Purpose. To address the unmet need for biomarker-driven, effective, targeted therapy for human papillomavirus (HPV)-associated head and neck squamous cell carcinoma (HNSCC) and cervical epithelial squamous cell carcinoma (CESC), we conducted a high-throughput drug screen (HTDS) using 1122 compounds in all readily available HPV-positive HNSCC and CESC cell lines and an equal number of matched HPV-negative lines.
Methods. Cells were incubated in drug concentrations ranging from 0.01 μM to 3.16 μM for 72 h, fixed and stained with DAPI, and counted. Of the 1122 analyzable compounds, 865 unique drugs were tested because of overlap. All drugs were assigned to one of 36 classes based on their primary targets. Drug concentrations resulting in a 50% reduction in cell proliferation (GI50) and the area under the dose response curve were calculated. Two biological replicates were performed for all cell lines on separate days and at least 1 week apart.
Results. The HTDS was conducted using 24 cell lines. We identified 493 highly effective compounds, which we defined as those with GI50 values less than 0.5 μM in 2 or more of the cell lines screened. The most effective drug classes were inhibitors of polo-like kinase, proteasomes, histone deacetylase, and Aurora kinases. Of the 19 Aurora kinase inhibitors tested, 18 were highly effective. We confirmed the efficacy of 3 Aurora kinase inhibitors using colony formation assays in 15 cell lines. Treatment with a dual Aurora A/B inhibitor, danusertib, led to G2M arrest and apoptosis in all 6 tested cell lines. Additionally, danusertib treatment decreased tumor size compared to controls in patient-derived xenograft mouse models of HNSCC. To identify biomarkers predicting response to Aurora kinase inhibitors, we tested for associations between mutations in the cell lines and sensitivity to the Aurora kinase inhibitors using whole exome mutation data for the 50 most common driver mutations in HNSCC. To validate our findings in an independent dataset, we queried the Genomics of Drug Sensitivity in Cancer database. In both data sets, cancer cell lines with KMT2D (MLL2) mutations were more sensitive to Aurora kinase inhibitors than cells without mutations. KMT2D mutations are inactivating; experiments to knock down KMT2D in wild-type cell lines and assess sensitivity to Aurora kinase inhibitors are ongoing.
Conclusions. We identified Aurora kinase inhibitors as effective and understudied drugs in HNSCC and CESC. These drugs cause apoptosis and cell cycle arrest in vitro and decrease tumor size in vivo. This is the first published study to demonstrate that mutations in KMT2D (MLL2), which are common in many cancers (16% HNSCC, 12% CESC), correlate with drug sensitivity in 2 independent data sets.
Citation Format: Tuhina Mazumdar, Nene N. Kalu, Shaohua Peng, Pan Tong, Li Shen, Jing Wang, Jeffrey N. Myers, Curtis R. Pickering, David Brunell, Clifford C. Stephan, Faye M. Johnson. Pharmacogenomic screen identifies KMT2D mutations as a biomarker of sensitivity to Aurora kinase inhibition in head and neck and cervical squamous cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4646.