The Library of Integrated Network-Based Cellular Signatures NIH Program: System-Level Cataloging of Human Cells Response to Perturbations

Keenan, Alexandra B.; Jenkins, Sherry L.; Jagodnik, Kathleen M.; Koplev, Simon; He, Edward; Torre, Denis; Wang, Zichen; Dohlman, Anders B.; Silverstein, Moshe C.; Lachmann, Alexander; Kuleshov, Maxim V.; Ma’ayan, Avi; Stathias, Vasileios; Terryn, Raymond; Cooper, Daniel J.; Forlin, Michele; Koleti, Amar; Vidović, Dušica; Chung, Caty; Schürer, Stephan C.; Vasiliauskas, Jouzas; Pilarczyk, Marcin; Shamsaei, Behrouz; Fazel, Mehdi; Ren, Yan; Niu, Wen; Clark, Nicholas; White, Shana; Mahi, Naim Al; Zhang, Lixia; Kouril, Michal; Reichard, John F.; Sivaganesan, Siva; Medvedovic, Mario; Meller, Jarosław; Koch, Rick J.; Birtwistle, Marc R.; Iyengar, Ravi; Sobie, Eric A.; Azeloglu, Evren U.; Kaye, Julia; Osterloh, Jeannette M.; Haston, Kelly; Kalra, Jaslin; Finkbiener, Steve; Li, Jonathan; Milani, Pamela; Adam, Miriam; Escalante-Chong, Renan; Sachs, Karen; Lenail, Alex; Ramamoorthy, Divya; Fraenkel, Ernest; Daigle, Gavin; Hussain, Uzma; Coye, Alyssa; Rothstein, Jeffrey; Sareen, Dhruv; Ornelas, Loren; Bañuelos, Maria G.; Mandefro, Berhan; Ho, Ritchie; Svendsen, Clive N.; Lim, Ryan G.; Stocksdale, Jennifer; Casale, Malcolm; Thompson, Terri G.; Wu, Jie; Thompson, Leslie M.; Dardov, Victoria; Venkatraman, Vidya; Matlock, Andrea; Eyk, Jennifer E. Van; Jaffe, Jacob D.; Papanastasiou, Malvina; Subramanian, Aravind; Golub, Todd R.; Erickson, Sean D.; Fallahi-Sichani, Mohammad; Hafner, Marc; Gray, Nathanael S.; Lin, Jia-Ren; Mills, Caitlin E.; Muhlich, Jeremy; Niepel, Mario; Shamu, Caroline E.; Williams, Liz; Wrobel, David M.; Sorger, Peter K.; Heiser, Laura M.; Gray, Joe W.; Korkola, James E.; Mills, Gordon B.; LaBarge, Mark A.; Feiler, Heidi S.; Dane, Mark; Bucher, Elmar; Nederlof, Michel; Sudar, Damir; Gross, Sean M.; Kilburn, David; Smith, Rebecca; Devlin, Kaylyn L.; Margolis, Ron; Derr, Leslie; Lee, Albert; Pillai, Ajay S.

doi:10.1016/j.cels.2017.11.001

Cited by 360 publications

(308 citation statements)

References 46 publications

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“…[50][51][52] The LINCS consortium has systematically collected gene expression profiles of cell cultures treated with every FDA-approved drug along with several experimental treatments. [50][51][52] The LINCS consortium has systematically collected gene expression profiles of cell cultures treated with every FDA-approved drug along with several experimental treatments.…”

Section: Strategies For Therapeutic Developmentmentioning

confidence: 99%

“…The GBT strategy has been implemented at the level of the full US Food and Drug Administration (FDA)-approved pharmacopoeia by the Library of Integrated Network-Based Cellular Signatures (LINCS) and Connectivity Map projects. [50][51][52] The LINCS consortium has systematically collected gene expression profiles of cell cultures treated with every FDA-approved drug along with several experimental treatments. Along with these public data, LINCS provides analytical tools to match user-supplied lists of up-and downregulated genes in a disease condition to molecules that have an opposing effect on gene expression in vitro.…”

Section: Strategies For Therapeutic Developmentmentioning

confidence: 99%

“…The LINCS database has been used extensively for drug repurposing efforts in many diseases. 50 Delahaye-Duriez et al recently reported on a 320 gene coexpression network that is commonly dysregulated across both patients with monogenic and patients with polygenic epilepsies. 53 Thus, despite the incredible heterogeneity of the epilepsies, there appear to be some commonly dysregulated pathways across many distinct disorders.…”

Section: Strategies For Therapeutic Developmentmentioning

confidence: 99%

“…Allows unbiased screen of all FDA-approved compounds, but signatures are limited to cell lines, which may not be representative of target tissues. [50][51][52] Machine learning…”

Section: Network Inference Biological Data Interpretationmentioning

confidence: 99%

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2017 WONOEP appraisal: Studying epilepsy as a network disease using systems biology approaches

et al. 2019

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The revolution in high‐throughput omics technologies has dramatically expanded our understanding of the epilepsies as complex diseases. It is now clear that further progress in treating the full spectrum of seizure disorders requires a systems‐level framework for analyzing and integrating data from multiple omics technologies that moves beyond the search for single molecular alterations to an understanding of dysregulated pathways in epilepsy. Taking such a pathway‐centered view requires further integrating the tools of systems biology into epilepsy research. In this appraisal, we highlight and summarize systems biology approaches in basic epilepsy studies as they were discussed during the 2017 Workshop on the Neurobiology of Epilepsy (WONOEP). During the 3‐day event, participants exchanged emerging results and thoughts on developing the systems biology of epilepsy, and the promise and limitations of these approaches for the near term.

