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Methylation gene probe selection and correlation to expression level.

To organize the rapidly accumulating information on bioregulatory networks related to the histone γ-H2AX-ATM-Chk2-p53-Mdm2 pathways in concise and unambiguous diagrams, we used the molecular interaction map notation (http://discover.nci.nih.gov/min). Molecular interaction maps are particularly useful for networks that include protein-protein binding and posttranslational modifications (e.g., phosphorylation). Both are important for nearly all of the proteins involved in DNA double-strand break signaling. Visualizing the regulatory circuits underlying cellular signaling may help identify key regulatory reactions and defects that can serve as targets for anticancer drugs.

Supplementary Figure 5 from Chk2 Molecular Interaction Map and Rationale for Chk2 Inhibitors

Supplementary Figure 4 from Chk2 Molecular Interaction Map and Rationale for Chk2 Inhibitors

Legends for Supplemental Figures S1-S2.

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.

Correlations between expression and DNA methylation for 17,144 transcripts.

As part of the Spotlight on Molecular Profiling series, we present here new profiling studies of mRNA and microRNA expression for the 60 cell lines of the National Cancer Institute (NCI) Developmental Therapeutics program (DTP) drug screen (NCI-60) using the 41,000-probe Agilent Whole Human Genome Oligo Microarray and the 15,000-feature Agilent Human microRNA Microarray V2. The expression levels of ∼21,000 genes and 723 human microRNAs were measured. These profiling studies include quadruplicate technical replicates for six and eight cell lines for mRNA and microRNA, respectively, and duplicates for the remaining cell lines. The resulting data sets are freely available and searchable online in our CellMiner database. The result indicates high reproducibility for both platforms and an essential biological similarity across the various cell types. The mRNA and microRNA expression levels were integrated with our previously published 1,429-compound database of anticancer activity obtained from the NCI DTP drug screen. Large blocks of both mRNAs and microRNAs were identified with predominately unidirectional correlations to ∼1,300 drugs, including 121 drugs with known mechanisms of action. The data sets presented here will facilitate the identification of groups of mRNAs, microRNAs, and drugs that potentially affect and interact with one another. Mol Cancer Ther; 9(5); 1080–91. ©2010 AACR.

Supplementary Data from mRNA and microRNA Expression Profiles of the NCI-60 Integrated with Drug Activities

Figure 4, Tables 4-8, supplementary text

Supplementary Table S5A from Cancers as Wounds that Do Not Heal: Differences and Similarities between Renal Regeneration/Repair and Renal Cell Carcinoma

Topoisomerase I (Top1) relaxes DNA supercoiling by forming transient cleavage complexes (Top1cc) up- and downstream of transcription complexes. Top1cc can be trapped by carcinogenic and endogenous DNA lesions and by camptothecin, resulting in transcription blocks. Here, we undertook genome-wide analysis of camptothecin-treated cells at exon resolution. RNA samples from HCT116 and MCF7 cells were analyzed with the Affy Exon Array platform, allowing high-resolution mapping along 18,537 genes. Long genes that are highly expressed were the most susceptible to downregulation, whereas short genes were preferentially upregulated. Along the body of genes, downregulation was most important toward the 3′-end and increased with the number of exon–intron junctions. Ubiquitin and RNA degradation-related pathway genes were selectively downregulated. Parallel analysis of microRNA with the Agilent miRNA microarray platform revealed that miR-142-3p was highly induced by camptothecin. More than 10% of the downregulated genes were targets of this p53-dependent microRNA. Our study shows the profound impact of Top1cc on transcription elongation, especially at intron–exon junctions and on transcript stability by microRNA miR-142-3p upregulation. Cancer Res; 73(15); 4830–9. ©2013 AACR.

PDF file - 2059K, Importance of Exon numbers for transcript downregulation by CPT (SF1); CPT up-regulates the p53 signaling pathway genes (SF2); Down-regulation of RNA degradation-related genes after CPT treatment (SF3); Down-regulation of ubiquitin-related genes after CPT treatment (SF4); Top1 down-regulation does not impacts gene expression for CUL5 and UBE2W (SF5); miR-142-3p decreases cell survival, and down-regulation of its target genes BCL2L1 and MAP3K7IP2 sensitize to CPT (SF6). *** List of Primers (ST1); List of the 3808 down-regulated genes in CPT-treated HCT116 cells, by 2-fold or more (ST2); List of the 835 up-regulated genes in CPT-treated HCT116 cells, by 2-fold or more (ST3).

