Imatinib binding and cKIT inhibition is abrogated by the cKIT kinase domain I missense mutation Val654Ala

VGAEDTI: drug-target interaction prediction based on variational inference and graph autoencoder

MOTIVATION

Accurate identification of Drug-Target Interactions (DTIs) plays a crucial role in many stages of drug development and drug repurposing. (i) Traditional methods do not consider the use of multi-source data and do not consider the complex relationship between data sources. (ii) How to better mine the hidden features of drug and target space from high-dimensional data, and better solve the accuracy and robustness of the model.

RESULTS

To solve the above problems, a novel prediction model named VGAEDTI is proposed in this paper. We constructed a heterogeneous network with multiple sources of information using multiple types of drug and target dataIn order to obtain deeper features of drugs and targets, we use two different autoencoders. One is variational graph autoencoder (VGAE) which is used to infer feature representations from drug and target spaces. The second is graph autoencoder (GAE) propagating labels between known DTIs. Experimental results on two public datasets show that the prediction accuracy of VGAEDTI is better than that of six DTIs prediction methods. These results indicate that model can predict new DTIs and provide an effective tool for accelerating drug development and repurposing.

DeepMPF: deep learning framework for predicting drug-target interactions based on multi-modal representation with meta-path semantic analysis

BACKGROUND

Drug-target interaction (DTI) prediction has become a crucial prerequisite in drug design and drug discovery. However, the traditional biological experiment is time-consuming and expensive, as there are abundant complex interactions present in the large size of genomic and chemical spaces. For alleviating this phenomenon, plenty of computational methods are conducted to effectively complement biological experiments and narrow the search spaces into a preferred candidate domain. Whereas, most of the previous approaches cannot fully consider association behavior semantic information based on several schemas to represent complex the structure of heterogeneous biological networks. Additionally, the prediction of DTI based on single modalities cannot satisfy the demand for prediction accuracy.

METHODS

We propose a multi-modal representation framework of 'DeepMPF' based on meta-path semantic analysis, which effectively utilizes heterogeneous information to predict DTI. Specifically, we first construct protein-drug-disease heterogeneous networks composed of three entities. Then the feature information is obtained under three views, containing sequence modality, heterogeneous structure modality and similarity modality. We proposed six representative schemas of meta-path to preserve the high-order nonlinear structure and catch hidden structural information of the heterogeneous network. Finally, DeepMPF generates highly representative comprehensive feature descriptors and calculates the probability of interaction through joint learning.

RESULTS

To evaluate the predictive performance of DeepMPF, comparison experiments are conducted on four gold datasets. Our method can obtain competitive performance in all datasets. We also explore the influence of the different feature embedding dimensions, learning strategies and classification methods. Meaningfully, the drug repositioning experiments on COVID-19 and HIV demonstrate DeepMPF can be applied to solve problems in reality and help drug discovery. The further analysis of molecular docking experiments enhances the credibility of the drug candidates predicted by DeepMPF.

CONCLUSIONS

All the results demonstrate the effectively predictive capability of DeepMPF for drug-target interactions. It can be utilized as a useful tool to prescreen the most potential drug candidates for the protein. The web server of the DeepMPF predictor is freely available at http://120.77.11.78/DeepMPF/ , which can help relevant researchers to further study.

Discovery and optimization of natural-based nanomolar c-Kit inhibitors via in silico and in vitro studies

