The Cancer Cell Map Initiative: Defining the Hallmark Networks of Cancer

Krogan, Nevan J.; Lippman, Scott M.; Agard, David A.; Ashworth, Alan; Ideker, Trey

doi:10.1016/j.molcel.2015.05.008

Cited by 122 publications

(93 citation statements)

References 120 publications

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“…This contrasts with the results of studies described in recent publications about the reliability of the data associated with two very large cancer cell line screens pursued by different groups at different sites [5, 6, 8, 9]. We also observed that the main source of variation in our study could be attributed to the drugs used–further indicating that in certain settings, HTS data may indeed be reliable.…”

Section: Discussioncontrasting

confidence: 99%

Analysis of variability in high throughput screening data: applications to melanoma cell lines and drug responses

et al. 2017

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High-throughput screening (HTS) strategies and protocols have undergone significant development in the last decade. It is now possible to screen hundreds of thousands of compounds, each exploring multiple biological phenotypes and parameters, against various cell lines or model systems in a single setting. However, given the vast amount of data such studies generate, the fact that they use multiple reagents, and are often technician-intensive, questions have been raised about the variability, reliability and reproducibility of HTS results. Assessments of the impact of the multiple factors in HTS studies could arguably lead to more compelling insights into the robustness of the results of a particular screen, as well as the overall quality of the study. We leveraged classical, yet highly flexible, analysis of variance (ANOVA)-based linear models to explore how different factors contribute to the variation observed in a screening study of four different melanoma cell lines and 120 drugs over nine dosages studied in two independent academic laboratories. We find that factors such as plate effects, appropriate dosing ranges, and to a lesser extent, the laboratory performing the screen, are significant predictors of variation in drug responses across the cell lines. Further, we show that when sources of variation are quantified and controlled for, they contextualize claims of inconsistencies and reveal the overall quality of the HTS studies performed at each participating laboratory. In the context of the broader screening study, we show that our analysis can also elucidate the robust effects of drugs, even those within specific cell lines.

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Section: Discussioncontrasting

confidence: 99%

Analysis of variability in high throughput screening data: applications to melanoma cell lines and drug responses

et al. 2017

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“…Reactome FI (RFI), a Cytoscape (Shannon et al . 2003) plugin app, combines gene ontology and biological pathways curated from online sources including CellMap (Krogan et al . 2015), Reactome (Milacic et al .…”

Section: Discussionmentioning

confidence: 99%

Single-Base Resolution Mapping of 5-Hydroxymethylcytosine Modifications in Hippocampus of Alzheimer’s Disease Subjects

2017

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Epigenetic modifications to cytosine have been shown to regulate transcription in cancer, embryonic development, and recently neurodegeneration. While cytosine methylation studies are now common in neurodegenerative research, hydroxymethylation studies are rare, particularly genome-wide mapping studies. As an initial study to analyze 5-hydroxymethylcytosine (5-hmC) in the Alzheimer’s disease (AD) genome, reduced representation hydroxymethylation profiling (RRHP) was used to analyze more than 2 million sites of possible modification in hippocampal DNA of sporadic AD and normal control subjects. Genes with differentially hydroxymethylated regions were filtered based on previously published microarray data for altered gene expression in hippocampal DNA of AD subjects. Our data show significant pathways for altered levels of 5- hmC in the hippocampus of AD subjects compared to age-matched normal controls involved in signaling, energy metabolism, cell function, gene expression, protein degradation, and cell structure and stabilization. Overall, our data suggest a possible role for the dysregulation of epigenetic modifications to cytosine in late stage AD.

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“…SignaLink v 2.0 (Fazekas et al, 2013) was mined to identify regulatory interactions of transcriptional, post-transcriptional and pathway regulators. Additional interactions present in non-disease conditions were identified from the Cancer Cell Map (Krogan et al, 2015), and the BioGRID database (Chatr-Aryamontri et al, 2015) was mined to identify unique interactions not reported by the other resources used. In addition to PPI databases and resources, primary literature was explored to identify experimentally verified interacting proteins in the human proteome.…”

Section: Methodsmentioning

confidence: 99%

Unbiased Identification of Blood-based Biomarkers for Pulmonary Tuberculosis by Modeling and Mining Molecular Interaction Networks

et al. 2017

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Efficient diagnosis of tuberculosis (TB) is met with multiple challenges, calling for a shift of focus from pathogen-centric diagnostics towards identification of host-based multi-marker signatures. Transcriptomics offer a list of differentially expressed genes, but cannot by itself identify the most influential contributors to the disease phenotype. Here, we describe a computational pipeline that adopts an unbiased approach to identify a biomarker signature. Data from RNA sequencing from whole blood samples of TB patients were integrated with a curated genome-wide molecular interaction network, from which we obtain a comprehensive perspective of variations that occur in the host due to TB. We then implement a sensitive network mining method to shortlist gene candidates that are most central to the disease alterations. We then apply a series of filters that include applicability to multiple publicly available datasets as well as additional validation on independent patient samples, and identify a signature comprising 10 genes — FCGR1A, HK3, RAB13, RBBP8, IFI44L, TIMM10, BCL6, SMARCD3, CYP4F3 and SLPI, that can discriminate between TB and healthy controls as well as distinguish TB from latent tuberculosis and HIV in most cases. The signature has the potential to serve as a diagnostic marker of TB.

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The Cancer Cell Map Initiative: Defining the Hallmark Networks of Cancer

Cited by 122 publications

References 120 publications

Analysis of variability in high throughput screening data: applications to melanoma cell lines and drug responses

Analysis of variability in high throughput screening data: applications to melanoma cell lines and drug responses

Single-Base Resolution Mapping of 5-Hydroxymethylcytosine Modifications in Hippocampus of Alzheimer’s Disease Subjects

Unbiased Identification of Blood-based Biomarkers for Pulmonary Tuberculosis by Modeling and Mining Molecular Interaction Networks

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