Supplementary Figure S4 from Cancers as Wounds that Do Not Heal: Differences and Similarities between Renal Regeneration/Repair and Renal Cell Carcinoma

Cancers have been described as wounds that do not heal, suggesting that the two share common features. By comparing microarray data from a model of renal regeneration and repair (RRR) with reported gene expression in renal cell carcinoma (RCC), we asked whether those two processes do, in fact, share molecular features and regulatory mechanisms. The majority (77%) of the genes expressed in RRR and RCC were concordantly regulated, whereas only 23% were discordant (i.e., changed in opposite directions). The orchestrated processes of regeneration, involving cell proliferation and immune response, were reflected in the concordant genes. The discordant gene signature revealed processes (e.g., morphogenesis and glycolysis) and pathways (e.g., hypoxia-inducible factor and insulin-like growth factor-I) that reflect the intrinsic pathologic nature of RCC. This is the first study that compares gene expression patterns in RCC and RRR. It does so, in particular, with relation to the hypothesis that RCC resembles the wound healing processes seen in RRR. However, careful attention to the genes that are regulated in the discordant direction provides new insights into the critical differences between renal carcinogenesis and wound healing. The observations reported here provide a conceptual framework for further efforts to understand the biology and to develop more effective diagnostic biomarkers and therapeutic strategies for renal tumors and renal ischemia. (Cancer Res 2006; 66(14): 7216-24)

Supplementary Figures 1-2, Tables 1-4 from Genome-wide Analysis of Novel Splice Variants Induced by Topoisomerase I Poisoning Shows Preferential Occurrence in Genes Encoding Splicing Factors

SUMMARY 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., In Brief Tlemsani et al. provide a unique resource, SCLC-CellMiner, integrating drug sensitivity and multi-omics data from 118 small cell lung cancer (SCLC) cell lines. They demonstrate that SCLCs have differential transcriptional networks driven by lineage-specific transcription factors (NEUROD1, ASCL1, POU2F3, and YAP1). Furthermore, YAP1-driven SCLCs have distinct drug sensitivity profiles., Graphical Abstract

SummaryModel systems are necessary to understand the biology of SCLC and develop new therapies against this recalcitrant disease. Here we provide the first online resource, CellMiner-SCLC (https://discover.nci.nih.gov/SclcCellMinerCDB) incorporating 118 individual SCLC cell lines and extensive omics and drug sensitivity datasets, including high resolution methylome performed for the purpose of the current study. We demonstrate the reproducibility of the cell lines and genomic data across the CCLE, GDSC, CTRP, NCI and UTSW datasets. We validate the SCLC classification based on four master transcription factors: NEUROD1, ASCL1, POU2F3 and YAP1 (NAPY classification) and show transcription networks connecting each them with their downstream and upstream regulators as well as with the NOTCH and HIPPO pathways and the MYC genes (MYC, MYCL1 and MYCN). We find that each of the 4 subsets express specific surface markers for antibody-targeted therapies. The SCLC-Y cell lines differ from the other subsets by expressing the NOTCH pathway and the antigen-presenting machinery (APM), and responding to mTOR and AKT inhibitors. Our analyses suggest the potential value of NOTCH activators, YAP1 inhibitors and immune checkpoint inhibitors in SCLC-Y tumors that can now be independently validated.Graphical AbstractHighlightsSCLC-CellMiner provides the most extensive SCLC resource in terms of number of cell lines (118 cell lines), extensive omics data (exome, microarray, RNA-seq, copy number, methylomes and microRNA) and drug sensitivity testing.We find evidence of distinct epigenetic profile of SCLC cell lines (global hypomethylation and histone gene methylation), which is consistent with their plasticity.Transcriptome analyses demonstrate the coherent transcriptional networks associated with the 4 main genomic subgroups (NEUROD1, ASCL1, POU2F3 & YAP1 = NAPY classification) and their connection with the NOTCH and HIPPO signaling pathways.SCLC-CellMiner provides a conceptual framework for the selection of therapies for SCLC in a personalized fashion allowing putative biomarkers according molecular classifications and molecular characteristics.SCLC-Y cell lines differ from the other cancer cell lines; their transcriptome resemble NSCLC cell lines. YAP1 cell lines while being the most resistant to standard of care treatments (etoposide, cisplatin and topotecan) respond to mTOR and AKT inhibitors and present native immune predisposition suggesting sensitivity to immune checkpoint inhibitors.

