Rigosertib Induces Mitotic Arrest and Apoptosis in RAS-Mutated Rhabdomyosarcoma and Neuroblastoma.
Joshua T KowalczykXiaolin WanEdjay R HernandezRuibai LuoGaelyn C LyonsKelli M WilsonDevorah C GallardoKristine A IsanogleChristina M RobinsonArnulfo MendozaChristine M HeskeJinqui-Qiu ChenAlexander E KellySimone DifilippantinioRobert W RobeyCraig J ThomasDan L SackettDeborah K MorrisonPaul A RandazzoLisa M Miller JenkinsMarielle E YohePublished in: Molecular cancer therapeutics (2020)
Relapsed pediatric rhabdomyosarcomas (RMS) and neuroblastomas (NBs) have a poor prognosis despite multimodality therapy. In addition, the current standard of care for these cancers includes vinca alkaloids that have severe toxicity profiles, further underscoring the need for novel therapies for these malignancies. Here, we show that the small-molecule rigosertib inhibits the growth of RMS and NB cell lines by arresting cells in mitosis, which leads to cell death. Our data indicate that rigosertib, like the vinca alkaloids, exerts its effects mainly by interfering with mitotic spindle assembly. Although rigosertib has the ability to inhibit oncogenic RAS signaling, we provide evidence that rigosertib does not induce cell death through inhibition of the RAS pathway in RAS-mutated RMS and NB cells. However, the combination of rigosertib and the MEK inhibitor trametinib, which has efficacy in RAS-mutated tumors, synergistically inhibits the growth of an RMS cell line, suggesting a new avenue for combination therapy. Importantly, rigosertib treatment delays tumor growth and prolongs survival in a xenograft model of RMS. In conclusion, rigosertib, through its impact on the mitotic spindle, represents a potential therapeutic for RMS.
Keyphrases
- cell cycle arrest
- cell death
- wild type
- poor prognosis
- combination therapy
- small molecule
- induced apoptosis
- cell cycle
- pi k akt
- oxidative stress
- healthcare
- endoplasmic reticulum stress
- long non coding rna
- acute lymphoblastic leukemia
- cell proliferation
- stem cells
- palliative care
- machine learning
- protein protein
- chronic pain
- big data
- affordable care act