Global and context-specific transcriptional consequences of oncogenic Fbw7 mutations.

The Fbw7 ubiquitin ligase targets many proteins for proteasomal degradation, which include oncogenic transcription factors (TFs) (e.g., c-Myc, c-Jun, and Notch).

Two diphosphorylated degrons control c-Myc degradation by the Fbw7 tumor suppressor.

c-Myc (hereafter, Myc) is a cancer driver whose abundance is regulated by the SCF ubiquitin ligase and proteasomal degradation. Fbw7 binds to a phosphorylated Myc degron centered at threonine 58 (T58), and mutations of Fbw7 or T58 impair Myc degradation in cancers. Here, we identify a second Fbw7 phosphodegron at Myc T244 that is required for Myc ubiquitylation and acts in concert with T58 to engage Fbw7.

FOXM1 drives HPV+ HNSCC sensitivity to WEE1 inhibition.

Head and neck squamous cell carcinoma (HNSCC) associated with high-risk human papilloma virus (HPV) infection is a growing clinical problem. The WEE1 kinase inhibitor AZD1775 (WEE1i) overrides cell cycle checkpoints and is being studied in HNSCC regimens. We show that the HPV16 E6/E7 oncoproteins sensitize HNSCC cells to single-agent WEE1i treatment through activation of a FOXM1-CDK1 circuit that drives mitotic gene expression and DNA damage.

A novel landscape of nuclear human CDK2 substrates revealed by in situ phosphorylation.

Cyclin-dependent kinase 2 (CDK2) controls cell division and is central to oncogenic signaling. We used an "in situ" approach to identify CDK2 substrates within nuclei isolated from cells expressing CDK2 engineered to use adenosine 5'-triphosphate analogs. We identified 117 candidate substrates, ~40% of which are known CDK substrates.

Pan-cancer transcriptional signatures predictive of oncogenic mutations reveal that Fbw7 regulates cancer cell oxidative metabolism.

The Fbw7 (F-box/WD repeat-containing protein 7) ubiquitin ligase targets multiple oncoproteins for degradation and is commonly mutated in cancers. Like other pleiotropic tumor suppressors, Fbw7's complex biology has impeded our understanding of how Fbw7 mutations promote tumorigenesis and hindered the development of targeted therapies. To address these needs, we employed a transfer learning approach to derive gene-expression signatures from The Cancer Gene Atlas datasets that predict Fbw7 mutational status across tumor types and identified the pathways enriched within these signatures.