Recent advances in quantitative high throughput and high content data analysis

KiRNet: Kinase-centered network propagation of pharmacological screen results

The ever-increasing size and scale of biological information have popularized network-based approaches as a means to interpret these data. We develop a network propagation method that integrates kinase-inhibitor-focused functional screens with known protein-protein interactions (PPIs). This method, dubbed KiRNet, uses an a priori edge-weighting strategy based on node degree to establish a pipeline from a kinase inhibitor screen to the generation of a predictive PPI subnetwork. We apply KiRNet to uncover molecular regulators of mesenchymal cancer cells driven by overexpression of Frizzled 2 (FZD2). KiRNet produces a network model consisting of 166 high-value proteins. These proteins exhibit FZD2-dependent differential phosphorylation, and genetic knockdown studies validate their role in maintaining a mesenchymal cell state. Finally, analysis of clinical data shows that mesenchymal tumors exhibit significantly higher average expression of the 166 corresponding genes than epithelial tumors for nine different cancer types.

The right tools for the job: the central role for next generation chemical probes and chemistry-based target deconvolution methods in phenotypic drug discovery

The reconnection of the scientific community with phenotypic drug discovery has created exciting new possibilities to develop therapies for diseases with highly complex biology. It promises to revolutionise fields such as neurodegenerative disease and regenerative medicine, where the development of new drugs has consistently proved elusive. Arguably, the greatest challenge in readopting the phenotypic drug discovery approach exists in establishing a crucial chain of translatability between phenotype and benefit to patients in the clinic. This remains a key stumbling block for the field which needs to be overcome in order to fully realise the potential of phenotypic drug discovery. Excellent quality chemical probes and chemistry-based target deconvolution techniques will be a crucial part of this process. In this review, we discuss the current capabilities of chemical probes and chemistry-based target deconvolution methods and evaluate the next advances necessary in order to fully support phenotypic screening approaches in drug discovery.

High-Throughput Screening: today's biochemical and cell-based approaches

High-throughput screening (HTS) provides starting chemical matter in the adventure of developing a new drug. In this review, we survey several HTS methods used today for hit identification, organized in two main flavors: biochemical and cell-based assays. Biochemical assays discussed include fluorescence polarization and anisotropy, FRET, TR-FRET, and fluorescence lifetime analysis. Binding-based methods are also surveyed, including NMR, SPR, mass spectrometry, and DSF. On the other hand, cell-based assays discussed include viability, reporter gene, second messenger, and high-throughput microscopy assays. We devote some emphasis to high-content screening, which is becoming very popular. An advisable stage after hit discovery using phenotypic screens is target deconvolution, and we provide an overview of current chemical proteomics, in silico, and chemical genetics tools. Emphasis is made on recent CRISPR/dCas-based screens. Lastly, we illustrate some of the considerations that inform the choice of HTS methods and point to some areas with potential interest for future research.

Synthetic lethality in lung cancer and translation to clinical therapies

Lung cancer is a heterogeneous disease consisting of multiple histological subtypes each driven by unique genetic alterations. Despite the development of targeted therapies that inhibit the oncogenic mutations driving a subset of lung cancer cases, there is a paucity of effective treatments for the majority of lung cancer patients and new strategies are urgently needed. In recent years, the concept of synthetic lethality has been established as an effective approach for discovering novel cancer-specific targets as well as a method to improve the efficacy of existing drugs which provide partial but insufficient benefits for patients. In this review, we discuss the concept of synthetic lethality, the various types of synthetic lethal interactions in the context of oncology and the approaches used to identify these interactions, including recent advances that have transformed the ability to discover novel synthetic lethal combinations on a global scale. Lastly, we describe the specific synthetic lethal interactions identified in lung cancer to date and explore the pharmacological challenges and considerations in translating these discoveries to the clinic.