Journal: iScience

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

doi: 10.1016/j.isci.2018.11.029

Figure Lengend Snippet: CellMinerCDB Overview (A) CellMinerCDB integrates cancer cell line information from principal resources and provides powerful, user-friendly analysis tools. (B) Summary of molecular and drug activity data for the five data sources currently included in CellMinerCDB. For molecular data types, the numbers indicate the number of genes with a particular data type. GDSC gene-level mutation and methylation data (numbers in red) were prepared from raw data as part of the development of CellMinerCDB. Asterisks indicate molecular data under development, but not publicly available. Protein expression was determined by reverse-phase protein array. (C) Cell line and drug overlaps between data sources. (D) Drug overlaps between data sources. (E) Small cell lung cancer (SCLC) cell line overlaps between data sources. (F) SCLC cell line-tested drug overlaps between data sources.

Article Snippet: CellMinerCDB integrates four prominent cancer cell line data sources: the CellMiner NCI-60 ( , , , ), Sanger/Massachusetts General Hospital GDSC , the Broad/Novartis CCLE, the Broad CTRP ( , ), and a tissue-specific dataset encompassing 66 small cell lung cancer lines (NCI-SCLC) ( ) ( ).

Techniques: Activity Assay, Mutagenesis, Methylation, Expressing, Protein Array

Journal: iScience

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

doi: 10.1016/j.isci.2018.11.029

Figure Lengend Snippet: Molecular Data Reproducibility across Sources Comparison of the available genomic features of the cell lines shared between the CellMinerCDB data sources. Bar plots indicate the median and inter-quartile range. (A) Pearson's correlation distributions for comparable expression (exp), DNA copy number (cop), and DNA methylation (met) data. (B) Jaccard coefficient distributions for comparable binary mutation (mut) data. The Jaccard coefficient for a pair of gene-specific mutation profiles is the ratio of the number of mutated cell lines reported by both sources to the number of mutated lines reported by either source. (C and D) Overlaps of function-impacting mutations as predicted using SIFT/PolyPhen2 for selected tumor suppressor genes and oncogenes. Matched cell line mutation data were binarized by assigning a value of 1 to lines with a homozygous mutation probability greater than a threshold, which was set to 0.3 for (B) and for oncogenes in (C) and to 0.7 for tumor suppressor genes in (D).

Article Snippet: CellMinerCDB integrates four prominent cancer cell line data sources: the CellMiner NCI-60 ( , , , ), Sanger/Massachusetts General Hospital GDSC , the Broad/Novartis CCLE, the Broad CTRP ( , ), and a tissue-specific dataset encompassing 66 small cell lung cancer lines (NCI-SCLC) ( ) ( ).

Techniques: Comparison, Expressing, DNA Methylation Assay, Mutagenesis

Journal: iScience

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

doi: 10.1016/j.isci.2018.11.029

Figure Lengend Snippet: Drug Activity Data Reproducibility (A and B) GDSC versus NCI-60 drug activity data in matched cell lines for (A) oxyphenisatin acetate (acetalax; NSC59687) and (B) MJ-III-65 (LMP744; NSC706744). Each point represents a matched cell line. Red points in (A) indicate triple-negative breast cancer cell lines. (C–H) (C and D) A total of 38 drugs were tested in the NCI-60, GDSC, and CTRP. CCLE was excluded because of its small drug dataset (24 drugs), which is largely included in CTRP. For each of the three inter-source comparisons, drugs were ranked by activity correlation strength (q-value), with ranks scaled between 0 (lowest) and 1 (highest). Specifically active compounds, such as the BRAF inhibitor dabrafenib, show strong correlations based on the response of melanoma lines shown in red (E and F), whereas broadly active compounds, such as the topoisomerase I inhibitor topotecan, show strong correlations based on broad response patterns (G and H). The NCI-60-matched data in (F) and (H) capture the pattern observed with matched data between the larger GDSC and CTRP collections. The full data table excerpted in (D) is shown in Figure S6 .

Article Snippet: CellMinerCDB integrates four prominent cancer cell line data sources: the CellMiner NCI-60 ( , , , ), Sanger/Massachusetts General Hospital GDSC , the Broad/Novartis CCLE, the Broad CTRP ( , ), and a tissue-specific dataset encompassing 66 small cell lung cancer lines (NCI-SCLC) ( ) ( ).

Techniques: Activity Assay

Journal: iScience

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

doi: 10.1016/j.isci.2018.11.029

Figure Lengend Snippet: Exploring Gene Expression Determinants Reduced mRNA expression (xai, average log2 intensity) of the cell cycle inhibitor and tumor suppressor CDKN2A (p16) is associated with DNA copy loss (cop) (A) and promoter methylation (met) (B) in the NCI-60 lines. In a subset of NCI-60 lines, enclosed in the red box, (C), DNA copy loss accompanies higher levels of promoter methylation. DNA copy number and promoter methylation data from the CCLE and GDSC, respectively, can be also be visualized over matched cell lines to verify a similar pattern in larger cell line collections (D–F). Note that the corroboration of the NCI-60 regulatory relationships in a far larger and more diverse cell line set is uniquely enabled by CellMinerCDB, which allows gene-level methylation data only available in the GDSC to complement gene-level DNA copy number data only available in the CCLE (for automatically matched cell lines). DNA copy number gain is associated with increased expression (exp, Z score microarray log2 intensity data) of the oncogenes MYC (G) and KRAS (H) in selected CCLE cell lines. In (G), small cell lung cancer lines are indicated in red to highlight a subset potentially derived from MYC-driven tumors (within red box).

Article Snippet: CellMinerCDB integrates four prominent cancer cell line data sources: the CellMiner NCI-60 ( , , , ), Sanger/Massachusetts General Hospital GDSC , the Broad/Novartis CCLE, the Broad CTRP ( , ), and a tissue-specific dataset encompassing 66 small cell lung cancer lines (NCI-SCLC) ( ) ( ).

Techniques: Gene Expression, Expressing, Methylation, Microarray, Derivative Assay

Journal: Frontiers in Endocrinology

Article Title: Identification of a novel prognostic and therapeutic prediction model in clear cell renal carcinoma based on Renin-angiotensin system related genes

doi: 10.3389/fendo.2025.1521940

Figure Lengend Snippet: The role of SLC6A19 in ccRCC; (A) Univariate Cox regression for SLC6A19, SLC6A12 and SMIM24. (B) Differential expression of SLC6A19 between tumor and normal tissues in TCGA,GSE53757; (C) Differential expression of SLC6A19 in various clinical stages; (D) Immunohistochemical result from the HPA database showing SLC6A19 expression in normal tissue; (E) Immunohistochemical result from the HPA database showing SLC6A19 expression in ccRCC tissue; (F) Transwell assay showing the impact of SLC6A19 to invasive ability of 786O and A498 cell lines; (G) CCK8 assay showing the impact of SLC6A19 to proliferation of 786O and A498 cell lines. *p < 0.05, **p < 0.01, ***p < 0.005, ****p < 0.001. ns, not significant.

Article Snippet: The 786O and A498 cancer cell lines were purchased from CellSource China.

Techniques: Quantitative Proteomics, Immunohistochemical staining, Expressing, Transwell Assay, CCK-8 Assay