Summary 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., Graphical Abstract, Highlights • CellMinerCDB integrates pharmacogenomic data of the major cancer cell line databases • It seamlessly enables genomic and drug data exploration within and across databases • It tests genomic data reproducibility and proposes drug response determinants • We expand the GDSC drug panel and advance LIX1L as a novel mesenchymal gene, Genomics; Bioinformatics; Biological Database; Cancer Systems Biology

Model systems are necessary to understand the biology of SCLC and develop new therapies against this recalcitrant disease. Here we provide the first online resource, CellMiner-SCLC (https://discover.nci.nih.gov/sclcCellMinerCDB) incorporating 118 individual SCLC cell lines and extensive omics and drug sensitivity datasets, including high resolution methylome performed for the purpose of the current study. We demonstrate the reproducibility of the cell lines and genomic data across the CCLE, GDSC, CTRP, NCI and UTSW datasets. We validate the SCLC classification based on four master transcription factors: NEUROD1, ASCL1, POU2F3 and YAP1 (NAPY classification) and show transcription networks connecting each them with their downstream and upstream regulators as well as with the NOTCH and HIPPO pathways and the MYC genes (MYC, MYCL1 and MYCN). We find that each of the 4 subsets express specific surface markers for antibody-targeted therapies. The SCLC-Y cell lines differ from the other subsets by expressing the NOTCH pathway and the antigen-presenting machinery (APM), and responding to mTOR and AKT inhibitors. Our analyses suggest the potential value of NOTCH activators, YAP1 inhibitors and immune checkpoint inhibitors in SCLC-Y tumors that can now be independently validated.

The typical low life expectancy and limited therapeutic options for patients with small cell lung cancer (SCLC) caused the National Cancer Institute (NCI) to categorize SCLC as “recalcitrant” cancer. SCLC-CellMiner (https://discover.nci.nih.gov/SclcCellMinerCDB) integrates drug sensitivity and genomic data from 118 patient-derived SCLC cell lines, providing a unique genomic and pharmacological resource. Transcriptomic profiling validates the SCLC consensus nomenclature based on expression of 4 master transcription factors NEUROD1, ASCL1, POU2F3 and YAP1 (NAPY classification) and demonstrate differential transcriptional networks driven by these lineage specific transcription factors. Our analyses reveal transcription networks linking SCLC subtypes with MYC and its paralogs MYCL and MYCN and inactivation of the NOTCH pathway in the neuroendocrine SCLC (N, A & P subgroups). By contrast, YAP1-driven SCLC (SCLC-Y) express the NOTCH pathway and co-express both YAP/TAZ and its negative regulator genes driving the Hippo pathway. SCLC-Y cell lines show the greatest resistance to the standard of care drugs (etoposide, cisplatin and topotecan) while PI3K-AKT-mTOR inhibitors show a higher activity in this subgroup. To explore the immune pathways and the potential value of the transciption factors based classification for selecting SCLC patients likely to respond to immune checkpoint inhibitors, we explored a transcriptome signature based on 18 established native immune response and antigen-presenting genes (APM score). The SCLC-Y cell lines are the only subset expressing innate immune response genes. SCLC-CellMiner is a powerfull tool demonstrating the value of cancer cell line genomic and pharmacological databases. Our analyses suggest the potential genomic molecular classifications to select patients for targeted therapies and immunotherapy, such as patients in the SCLC-Y subgroup who may be most responsive to immune checkpoints modulators. Citation Format: Camille Tlemsani, Lorinc Pongor, Fathi Elloumi, Luc Girard, Kenneth Huffman, Nitin Roper, Sudhir Varma, Augustin Luna, Vinodh Rajapakse, Pascaline Boudou-Rouquette, Robin Sebastian, Kurt Kohn, Julia Krushkal, Mirit Aladjem, Beverly Teicher, Paul Meltzer, William Reinhold, John Minna, Anish Thomas, Yves Pommier. SCLC-CellMiner: An extensive cell line genomic and pharmacology resource identifies a subgroup of small cell lung cancers sensitive to targeted therapies and immunotherapies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 203.

