The molecular basis of targeting protein kinases in cancer therapeutics

'Latent drivers' expand the cancer mutational landscape

A major challenge facing the community involves identification of mutations that drive cancer. Analyses of cancer genomes to detect, and distinguish, 'driver' from 'passenger' mutations are daunting tasks. Here we suggest that there is a third 'latent driver' category. 'Latent driver' mutations behave as passengers, and do not confer a cancer hallmark. However, coupled with other emerging mutations, they drive cancer development and drug resistance. 'Latent drivers' emerge prior to and during cancer evolution. These allosteric mutations can work through 'AND' all-or-none or incremental 'Graded' logic gate mechanisms. Current diagnostic platforms generally assume that actionable 'driver' mutations are those appearing most frequently in cancer. We propose that 'latent driver' detection may help forecast cancer progression and modify personalized drug regimes.

Novel non-substrate modulators of the transmembrane efflux pump P-glycoprotein (ABCB1)

Novel non-substrate modulators of the transmembrane efflux pump P-gp have been profiled as nontoxic and mdr reversing agents.

The structural basis for cancer treatment decisions

Cancer treatment decisions rely on genetics, large data screens and clinical pharmacology. Here we point out that genetic analysis and treatment decisions may overlook critical elements in cancer development, progression and drug resistance. Two critical structural elements are missing in genetics-based decision-making: the mechanisms of oncogenic mutations and the cellular network which is rewired in cancer. These lay the foundation for the structural basis for cancer treatment decisions, which is rooted in the physical principles of the molecular conformational behavior of single molecules and their interactions. Improved tumor mutational analysis platforms and knowledge of the redundant pathways which can take over in cancer, may not only supplement known actionable findings, but forecast possible cancer progression and resistance. Such forward-looking can be powerful, endowing the oncologist with mechanistic insight and cancer prognosis, and consequently more informed treatment options. Examples include redundant pathways taking over after inhibition of EGFR constitutive activation, mutations in PIK3CA p110α and p85, and the non-hotspot AKT1 mutants conferring constitutive membrane localization.

Small-molecule inhibitors of the receptor tyrosine kinases: promising tools for targeted cancer therapies

Chemotherapeutic and cytotoxic drugs are widely used in the treatment of cancer. In spite of the improvements in the life quality of patients, their effectiveness is compromised by several disadvantages. This represents a demand for developing new effective strategies with focusing on tumor cells and minimum side effects. Targeted cancer therapies and personalized medicine have been defined as a new type of emerging treatments. Small molecule inhibitors (SMIs) are among the most effective drugs for targeted cancer therapy. The growing number of approved SMIs of receptor tyrosine kinases (RTKs) i.e., tyrosine kinase inhibitors (TKIs) in the clinical oncology imply the increasing attention and application of these therapeutic tools. Most of the current approved RTK-TKIs in preclinical and clinical settings are multi-targeted inhibitors with several side effects. Only a few specific/selective RTK-TKIs have been developed for the treatment of cancer patients. Specific/selective RTK-TKIs have shown less deleterious effects compared to multi-targeted inhibitors. This review intends to highlight the importance of specific/selective TKIs for future development with less side effects and more manageable agents. This article provides an overview of: (1) the characteristics and function of RTKs and TKIs; (2) the recent advances in the improvement of specific/selective RTK-TKIs in preclinical or clinical settings; and (3) emerging RTKs for targeted cancer therapies by TKIs.

The receptor tyrosine kinase ROR1--an oncofetal antigen for targeted cancer therapy

Targeted cancer therapies have emerged as new treatment options for various cancer types. Among targets, receptor tyrosine kinases (RTKs) are among the most promising. ROR1 is a transmembrane RTK of importance during the normal embryogenesis for the central nervous system, heart, lung and skeletal systems, but is not expressed in normal adult tissues. However, ROR1 is overexpressed in several human malignancies and may act as a survival factor for tumor cells. Its unique expression by malignant cells may provide a target for novel therapeutics including monoclonal antibodies (mAbs) and small molecule inhibitors of tyrosine kinases (TKI) for the treatment of cancer. Promising preclinical results have been reported in e.g. chronic lymphocytic leukemia, pancreatic carcinoma, lung and breast cancer. ROR1 might also be an interesting oncofetal antigen for active immunotherapy. In this review, we provide an overview of the ROR1 structure and functions in cancer and highlight emerging therapeutic options of interest for targeting ROR1 in tumor therapy.

