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• W2904362605 abstract "•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 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. 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. A critical aim of precision medicine is to match drugs with genomic determinants of response. Identifying tumor molecular features that affect response to specific drug treatments is especially challenging because of the typically encountered diversity of patient experiences, incomplete knowledge of the multiple molecular determinants of response and resistance factors downstream of the primary drug targets, and tumor heterogeneity. In this setting, the relative homogeneity of cell lines is advantageous, making them model systems for resolving and establishing cellular intrinsic drug response mechanisms. These features motivated the development of cancer cell line pharmacogenomic databases. Building on the NCI-60 paradigm (Abaan et al., 2013Abaan O.D. Polley E.C. Davis S.R. Zhu Y.J. Bilke S. Walker R.L. Pineda M. Gindin Y. Jiang Y. Reinhold W.C. et al.The exomes of the NCI-60 panel: a genomic resource for cancer biology and systems pharmacology.Cancer Res. 2013; 73: 4372-4382Crossref PubMed Scopus (208) Google Scholar, Reinhold et al., 2012Reinhold W.C. Sunshine M. Liu H. Varma S. Kohn K.W. Morris J. Doroshow J. Pommier Y. CellMiner: a web-based suite of genomic and pharmacologic tools to explore transcript and drug patterns in the NCI-60 cell line set.Cancer Res. 2012; 72: 3499-3511Crossref PubMed Scopus (429) Google Scholar, Reinhold et al., 2015Reinhold W.C. Sunshine M. Varma S. Doroshow J.H. Pommier Y. Using cellminer 1.6 for systems pharmacology and genomic analysis of the NCI-60.Clin. Cancer Res. 2015; 21: 3841-3852Crossref PubMed Scopus (59) Google Scholar, Reinhold et al., 2017Reinhold W.C. Varma S. Sunshine M. Rajapakse V. Luna A. Kohn K.W. Stevenson H. Wang Y. Heyn H. Nogales V. et al.The NCI-60 methylome and its integration into cellminer.Cancer Res. 2017; 77: 601-612Crossref PubMed Scopus (35) Google Scholar, Zoppoli et al., 2012Zoppoli G. Regairaz M. Leo E. Reinhold W.C. Varma S. Ballestrero A. Doroshow J.H. Pommier Y. Putative DNA/RNA helicase Schlafen-11 (SLFN11) sensitizes cancer cells to DNA-damaging agents.Proc. Natl. Acad. Sci. U S A. 2012; 109: 15030-15035Crossref PubMed Scopus (199) Google Scholar), pharmacogenomic data portals such as the Genomics of Drug Sensitivity in Cancer (GDSC) (Garnett et al., 2012Garnett M.J. Edelman E.J. Heidorn S.J. Greenman C.D. Dastur A. Lau K.W. Greninger P. Thompson I.R. Luo X. Soares J. et al.Systematic identification of genomic markers of drug sensitivity in cancer cells.Nature. 2012; 483: 570-575Crossref PubMed Scopus (1695) Google Scholar, Iorio et al., 2016Iorio F. Knijnenburg T.A. Vis D.J. Bignell G.R. Menden M.P. Schubert M. Aben N. Gonçalves E. Barthorpe S. Lightfoot H. et al.A landscape of pharmacogenomic interactions in cancer.Cell. 2016; 166: 740-754Abstract Full Text Full Text PDF PubMed Scopus (944) Google Scholar), the Cancer Cell Line Encyclopedia (CCLE) (Barretina et al., 2012Barretina J. Caponigro G. Stransky N. Venkatesan K. Margolin A.A. Kim S. Wilson C.J. Lehár J. Kryukov G.V. Sonkin D. et al.The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity.Nature. 2012; 483: 603-607Crossref PubMed Scopus (4820) Google Scholar, Cancer Cell Line Encyclopedia Consortium and Genomics of Drug Sensitivity in Cancer Consortium, 2015Cancer Cell Line Encyclopedia ConsortiumGenomics of Drug Sensitivity in Cancer ConsortiumPharmacogenomic agreement between two cancer cell line data sets.Nature. 