Controlling distinct signaling states in cultured cancer cells provides a new platform for drug discovery

Poser, Steven; Otto, Oliver; Arps-Forker, Carina; Ge, Yan; Herbig, Maik; Andree, Cordula; Gruetzmann, Konrad; Adasme, Melissa F.; Stodolak, Szymon; Nikolakopoulou, Polyxeni; Park, Deric M.; McIntyre, Alan; Lesche, Mathias; Dahl, Andreas; Lennig, Petra; Bornstein, Stefan R.; Schroeck, Evelin; Klink, Barbara; Leker, Ronen R.; Bickle, Marc; Chrousos, George P.; Schroeder, Michael; Cannistraci, Carlo Vittorio; Guck, Jochen; Androutsellis-Theotokis, Andreas

doi:10.1096/fj.201802603rr

Cited by 11 publications

(23 citation statements)

References 55 publications

(91 reference statements)

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“…Here we extend the concept of mechanomics to a data-driven methodology for de novo identification of genes contributing to the mechanical phenotype based on omics data (Figure 1). To demonstrate this approach, we perform a machine learning-based discriminative network analysis termed PC-corr 41 on transcriptomics data from two unrelated biological systems with known mechanical phenotype changes 42,43 and elucidate a conserved functional module of five candidate genes putatively involved in the regulation of cell mechanics. We then test the ability of each gene to classify cell states according to cell stiffness in silico on four further datasets.…”

Section: Introductionmentioning

confidence: 99%

De novo identification of universal cell mechanics gene signatures

Urbanska

Winzi

et al. 2021

Preprint

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Mechanical proprieties determine many cellular functions, such as cell fate specification, migration, or circulation through vasculature. Identifying factors governing cell mechanical phenotype is therefore a subject of great interest. Here we present a mechanomics approach for establishing links between mechanical phenotype changes and the genes involved in driving them. We employ a machine learning-based discriminative network analysis method termed PC-corr to associate cell mechanical states, measured by real-time deformability cytometry (RT-DC), with large-scale transcriptome datasets ranging from stem cell development to cancer progression, and originating from different murine and human tissues. By intersecting the discriminative networks inferred from two selected datasets, we identify a conserved module of five genes with putative roles in the regulation of cell mechanics. We validate the power of the individual genes to discriminate between soft and stiff cell states in silico, and demonstrate experimentally that the top scoring gene, CAV1, changes the mechanical phenotype of cells when silenced or overexpressed. The data-driven approach presented here has the power of de novo identification of genes involved in cell mechanics regulation and paves the way towards engineering cell mechanical properties on demand to explore their impact on physiological and pathological cell functions.

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Section: Introductionmentioning

confidence: 99%

De novo identification of universal cell mechanics gene signatures

Urbanska

Winzi

et al. 2021

Preprint

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“…Antifungal agents have recently been identified as potential anti-GBM agents. Among this category of drugs, azoles have shown some potential for clinical translation [92,93]. The current understanding of the antineoplastic action of azoles involves their ability to inhibit hexokinase II leading to restriction of GSC proliferation and GBM growth [93].…”

Section: Antifungals/antimalarialsmentioning

confidence: 99%

“…The current understanding of the antineoplastic action of azoles involves their ability to inhibit hexokinase II leading to restriction of GSC proliferation and GBM growth [93]. Specifically, in experimental GBM, ketoconazole and posaconazole decreased metabolic activity leading to increased animal survival and decreased tumor growth [92]. These preclinical studies led to an ongoing open-label, non-randomized Phase I trial investigating posaconazole and ketoconazole in patients with recurrent HGG [94].…”

Section: Antifungals/antimalarialsmentioning

confidence: 99%

“…The trial began in 2018 with plans to enroll 30 patients with a primary outcome of intratumoral ketoconazole and/or posaconazole concentration. Clioquinol is another topical antifungal being investigated in GBM [92,95]. Together, these results suggest that although there is some preclinical evidence for the potential use of azoles and clioquinol for GBM, their clinical safety and efficacy remain to be determined.…”

Section: Antifungals/antimalarialsmentioning

confidence: 99%

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An Alternative Pipeline for Glioblastoma Therapeutics: A Systematic Review of Drug Repurposing in Glioblastoma

Lyne

Yamini

2021

Cancers

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The treatment of glioblastoma (GBM) remains a significant challenge, with outcome for most patients remaining poor. Although novel therapies have been developed, several obstacles restrict the incentive of drug developers to continue these efforts including the exorbitant cost, high failure rate and relatively small patient population. Repositioning drugs that have well-characterized mechanistic and safety profiles is an attractive alternative for drug development in GBM. In addition, the relative ease with which repurposed agents can be transitioned to the clinic further supports their potential for examination in patients. Here, a systematic analysis of PubMed and ClinicalTrials.gov (August 17, 2020) provides a comprehensive review of primary articles and unpublished trials that use repurposed drugs for the treatment of GBM. The findings demonstrate that numerous drug classes that have a range of initial indications have efficacy against preclinical GBM models and that certain agents have shown significant potential for clinical benefit. With examination in randomized, placebo-controlled trials and the targeting of particular GBM subgroups, it is possible that repurposing can be a cost-effective approach to identify agents for use in multimodal anti-GBM strategies.

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“…The sorted cells can subsequently be subjected to refined analysis and inquiry into their proteomic, transcriptomic, or genetic identity and function 4,5 . Alternatively, they can be used for culture and serve the establishment of specific drugs 4,6,7 , or for transplantation into patients in regenerative medicine applications [8][9][10][11][12] .…”

Section: Introductionmentioning

confidence: 99%

Using real-time fluorescence and deformability cytometry and deep learning to transfer molecular specificity to label-free sorting

Nawaz

Urbanska

Herbig

et al. 2019

Preprint

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The identification and separation of specific cells from heterogeneous populations is an essential prerequisite for further analysis or use. Conventional passive and active separation approaches rely on fluorescent or magnetic tags introduced to the cells of interest through molecular markers. Such labeling is time-and cost-intensive, can alter cellular properties, and might be incompatible with subsequent use, for example, in transplantation. Alternative label-free approaches utilizing morphological or mechanical features are attractive, but lack molecular specificity. Here we combine image-based real-time fluorescence and deformability cytometry (RT-FDC) with downstream cell sorting using standing surface acoustic waves (SSAW). We demonstrate basic sorting capabilities of the device by separating cell mimics and blood cell types based on fluorescence as well as deformability and other image parameters. The identification of blood sub-populations is enhanced by flow alignment and deformation of cells in the microfluidic channel constriction. In addition, the classification of blood cells using established fluorescence-based markers provides hundreds of thousands of labeled cell images used to train a deep neural network. The trained algorithm, with latency optimized to below 1 ms, is then used to identify and sort unlabeled blood cells at rates of 100 cells/sec. This approach transfers molecular specificity into labelfree sorting and opens up new possibilities for basic biological research and clinical therapeutic applications. Keywords: mechanical phenotype | label-free cell sorting | standing surface acoustic waves (SSAW) | artificial intelligence | deep neural networks | flow cytometry | image-based sorting

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Controlling distinct signaling states in cultured cancer cells provides a new platform for drug discovery

Cited by 11 publications

References 55 publications

De novo identification of universal cell mechanics gene signatures

De novo identification of universal cell mechanics gene signatures

An Alternative Pipeline for Glioblastoma Therapeutics: A Systematic Review of Drug Repurposing in Glioblastoma

Using real-time fluorescence and deformability cytometry and deep learning to transfer molecular specificity to label-free sorting

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