Guinney, Justin

Abstract Zinc finger E-box–binding homeobox 1 (ZEB1) is a transcription factor that can promote tumor invasion and metastasis by inducing epithelial-to-mesenchymal transition (EMT). To date, regulation of ZEB1 by RAS/RAF signaling remains unclear, and few studies have examined posttranslation modification of ZEB1, including its ubiquitination. In human colorectal cancer cell lines with RAS/RAF/MEK/ERK activation, an interaction of ZEB1 with the deubiquitinase ubiquitin-specific protease 10 (USP10) was identified whereby USP10 modifies ZEB1 ubiquitination and promotes its proteasomal degradation. Regulation of the USP10–ZEB1 interaction by MEK–ERK signaling was shown whereby constitutive activation of ERK can phosphorylate USP10 at Ser236 to impair its interaction with ZEB1 and enable ZEB1 protein stabilization. Stabilized ZEB1 was shown to promote colorectal cancer metastatic colonization in a mouse tail vein injection model. Conversely, MEK–ERK inhibition blocked USP10 phosphorylation and enhanced the USP10–ZEB1 interaction shown to suppress ZEB1-mediated tumor cell migration and metastasis. In conclusion, we demonstrate a novel function of USP10 in the regulation of ZEB1 protein stability and its ability to mediate tumor metastasis in a preclinical model. Implications: The MEK–ERK-regulated interaction of USP10 with ZEB1 can promote the proteasomal degradation of ZEB1 and thereby suppress its demonstrated ability to mediate tumor metastasis.

The American Joint Committee on Cancer (AJCC) has increasingly recognized the need for more personalized probabilistic predictions than those delivered by ordinal staging systems, particularly through the use of accurate risk models or calculators. However, judging the quality and acceptability of a risk model is complex. The AJCC Precision Medicine Core conducted a 2‐day meeting to discuss characteristics necessary for a quality risk model in cancer patients. More specifically, the committee established inclusion and exclusion criteria necessary for a risk model to potentially be endorsed by the AJCC. This committee reviewed and discussed relevant literature before creating a checklist unique to this need of AJCC risk model endorsement. The committee identified 13 inclusion and 3 exclusion criteria for AJCC risk model endorsement in cancer. The emphasis centered on performance metrics, implementation clarity, and clinical relevance. The facilitation of personalized probabilistic predictions for cancer patients holds tremendous promise, and these criteria will hopefully greatly accelerate this process. Moreover, these criteria might be useful for a general audience when trying to judge the potential applicability of a published risk model in any clinical domain. CA Cancer J Clin 2016;66:370–374. © 2016 American Cancer Society.