2015; 528: 84-87Crossref PubMed Scopus (218) Google Scholar), and the Cancer Therapeutics Response Portal (CTRP) (Rees et al., 2016Rees M.G. Seashore-Ludlow B. Cheah J.H. Adams D.J. Price E.V. Gill S. Javaid S. Coletti M.E. Jones V.L. Bodycombe N.E. et al.Correlating chemical sensitivity and basal gene expression reveals mechanism of action.Nat. Chem. Biol. 2016; 12: 109-116Crossref PubMed Scopus (382) Google Scholar) have expanded to span ∼1,400 cancer cell lines. Each database provides a readily available resource for translational research, and proposals have been advanced to further enrich them to over 10,000 cancer cell lines for better coverage of tumor type diversity (Boehm and Golub, 2015Boehm J.S. Golub T.R. An ecosystem of cancer cell line factories to support a cancer dependency map.Nat. Rev. Genet. 2015; 16: 373-374Crossref PubMed Scopus (39) Google Scholar). The NCI-60 dataset includes drug activity data for over 21,000 compounds, together with a wide range of molecular profiling data (gene expression, mutations, copy number, methylation, and protein expression). The GDSC and CCLE collections focus on drug activity data for clinically relevant drugs over larger cell line sets, together with an array of molecular profiling data that match the NCI-60 and clinical genomic analyses. The CTRP provides independent drug activity data for nearly 500 compounds over cell lines spanning most of the CCLE and GDSC collections. Each source-specific portal allows deep exploration of its associated datasets, but does not allow immediate cross-database analyses. Yet, substantial overlaps in both cell lines and drugs have the potential to empower integrative analyses, building on the complementarity of the cancer cell line datasets. However, data complexity and mundane (but significant) sources of friction, such as differences in entity naming (cell lines, drugs) and data preparation, have until now made working across databases challenging, even for those with informatics training. To enable integrative analyses within and across data sources, we are introducing CellMinerCDB (https://discover.nci.nih.gov/cellminercdb/), a web application allowing immediate, interactive exploration of the richest cancer cell line genomic and pharmacogenomic databases (Figure 1). In CellMinerCDB, named entities are transparently matched across sources, allowing cell line molecular features and drug responses to be readily compared using bivariate scatterplots and correlation analyses. Multivariate models of drug response or any genomic cell line attribute can also be assessed. Analyses can be restricted to tissues of origin, with cell lines across all sources mapped to a uniform tissue type hierarchy. Gene pathway annotations allow assessment and filtering of analysis results. CellMinerCDB is built using the publicly available rcellminer R/Bioconductor package, which provides analyses and a standard data representation format (Luna et al., 2016Luna A. Rajapakse V.N. Sousa F.G. Gao J. Schultz N. Varma S. Reinhold W. Sander C. Pommier Y. rcellminer: exploring molecular profiles and drug response of the NCI-60 cell lines in R.Bioinformatics. 2016; 32: 1272-1274Crossref PubMed Scopus (24) Google Scholar). The latter also allows CellMinerCDB to be readily updated to include additional data. Although the rcellminer package (Luna et al., 2016Luna A. Rajapakse V.N. Sousa F.G. Gao J. Schultz N. Varma S. Reinhold W. Sander C. Pommier Y. rcellminer: exploring molecular profiles and drug response of the NCI-60 cell lines in R.Bioinformatics. 2016; 32: 1272-1274Crossref PubMed Scopus (24) Google Scholar) is available for bioinformaticists, it requires knowledge of the R programming language to install, configure, and conduct analyses. CellMinerCDB, by contrast, is accessible via a web-based interface meant for direct, general use. Furthermore, CellMinerCDB is enhanced with new data sources and analyses, including a wide range of fully interoperable pharmacogenomics datasets, as well as multivariate analyses that can be used to explore the biological complexity of these data. The accessibility of these analyses and breadth of available data make CellMinerCDB a unique resource for cancer cell line pharmacogenomic data exploration and hypothesis generation. Here we present