Sarcomas represent a heterogeneous group of cancers with many histological subtypes. Their prognosis remains poor and treatment is mainly based on palliative chemotherapy at metastatic stage. Tumor genome sequencing failed to identify recurrent somatic drivers while several oncogenic gene fusion-translocations have been identified in specific sarcoma subtypes. Because of the rarity and heterogeneity of sarcomas, only few sarcoma patients are enrolled in clinicals trials according their subtype. Increased translational research according subtypes of sarcoma patients is needed to improve patient management. To characterize the differences between subgroups and build translational hypotheses, we built a novel resource, Sarcoma-Cellminer, which integrates drug sensitivity and genomic data from 112 patient-derived sarcoma cell lines. These data will be available from a web-based tool (https://discover.nci.nih.gov/SclcCellMinerCDB/) derived from our CellMiner cross-database web application (https://discover.nci.nih.gov/cellminercdb). Among the 112 cell lines, 65 are bone sarcomas (including 38 Ewing sarcomas and 22 osteosarcomas), 45 are soft tissue sarcomas (including 21 rhabdomyosarcomas). Transcriptome (RNAseq and microarray), copy number, microRNA, genome-wide methylation, and drug sensitivity data are included and made publicly available. We also generated new genomic data including copy number and methylation (850 k) for 79 cell lines from the NCI in addition to the 42 cell lines from Broad Institute (CCLE) and the 40 cell lines from the MGH-Sanger (GDSC). We created the “SCLC-Global” expression set by regrouping all datasets by Z-score normalization, which enables cross-database analyses of gene expression and molecular pathways. Hierarchical clustering based on expression and methylation data identifies subtypes of sarcomas. Histone genes stand out suggesting that epigenetic regulation of canonical histones is a feature of sarcoma genesis. Sarcoma-CellMiner includes drug sensitivity data for over 500 different drugs tested in the NCI, CCLE and GDSC databases. They show two subgroups of Ewing sarcomas: one sensitive to PARP inhibitors and one resistant. Similarly, profile of response to dasatinib is different when comparing alveolar and embryonal rhabdomyosarcomas. Sarcoma-CellMiner is a powerful tool demonstrating the value of patient-derived cancer cell line databases. It provides hypothesis-driven rationale for using omics, especially transcriptome and epigenetic data to better understand sarcoma heterogeneity and select personalized treatments for clinical trials. Citation Format: Camille Tlemsani, Lorinc Pongor, Javed Khan, Fathi Elloumi, Sudhir Varma, Augustin Luna, Vinodh Rajapakse, Kurt Kohn, Julia Krushkal, Mirit Aladjem, Beverly Teicher, Paul Meltzer, William Reinhold, Christine Heske, Yves Pommier. Sarcoma-CellMiner: An extensive resource for patient-derived sarcoma cell line epigenetics, genomics and pharmacology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 212.

As precision medicine demands molecular determinants of drug response, CellMinerCDB provides (https://discover.nci.nih.gov/cellminercdb/) a web-based portal for multiple forms of pharmacological, molecular, and genomic analyses, unifying the richest cancer cell line datasets (NCI-60, NCI-SCLC, Sanger/MGH GDSC, and Broad CCLE/CTRP). CellMinerCDB enables genomic and pharmacological data queries for identifying pharmacogenomic determinants, drug signatures, and gene regulatory networks for researchers without requiring specialized bioinformatics support. It leverages overlaps of cell lines and tested drugs to allow assessment of data reproducibility. It builds on the complementarity and strength of each dataset. A panel of 41 drugs evaluated in parallel in the NCI-60 and GDSC is reported, supporting drug reproducibility across databases, repositioning of bisacodyl and acetalax for triple negative breast cancer, and identifying novel drug response determinants and genomic signatures for topoisomerase inhibitors and schweinfurthins in development. CellMinerCDB also allowed the identification of LIX1L as a novel mesenchymal gene regulating cellular migration and invasiveness.

CellMinerCDB provides a web-based resource and portal for integrating multiple forms of pharmacological and genomic analyses, 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 identifying pharmacogenomic determinants and drug signatures. It leverages overlaps of cell lines and drugs. A panel of 41 drugs was evaluated in parallel in the NCI-60 and GDSC, supporting reproducibility and further pathway analyses across databases. We demonstrate the value of CellMinerCDB for elucidating gene expression determinants, such as methylation and copy number variation; highlight difficulties in assessing mutation burden; the dominant role of SLFN11 for drug response; novel response determinants and genomic signatures for topoisomerase inhibitors and schweinfurthins; drug repositioning for bisacodyl and acetalax for triple negative breast cancer; and the discovery of LIX1L as a novel mesenchymal gene regulating cancer cellular migration and invasiveness.

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 from, 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.

