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Motivation: Targeted kinase inhibitors have dramatically improved cancer treatment, but kinase dependency for an individual patient or cancer cell can be challenging to predict. Kinase dependency does not always correspond with gene expression and mutation status. High-throughput drug screens are powerful tools for determining kinase dependency, but drug polypharmacology can make results difficult to interpret., Results: We developed Kinase Addiction Ranker (KAR), an algorithm that integrates high-throughput drug screening data, comprehensive kinase inhibition data and gene expression profiles to identify kinase dependency in cancer cells. We applied KAR to predict kinase dependency of 21 lung cancer cell lines and 151 leukemia patient samples using published datasets. We experimentally validated KAR predictions of FGFR and MTOR dependence in lung cancer cell line H1581, showing synergistic reduction in proliferation after combining ponatinib and AZD8055., Availability and Implementation: KAR can be downloaded as a Python function or a MATLAB script along with example inputs and outputs at: http://tanlab.ucdenver.edu/KAR/., Contact: aikchoon.tan@ucdenver.edu., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Purpose: FGFR1 copy-number gain (CNG) occurs in head and neck squamous cell cancers (HNSCC) and is used for patient selection in FGFR-specific inhibitor clinical trials. This study explores FGFR1 mRNA and protein levels in HNSCC cell lines, primary tumors, and patient-derived xenografts (PDX) as predictors of sensitivity to the FGFR inhibitor, NVP-BGJ398., Experimental Design: FGFR1 status, expression levels, and BGJ398 sensitive growth were measured in 12 HNSCC cell lines. Primary HNSCCs (n = 353) were assessed for FGFR1 CNG and mRNA levels, and HNSCC TCGA data were interrogated as an independent sample set. HNSCC PDXs (n = 39) were submitted to FGFR1 copy-number detection and mRNA assays to identify putative FGFR1-dependent tumors., Results: Cell line sensitivity to BGJ398 is associated with FGFR1 mRNA and protein levels, not FGFR1 CNG. Thirty-one percent of primary HNSCC tumors expressed FGFR1 mRNA, 18% exhibited FGFR1 CNG, 35% of amplified tumors were also positive for FGFR1 mRNA. This relationship was confirmed with the TCGA dataset. Using high FGFR1 mRNA for selection, 2 HNSCC PDXs were identified, one of which also exhibited FGFR1 CNG. The nonamplified tumor with high mRNA levels exhibited in vivo sensitivity to BGJ398., Conclusions: FGFR1 expression associates with BGJ398 sensitivity in HNSCC cell lines and predicts tyrosine kinase inhibitor sensitivity in PDXs. Our results support FGFR1 mRNA or protein expression, rather than FGFR1 CNG as a predictive biomarker for the response to FGFR inhibitors in a subset of patients suffering from HNSCC., (©2015 American Association for Cancer Research.)

Purpose: FGFR1 gene copy number (GCN) is being evaluated as a biomarker for FGFR tyrosine kinase inhibitor (TKI) response in squamous cell lung cancers (SCC). The exclusive use of FGFR1 GCN for predicting FGFR TKI sensitivity assumes increased GCN is the only mechanism for biologically relevant increases in FGFR1 signaling. Herein, we tested whether FGFR1 mRNA and protein expression may serve as better biomarkers of FGFR TKI sensitivity in lung cancer., Experimental Design: Histologically diverse lung cancer cell lines were submitted to assays for ponatinib sensitivity, a potent FGFR TKI. A tissue microarray composed of resected lung tumors was submitted to FGFR1 GCN, and mRNA analyses and the results were validated with The Cancer Genome Atlas (TCGA) lung cancer data., Results: Among 58 cell lines, 14 exhibited ponatinib sensitivity (IC50 values ≤ 50 nmol/L) that correlated with FGFR1 mRNA and protein expression, but not with FGFR1 GCN or histology. Moreover, ponatinib sensitivity associated with mRNA expression of the ligands, FGF2 and FGF9. In resected tumors, 22% of adenocarcinomas and 28% of SCCs expressed high FGFR1 mRNA. Importantly, only 46% of SCCs with increased FGFR1 GCN expressed high mRNA. Lung cancer TCGA data validated these findings and unveiled overlap of FGFR1 mRNA positivity with KRAS and PIK3CA mutations., Conclusions: FGFR1 dependency is frequent across various lung cancer histologies, and FGFR1 mRNA may serve as a better biomarker of FGFR TKI response in lung cancer than FGFR1 GCN. The study provides important and timely insight into clinical testing of FGFR TKIs in lung cancer and other solid tumor types., (©2014 American Association for Cancer Research.)

The targeting of oncogenic 'driver' kinases with small molecule inhibitors has proven to be a highly effective therapeutic strategy in selected non-small cell lung cancer (NSCLC) patients. However, acquired resistance to targeted therapies invariably arises and is a major limitation to patient care. ROS1 fusion proteins are a recently described class of oncogenic driver, and NSCLC patients that express these fusions generally respond well to ROS1-targeted therapy. In this study, we sought to determine mechanisms of acquired resistance to ROS1 inhibition. To accomplish this, we analyzed tumor samples from a patient who initially responded to the ROS1 inhibitor crizotinib but eventually developed acquired resistance. In addition, we generated a ROS1 inhibition-resistant derivative of the initially sensitive NSCLC cell line HCC78. Previously described mechanisms of acquired resistance to tyrosine kinase inhibitors including target kinase-domain mutation, target copy number gain, epithelial-mesenchymal transition, and conversion to small cell lung cancer histology were found to not underlie resistance in the patient sample or resistant cell line. However, we did observe a switch in the control of growth and survival signaling pathways from ROS1 to EGFR in the resistant cell line. As a result of this switch, ROS1 inhibition-resistant HCC78 cells became sensitive to EGFR inhibition, an effect that was enhanced by co-treatment with a ROS1 inhibitor. Our results suggest that co-inhibition of ROS1 and EGFR may be an effective strategy to combat resistance to targeted therapy in some ROS1 fusion-positive NSCLC patients.