CellMinerCDB (https://discover.nci.nih.gov/cellminercdb/), highlighting key features of molecular and drug data reproducibility, and complementarity across sources. We provide examples illustrating cancer biology explorations and drug response determinants. We propose the potential repurposing of oxyphenisatin acetate (acetalax; NSC59687) as an anticancer agent for triple-negative breast cancer. We demonstrate multivariate analyses for the exploration of genomic response determinants for topoisomerase inhibitors and schweinfurthins, a class of National Cancer Institute (NCI)-developed compounds derived from natural products. CellMinerCDB also provides phenotypic genomic signatures for cancer cell lines, including a gene-expression-based measure of epithelial-to-mesenchymal (EMT) transition status. We demonstrate the use of the latter to assess EMT stratification within specific tissues of origin, leading to the identification of a novel EMT gene, LIX1L. Detailed use of CellMinerCDB is described in a video tutorial (https://youtu.be/XljXazRGkQ8). CellMinerCDB integrates four prominent cancer cell line data sources: the CellMiner NCI-60 (Abaan et al., 2013Abaan O.D. Polley E.C. Davis S.R. Zhu Y.J. Bilke S. Walker R.L. Pineda M. Gindin Y. Jiang Y. Reinhold W.C. et al.The exomes of the NCI-60 panel: a genomic resource for cancer biology and systems pharmacology.Cancer Res. 2013; 73: 4372-4382Crossref PubMed Scopus (208) Google Scholar, Luna et al., 2016Luna A. Rajapakse V.N. Sousa F.G. Gao J. Schultz N. Varma S. Reinhold W. Sander C. Pommier Y. rcellminer: exploring molecular profiles and drug response of the NCI-60 cell lines in R.Bioinformatics. 2016; 32: 1272-1274Crossref PubMed Scopus (24) Google Scholar, Reinhold et al., 2015Reinhold W.C. Sunshine M. Varma S. Doroshow J.H. Pommier Y. Using cellminer 1.6 for systems pharmacology and genomic analysis of the NCI-60.Clin. Cancer Res. 2015; 21: 3841-3852Crossref PubMed Scopus (59) Google Scholar, Reinhold et al., 2017Reinhold W.C. Varma S. Sunshine M. Rajapakse V. Luna A. Kohn K.W. Stevenson H. Wang Y. Heyn H. Nogales V. et al.The NCI-60 methylome and its integration into cellminer.Cancer Res. 2017; 77: 601-612Crossref PubMed Scopus (35) Google Scholar), Sanger/Massachusetts General Hospital GDSC (Garnett et al., 2012Garnett M.J. Edelman E.J. Heidorn S.J. Greenman C.D. Dastur A. Lau K.W. Greninger P. Thompson I.R. Luo X. Soares J. et al.Systematic identification of genomic markers of drug sensitivity in cancer cells.Nature. 2012; 483: 570-575Crossref PubMed Scopus (1695) Google Scholar), the Broad/Novartis CCLE, the Broad CTRP (Barretina et al., 2012Barretina J. Caponigro G. Stransky N. Venkatesan K. Margolin A.A. Kim S. Wilson C.J. Lehár J. Kryukov G.V. Sonkin D. et al.The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity.Nature. 2012; 483: 603-607Crossref PubMed Scopus (4820) Google Scholar, Rees et al., 2016Rees M.G. Seashore-Ludlow B. Cheah J.H. Adams D.J. Price E.V. Gill S. Javaid S. Coletti M.E. Jones V.L. Bodycombe N.E. et al.Correlating chemical sensitivity and basal gene expression reveals mechanism of action.Nat. Chem. Biol. 2016; 12: 109-116Crossref PubMed Scopus (382) Google Scholar), and a tissue-specific dataset encompassing 66 small cell lung cancer lines (NCI-SCLC) (Polley et al., 2016Polley E. Kunkel M. Evans D. Silvers T. Delosh R. Laudeman J. Ogle C. Reinhart R. Selby M. Connelly J. et al.Small cell lung cancer screen of oncology drugs, investigational agents, and gene and microRNA expression.J. Natl. Cancer Inst. 2016; 108https://doi.org/10.1093/jnci/djw122Crossref PubMed Scopus (97) Google Scholar) (Figure 1). Collectively, these databases provide drug activity and molecular profiling data for approximately 1,400 distinct cancer cell lines (Figure 1B, Supplemental Information). Each source has particular strengths. The NCI-60 is unmatched with respect to the breadth of molecular profiling data, as well as the number of tested drugs, compounds, and