Synthesis and biological evaluation of new pyrimidine-4-yl-ethanol derivatives as ROS1 kinase inhibitors

As a part of trials to target ROS1 kinase with potential inhibitors, a novel series of pyrimidin-4-yl-ethanol and ethanone derivatives (4a-f, 5a-f, 6a-f and 7a-f) have been designed based on previously discovered lead compounds KIST301072 and KIST301080, and synthesized on 4-5 steps according to compounds. The structures of the newly synthesized compounds have been confirmed on (1)H-NMR, (13)C-NMR and IR. Most of the tested compounds showed ROS1 kinase inhibitory activity in micromolar range.

Solid-phase biological assays for drug discovery

In the past 30 years, there has been a significant growth in the use of solid-phase assays in the area of drug discovery, with a range of new assays being used for both soluble and membrane-bound targets. In this review, we provide some basic background to typical drug targets and immobilization protocols used in solid-phase biological assays (SPBAs) for drug discovery, with emphasis on particularly labile biomolecular targets such as kinases and membrane-bound receptors, and highlight some of the more recent approaches for producing protein microarrays, bioaffinity columns, and other devices that are central to small molecule screening by SPBA. We then discuss key applications of such assays to identify drug leads, with an emphasis on the screening of mixtures. We conclude by highlighting specific advantages and potential disadvantages of SPBAs, particularly as they relate to particular assay formats.

Prediction and prioritization of rare oncogenic mutations in the cancer Kinome using novel features and multiple classifiers

Cancer is a genetic disease that develops through a series of somatic mutations, a subset of which drive cancer progression. Although cancer genome sequencing studies are beginning to reveal the mutational patterns of genes in various cancers, identifying the small subset of “causative” mutations from the large subset of “non-causative” mutations, which accumulate as a consequence of the disease, is a challenge. In this article, we present an effective machine learning approach for identifying cancer-associated mutations in human protein kinases, a class of signaling proteins known to be frequently mutated in human cancers. We evaluate the performance of 11 well known supervised learners and show that a multiple-classifier approach, which combines the performances of individual learners, significantly improves the classification of known cancer-associated mutations. We introduce several novel features related specifically to structural and functional characteristics of protein kinases and find that the level of conservation of the mutated residue at specific evolutionary depths is an important predictor of oncogenic effect. We consolidate the novel features and the multiple-classifier approach to prioritize and experimentally test a set of rare unconfirmed mutations in the epidermal growth factor receptor tyrosine kinase (EGFR). Our studies identify T725M and L861R as rare cancer-associated mutations inasmuch as these mutations increase EGFR activity in the absence of the activating EGF ligand in cell-based assays.

Cancer progresses by accumulation of mutations in a subset of genes that confer growth advantage. The 518 protein kinase genes encoded in the human genome, collectively called the kinome, represent one of the largest families of oncogenes. Targeted sequencing studies of many different cancers have shown that the mutational landscape comprises both cancer-causing “driver” mutations and harmless “passenger” mutations. While the frequent recurrence of some driver mutations in human cancers helps distinguish them from the large number of passenger mutations, a significant challenge is to identify the rare “driver” mutations that are less frequently observed in patient samples and yet are causative. Here we combine computational and experimental approaches to identify rare cancer-associated mutations in Epidermal Growth Factor receptor kinase (EGFR), a signaling protein frequently mutated in cancers. Specifically, we evaluate a novel multiple-classifier approach and features specific to the protein kinase super-family in distinguishing known cancer-associated mutations from benign mutations. We then apply the multiple classifier to identify and test the functional impact of rare cancer-associated mutations in EGFR. We report, for the first time, that the EGFR mutations T725M and L861R, which are infrequently observed in cancers, constitutively activate EGFR in a manner analogous to the frequently observed driver mutations.

The free energy landscape in translational science: how can somatic mutations result in constitutive oncogenic activation?

The free energy landscape theory has transformed the field of protein folding. The significance of perceiving function in terms of conformational heterogeneity is gradually shifting the interest in the community from folding to function. From the free energy landscape standpoint the principles are unchanged: rather than considering the entire protein conformational landscape, the focus is on the ensemble around the bottom of the folding funnel. The protein can be viewed as populating one of two states: active or inactive. The basins of the two states are separated by a surmountable barrier, which allows the conformations to switch between the states. Unless the protein is a repressor, under physiological conditions it typically populates the inactive state. Ligand binding (or post-translational modification) triggers a switch to the active state. Constitutive allosteric mutations work by shifting the population from the inactive to the active state and keeping it there. This can happen by either destabilizing the inactive state, stabilizing the active state, or both. Identification of the mechanism through which they work is important since it may assist in drug discovery. Here we spotlight the usefulness of the free energy landscape in translational science, illustrating how oncogenic mutations can work in key proteins from the EGFR/Ras/Raf/Erk/Mek pathway, the main signaling pathway in cancer. Finally, we delineate the key components which are needed in order to trace the mechanism of allosteric events.