Data Portal for the Library of Integrated Network-based Cellular Signatures (LINCS) program: integrated access to diverse large-scale cellular perturbation response data

Koleti, Amar; Terryn, Raymond; Stathias, Vasileios; Chung, Caty; Cooper, Daniel J.; Turner, John Paul; Vidović, Dušica; Forlin, Michele; Kelley, Tanya; D’Urso, Alessandro; Allen, Bryce K.; Torre, Denis; Jagodnik, Kathleen M.; Wang, Lily; Jenkins, Sherry L.; Mader, Christopher C.; Niu, Wen; Fazel, Mehdi; Mahi, Naim Al; Pilarczyk, Marcin; Clark, Nicholas; Shamsaei, Behrouz; Meller, Jarosław; Vasiliauskas, Juozas; Reichard, John F.; Medvedovic, Mario; Ma’ayan, Avi; Pillai, Ajay S.; Schürer, Stephan C.

doi:10.1093/nar/gkx1063

Cited by 151 publications

(123 citation statements)

References 19 publications

(23 reference statements)

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“…Here, the mRNA transcription patterns displayed by cells following their exposure to MAPK pathway inhibitors should provide a clear representation. To evaluate this hypothesis, we used the publicly accessible LINCS dataset which details the mRNA transcription patterns of ~1000 genes in cell lines 25,26 following exposure of the cell lines to, amongst other small molecules, seven MAPK pathway inhibitors. Here we focused on the two cell lines (MCF7 and A549) and two MAPK pathway inhibitors selumetinib and PD-0325901 (both of which target mitogen-activated protein kinase kinase), that are common between the LINCS datasets that we retrieved, and the datasets provided by the GDSC or CCLE projects.…”

Section: Transcription Responses Of the Mapk Pathway Genes To Mapk Pamentioning

confidence: 99%

“…The efforts of the TCGA, Achilles, GDSC and CCLE projects are complemented by the library of integrated cellular-based signatures (LINCS) project which aspires to illuminate the responses of complex cellular systems 23,24 to drug perturbation.…”

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confidence: 99%

“…The LINCS project provides datasets detailing the molecular and functional changes that occur in thousands of different human cell types following their exposure to thousands of drugs and/or genetic perturbations 25,26 .…”

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confidence: 99%

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Integrated Molecular Characterisation of the MAPK Pathways in Human Cancers

Sinkala

Nkhoma

Mulder

et al. 2020

Preprint

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MAPK signalling [5][6][7][8] . We now have many anticancer drugs that target the components of the MAPK pathways, most of which have been successful in treating cancers of, among other tissues, the skin and kidney 9-12 .Several genetic aberrations, including mutations in, and copy number variations of, MAPK pathway genes have been identified in human cancers and several of proteins encoded by these genes are promising drug targets [13][14][15][16] . However, we still do not have a complete understanding of the extent to which MAPK pathways are altered across the entire spectrum of human cancers, and whether these alterations impact either disease outcomes, or the responses of tumours to anti-MAPK pathway drugs.

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Section: Transcription Responses Of the Mapk Pathway Genes To Mapk Pamentioning

confidence: 99%

mentioning

confidence: 99%

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Integrated Molecular Characterisation of the MAPK Pathways in Human Cancers

Sinkala

Nkhoma

Mulder

et al. 2020

Preprint

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“…Based on these computations, the posterior motif influenceωT,C is then computed and compared to the simulated motif influenceωT,C with a Pearson correlation (Pearson, 1895) over all conditions. As gene set we use the 978 landmark genes from the LINCS project (Koleti et al, 2018). In a secondary simulation we increase the size of the gene set to 5000 genes, which originate from an analysis of the most variational genes across all samples from the GTEx project (Genotype Tissue Expression, https://gtexportal.org/home/).…”

Section: Simulating Datamentioning

confidence: 99%

Bayesian Linear Mixed Models for Motif Activity Analysis

Lederer

Heskes²,

Heeringen

et al. 2019

Preprint

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Motivation: Cellular identity and behavior is controlled by complex gene regulatory networks. Transcription factors (TFs) bind to specific DNA sequences to regulate the transcription of their target genes. On the basis of these TF motifs in cis-regulatory elements we can model the influence of TFs on gene expression. In such models of TF motif activity the data is usually modeled assuming a linear relationship between the motif activity and the gene expression level. A commonly used method to model motif influence is based on Ridge Regression. One important assumption of linear regression is the independence between samples. However, if samples are generated from the same cell line, tissue, or other biological source, this assumption may be invalid. This same assumption of independence is also applied to different, yet similar, experimental conditions, which may also be inappropriate. In theory, the independence assumption between samples could lead to loss in signal detection. Here we investigate whether a Bayesian model that allows for correlations results in more accurate inference of motif activities. Results: We extend the Ridge Regression to a Bayesian Linear Mixed Model, which allows us to model dependence between different samples. In a simulation study, we investigate the differences between the two model assumptions. We show that our Bayesian Linear Mixed Model implementation outperforms Ridge Regression in a simulation scenario where the noise, the signal that can not be explained by TF motifs, is uncorrelated. However, we demonstrate that there is no such gain in performance if the noise has a similar covariance structure over samples as the signal that can be explained by motifs. We give a mathematical explanation to why this is the case. Using two representative real data sets we show that at most ∼ 40% of the signal is explained by motifs using the linear model. With these data there is no advantage to using the Bayesian Linear Mixed Model, due to the similarity of the covariance structure.