natural products (>20,000). It also includes replicate data readily accessible via the established CellMiner data portal (Reinhold et al., 2015Reinhold W.C. Sunshine M. Varma S. Doroshow J.H. Pommier Y. Using cellminer 1.6 for systems pharmacology and genomic analysis of the NCI-60.Clin. Cancer Res. 2015; 21: 3841-3852Crossref PubMed Scopus (59) Google Scholar). The GDSC, CCLE, and CTRP sources feature much larger numbers of cell lines, spanning tissues of origin not included in the NCI-60. The range of tested compounds in these expanded cell line panels is narrow relative to the NCI-60, although the GDSC and CTRP focus on a wide range of clinically relevant anticancer drugs. The CTRP provides data for 170 US Food and Drug Administration (FDA)-approved or investigational anticancer drugs and 196 other compounds with mechanism of action information. The CTRP molecular data in CellMinerCDB are from the CCLE (Figure 1B). Despite ongoing data acquisition and processing efforts, gaps exist with respect to genomic profiling data (Figure 1B, dark gray table entries). For the GDSC gene mutation and methylation data, we took advantage of processing pipelines developed for the NCI-60 (Reinhold et al., 2014Reinhold W.C. Varma S. Sousa F. Sunshine M. Abaan O.D. Davis S.R. Reinhold S.W. Kohn K.W. Morris J. Meltzer P.S. et al.NCI-60 whole exome sequencing and pharmacological CellMiner analyses.PLoS One. 2014; 9: e101670Crossref PubMed Scopus (28) Google Scholar, Reinhold et al., 2017Reinhold W.C. Varma S. Sunshine M. Rajapakse V. Luna A. Kohn K.W. Stevenson H. Wang Y. Heyn H. Nogales V. et al.The NCI-60 methylome and its integration into cellminer.Cancer Res. 2017; 77: 601-612Crossref PubMed Scopus (35) Google Scholar) to compute gene-level summary data from publicly available raw data. Remaining source-specific molecular profiling data gaps can be filled within CellMinerCDB by effectively extending data provided by one source to another. This is possible because of extensive overlaps between tested cell lines and drugs (Figure 1). For example, gene-level methylation data are not publicly available for the CCLE, but GDSC methylation data are available for the matching 671 CCLE lines and 597 CTRP lines (Figure 1C). CellMinerCDB automatically matches synonymous cell line and drug names (https://discover.nci.nih.gov/cellminercdb/), freeing users from a mundane but time-consuming impediment to work across data sources. Integrative analyses presuppose data concordance across sources. Such analyses can be readily performed with CellMinerCDB because of the extensive overlaps across the cancer cell line databases: 55 of the NCI-60 lines are in GDSC and 44 are in CCLE, 671 lines (∼60%) are shared between CCLE and GDSC (Figure 1C), 40 of the 67 NCI-SCLC lines are in GDSC and 36 are in CCLE (Figure 1E), 74 drugs are in both GDSC and CTRP, and 63 drugs are in both NCI-60 and CTRP (Figure 1D). For the genomic data, we assessed concordance by computing Pearson's correlations between gene-specific molecular profiles over matched cell lines for all pairs of sources and comparable data types. The distributions of expression, copy number, and methylation data correlations indicate highly significant concordance across sources (Figure 2A). Concordance was also evident based on non-parametric Spearman's rank correlations (Figure S1, related to Figure 2A). For these analyses, gene-level transcript expression and methylation patterns with uniformly low values across matched cell lines were excluded due to their lack of meaningful pattern (Transparent Methods). The median correlations exceed 0.7 in all cases (Figure 2A). The striking concordance between NCI-60 and GDSC methylation data (median R = 0.97, median n = 52) may derive in part from the use of same technology platform (Reinhold et al., 2017Reinhold W.C. Varma S. Sunshine M. Rajapakse V. Luna A. Kohn K.W. Stevenson H. Wang Y. Heyn H. Nogales V. et al.The NCI-60 methylome and its