell line data may apply to human tissue samples.

Among the best-defined cell phenotypes are those of epithelia. Epithelial cells have polarity that distinguishes an apical region, directed towards a surface or lumen, from a basolateral region resting on a connective tissue basement membrane. Polarity is created by transport of appropriate molecular components as cargo in vesicles that are moved by motor proteins along cytoskeletal tracks to the relevant cell regions. Epithelial polarity is maintained by molecular structures, such as tight junctions, that prevent back-flow between the apical and basolateral surfaces of the cell.

We initially defined an epithelial-like phenotype on the basis of expression of genes coding for tight junction and adherence junction proteins and their family members. Among the NCI-60, we found 11 cell lines that express a mutually correlated subset of those genes. The relevant cell lines were breast cancer MCF7 and T47D; colon cancer COLO205, HCC_2998, HCT_116, HCT_15, HT29, and KM12; lung cancer NCI_H322M; and ovary cancer OVCAR_3 and OVCAR_4; we call these the NCI-60 epithelial consensus (NEC) cell lines. The following tight junction genes were selectively expressed in the NEC cell lines: TJP3, CLDN3, CLDN4, CLDN7, OCLN, MARVELD2, and MARVELD3. Highly correlated with these genes was the adherence junction gene CDH1/E-cadherin (as expected).

We then assembled an expanded list of 75 genes that were selectively expressed in the NEC cell lines, and found that many of them function in the epithelia-specific processes summarized above.

With some notable exceptions, the tight junction and adherence junction genes co-expressed in the NEC cell lines were also co-expressed in the normal breast and breast cancer tissue samples. An unexpected finding was that some of the normal breast tissue samples expressed a different set of tight junction family genes.

This work delineates similarities and differences between epithelial-like NCI-60 cell lines and TCGA breast tissue samples in regard to the expression of functionally defined genes.

Citation Format: Kurt W. Kohn, Barry R. Zeeberg, William C. Reinhold, Ari Kahn, Yves Pommier. Molecular phenotype of an epithelial-like subset of the NCI-60 human tumor cell lines and relevance to gene expression patterns in TCGA normal breast and breast cancer tissue samples. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5219. doi:10.1158/1538-7445.AM2013-5219

Abstract 2994: Using embryonic melanoblast transcriptome analysis to identify novel mechanisms promoting metastatic melanoma

Melanoma is among the most aggressive of cancers in its ability to metastasize. Although melanoma makes up &lt;6% of all skin cancers, it contributes to over 70% of skin cancer deaths. There is no other tumor type that consistently gains metastatic potential in a matter of millimeters. However, the genes and pathways involved in this deadly manifestation remain elusive. The enhanced ability of melanoma cells to metastasize is reminiscent of the innate propensity of melanoblasts to migrate for long distances during embryonic development - from the neural crest to the eventual niche across the skin of the whole body. Once transformed, melanoma cells mimic migratory and growth capability similar to that of the embryonic melanoblasts. Therefore we hypothesize that late stage metastatic melanoma can exploit pathways employed by embryonic melanoblasts to achieve a more aggressive malignant phenotype.

In the present study, we paint a novel picture of the oncological landscape based on the mouse melanoblast signature to reveal an intimate connection between tumorigenesis and developmental processes. We have, for the first time isolated and sequenced the transcriptomes of murine embryonic melanoblasts at several key representative developmental stages utilizing a newly developed genetically engineered mouse model with melanocyte-specific GFP expression. To uncover the overall classes of gene expression and to identify and characterize genesets whose expression is common and equally important to melanomagenic and developmental processes, a heat-map of the top 1000 most variable developmental genes was generated, and then shortlisted based on compared levels of expression in human and mouse metastatic melanomas and a relationship with melanoma patient survival data. A series of bioinformatics and meta-analyses led us to identify a small number of candidate genes. The resulting gene set was found to be related to early neural expression, epigenetic regulation, collagens, G-protein coupled receptors and calcium regulators. As a final step toward the identification and characterization of genes whose expression is common and equally important to both melanoma metastasis and melanoblast developmental processes, we are determining the consequences of RNAi-based knockdown on experimental metastasis potential in mouse models. Developmental genes that regulate melanoma metastatic behavior will be fully characterized. This approach should facilitate identification of novel therapeutic targets for melanoma treatment and diagnosis. Our study will attempt to provide insight into elements of melanocyte development that might prime them for metastasis in future malignancies. Pathways parallel between embryonic and metastatic melanoma cells will be identified and validated, offering both mechanistic and prognostic significance to our understanding of this fatal disease.