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Section: Strategies For Therapeutic Developmentmentioning

confidence: 99%

Section: Strategies For Therapeutic Developmentmentioning

confidence: 99%

Section: Strategies For Therapeutic Developmentmentioning

confidence: 99%

“…Allows unbiased screen of all FDA-approved compounds, but signatures are limited to cell lines, which may not be representative of target tissues. [50][51][52] Machine learning…”

Section: Network Inference Biological Data Interpretationmentioning

confidence: 99%

See 2 more Smart Citations

2017 WONOEP appraisal: Studying epilepsy as a network disease using systems biology approaches

et al. 2019

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“…We have also measured an empirical relation between the scale parameter σ and the log2 expression value x, shown in Fig. 8, which can be fitted by an analytical expression as σ x = 0.15 + 5.3 e −0.52x (1) with a mean absolute error of 0.009. The shape of a single peak centered at log2 expression level x is therefore given by…”

Section: Peak Deconvolutionmentioning

confidence: 99%

A Bayesian approach to accurate and robust signature detection on LINCS L1000 data

Qiu

Lim

et al. 2019

Preprint

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LINCS L1000 dataset produced by L1000 assay contains numerous cellular expression data induced by large sets of perturbagens. Although it provides invaluable resources for drug discovery as well as understanding of disease mechanisms, severe noise in the dataset makes the detection of reliable gene expression signals difficult. Existing methods for the peak deconvolution, either k-means based or Gaussian mixture model, cannot reliably recover the accurate expression level of genes in many cases, thereby limiting their robust applications in biomedical studies. Here, we have developed a novel Bayes' theory based deconvolution algorithm that gives unbiased likelihood estimations for peak positions and characterizes the peak with a probability based z-scores. Based on above algorithm, a pipeline is built to process raw data from L1000 assay into signatures that represent the features of perturbagen. The performance of the proposed new pipeline is rigorously evaluated using the similarity between bio-replicates and between drugs with shared targets. The results show that the new signature derived from the proposed algorithm gives a substantially more reliable and informative representation for perturbagens than existing methods. Thus, our new Bayesian-based peak deconvolution and z-score calculation method may significantly boost the performance of invaluable L1000 data in the down-stream applications such as drug repurposing, disease modeling, and gene function prediction.

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Association of Antineoplastic Therapy With Decreased SARS-CoV-2 Infection Rates in Patients With Cancer

et al. 2021

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IMPORTANCE Novel therapies for SARS-CoV-2 infection are urgently needed. Antineoplastic compounds that target cellular machinery used by SARS-CoV-2 for entry and replication, including angiotensin-converting enzyme 2 (ACE2), may disrupt SARS-CoV-2 activity.OBJECTIVES To determine whether patients with cancer treated with potential ACE2-lowering antineoplastic compounds exhibit lower SARS-CoV-2 infection rates.DESIGN, SETTING, AND PARTICIPANTS We used the Library of Integrated Network-Based Cellular Signatures database to identify antineoplastic compounds associated with decreased ACE2 gene expression across cell lines. We then evaluated a retrospective cohort of 1701 patients who were undergoing antineoplastic therapy at Memorial Sloan Kettering Cancer Center in New York, New York, during the COVID-19 pandemic to determine if treatment with an ACE2-lowering antineoplastic was associated with a decreased odds ratio (OR) of SARS-CoV-2 infection. Patients included in the analysis underwent active treatment for cancer and received a SARS-CoV-2 test between March 10 and May 28, 2020. MAIN OUTCOME AND MEASUREThe association between potential ACE2-lowering antineoplastic treatment and a positive SARS-CoV-2 test. RESULTSIn the cohort of 1701 patients, SARS-CoV-2 infection rates were determined for 949 (55.8%) female and 752 (44.2%) male patients (mean [SD] age, 63.1 [13.1] years) with diverse cancers receiving antineoplastic therapy. In silico analysis of gene expression signatures after drug treatment identified 91 compounds associated with downregulation of ACE2 across cell lines. Of the total cohort, 215 (12.6%) patients were treated with 8 of these compounds, including 3 mTOR/PI3K inhibitors and 2 antimetabolites. In a multivariable analysis of patients who received an ACE2-lowering antineoplastic adjusting for confounders, 15 of 215 (7.0%) patients had a positive SARS-CoV-2 test compared with 191 of 1486 (12.9%) patients who received other antineoplastic therapies (OR, 0.53; 95% CI, 0.29-0.88). Findings were confirmed in additional sensitivity analyses including cancer type, steroid use, and a propensity-matched subcohort. Gemcitabine treatment was associated with reduced SARS-CoV-2 infection (OR, 0.42; 95% CI, 0.17-0.87). CONCLUSIONS AND RELEVANCEIn this cohort study, in silico analysis of drug-associated gene expression signatures identified potential ACE2-lowering antineoplastic compounds, including mTOR/PI3K inhibitors and antimetabolites. Patients who received these compounds exhibited statistically significantly lower rates of SARS-CoV-2 infection compared with patients given other antineoplastics. Further evaluation of the biological and clinical anti-SARS-CoV-2 properties of identified antineoplastic compounds is warranted.

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The Library of Integrated Network-Based Cellular Signatures NIH Program: System-Level Cataloging of Human Cells Response to Perturbations

Cited by 360 publications

References 46 publications

2017 WONOEP appraisal: Studying epilepsy as a network disease using systems biology approaches

2017 WONOEP appraisal: Studying epilepsy as a network disease using systems biology approaches

A Bayesian approach to accurate and robust signature detection on LINCS L1000 data

Association of Antineoplastic Therapy With Decreased SARS-CoV-2 Infection Rates in Patients With Cancer

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