integration into cellminer.Cancer Res. 2017; 77: 601-612Crossref PubMed Scopus (35) Google Scholar) and gene-level data summarization approach (Transparent Methods). Examples for specific genes are displayed in Figure S2 (related to Figure 2A), demonstrating the high data reproducibility for SLFN11 (Schlafen 11) expression in the NCI-60 versus GDSC, CDH1 (E-cadherin) expression in GDSC versus CCLE, SLFN11 methylation in the GDSC versus NCI-60, and CDKN2A (p16INK4/p19ARF) copy number in NCI-60 versus CCLE. Readers are invited to explore their own queries at https://discover.nci.nih.gov/cellminercdb/ by selecting a genomic feature for any given gene in two different datasets of their choice. Gene-level mutation values in CellMinerCDB indicate the probability that an observed mutation is homozygous and is function impacting. For genes with multiple deleterious mutations in a given cell line, values are converted to cumulative probability values (Reinhold et al., 2014Reinhold W.C. Varma S. Sousa F. Sunshine M. Abaan O.D. Davis S.R. Reinhold S.W. Kohn K.W. Morris J. Meltzer P.S. et al.NCI-60 whole exome sequencing and pharmacological CellMiner analyses.PLoS One. 2014; 9: e101670Crossref PubMed Scopus (28) Google Scholar), and are available in graphical and tabular forms at https://discover.nci.nih.gov/cellminercdb/. To compare mutation profiles across sources, we binarized the matched cell line data by assigning a value of 1 to lines with an aforementioned probability value greater than 0.3. This value was selected to be below the formally expected value of 0.5 for a heterozygous mutation to allow for technical variability. Entirely matched mutation profiles across sources should have a Jaccard index value of 1. As such, the similarity index distributions indicate greater discordance for the mutation data (Figure 2B) than for the other types of genomic data (Figure 2A). The similarity distribution values are higher for the NCI-60 (NCI-60/GDSC median J = 0.5, n = 55; NCI-60/CCLE median J = 0.71, n = 39) than for the GDSC/CCLE comparison (median J = 0.38, n = 593). One caveat, however, is that the large cell line database comparisons entail far larger numbers of matched cell lines. Indeed, the Jaccard similarity values approaching 1 with the NCI-60 comparisons often derive from just one or two matched mutant cell lines. We used similar processing steps to derive gene-level mutation data from variant call data for the NCI-60, GDSC, and CCLE (Transparent Methods). Still, inconsistencies were notable. Differences between the underlying sequencing technologies and initial data preparation methods are likely to account for the observed discrepancies between the gene mutation data across the datasets. For example, the CCLE mutation data were obtained for a selected set of 1,667 cancer-associated genes subject to high-depth exome capture sequencing (Barretina et al., 2012Barretina J. Caponigro G. Stransky N. Venkatesan K. Margolin A.A. Kim S. Wilson C.J. Lehár J. Kryukov G.V. Sonkin D. et al.The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity.Nature. 2012; 483: 603-607Crossref PubMed Scopus (4820) Google Scholar). They consistently yielded the largest numbers of cell lines with function-impacting mutations. The greater number of mutations found for KRAS, PTEN, BRAF, NRAS, or MSH6 in CCLE relative to the GDSC or NCI-60 databases (evaluated by global exome sequencing; Figure S3, related to Figures 2C and 2D) reflects the importance of sequencing depth for accurate assessment of mutations. For a more focused and translational assessment of mutation data concordance, we examined the overlap between sources for established oncogenes and tumor suppressor genes (Figures 2C and 2D, Table S1). For the tumor suppressors, we binarized the data using a probability threshold of 0.7 (to account for the recessive nature of such mutations), whereas for the oncogenes, a 0.3 threshold was used (to account for the dominance of oncogene-activating