RefPlus: an R package extending the RMA Algorithm

Harbron, Chris; Chang, Kai-Ming; South, Marie C.

doi:10.1093/bioinformatics/btm357

Cited by 43 publications

(31 citation statements)

References 5 publications

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“…The reference quantiles and probe effects were defined from a compendium of publically available arrays representing a broad diversity of oncology indications (through the expO data set but not exclusively) and human body normal tissues (GEO series accessions GSE5764, GSE6338, GSE2109, GSE28504, GSE6764, GSE7307, GSE32317, GSE7753, GSE8507, GSE7904, GSE8671, GSE8762, GSE4107, GSE4183, GSE8977, GSE20238, GSE20596, GSE13911, GSE10282, GSE9829, GSE9843, GSE7553, GSE9891, GSE9899, GSE6004 and GSE4237). The RefPlus R package was used to that aim (Harbron et al, 2007). …”

Section: Methodsmentioning

confidence: 99%

A distinct p53 target gene set predicts for response to the selective p53–HDM2 inhibitor NVP-CGM097

Jeay

Gaulis

Ferretti

et al. 2015

eLife

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Biomarkers for patient selection are essential for the successful and rapid development of emerging targeted anti-cancer therapeutics. In this study, we report the discovery of a novel patient selection strategy for the p53–HDM2 inhibitor NVP-CGM097, currently under evaluation in clinical trials. By intersecting high-throughput cell line sensitivity data with genomic data, we have identified a gene expression signature consisting of 13 up-regulated genes that predicts for sensitivity to NVP-CGM097 in both cell lines and in patient-derived tumor xenograft models. Interestingly, these 13 genes are known p53 downstream target genes, suggesting that the identified gene signature reflects the presence of at least a partially activated p53 pathway in NVP-CGM097-sensitive tumors. Together, our findings provide evidence for the use of this newly identified predictive gene signature to refine the selection of patients with wild-type p53 tumors and increase the likelihood of response to treatment with p53–HDM2 inhibitors, such as NVP-CGM097.DOI: http://dx.doi.org/10.7554/eLife.06498.001

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Section: Methodsmentioning

confidence: 99%

A distinct p53 target gene set predicts for response to the selective p53–HDM2 inhibitor NVP-CGM097

Jeay

Gaulis

Ferretti

et al. 2015

eLife

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“…The raw microarray data (CEL files) for all analyzed compounds and conditions were downloaded from the Open TG-GATEs website (http://toxico.nibio.go.jp/). For the normalization of the entire set of expression arrays, the Robust Multi-Array Average (RMA) algorithm was used that applies background correction, log2 transformation, quantile normalization, and a linear model fit to the normalized data to obtain a value for each probe set (PS) on each array (Krug et al 2013;Harbron et al 2007). The difference in gene expression (fold change) between treated samples and corresponding untreated controls was calculated for each compound, and for each concentration and incubation time, based on the average of replicate values.…”

