Network-based assessment of HDAC6 activity predicts preclinical and clinical responses to the HDAC6 inhibitor ricolinostat in breast cancer.

Abstract

Inhibiting individual histone deacetylase (HDAC) is emerging as well-tolerated anticancer strategy compared with pan-HDAC inhibitors. Through preclinical studies, we demonstrated that the sensitivity to the leading HDAC6 inhibitor (HDAC6i) ricolinstat can be predicted by a computational network-based algorithm (HDAC6 score). Analysis of ~3,000 human breast cancers (BCs) showed that ~30% of them could benefice from HDAC6i therapy. Thus, we designed a phase 1b dose-escalation clinical trial to evaluate the activity of ricolinostat plus nab-paclitaxel in patients with metastatic BC (MBC) (NCT02632071). Study results showed that the two agents can be safely combined, that clinical activity is identified in patients with HR+/HER2- disease and that the HDAC6 score has potential as predictive biomarker. Analysis of other tumor types also identified multiple cohorts with predicted sensitivity to HDAC6i's. Mechanistically, we have linked the anticancer activity of HDAC6i's to their ability to induce c-Myc hyperacetylation (ac-K148) promoting its proteasome-mediated degradation in sensitive cancer cells.