High-throughput and high-content databases are increasingly important resources in molecular medicine, systems biology, and pharmacology. However, the information usually resides in unwieldy databases, limiting ready data analysis and integration. One resource that offers substantial potential for improvement in this regard is the NCI-60 cell line database compiled by the U.S. National Cancer Institute, which has been extensively characterized across numerous genomic and pharmacologic response platforms. In this report, we introduce a CellMiner (http://discover.nci.nih.gov/cellminer/) web application designed to improve the use of this extensive database. CellMiner tools allowed rapid data retrieval of transcripts for 22,379 genes and 360 microRNAs along with activity reports for 20,503 chemical compounds including 102 drugs approved by the U.S. Food and Drug Administration. Converting these differential levels into quantitative patterns across the NCI-60 clarified data organization and cross-comparisons using a novel pattern match tool. Data queries for potential relationships among parameters can be conducted in an iterative manner specific to user interests and expertise. Examples of the in silico discovery process afforded by CellMiner were provided for multidrug resistance analyses and doxorubicin activity; identification of colon-specific genes, microRNAs, and drugs; microRNAs related to the miR-17-92 cluster; and drug identification patterns matched to erlotinib, gefitinib, afatinib, and lapatinib. CellMiner greatly broadens applications of the extensive NCI-60 database for discovery by creating web-based processes that are rapid, flexible, and readily applied by users without bioinformatics expertise. Cancer Res; 72(14); 3499–511. ©2012 AACR.

The “apoptotic ring” is characterized by the phosphorylation of histone H2AX at serine 139 (γ-H2AX) by DNA-dependent protein kinase (DNA-PK). The γ-H2AX apoptotic ring differs from the nuclear foci patterns observed in response to DNA-damaging agents. It contains phosphorylated DNA damage response proteins including activated Chk2, activated ATM, and activated DNA-PK itself but lacks MDC1 and 53BP1, which are required to initiate DNA repair. Because DNA-PK can phosphorylate heat shock protein 90α (HSP90α) in biochemical assays, we investigated whether HSP90α is involved in the apoptotic ring. Here we show that HSP90α is phosphorylated by DNA-PK on threonines 5 and 7 early during apoptosis and that both phosphorylated HSP90α and DNA-PK colocalize in the apoptotic ring. We also show that DNA-PK is a client of HSP90α and that HSP90α is required for full DNA-PK activation, γ-H2AX formation, DNA fragmentation, and apoptotic body formation. In contrast, HSP90 inhibition by geldanamycin markedly enhances TRAIL-induced DNA-PK and H2AX activation. Together, our results reveal that HSP90α is a substrate and chaperone of DNA-PK in the apoptotic response. The response of phosphorylated HSP90α to TRAIL and its localization to the γ-H2AX ring represent epigenetic features of apoptosis that offer insights for studying and monitoring nuclear apoptosis.

RNA splicing is required to remove introns from pre-mRNA, and alternative splicing generates protein diversity. Topoisomerase I (Top1) has been shown to be coupled with splicing by regulating serine/arginine-rich splicing proteins. Prior studies on isolated genes also showed that Top1 poisoning by camptothecin (CPT), which traps Top1 cleavage complexes (Top1cc), can alter RNA splicing. Here, we tested the effect of Top1 inhibition on splicing at the genome-wide level in human colon carcinoma HCT116 and breast carcinoma MCF7 cells. The RNA of HCT116 cells treated with CPT for various times was analyzed with ExonHit Human Splice Array. Unlike other exon array platforms, the ExonHit arrays include junction probes that allow the detection of splice variants with high sensitivity and specificity. We report that CPT treatment preferentially affects the splicing of splicing-related factors, such as RBM8A, and generates transcripts coding for inactive proteins lacking key functional domains. The splicing alterations induced by CPT are not observed with cisplatin or vinblastine and are not simply due to reduced Top1 activity, as Top1 downregulation by short interfering RNA did not alter splicing like CPT treatment. Inhibition of RNA polymerase II (Pol II) hyperphosphorylation by 5,6-dichloro-1-β-d-ribofuranosylbenzimidazole (DRB) blocked the splicing alteration induced by CPT, which suggests that the rapid Pol II hyperphosphorylation induced by CPT interferes with normal splicing. The preferential effect of CPT on genes encoding splicing factors may explain the abnormal splicing of a large number of genes in response to Top1cc. Cancer Res; 70(20); 8055–65. ©2010 AACR.