c-Kit is a receptor tyrosine kinase which is involved in intracellular signaling and mutations of c-Kit have been associated with various types of cancers. Investigations have shown that inhibition of c-Kit, using tyrosine kinase inhibitors, yielded promising results in cancer treatment marking it as a promising target for cancer therapy. However, the emerging resistance for the current therapy necessitates the development of more potent inhibitors which are not affected by these mutations. Herein, virtual screening of a library of natural-based compounds yielded three hits (2, 5 and 6) which possessed nanomolar inhibitory (2.02, 4.33 and 2.80 nM, respectively) activity when tested in vitro against c-Kit. Single point mutation docking studies showed the hits to be unaffected by the most common resistance mutation in imatinib-resistant cells, mutation of Val654. Although, the top hits exhibited around 3000 higher inhibitory potency toward c-Kit when compared to imatinib (5.4 µM), previous studies have shown that they are metabolically unstable. Fragment-based drug design approaches were then employed to enhance binding affinity of the top hit and make it more metabolically stable. Screening of the generated fragments yielded a new derivative, F1, which demonstrated stronger binding affinity, stability and binding free energy when compared to the hit compound 2.Communicated by Ramaswamy H. Sarma.

New treatment strategies for advanced-stage gastrointestinal stromal tumours

When gastrointestinal stromal tumour (GIST), the most common form of sarcoma, was first recognized as a distinct pathological entity in the 1990s, patients with advanced-stage disease had a very poor prognosis owing to a lack of effective medical therapies. The discovery of KIT mutations as the first and most prevalent drivers of GIST and the subsequent development of the first KIT tyrosine kinase inhibitor (TKI), imatinib, revolutionized the treatment of patients with this disease. We can now identify the driver mutation in 99% of patients with GIST via molecular diagnostic testing, and therapies have been developed to treat many, but not all, molecular subtypes of the disease. At present, seven drugs are approved by the FDA for the treatment of advanced-stage GIST (imatinib, sunitinib, regorafenib, ripretinib, avapritinib, larotrectinib and entrectinib), all of which are TKIs. Although these agents can be very effective for treating certain GIST subtypes, challenges remain and new therapeutic approaches are needed. In this Review, we discuss the molecular subtypes of GIST and the evolution of current treatments, as well as their therapeutic limitations. We also highlight emerging therapeutic approaches that might overcome clinical challenges through novel strategies predicated on the biological features of the distinct GIST molecular subtypes.

Multiview network embedding for drug-target Interactions prediction by consistent and complementary information preserving

Accurate prediction of drug-target interactions (DTIs) can reduce the cost and time of drug repositioning and drug discovery. Many current methods integrate information from multiple data sources of drug and target to improve DTIs prediction accuracy. However, these methods do not consider the complex relationship between different data sources. In this study, we propose a novel computational framework, called MccDTI, to predict the potential DTIs by multiview network embedding, which can integrate the heterogenous information of drug and target. MccDTI learns high-quality low-dimensional representations of drug and target by preserving the consistent and complementary information between multiview networks. Then MccDTI adopts matrix completion scheme for DTIs prediction based on drug and target representations. Experimental results on two datasets show that the prediction accuracy of MccDTI outperforms four state-of-the-art methods for DTIs prediction. Moreover, literature verification for DTIs prediction shows that MccDTI can predict the reliable potential DTIs. These results indicate that MccDTI can provide a powerful tool to predict new DTIs and accelerate drug discovery. The code and data are available at: https://github.com/ShangCS/MccDTI.

A computational approach to aid clinicians in selecting anti-viral drugs for COVID-19 trials