Section: Download and Preprocessing Of Open Tg-gates Datamentioning

confidence: 99%

Toxicogenomics directory of chemically exposed human hepatocytes

et al. 2014

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A long-term goal of numerous research projects is to identify biomarkers for in vitro systems predicting toxicity in vivo. Often, transcriptomics data are used to identify candidates for further evaluation. However, a systematic directory summarizing key features of chemically influenced genes in human hepatocytes is not yet available. To bridge this gap, we used the Open TG-GATES database with Affymetrix files of cultivated human hepatocytes incubated with chemicals, further sets of gene array data with hepatocytes from human donors generated in this study, and publicly available genome-wide datasets of human liver tissue from patients with non-alcoholic steatohepatitis (NASH), cirrhosis, and hepatocellular cancer (HCC). After a curation procedure, expression data of 143 chemicals were included into a comprehensive biostatistical analysis. The results are summarized in the publicly available toxicotranscriptomics directory (http://wiki.toxbank.net/toxicogenomics-map/) which provides information for all genes whether they are up- or downregulated by chemicals and, if yes, by which compounds. The directory also informs about the following key features of chemically influenced genes: (1) Stereotypical stress response. When chemicals induce strong expression alterations, this usually includes a complex but highly reproducible pattern named 'stereotypical response.' On the other hand, more specific expression responses exist that are induced only by individual compounds or small numbers of compounds. The directory differentiates if the gene is part of the stereotypical stress response or if it represents a more specific reaction. (2) Liver disease-associated genes. Approximately 20 % of the genes influenced by chemicals are up- or downregulated, also in liver disease. Liver disease genes deregulated in cirrhosis, HCC, and NASH that overlap with genes of the aforementioned stereotypical chemical stress response include CYP3A7, normally expressed in fetal liver; the phase II metabolizing enzyme SULT1C2; ALDH8A1, known to generate the ligand of RXR, one of the master regulators of gene expression in the liver; and several genes involved in normal liver functions: CPS1, PCK1, SLC2A2, CYP8B1, CYP4A11, ABCA8, and ADH4. (3) Unstable baseline genes. The process of isolating and the cultivation of hepatocytes was sufficient to induce some stress leading to alterations in the expression of genes, the so-called unstable baseline genes. (4) Biological function. Although more than 2,000 genes are transcriptionally influenced by chemicals, they can be assigned to a relatively small group of biological functions, including energy and lipid metabolism, inflammation and immune response, protein modification, endogenous and xenobiotic metabolism, cytoskeletal organization, stress response, and DNA repair. In conclusion, the introduced toxicotranscriptomics directory offers a basis for a rationale choice of candidate genes for biomarker evaluation studies and represents an easy to use source of background information on chemically infl...

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“…15) with an extended reference-based RMA summarization method (16). Fourteen-hundred samples from the entire data set were randomly selected to estimate parameters of the RMA fit, which were then applied directly to the remaining microarray samples to produce gene-level expressions.…”

Section: Microarray Data Processing and Analysismentioning

confidence: 99%

Pan-PIM Kinase Inhibition Provides a Novel Therapy for Treating Hematologic Cancers

García

Langowski

Wang

et al. 2014

Clinical Cancer Research

107

112

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Purpose: PIM kinases have been shown to act as oncogenes in mice, with each family member being able to drive progression of hematologic cancers. Consistent with this, we found that PIMs are highly expressed in human hematologic cancers and show that each isoform has a distinct expression pattern among disease subtypes. This suggests that inhibitors of all three PIMs would be effective in treating multiple hematologic malignancies.Experimental Design: Pan-PIM inhibitors have proven difficult to develop because PIM2 has a low K m for ATP and, thus, requires a very potent inhibitor to effectively block the kinase activity at the ATP levels in cells. We developed a potent and specific pan-PIM inhibitor, LGB321, which is active on PIM2 in the cellular context.Results:LGB321 is active on PIM2-dependent multiple myeloma cell lines, where it inhibits proliferation, mTOR-C1 signaling and phosphorylation of BAD. Broad cancer cell line profiling of LGB321 demonstrates limited activity in cell lines derived from solid tumors. In contrast, significant activity in cell lines derived from diverse hematological lineages was observed, including acute lymphoblastic leukemia (ALL), acute myelogenous leukemia (AML), multiple myeloma and non-Hodgkin lymphoma (NHL). Furthermore, we demonstrate LGB321 activity in the KG-1 AML xenograft model, in which modulation of pharmacodynamics markers is predictive of efficacy. Finally, we demonstrate that LGB321 synergizes with cytarabine in this model. Conclusions:We have developed a potent and selective pan-PIM inhibitor with single-agent antiproliferative activity and show that it synergizes with cytarabine in an AML xenograft model. Our results strongly support the development of Pan-PIM inhibitors to treat hematologic malignancies.

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RefPlus: an R package extending the RMA Algorithm

Abstract: Further details of the workings and evaluation of these functions are given in the documentation available on the Bioconductor website.

Cited by 43 publications

References 5 publications

A distinct p53 target gene set predicts for response to the selective p53–HDM2 inhibitor NVP-CGM097

A distinct p53 target gene set predicts for response to the selective p53–HDM2 inhibitor NVP-CGM097

Toxicogenomics directory of chemically exposed human hepatocytes

Pan-PIM Kinase Inhibition Provides a Novel Therapy for Treating Hematologic Cancers

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