mutations). These values were set below the formally expected values of 1 and 0.5 for homozygous and heterozygous mutations, respectively, to allow for technical variability. As expected, the most frequently mutated genes were TP53, KRAS, BRAF, APC, RB1, NF1, PTEN, SMARCA4, and MLH1 (Table S1, related to Figure 2). BRAF mutation profiles showed the expected overlap (J > 0.7) across datasets, as was the case for the TP53 gene across the GDSC and CCLE (J = 0.69). On the other hand, PIK3CA, BRCA2, BRCA1, MLH1, MSH6, and MSH2 mutation comparisons were largely divergent. These discrepancies reflect the ongoing challenges and trade-offs with mutation profiling technologies and mutation calling procedures. The ability of CellMinerCDB to compare and integrate data across sources highlights the fundamental research efforts and technological standards still required for the accurate identification of mutations. As a practical matter, CellMinerCDB users can readily compare cell line mutation calls across sources for any given gene of interest. For follow-up studies, they can then select either cell lines that are consistently identified as mutant across sources or the larger set of mutant lines (according to one or more sources). Independent studies have examined drug data reproducibility, noting potential sources of data divergence such as assay type and duration of drug treatments (Cancer Cell Line Encyclopedia Consortium and Genomics of Drug Sensitivity in Cancer Consortium, 2015Cancer Cell Line Encyclopedia ConsortiumGenomics of Drug Sensitivity in Cancer ConsortiumPharmacogenomic agreement between two cancer cell line data sets.Nature. 2015; 528: 84-87Crossref PubMed Scopus (218) Google Scholar, Haibe-Kains et al., 2013Haibe-Kains B. El-Hachem N. Birkbak N.J. Jin A.C. Beck A.H. Aerts H.J.W.L. Quackenbush J. Inconsistency in large pharmacogenomic studies.Nature. 2013; 504: 389-393Crossref PubMed Scopus (343) Google Scholar, Haverty et al., 2016Haverty P.M. Lin E. Tan J. Yu Y. Lam B. Lianoglou S. Neve R.M. Martin S. Settleman J. Yauch R.L. et al.Reproducible pharmacogenomic profiling of cancer cell line panels.Nature. 2016; 533: 333-337Crossref PubMed Scopus (176) Google Scholar). To explore the reproducibility and the ability of CellMinerCDB to identify genomic signatures over a larger number of cell lines from different tissues of origin, we tested a selected set of NCI-60-screened compounds in the larger GDSC panel (Table S2, related to Figure 3). Noting that the GDSC and the NCI/Developmental Therapeutics Program (DTP) used different assays to determine their IC50 values (Cell Titer Glo measurements of ATP at 72 hr post-treatment versus sulforhodamine B measurement of total protein at 48 hr post-treatment, with additional differences in cell seeding densities and drug dose ranges), we tested in parallel 19 drugs referenced by their NSCs (National Service Center identifiers) and associated with a range of mechanisms of action. Two drugs with the strongest correlations were oxyphenisatin acetate (acetalax) and bisacodyl (Figure 3A, R = 0.84, p = 8.6 × 10−13, N = 44 and R = 0.80, N = 43, p = 1.0 × 10−10, respectively). These FDA-approved laxatives were included in our comparative analysis based on their range of antiproliferative activity in the NCI-60 (further corroborated by NCI-60 activity data for several derivatives), unique pattern of activity compared with the FDA-approved anticancer drugs, outstanding activity in two of the three NCI-60 triple-negative breast cancer cell lines, and lack of pre-existing data in the CTRP, CCLE, or GDSC. The GDSC results confirmed that oxyphenisatin acetate (acetalax) elicits a broad range of cytotoxic responses in the expanded GDSC cell line collection. Extending our NCI-60 observations, it is more active than any of the 15 tested oncologic drugs by a significant margin (p < 7 × 10−10) in the 22 GDSC triple-negative breast cancer lines (Table S3, related to Figure 3). Overall, 