Evidence map›Paper›PMID 34907165›Full record

ArticleNature communications2021

Metabolic drug survey highlights cancer cell dependencies and vulnerabilities.

Tea Pemovska, Johannes W Bigenzahn, Ismet Srndic, Alexander Lercher, Andreas Bergthaler, Adrián César-Razquin, Felix Kartnig, Christoph Kornauth, Peter Valent, Philipp B Staber and 1 more

Abstract read
In one paragraph

Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Tea PemovskaCeMM-Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.ORCID 0000-0003-2951-4905
Johannes W BigenzahnCeMM-Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.
Ismet SrndicCeMM-Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.
Alexander LercherCeMM-Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.ORCID 0000-0003-3478-5304
Andreas BergthalerCeMM-Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.ORCID 0000-0003-0597-1976
Adrián César-RazquinCeMM-Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.
Felix KartnigCeMM-Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.
Christoph KornauthDepartment of Medicine I, Division of Hematology & Hemostaseology, Medical University of Vienna, Vienna, Austria.
Peter ValentDepartment of Medicine I, Division of Hematology & Hemostaseology, Medical University of Vienna, Vienna, Austria.ORCID 0000-0003-0456-5095
Philipp B StaberDepartment of Medicine I, Division of Hematology & Hemostaseology, Medical University of Vienna, Vienna, Austria.
Giulio Superti-FurgaCeMM-Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria. GSuperti@cemm.oeaw.ac.at.ORCID 0000-0002-0570-1768

Funding

Austrian Science Fund FWF I 4154Austrian Science Fund FWF I 4156
6 · The paper itself

Abstract

Interrogation of cellular metabolism with high-throughput screening approaches can unravel contextual biology and identify cancer-specific metabolic vulnerabilities. To systematically study the consequences of distinct metabolic perturbations, we assemble a comprehensive metabolic drug library (CeMM Library of Metabolic Drugs; CLIMET) covering 243 compounds. We, next, characterize it phenotypically in a diverse panel of myeloid leukemia cell lines and primary patient cells. Analysis of the drug response profiles reveals that 77 drugs affect cell viability, with the top effective compounds targeting nucleic acid synthesis, oxidative stress, and the PI3K/mTOR pathway. Clustering of individual drug response profiles stratifies the cell lines into five functional groups, which link to specific molecular and metabolic features. Mechanistic characterization of selective responses to the PI3K inhibitor pictilisib, the fatty acid synthase inhibitor GSK2194069, and the SLC16A1 inhibitor AZD3965, bring forth biomarkers of drug response. Phenotypic screening using CLIMET represents a valuable tool to probe cellular metabolism and identify metabolic dependencies at large.

Indexed as

Biomarkers, TumorCell SurvivalCluster AnalysisFatty AcidsGenotypeHumansLeukemia, MyeloidMonocarboxylate Transport Protein 1Monocarboxylic Acid TransportersPhenotypePhosphatidylinositol 3-KinasePhosphoinositide-3 Kinase InhibitorsPyrimidinonesPyrrolidinesSignal TransductionSmall Molecule LibrariesAZD3965Biomarkers, TumorFatty AcidsGSK2194069Monocarboxylate Transport Protein 1Monocarboxylic Acid TransportersPhosphatidylinositol 3-KinasePhosphoinositide-3 Kinase InhibitorsPyrimidinonesPyrrolidinesSLC16A3 protein, humanSmall Molecule LibrariesSymportersThiophenesTriazoles

Identifiers

PMID34907165
PMCPMC8671470

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.