Aminoflavone (AF, NSC 686,288) is beginning clinical trials. It induces replication-mediated histone H2AX phosphorylation, DNA-protein crosslinks and activates p53. Here, we studied p21(CIP1/WAF1) and Mdm2 responses to AF. Although p53 stabilization and phosphorylation at serine 15 increased with dose and time of exposure, Mdm2 and p21(CIP1/WAF1) protein levels displayed a biphasic response, as they accumulated at submicromolar doses and then decreased with increasing AF. As both Mdm2 and p21(CIP1/WAF1) mRNA levels increased with AF concentration without reduction at higher concentrations, we measured the half-lives of Mdm2 and p21(CIP1/WAF1) proteins. Mdm2 and p21(CIP1/WAF1) half-lives were shortened with increasing AF concentrations. Proteasomal degradation appears responsible for the decrease of both Mdm2 and p21(CIP1/WAF1), as MG-132 prevented their degradation and revealed AF-induced Mdm2 polyubiquitylation. AF also induced protein kinase B (Akt) activation, which was reduced with increasing AF concentrations. Suppression of Akt by small interfering RNA was associated with downregulation of Mdm2 and p21(CIP1/WAF1) and with enhanced apoptosis. These results suggest that the cellular responses to AF are determined at least in part by Mdm2 and p21(CIP1/WAF1) protein levels, as well as by Akt activity, leading either to cell cycle arrest when Mdm2 and p21(CIP1/WAF1) are elevated, or to apoptosis when Mdm2 and p21(CIP1/WAF1) are degraded by the proteasome and Akt insufficiently activated to protect against apoptosis.

Cancers have been described as wounds that do not heal, suggesting that the two share common features. By comparing microarray data from a model of renal regeneration and repair (RRR) with reported gene expression in renal cell carcinoma (RCC), we asked whether those two processes do, in fact, share molecular features and regulatory mechanisms. The majority (77%) of the genes expressed in RRR and RCC were concordantly regulated, whereas only 23% were discordant (i.e., changed in opposite directions). The orchestrated processes of regeneration, involving cell proliferation and immune response, were reflected in the concordant genes. The discordant gene signature revealed processes (e.g., morphogenesis and glycolysis) and pathways (e.g., hypoxia-inducible factor and insulin-like growth factor-I) that reflect the intrinsic pathologic nature of RCC. This is the first study that compares gene expression patterns in RCC and RRR. It does so, in particular, with relation to the hypothesis that RCC resembles the wound healing processes seen in RRR. However, careful attention to the genes that are regulated in the discordant direction provides new insights into the critical differences between renal carcinogenesis and wound healing. The observations reported here provide a conceptual framework for further efforts to understand the biology and to develop more effective diagnostic biomarkers and therapeutic strategies for renal tumors and renal ischemia. (Cancer Res 2006; 66(14): 7216-24)

RecQ helicase BLM-deficient cells are characteristically hypersensitive to 4-nitroquinoline-1-oxide (4NQO). We recently reported that isogenic BLM-deficient cells (PNSG13) are more sensitive than BLM-complemented cells (PNSF5) to camptothecin, which specifically traps topoisomerase I cleavage complexes (Top1cc). We now report that PNSG13 are also 3.5-fold more sensitive to 4NQO compared with PNSF5 and that 4NQO induces higher levels of Top1cc and reduced histone γ-H2AX in PSNG13 than in PNSF5. Similarly, 4NQO induces more Top1cc in primary fibroblasts from a patient with Bloom syndrome than in normal human fibroblasts. 4NQO also induces Top1cc in colon cancer HCT116 and HT29 cells in a time- and concentration-dependent fashion. Of note, distinct from camptothecin, the Top1cc produced by 4NQO accumulate progressively after 4NQO addition and persist following 4NQO removal. The Top1cc induced by 4NQO are detectable by alkaline elution. To examine the functional relevance of the Top1cc induced by 4NQO, we used two stable topoisomerase I small interfering RNA (siRNA) cell lines derived from HCT116 and MCF7 cells. Both topoisomerase I siRNA cell lines are resistant to 4NQO, indicating that Top1cc contribute to the cellular activity of 4NQO. Collectively, these data show that 4NQO is an effective inducer of cellular Top1cc. Because 4NQO does not directly trap Top1cc in biochemical assays, we propose that active metabolites of 4NQO trap Top1cc by forming DNA adducts. Induction of Top1cc and histone γ-H2AX by 4NQO may contribute to the cellular effects of 4NQO, including its selective activity toward RecQ helicase BLM-deficient cells.(Cancer Res 2006; 66(13): 6540-5)

Camptothecin, originally discovered in 1957 as an antitumor activity in plant extracts, has recently become one of the most promising leads to new anticancer drugs. After lingering for many years, interest in camptothecin was revitalized in 1985 upon discovery of its specific action on topoisomerase I. Detailed elucidation of action mechanisms at the molecular, cellular, and pharmacologic levels has made camptothecin and its congeners perhaps the best understood among clinical anticancer drugs. Promising chemical variants of camptothecin, and recently other chemical categories of topoisomerase I-targeted drugs, provide unusually rich opportunities for rational drug selection and design. This is made possible by current concepts based, for the most part, on a sound experimental foundation, which points the way towards optimally effective therapy.

