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Schukken, K.M.* ; Lin, Y.-C.* ; Bakker, P.L.* ; Schubert, M.* ; Preuss, S.F.* ; Simon, J.E.* ; van den Bos, H.* ; Storchova, Z.* ; Colomé-Tatché, M. ; Bastians, H.* ; Spierings, D.C.* ; Foijer, F.*

Altering microtubule dynamics is synergistically toxic with spindle assembly checkpoint inhibition.

Life Sci. All. 3:e201900499 (2020)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Chromosomal instability (CIN) and aneuploidy are hallmarks of cancer. As most cancers are aneuploid, targeting aneuploidy or CIN may be an effective way to target a broad spectrum of cancers. Here, we perform two small molecule compound screens to identify drugs that selectively target cells that are aneuploid or exhibit a CIN phenotype. We find that aneuploid cells are much more sensitive to the energy metabolism regulating drug ZLN005 than their euploid counterparts. Furthermore, cells with an ongoing CIN phenotype, induced by spindle assembly checkpoint (SAC) alleviation, are significantly more sensitive to the Src kinase inhibitor SKI606. We show that inhibiting Src kinase increases microtubule polymerization rates and, more generally, that deregulating microtubule polymerization rates is particularly toxic to cells with a defective SAC. Our findings, therefore, suggest that tumors with a dysfunctional SAC are particularly sensitive to microtubule poisons and, vice versa, that compounds alleviating the SAC provide a powerful means to treat tumors with deregulated microtubule dynamics.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Chromosome Segregation Errors; Dna-damage Response; Mis-segregation; Colorectal-cancer; C-src; Aneuploidy; Instability; Cells; Mps1; Consequences
ISSN (print) / ISBN 2575-1077
e-ISSN 2575-1077
Zeitschrift Life Science Alliance
Quellenangaben Band: 3, Heft: 2, Seiten: , Artikelnummer: e201900499 Supplement: ,
Verlag EMBO Press
Verlagsort Heidelberg
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)