The year 2020 witnessed a heavy death toll due to COVID-19, calling for a global emergency. The continuous ongoing research and clinical trials paved the way for vaccines. But, the vaccine efficacy in the long run is still questionable due to the mutating coronavirus, which makes drug re-positioning a reasonable alternative. COVID-19 has hence fast-paced drug re-positioning for the treatment of COVID-19 and its symptoms. This work builds computational models using matrix completion techniques to predict drug-virus association for drug re-positioning. The aim is to assist clinicians with a tool for selecting prospective antiviral treatments. Since the virus is known to mutate fast, the tool is likely to help clinicians in selecting the right set of antivirals for the mutated isolate. The main contribution of this work is a manually curated database publicly shared, comprising of existing associations between viruses and their corresponding antivirals. The database gathers similarity information using the chemical structure of drugs and the genomic structure of viruses. Along with this database, we make available a set of state-of-the-art computational drug re-positioning tools based on matrix completion. The tools are first analysed on a standard set of experimental protocols for drug target interactions. The best performing ones are applied for the task of re-positioning antivirals for COVID-19. These tools select six drugs out of which four are currently under various stages of trial, namely Remdesivir (as a cure), Ribavarin (in combination with others for cure), Umifenovir (as a prophylactic and cure) and Sofosbuvir (as a cure). Another unanimous prediction is Tenofovir alafenamide, which is a novel Tenofovir prodrug developed in order to improve renal safety when compared to its original counterpart (older version) Tenofovir disoproxil. Both are under trail, the former as a cure and the latter as a prophylactic. These results establish that the computational methods are in sync with the state-of-practice. We also demonstrate how the drugs to be used against the virus would vary as SARS-Cov-2 mutates over time by predicting the drugs for the mutated strains, suggesting the importance of such a tool in drug prediction. We believe this work would open up possibilities for applying machine learning models to clinical research for drug-virus association prediction and other similar biological problems.

A comparative chemogenic analysis for predicting Drug-Target Pair via Machine Learning Approaches

A computational technique for predicting the DTIs has now turned out to be an indispensable job during the process of drug finding. It tapers the exploration room for interactions by propounding possible interaction contenders for authentication through experiments of wet-lab which are known for their expensiveness and time consumption. Chemogenomics, an emerging research area focused on the systematic examination of the biological impact of a broad series of minute molecular-weighting ligands on a broad raiment of macromolecular target spots. Additionally, with the advancement in time, the complexity of the algorithms is increasing which may result in the entry of big data technologies like Spark in this field soon. In the presented work, we intend to offer an inclusive idea and realistic evaluation of the computational Drug Target Interaction projection approaches, to perform as a guide and reference for researchers who are carrying out work in a similar direction. Precisely, we first explain the data utilized in computational Drug Target Interaction prediction attempts like this. We then sort and explain the best and most modern techniques for the prediction of DTIs. Then, a realistic assessment is executed to show the projection performance of several illustrative approaches in various situations. Ultimately, we underline possible opportunities for additional improvement of Drug Target Interaction projection enactment and also linked study objectives.

Drug-target interaction prediction using Multi Graph Regularized Nuclear Norm Minimization

The identification of potential interactions between drugs and target proteins is crucial in pharmaceutical sciences. The experimental validation of interactions in genomic drug discovery is laborious and expensive; hence, there is a need for efficient and accurate in-silico techniques which can predict potential drug-target interactions to narrow down the search space for experimental verification. In this work, we propose a new framework, namely, Multi-Graph Regularized Nuclear Norm Minimization, which predicts the interactions between drugs and target proteins from three inputs: known drug-target interaction network, similarities over drugs and those over targets. The proposed method focuses on finding a low-rank interaction matrix that is structured by the proximities of drugs and targets encoded by graphs. Previous works on Drug Target Interaction (DTI) prediction have shown that incorporating drug and target similarities helps in learning the data manifold better by preserving the local geometries of the original data. But, there is no clear consensus on which kind and what combination of similarities would best assist the prediction task. Hence, we propose to use various multiple drug-drug similarities and target-target similarities as multiple graph Laplacian (over drugs/targets) regularization terms to capture the proximities exhaustively. Extensive cross-validation experiments on four benchmark datasets using standard evaluation metrics (AUPR and AUC) show that the proposed algorithm improves the predictive performance and outperforms recent state-of-the-art computational methods by a large margin. Software is publicly available at https://github.com/aanchalMongia/MGRNNMforDTI.