16 of the 19 newly tested compounds across the NCI-60 and GDSC gave significant correlations (Table S2, related to Figure 3). Technical discrepancies were evident for three drugs. Dacarbazine, an alkylating agent related to temozolomide, and vincristine, an anti-tubulin, both had poor reproducibility even within DTP assay replicates. Fulvestrant appeared to be out of the proper concentration range in the DTP assay (Figure S4, related to Figure 3). The non-camptothecin indenoisoquinoline-based topoisomerase I inhibitor in clinical trial, LMP744 (NSC 706744; MJ-III-65) (Burton et al., 2018Burton J.H. Mazcko C.N. LeBlanc A.K. Covey J.M. Ji J.J. Kinders R.J. Parchment R.E. Khanna C. Paoloni M. Lana S.E. et al.NCI comparative oncology program testing of non-camptothecin indenoisoquinoline topoisomerase i inhibitors in naturally occurring canine lymphoma.Clin. Cancer Res. 2018; https://doi.org/10.1158/1078-0432.CCR-18-1498Crossref PubMed Scopus (29) Google Scholar), was also included in our 19-compound test set to assess the similarity of its activity profile with that of topotecan over a larger cell line collection and to enrich the genomic signature associated with its activity (see section Exploring Drug Response Determinants). Consistent with its activity as a topoisomerase I inhibitor (Antony et al., 2003Antony S. Jayaraman M. Laco G. Kohlhagen G. Kohn K.W. Cushman M. Pommier Y. Differential induction of topoisomerase I-DNA cleavage complexes by the indenoisoquinoline MJ-III-65 (NSC 706744) and camptothecin: base sequence analysis and activity against camptothecin-resistant topoisomerases I.Cancer Res. 2003; 63: 7428-7435PubMed Google Scholar, Burton et al., 2018Burton J.H. Mazcko C.N. LeBlanc A.K. Covey J.M. Ji J.J. Kinders R.J. Parchment R.E. Khanna C. Paoloni M. Lana S.E. et al.NCI comparative oncology program testing of non-camptothecin indenoisoquinoline topoisomerase i inhibitors in naturally occurring canine lymphoma.Clin. Cancer Res. 2018; https://doi.org/10.1158/1078-0432.CCR-18-1498Crossref PubMed Scopus (29) Google Scholar), LMP744 is highly correlated with topotecan in the GDSC testing (R = 0.83, p = 4.2 × 10−187, N = 715) (Figure S5, related to Figure 3), and exhibits significant activity data concordance between NCI-60 and GDSC (R = 0.66, p = 9.8 × 10−7, N = 44) (Figure 3B). Further focusing on drug activity data reproducibility, we analyzed the 38 drugs previously tested in each of the three databases with larger numbers of tested drugs (NCI-60, GDSC, and CTRP) (Figure 3C). For each of the three inter-source comparisons, drugs were ranked by activity correlation strength (q-value, scaled between 0 [lowest] and 1 [highest]). The drugs were then ordered by the average of the three inter-source comparison rank scores (Figures 3D and S6, related to Figure 3). As noted in earlier studies of drug activity data reproducibility (Haverty et al., 2016Haverty P.M. Lin E. Tan J. Yu Y. Lam B. Lianoglou S. Neve R.M. Martin S. Settleman J. Yauch R.L. et al.Reproducible pharmacogenomic profiling of cancer cell line panels.Nature. 2016; 533: 333-337Crossref PubMed Scopus (176) Google Scholar), strong activity correlations were observed for specifically active compounds (Figures 3E and 3F), such as the BRAF inhibitor dabrafenib, wherein outstanding response occurs in cell lines with the activated kinase target. Notably, we also observed high correlations for broadly active drugs, such as the topoisomerase I inhibitor topotecan (Figures 3G and 3H), indicating that the cancer cell line responses are reproducible across databases and assays and are not limited to protein" @default.
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• W2904362605 date "2018-12-01" @default.
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• W2904362605 title "CellMinerCDB for Integrative Cross-Database Genomics and Pharmacogenomics Analyses of Cancer Cell Lines" @default.
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