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“…The list of human protein kinases in this 375 study is defined by ProKinO (version 2.0) [57]. Drug-kinase associations were extracted 376 from DrugBank (version 5.1.0) [58], Therapeutic Target Database (TTD, last accessed 377 on September 15th, 2017) [59], Pharos (last accessed on May 15th, 2018) [60], and 378 LINCS Data Portal (last accessed on May 15th, 2018) [61]. We define a drug as a PKI 379 if it is annotated as an "inhibitor", "antagonist", or "suppressor" in the drug-kinase Descriptor Calculator GUI (version 1.4.6) [63] generated 881 PubChem fingerprints and 400 286 chemical descriptors including constitutional, topological, electronic, geometric, and 401 bridge descriptors.…”

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confidence: 99%

Quantitative Structure-Mutation-Activity Relationship Tests (QSMART) Model for Protein Kinase Inhibitor Response Prediction

Huang

Yeung

Wang

et al. 2019

Preprint

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Predicting drug sensitivity profiles from genotypes is a major challenge in personalized medicine. Machine learning and deep neural network methods have shown promise in addressing this challenge, but the "black-box" nature of these methods precludes a mechanistic understanding of how and which genomic and proteomic features contribute to the observed drug sensitivity profiles. Here we provide a combination of statistical and neural network framework that not only estimates drug IC 50 in cancer cell lines with high accuracy (R 2 = 0.861 and RMSE = 0.818) but also identifies features contributing to the accuracy, thereby enhancing explainability. Our framework, termed QSMART, uses a multi-component approach that includes (1) collecting drug fingerprints, cancer cell line's multi-omics features, and drug responses, (2) testing the statistical significance of interaction terms, (3) selecting features by Lasso with Bayesian information criterion, and (4) using neural networks to predict drug response. We evaluate the contribution of each of these components and use a case study to explain the biological relevance of several selected features to protein kinase inhibitor response in non-small cell lung cancer cells. Specifically, we illustrate how interaction terms that capture associations between drugs and mutant kinases quantitatively contribute to the response of two EGFR inhibitors (afatinib and lapatinib) in non-small cell lung cancer cells. Although we have tested QSMART on protein kinase inhibitors, it can be extended across the proteome to investigate the complex relationships connecting genotypes and drug sensitivity profiles. Introduction 1 Protein kinases are a class of signaling proteins, greatly valued as therapeutic targets for 2 their key roles in human diseases, such as cancer [1]. For decades, chemotherapy has 3 served as part of a standard set of cancer treatments; however, the resistance of cancer 4 cells to chemotherapy is still a major clinical challenge [2]. Mutations in protein kinase 5 are known to play important roles not only in drug resistance [3] but also in drug 6 sensitivity [4]. Depending on the structural location, mutations can have varying 7 impacts on drug sensitivity. For example, non-small cell lung cancer (NSCLC) cells 8 December 28, 2019 1/25 harboring either the EGFR T790M or L858R mutation respectively leads to resistance 9 or hypersensitivity to the cancer drug gefitinib [5, 6], while those with EGFR 10 T790M/L858R double mutant are only resistant to gefitinib [7]. As mutations impact 11 the efficacy of different cancer drugs, there is a need to incorporate structural 12 knowledge in drug response prediction methods. 13 To facilitate the understanding of the molecular mechanisms that cause drug 14 sensitivity and drug resistance in cancer cells, the Genomics of Drug Sensitivity in 15 Cancer (GDSC) Project [8] recently screened the drug responses of 266 anti-cancer 16 drugs against ∼1,000 human cancer cell lines and provided the largest publicly available 17 drug response datas...

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Data Portal for the Library of Integrated Network-based Cellular Signatures (LINCS) program: integrated access to diverse large-scale cellular perturbation response data

Cited by 151 publications

References 19 publications

Integrated Molecular Characterisation of the MAPK Pathways in Human Cancers

Integrated Molecular Characterisation of the MAPK Pathways in Human Cancers

Bayesian Linear Mixed Models for Motif Activity Analysis

Quantitative Structure-Mutation-Activity Relationship Tests (QSMART) Model for Protein Kinase Inhibitor Response Prediction

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