Aiding and Abetting: How the Tumor Microenvironment Protects Cancer from Chemotherapy

Disease recurrence following cancer therapy remains an intractable clinical problem and represents a major impediment to reducing the mortality attributable to malignant tumors. While research has traditionally focused on the cell-intrinsic mechanisms and mutations that render tumors refractory to both classical chemotherapeutics and targeted therapies, recent studies have begun to uncover myriad roles for the tumor microenvironment (TME) in modulating therapeutic efficacy. This work suggests that drug resistance is as much ecological as it is evolutionary. Specifically, cancers resident in organs throughout the body do not develop in isolation. Instead, tumor cells arise in the context of nonmalignant cellular components of a tissue. While the roles of these cell-extrinsic factors in cancer initiation and progression are well established, our understanding of the TME's influence on therapeutic outcome is in its infancy. Here, we focus on mechanisms by which neoplastic cells co-opt preexisting or treatment-induced signaling networks to survive chemotherapy.

Silencing of adaptor protein SH3BP2 reduces KIT/PDGFRA receptors expression and impairs gastrointestinal stromal tumors growth

Gastrointestinal stromal tumors (GISTs) represent about 80% of the mesenchymal neoplasms of the gastrointestinal tract. Most GISTs contain oncogenic KIT (85%) or PDGFRA (5%) receptors. The kinase inhibitor imatinib mesylate is the preferential treatment for these tumors; however, the development of drug resistance has highlighted the need for novel therapeutic strategies. Recently, we reported that the adaptor molecule SH3 Binding Protein 2 (SH3BP2) regulates KIT expression and signaling in human mast cells. Our current study shows that SH3BP2 is expressed in primary tumors and cell lines from GIST patients and that SH3BP2 silencing leads to a downregulation of oncogenic KIT and PDGFRA expression and an increase in apoptosis in imatinib-sensitive and imatinib-resistant GIST cells. The microphthalmia-associated transcription factor (MITF), involved in KIT expression in mast cells and melanocytes, is expressed in GISTs. Interestingly, MITF is reduced after SH3BP2 silencing. Importantly, reconstitution of both SH3BP2 and MITF restores cell viability. Furthermore, SH3BP2 silencing significantly reduces cell migration and tumor growth of imatinib-sensitive and imatinib-resistant cells in vivo. Altogether, SH3BP2 regulates KIT and PDGFRA expression and cell viability, indicating a role as a potential target in imatinib-sensitive and imatinib-resistant GISTs.

Computational prediction of drug–target interactions using chemogenomic approaches: an empirical survey

Computational prediction of drug–target interactions (DTIs) has become an essential task in the drug discovery process. It narrows down the search space for interactions by suggesting potential interaction candidates for validation via wet-lab experiments that are well known to be expensive and time-consuming. In this article, we aim to provide a comprehensive overview and empirical evaluation on the computational DTI prediction techniques, to act as a guide and reference for our fellow researchers. Specifically, we first describe the data used in such computational DTI prediction efforts. We then categorize and elaborate the state-of-the-art methods for predicting DTIs. Next, an empirical comparison is performed to demonstrate the prediction performance of some representative methods under different scenarios. We also present interesting findings from our evaluation study, discussing the advantages and disadvantages of each method. Finally, we highlight potential avenues for further enhancement of DTI prediction performance as well as related research directions.

SCF-KIT signaling induces endothelin-3 synthesis and secretion: Thereby activates and regulates endothelin-B-receptor for generating temporally- and spatially-precise nitric oxide to modulate SCF- and or KIT-expressing cell functions

