Department of Thoracic and Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
Clin Cancer Res. 2019 Jan 1;25(1):346-357. doi: 10.1158/1078-0432.CCR-18-1129. Epub 2018 Sep 26.
Despite a growing arsenal of approved drugs, therapeutic resistance remains a formidable and, often, insurmountable challenge in cancer treatment. The mechanisms underlying therapeutic resistance remain largely unresolved and, thus, examples of effective combinatorial or sequential strategies to combat resistance are rare. Here, we present Differential Sensitivity Analysis for Resistant Malignancies (DISARM), a novel, integrated drug screen analysis tool designed to address this dilemma.
DISARM, a software package and web-based application, analyzes drug response data to prioritize candidate therapies for models with resistance to a reference drug and to assess whether response to a reference drug can be utilized to predict future response to other agents. Using cisplatin as our reference drug, we applied DISARM to models from nine cancers commonly treated with first-line platinum chemotherapy including recalcitrant malignancies such as small cell lung cancer (SCLC) and pancreatic adenocarcinoma (PAAD).
In cisplatin-resistant models, DISARM identified novel candidates including multiple inhibitors of PI3K, MEK, and BCL-2, among other classes, across unrelated malignancies. Additionally, DISARM facilitated the selection of predictive biomarkers of response and identification of unique molecular subtypes, such as contrasting ASCL1-low/cMYC-high SCLC targetable by AURKA inhibitors and ASCL1-high/cMYC-low SCLC targetable by BCL-2 inhibitors. Utilizing these predictions, we assessed several of DISARM's top candidates, including inhibitors of AURKA, BCL-2, and HSP90, to confirm their activity in cisplatin-resistant SCLC models.
DISARM represents the first validated tool to analyze large-scale drug response data to statistically optimize candidate drug and biomarker selection aimed at overcoming candidate drug resistance.
尽管有越来越多的批准药物，但治疗耐药性仍然是癌症治疗中一个巨大且常常难以克服的挑战。治疗耐药性的机制在很大程度上仍未得到解决，因此，对抗耐药性的有效组合或序贯策略的例子很少。在这里，我们提出了用于耐药性恶性肿瘤的差异敏感性分析（DISARM），这是一种新的综合药物筛选分析工具，旨在解决这一困境。
DISARM 是一个软件包和基于网络的应用程序，用于分析药物反应数据，以确定对参考药物耐药的模型的候选治疗药物，并评估对参考药物的反应是否可用于预测对其他药物的未来反应。我们使用顺铂作为参考药物，将 DISARM 应用于包括难治性恶性肿瘤（如小细胞肺癌[SCLC]和胰腺腺癌[PAAD]）在内的 9 种常用一线铂类化疗治疗的癌症模型。
在顺铂耐药模型中，DISARM 确定了新的候选药物，包括 PI3K、MEK 和 BCL-2 等多种抑制剂，涉及多种不同的恶性肿瘤。此外，DISARM 促进了反应预测生物标志物的选择和独特分子亚型的鉴定，例如，ASCL1 低/cMYC 高 SCLC 可被 AURKA 抑制剂靶向，而 ASCL1 高/cMYC 低 SCLC 可被 BCL-2 抑制剂靶向。利用这些预测结果，我们评估了 DISARM 的一些顶级候选药物，包括 AURKA、BCL-2 和 HSP90 的抑制剂，以确认它们在顺铂耐药 SCLC 模型中的活性。
DISARM 是第一个经过验证的工具，用于分析大规模药物反应数据，以进行统计学优化候选药物和生物标志物选择，旨在克服候选药物耐药性。
