Network-based assessment of HDAC6 activity is highly predictive of pre-clinical and clinical responses to the HDAC6 inhibitor ricolinostat [high throughput sequencing]. Network-based assessment of HDAC6 activity is highly predictive of pre-clinical and clinical responses to the HDAC6 inhibitor ricolinostat [high throughput sequencing]

Despite the anticancer activity of pan-histone deacetylase (HDAC) inhibitors, their clinical use has been limited due to toxicity. However, the development of more specific inhibitors that selectively inhibit individual HDACs is emerging as a novel and well-tolerated alternative. Here, we present the results of the first clinical trial evaluating the activity of ricolinostat (the leading HDAC6 inhibitor) in breast cancer (BC) patients. We have developed a computational network-based algorithm to evaluate the activity of the HDAC6 protein, based on the enrichment of its transcriptional targets in differentially expressed genes (HDAC6 score). Through preclinical in vitro and in vivo studies, we confirmed that the HDAC6 score can stratify the sensitivity of BC cells to ricolinostat treatment and may thus have value as a predictive biomarker. Moreover, analysis of ~3,000 primary human breast cancers showed that ~30% of them present high HDAC6 scores. Based on these results, we designed a phase Ib clinical trial to evaluate the activity of ricolinostat plus nab-paclitaxel in metastatic BC patients. Study results showed that the two agents can be safely combined, that clinical activity is identified specifically in patients with HR+/HER2- disease, and that the HDAC6 score was predictive of response. Expansion of our analysis to other tumor types identified multiple cohorts enriched in high HDAC6 score samples. These results suggest that the HDAC6 score may provide an effective predictive biomarker of ricolinostat sensitivity in multiple human cancers. Overall design: Gene expression profiles of 1) 10 formalin fixed parafilm embedded biopsy samples from 10 patients of ductal breast cancer who were treated with HDAC6 inhibitor ricolinostat; and 2) 4 breast cancer cell lines treated by ricolinostat for 6 hours and 24 hours and by tunicamycin for 18 hours.

搜集汇总

背景与挑战

背景概述

该数据集通过高通量测序获取了人类乳腺癌患者活检样本和细胞系的基因表达数据，用于开发基于网络计算的HDAC6活性评分算法，该评分通过评估HDAC6转录靶标的富集程度来预测对HDAC6抑制剂ricolinostat的敏感性，并在临床前和临床研究中验证其作为预测性生物标志物的价值。