This study provides evidence for the importance of p21(CDKN1A) for the repair of replication-mediated DNA double-strand breaks (DSBs) induced by topoisomerase I. We report that defects of p21(CDKN1A) and p53 enhance camptothecin-induced histone H2AX phosphorylation (gammaH2AX), a marker for DNA DSBs. In human colon carcinoma HCT116 cells with wild-type (wt) p53, gammaH2AX reverses after camptothecin removal. By contrast, gammaH2AX increases after camptothecin removal in HCT116 cells deficient for p53 (p53-/-) or p21(CDKN1A) (p21-/-) as the cells reach the late-S and G2 phases. Since p21-/- cells exhibit similar S-phase arrest as wt cells in response to camptothecin and aphidicolin does not abrogate the enhanced gammaH2AX formation in p21-/- cells, we conclude that enhanced gammaH2AX formation in p21-/- cells is not due to re-replication. The cell cycle checkpoint abrogator and Chk1/Chk2 inhibitor 7-hydroxystaurosporine (UCN-01) also increases camptothecin-induced gammaH2AX formation and inhibits camptothecin-induced p21(CDKN1A) upregulation in HCT116 wt cells. TUNEL (terminal deoxynucleotidyl transferase (TdT)-mediated dUTP-biotin nick end labeling) assays demonstrate that gammaH2AX formation in late S and G2 cells following CPT treatment corresponds to DNA breaks. However, these breaks are not related to apoptotic DNA fragmentation. We propose that p21(CDKN1A) prevents the collapse of replication forks damaged by stabilized topoisomerase I cleavage complexes.

A standard for bioregulatory network diagrams is urgently needed in the same way that circuit diagrams are needed in electronics. Several graphical notations have been proposed, but none has become standard. We have prepared many detailed bioregulatory network diagrams using the molecular interaction map (MIM) notation, and we now feel confident that it is suitable as a standard. Here, we describe the MIM notation formally and discuss its merits relative to alternative proposals. We show by simple examples how to denote all of the molecular interactions commonly found in bioregulatory networks. There are two forms of MIM diagrams. “Heuristic” MIMs present the repertoire of interactions possible for molecules that are colocalized in time and place. “Explicit” MIMs define particular models (derived from heuristic MIMs) for computer simulation. We show also how pathways or processes can be highlighted on a canonical heuristic MIM. Drawing a MIM diagram, adhering to the rules of notation, imposes a logical discipline that sharpens one's understanding of the structure and function of a network.

Intrinsic (innate) and acquired (adaptive) resistance to chemotherapy critically limits the outcome of cancer treatments. For many years, it was assumed that the interaction of a drug with its molecular target would yield a lethal lesion, and that determinants of intrinsic drug resistance should therefore be sought either at the target level (quantitative changes or/and mutations) or upstream of this interaction, in drug metabolism or drug transport mechanisms. It is now apparent that independent of the factors above, cellular responses to a molecular lesion can determine the outcome of therapy. This review will focus on programmed cell death (apoptosis) and on survival pathways (Bcl-2, Apaf-1, AKT, NF-kappaB) involved in multidrug resistance. We will present our molecular interaction mapping conventions to summarize the AKT and IkappaB/NF-kappaB networks. They complement the p53, Chk2 and c-Abl maps published recently. We will also introduce the 'permissive apoptosis-resistance' model for the selection of multidrug-resistant cells.

