Transcriptional Characterization of Compounds: Lessons Learned from the Public LINCS Data

Wolf, Hans; Bondt, An De; Turner, Heather; Göhlmann, Hinrich W. H.

doi:10.1089/adt.2016.715

Cited by 22 publications

(16 citation statements)

References 21 publications

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“…That is the reason why we selected data sets originating from the same study design. GESs measured at a concentration of 10 µM after 24 h of treatment of the cell lines were extracted, as this is the most represented experimental condition (De Wolf et al, 2016;Lv et al, 2017).…”

Section: Biological Response Constraintsmentioning

confidence: 99%

Exploring the Use of Compound-Induced Transcriptomic Data Generated From Cell Lines to Predict Compound Activity Toward Molecular Targets

et al. 2020

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Pharmaceutical or phytopharmaceutical molecules rely on the interaction with one or more specific molecular targets to induce their anticipated biological responses. Nonetheless, these compounds are also prone to interact with many other non-intended biological targets, also known as off-targets. Unfortunately, off-target identification is difficult and expensive. Consequently, QSAR models predicting the activity on a target have gained importance in drug discovery or in the de-risking of chemicals. However, a restricted number of targets are well characterized and hold enough data to build such in silico models. A good alternative to individual target evaluations is to use integrative evaluations such as transcriptomics obtained from compound-induced gene expression measurements derived from cell cultures. The advantage of these particular experiments is to capture the consequences of the interaction of compounds on many possible molecular targets and biological pathways, without having any constraints concerning the chemical space. In this work, we assessed the value of a large public dataset of compound-induced transcriptomic data, to predict compound activity on a selection of 69 molecular targets. We compared such descriptors with other QSAR descriptors, namely the Morgan fingerprints (similar to extended-connectivity fingerprints). Depending on the target, active compounds could show similar signatures in one or multiple cell lines, whether these active compounds shared similar or different chemical structures. Random forest models using gene expression signatures were able to perform similarly or better than counterpart models built with Morgan fingerprints for 25% of the target prediction tasks. These performances occurred mostly using signatures produced in cell lines showing similar signatures for active compounds toward the considered target. We show that compound-induced transcriptomic data could represent a great opportunity for target prediction, allowing to overcome the chemical space limitation of QSAR models.

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Section: Biological Response Constraintsmentioning

confidence: 99%

Exploring the Use of Compound-Induced Transcriptomic Data Generated From Cell Lines to Predict Compound Activity Toward Molecular Targets

et al. 2020

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“…The Enrichr tool also facilitates queries of the Library of Network-Based Cellular Signatures (LINCS, www. lincsproject.org) database [48,49] , which is based on the [50] . Through an extraordinary effort, this resource has compiled gene signatures in 20 or more cell lines in response to over 20,000 drugs and compounds as well as numerous shRNA inhibitors and overexpression agents.…”

Section: Pathway-based Analysismentioning

confidence: 99%

Searching for a Lifeline: Transcriptome Profiling Studies of Influenza Susceptibility and Resistance

Kobzik¹

2017

J Innate Immun

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Excess or dysregulated host inflammatory responses cause much of the morbidity and mortality caused by severe influenza. Given the limitations of vaccines and antiviral drugs, novel therapeutics to modulate host responses and improve outcomes in severe influenza are needed. One strategy is to learn from the direct comparison of high-survivor versus high-mortality animal models. This review surveys the results of lung transcriptome profiling studies in murine models that directly compare susceptible versus resistant hosts challenged with identical influenza infections. The potential contributions and limitations of these studies are discussed. To amplify their power, the studies are subjected to a meta-analysis, which helps identify frequently dysregulated pathways and potentially novel areas for investigation. Using connectivity map-based tools (LINCS), transcriptome signatures linked to susceptibility can identify candidate drugs that merit testing for in vivo efficacy.

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“…High-throughput preclinical screens have started to provide a large amount of data for combinations of targeted therapies [6, 7], but such large-scale data sets are not easy to generate. Screens in cancer cell lines are limited to modeling the intracellular effects of drugs, but efforts to measure the molecular impact of individual drugs on these cells [63] should be extended to cover more drug combinations. Cell screens should also be expanded to include cell types other than cancer cells, such as stromal cells; only a few examples of such screens are available to date [28].…”

Section: Which Computational Approaches Can Identify These Multiscalementioning

confidence: 99%

Looking beyond the cancer cell for effective drug combinations

2016

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Combinations of therapies are being actively pursued to expand therapeutic options and deal with cancer’s pervasive resistance to treatment. Research efforts to discover effective combination treatments have focused on drugs targeting intracellular processes of the cancer cells and in particular on small molecules that target aberrant kinases. Accordingly, most of the computational methods used to study, predict, and develop drug combinations concentrate on these modes of action and signaling processes within the cancer cell. This focus on the cancer cell overlooks significant opportunities to tackle other components of tumor biology that may offer greater potential for improving patient survival. Many alternative strategies have been developed to combat cancer; for example, targeting different cancer cellular processes such as epigenetic control; modulating stromal cells that interact with the tumor; strengthening physical barriers that confine tumor growth; boosting the immune system to attack tumor cells; and even regulating the microbiome to support antitumor responses. We suggest that to fully exploit these treatment modalities using effective drug combinations it is necessary to develop multiscale computational approaches that take into account the full complexity underlying the biology of a tumor, its microenvironment, and a patient’s response to the drugs. In this Opinion article, we discuss preliminary work in this area and the needs—in terms of both computational and data requirements—that will truly empower such combinations.

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Transcriptional Characterization of Compounds: Lessons Learned from the Public LINCS Data

Cited by 22 publications

References 21 publications

Exploring the Use of Compound-Induced Transcriptomic Data Generated From Cell Lines to Predict Compound Activity Toward Molecular Targets

Exploring the Use of Compound-Induced Transcriptomic Data Generated From Cell Lines to Predict Compound Activity Toward Molecular Targets

Searching for a Lifeline: Transcriptome Profiling Studies of Influenza Susceptibility and Resistance

Looking beyond the cancer cell for effective drug combinations

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