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.

与泛 HDAC 抑制剂相比，抑制个体组蛋白脱乙酰酶 (HDAC) 正在成为一种耐受性良好的抗癌策略。通过临床前研究，我们证明对主要 HDAC6 抑制剂 (HDAC6i) ricolinstat 的敏感性可以通过基于计算网络的算法（HDAC6 评分）进行预测。对约 3,000 例人类乳腺癌 (BC) 的分析表明，其中约 30% 的患者可受益于 HDAC6i 治疗。因此，我们设计了一项 1b 期剂量递增临床试验，以评估利可林司他加白蛋白结合型紫杉醇在转移性 BC (MBC) 患者中的活性 (NCT02632071)。^{研究结果表明，这两种药物可以安全地组合，在 HR +} /HER2 ^-疾病患者中确定了临床活性，并且 HDAC6 评分有潜力作为预测生物标志物。对其他肿瘤类型的分析还确定了多个对 HDAC6i 具有预测敏感性的队列。从机制上讲，我们将 HDAC6i 的抗癌活性与其诱导 c-Myc 过度乙酰化 (ac-K148) 的能力联系起来，从而促进敏感癌细胞中蛋白酶体介导的降解。