Citation Format: Pravin J. Mishra, Theresa Guo, Raza Zaidi, Sean Davis, Heinz Arnheiter, William C. Reinhold, Paul Meltzer, Glenn Merlino. Using embryonic melanoblast transcriptome analysis to identify novel mechanisms promoting metastatic melanoma. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 2994. doi:10.1158/1538-7445.AM2013-2994

Abstract 3177: Web-based access using CellMiner for gene expression, DNA copy number, microRNA transcript levels, variant status, drug activity, and their pattern comparisons for the NCI-60

High-throughput data is increasingly being integrated into the fields of biology, molecular biology, and pharmacology. However, a difficult problem has been the rapid and fluid access to and integration of the data, which tends to reside in huge unwieldy databases. One set of cell lines with substantial potential for benefit from this type of access and integration is the NCI-60 cancerous cell lines. We present here a set of tools within our CellMiner web-application designed to address this need for the areas of transcript expression, microRNA expression, gene DNA copy number, variant status, and drug activity. CellMiner allows the user to rapidly access data for relative levels of transcript expression for 26,065 genes, 360 microRNAs, and 20,602 compounds including 102 Food and Drug Administration (FDA)-approved drugs. These levels in turn create patterns across the NCI-60 that can be compared to one another using our “pattern match” tool. Together, these tools allow one to query the data for potential relationships between these parameters, in a manner specific to a users area of expertise and interest, in a rapid and flexible manner without the need for expertise in computer science or bioinformatics. Comparisons of transcript/drug will be demonstrated with SLFN11/topotecan; variant/drug with BRAF V600E/vemurafinib; colon tissue-of origin specific genes with TRIM15, RNF43, and VIL1; and DNA copy number change with CDKN2A (p16). The data are publicly available at http://discover.nci.nih.gov/cellminer.

Citation Format: William C. Reinhold, Margot Sunshine, Sudir Varma, Hongfang Liu, Ogan Abaan, Paul Meltzer, Joel Morris, Kurt Kohn, James Doroshow, Yves Pommier. Web-based access using CellMiner for gene expression, DNA copy number, microRNA transcript levels, variant status, drug activity, and their pattern comparisons for the NCI-60. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3177. doi:10.1158/1538-7445.AM2013-3177

Tumor suppressor miR-375 regulates MYC expression via repression of CIP2A coding sequence through multiple miRNA-mRNA interactions

miR-375 interacts with five conserved target sites in the CIP2A coding region, whereas the CIP2A 3′ UTR is not conserved across mammalian species. Reexpression of the tumor suppressor miR-375 in cancer cells represses the expression of CIP2A, resulting in a decrease in the MYC protein level and leading to reduced cell proliferation, migration, and invasion.

MicroRNAs (miRNAs) are small, noncoding RNAs involved in posttranscriptional regulation of protein-coding genes in various biological processes. In our preliminary miRNA microarray analysis, miR-375 was identified as the most underexpressed in human oral tumor versus controls. The purpose of the present study is to examine the function of miR-375 as a candidate tumor suppressor miRNA in oral cancer. Cancerous inhibitor of PP2A (CIP2A), a guardian of oncoprotein MYC, is identified as a candidate miR-375 target based on bioinformatics. Luciferase assay accompanied by target sequence mutagenesis elucidates five functional miR-375–binding sites clustered in the CIP2A coding sequence close to the C-terminal domain. Overexpression of CIP2A is clearly demonstrated in oral cancers, and inverse correlation between miR-375 and CIP2A is observed in the tumors, as well as in NCI-60 cell lines, indicating the potential generalized involvement of the miR-375–CIP2A relationship in many other cancers. Transient transfection of miR-375 in oral cancer cells reduces the expression of CIP2A, resulting in decrease of MYC protein levels and leading to reduced proliferation, colony formation, migration, and invasion. Therefore this study shows that underexpression of tumor suppressor miR-375 could lead to uncontrolled CIP2A expression and extended stability of MYC, which contributes to promoting cancerous phenotypes.

USP7 and Daxx regulate mitosis progression and taxane sensitivity by affecting stability of Aurora-A kinase

A large number of patients are resistant to taxane-based chemotherapy. Functional mitotic checkpoints are essential for taxane sensitivity. Thus, mitotic regulators are potential markers for therapy response and could be targeted for anticancer therapy. In this study, we identified a novel function of ubiquitin (Ub)-specific processing protease-7 (USP7) that interacts and cooperates with protein death domain-associated protein (Daxx) in the regulation of mitosis and taxane resistance. Depletion of USP7 impairs mitotic progression, stabilizes cyclin B and reduces stability of the mitotic E3 Ub ligase, checkpoint with forkhead and Ring-finger (CHFR). Consequently, cells with depleted USP7 accumulate Aurora-A kinase, a CHFR substrate, thus elevating multipolar mitoses. We further show that these effects are independent of the USP7 substrate p53. Thus, USP7 and Daxx are necessary to regulate proper execution of mitosis, partially via regulation of CHFR and Aurora-A kinase stability. Results from colony formation assay, in silico analysis across the NCI60 platform and in breast cancer patients suggest that USP7 levels inversely correlate with response to taxanes, pointing at the USP7 protein as a potential predictive factor for taxane response in cancer patients. In addition, we demonstrated that inhibition of Aurora-A attenuates USP7-mediated taxane resistance, suggesting that combinatorial drug regimens of Taxol and Aurora-A inhibitors may improve the outcome of chemotherapy response in cancer patients resistant to taxane treatment. Finally, our study offers novel insights on USP7 inhibition as cancer therapy.