aikchoon.tan@ucdenver.edu.

Oncogenic rearrangements in RET are present in 1-2% of lung adenocarcinoma (LAD) patients. Ponatinib is a multi-kinase inhibitor with low-nanomolar potency against the RET kinase domain. Here, we demonstrate that ponatinib exhibits potent anti-proliferative activity in RET fusion positive LC-2/ad LAD cells and inhibits phosphorylation of the RET fusion protein and signaling through ERK1/2 and AKT. Using distinct dose-escalation strategies, two ponatinib-resistant LC-2/ad cell lines, PR1 and PR2, were derived. PR1 and PR2 cell lines retained expression, but not phosphorylation of the RET fusion and lacked evidence of a resistance mutation in the RET kinase domain. Both resistant lines retained activation of the MAPK pathway. Next-generation RNA sequencing revealed an oncogenic NRAS p.Q61K mutation in the PR1 cell. PR1 cell proliferation was preferentially sensitive to siRNA knockdown of NRAS compared to knockdown of RET, more sensitive to MEK inhibition than the parental line, and NRAS-dependence was maintained in the absence of chronic RET inhibition. Expression of NRAS p.Q61K in RET fusion expressing TPC1 cells conferred resistance to ponatinib. PR2 cells exhibited increased expression of EGFR and AXL. EGFR inhibition decreased cell proliferation and phosphorylation of ERK1/2 and AKT in PR2 cells but not LC-2/ad cells. Although AXL inhibition enhanced PR2 sensitivity to afatinib, it was unable to decrease cell proliferation by itself. Thus, EGFR and AXL cooperatively rescued signaling from RET inhibition in the PR2 cells. Collectively, these findings demonstrate that resistance to ponatinib in RET-rearranged LAD is mediated by bypass signaling mechanisms that result in restored RAS/MAPK activation.

The consistency of in vitro drug sensitivity data is of key importance for cancer pharmacogenomics. Previous attempts to correlate drug sensitivities from the large pharmacogenomics databases, such as the Cancer Cell Line Encyclopedia (CCLE) and the Genomics of Drug Sensitivity in Cancer (GDSC), have produced discordant results. We developed a new drug sensitivity metric, the area under the dose response curve adjusted for the range of tested drug concentrations, which allows integration of heterogeneous drug sensitivity data from the CCLE, the GDSC, and the Cancer Therapeutics Response Portal (CTRP). We show that there is moderate to good agreement of drug sensitivity data for many targeted therapies, particularly kinase inhibitors. The results of this largest cancer cell line drug sensitivity data analysis to date are accessible through the online portal, which serves as a platform for high power pharmacogenomics analysis.

Integrative Gene-set, Network and Pathway Analysis (GNPA) is a powerful data analysis approach developed to help interpret high-throughput omics data. In PAGER 1.0, we demonstrated that researchers can gain unbiased and reproducible biological insights with the introduction of PAGs (Pathways, Annotated-lists and Gene-signatures) as the basic data representation elements. In PAGER 2.0, we improve the utility of integrative GNPA by significantly expanding the coverage of PAGs and PAG-to-PAG relationships in the database, defining a new metric to quantify PAG data qualities, and developing new software features to simplify online integrative GNPA. Specifically, we included 84 282 PAGs spanning 24 different data sources that cover human diseases, published gene-expression signatures, drug–gene, miRNA–gene interactions, pathways and tissue-specific gene expressions. We introduced a new normalized Cohesion Coefficient (nCoCo) score to assess the biological relevance of genes inside a PAG, and RP-score to rank genes and assign gene-specific weights inside a PAG. The companion web interface contains numerous features to help users query and navigate the database content. The database content can be freely downloaded and is compatible with third-party Gene Set Enrichment Analysis tools. We expect PAGER 2.0 to become a major resource in integrative GNPA. PAGER 2.0 is available at http://discovery.informatics.uab.edu/PAGER/.

Supplementary data are available at Bioinformatics online.

Supplementary data are available at Bioinformatics online.

Various power saving and contrast enhancement (PSCE) techniques have been applied to an organic light emitting diode (OLED) display for reducing the power demands of the display while preserving the image quality. In this paper, we propose a new deep learning-based PSCE scheme that can save power consumed by the OLED display while enhancing the contrast of the displayed image. In the proposed method, the power consumption is saved by simply reducing the brightness a certain ratio, whereas the perceived visual quality is preserved as much as possible by enhancing the contrast of the image using a convolutional neural network (CNN). Furthermore, our CNN can learn the PSCE technique without a reference image by unsupervised learning. Experimental results show that the proposed method is superior to conventional ones in terms of image quality assessment metrics such as a visual saliency-induced index (VSI) and a measure of enhancement (EME). 1 Index Terms-Convolutional neural network, deep learning, energy efficiency, image enhancement.

Protein kinases play important roles in regulating signal transduction in eukaryotic cells. Due to evolutionary conserved binding sites in the catalytic domain of the kinases, most inhibitors that target these sites promiscuously inhibit multiple kinases. Quantitative analysis can reveal complex and unexpected interactions between protein kinases and kinase inhibitors, providing opportunities for identifying multi-targeted inhibitors of specific diverse kinases for drug repurposing and development. We have developed K-Map—a novel and user-friendly web-based program that systematically connects a set of query kinases to kinase inhibitors based on quantitative profiles of the kinase inhibitor activities. Users can use K-Map to find kinase inhibitors for a set of query kinases (obtained from high-throughput ‘omics’ experiments) or to reveal new interactions between kinases and kinase inhibitors for rational drug combination studies.Availability and implementationK-Map has been implemented in python scripting language and the website is freely available at: http://tanlab.ucdenver.edu/kMap.