Topoisomerase inhibitors are among the most efficient inducers of apoptosis. The main pathways leading from topoisomerase-mediated DNA damage to cell death involve activation of caspases in the cytoplasm by proapoptotic molecules released from mitochondria. In some cells, apoptotic response also involves the death receptor Fas (APO-1/CD95). The engagement of these apoptotic effector pathways is tightly controlled by upstream regulatory pathways that respond to DNA lesions-induced by topoisomerase inhibitors in cells undergoing apoptosis. These include the proapoptotic Chk2, c-Abl and SAPK/JNK pathways, the survival PI(3)kinase-Akt-dependent pathway and the transcription factors p53 and NF-kappaB. Initiation of cellular responses to DNA lesions-induced by topoisomerase inhibitors is ensured by the protein kinases DNA-PK, ATM and ATR, which bind to DNA breaks. These kinases commonly called "DNA sensors" mediate their effects (DNA repair, cell cycle arrest and/or apoptosis) by phosphorylating a large number of substrates, including several downstream kinases such as c-Abl and the checkpoint protein Chk2. c-Abl induces apoptosis by activating cell death pathways (e.g., SAPK, p53 and p73) and inhibiting cell survival pathways [e.g., PI(3)kinase]. The DNA-damage regulating kinase Chk2, in addition to its role in cell cycle arrest and/or DNA repair, can induce apoptosis by phosphorylation/activation of the promyelocytic leukemia (PML) protein and p53. Finally, we will review the recent observations that support a role for topoisomerases in chromatin fragmentation during the execution phase of apoptosis.

La découverte de l’existence des gènes Cdc (cell division cycle) et des complexes cycline:Cdk, et de leur conservation dans l’évolution a valu à Leland H. Hartwell, Paul M. Nurse et R. Timothy Hunt le prix Nobel de physiologie et de médecine en 2001. Cet article fait le point des connaissances actuelles sur les molécules de régulation du cycle cellulaire, et met en avant l’importance de ces connaissances dans la compréhension des mécanismes responsables des cancers et dans le développement de nouvelles thérapeutiques en oncologie., Leland H. Hartwell, Paul M. Nurse et R. Timothy Hunt just received the Nobel price for their discovery of the molecular components of the cell cycle and cell cycle checkpoints. This review is an update of the molecular networks driving the cell cycle and its regulation, and of the importance of this knowledge for understanding the mechanisms driving oncogenesis and therapeutic developments.

We report that point mutations causing alteration of the fourth alpha-helix (α4-helix) of the CAP homology domain of eukaryotic ( Saccharomyces cerevisiae ) type II topoisomerases (Ser 740 Trp, Gln 743 Pro, and Thr 744 Pro) change the selection of type II topoisomerase-mediated DNA cleavage sites promoted by Ca 2+ or produced by etoposide, the fluoroquinolone CP-115,953, or mitoxantrone. By contrast, Thr 744 Ala substitution had minimal effect on Ca 2+ - and drug-stimulated DNA cleavage sites, indicating the selectivity of single amino acid substitutions within the α4-helix on type II topoisomerase-mediated DNA cleavage. The equivalent mutation in the gene for Escherichia coli gyrase causing Ser 83 Trp also changed the DNA cleavage pattern generated by Ca 2+ or quinolones. Finally, Thr 744 Pro substitution in the yeast type II topoisomerase rendered the enzyme sensitive to antibacterial quinolones. This study shows that the α4-helix within the conserved CAP homology domain of type II topoisomerases is critical for selecting the sites of DNA cleavage. It also demonstrates that selective amino acid residues in the α4-helix are important in determining the activity and possibly the binding of quinolones to the topoisomerase II-DNA complexes.

We used cDNA microarrays to assess gene expression profiles in 60 human cancer cell lines used in a drug discovery screen by the National Cancer Institute. Using these data, we linked bioinformatics and chemoinformatics by correlating gene expression and drug activity patterns in the NCI60 lines. Clustering the cell lines on the basis of gene expression yielded relationships very different from those obtained by clustering the cell lines on the basis of their response to drugs. Gene-drug relationships for the clinical agents 5-fluorouracil and L-asparaginase exemplify how variations in the transcript levels of particular genes relate to mechanisms of drug sensitivity and resistance. This is the first study to integrate large databases on gene expression and molecular pharmacology.

Recent results suggest that potentially lethal DNA lesions may result when replication forks encounter trapped topoisomerase-DNA complexes or some other types of DNA damage. Such events produce what are called replication-encounter lesions. These lesions have the characteristic that they may allow single stranded DNA-associated replication protein A (RPA) to become juxtaposed to dsDNA end-associated DNA-protein kinase. Our results suggest that DNA-protein kinases may then hyperphosphorylate the RPA2 subunit. We discuss a possible pathway by which hyperphosphorylation of RPA2 could lead to the release of active p53. This could constitute a pathway for signaling the presence of replication-encounter lesions to the p53-dependent cell cycle arrest and/or apoptosis initiator systems. *** DIRECT SUPPORT *** A01Q2011 00008