We demonstrate that SCF-KIT signaling induces synthesis and secretion of endothelin-3 (ET3) in human umbilical vein endothelial cells and melanoma cells in vitro, gastrointestinal stromal tumors, human sun-exposed skin, and myenteric plexus of human colon post-fasting in vivo. This is the first report of a physiological mechanism of ET3 induction. Integrating our finding with supporting data from literature leads us to discover a previously unreported pathway of nitric oxide (NO) generation derived from physiological endothelial NO synthase (eNOS) or neuronal NOS (nNOS) activation (referred to as the KIT-ET3-NO pathway). It involves: (1) SCF-expressing cells communicate with neighboring KIT-expressing cells directly or indirectly (cleaved soluble SCF). (2) SCF-KIT signaling induces timely local ET3 synthesis and secretion. (3) ET3 binds to ETBR on both sides of intercellular space. (4) ET3-binding-initiated-ETBR activation increases cytosolic Ca²⁺, activates cell-specific eNOS or nNOS. (5) Temporally- and spatially-precise NO generation. NO diffuses into neighboring cells, thus acts in both SCF- and KIT-expressing cells. (6) NO modulates diverse cell-specific functions by NO/cGMP pathway, controlling transcriptional factors, or other mechanisms. We demonstrate the critical physiological role of the KIT-ET3-NO pathway in fulfilling high demand (exceeding basal level) of endothelium-dependent NO generation for coping with atherosclerosis, pregnancy, and aging. The KIT-ET3-NO pathway most likely also play critical roles in other cell functions that involve dual requirement of SCF-KIT signaling and NO. New strategies (e.g. enhancing the KIT-ET3-NO pathway) to harness the benefit of endogenous eNOS and nNOS activation and precise NO generation for correcting pathophysiology and restoring functions warrant investigation.

Targeted Therapies Combined With Immune Checkpoint Therapy

The age of personalized medicine continues to evolve within clinical oncology with the arsenal available to clinicians in a variety of malignancies expanding at an exponential rate. The development and advancement of molecular treatment modalities, including targeted therapy and immune checkpoint blockade, continue to flourish. Treatment with targeted therapy (BRAF, MEK, and other small molecule inhibitors) can be associated with swift disease control and high response rates, but limited durability when used as monotherapy. Conversely, treatment with immune checkpoint blockade monotherapy regimens (anti-cytotoxic T-lymphocyte antigen 4 and anti-programmed cell death protein 1/programmed cell death protein 1 ligand) tends to have lower response rates than that observed with BRAF-targeted therapy, although these treatments may offer long-term durable disease control. With the advent of these forms of therapy, there was interest early on in empirically combining targeted therapy with immune checkpoint blockade with the hopes of preserving high response rates and adding durability; however, there is now strong scientific rationale for combining these forms of therapy-and early evidence of synergy in preclinical models of melanoma. Clinical trials combining these strategies are ongoing, and mature data regarding response rates and durability are not yet available. Synergy may ultimately be apparent; however, it has also become clear that complexities exist regarding toxicity when combining these therapies. Nonetheless, this increased appreciation of the complex interplay between oncogenic mutations and antitumor immunity has opened up tremendous opportunities for studying targeted agents and immunotherapy in combination, which extends far beyond melanoma to other solid tumors and also to hematologic malignancies.

Distinct cellular properties of oncogenic KIT receptor tyrosine kinase mutants enable alternative courses of cancer cell inhibition

Large genomic sequencing analysis as part of precision medicine efforts revealed numerous activating mutations in receptor tyrosine kinases, including KIT. Unfortunately, a single approach is not effective for inhibiting cancer cells or treating cancers driven by all known oncogenic KIT mutants. Here, we show that each of the six major KIT oncogenic mutants exhibits different enzymatic, cellular, and dynamic properties and responds distinctly to different KIT inhibitors. One class of KIT mutants responded well to anti-KIT antibody treatment alone or in combination with a low dose of tyrosine kinase inhibitors (TKIs). A second class of KIT mutants, including a mutant resistant to imatinib treatment, responded well to a combination of TKI with anti-KIT antibodies or to anti-KIT toxin conjugates, respectively. We conclude that the preferred choice of precision medicine treatments for cancers driven by activated KIT and other RTKs may rely on clear understanding of the dynamic properties of oncogenic mutants.

Extended molecular dynamics of a c-kit promoter quadruplex

The 22-mer c-kit promoter sequence folds into a parallel-stranded quadruplex with a unique structure, which has been elucidated by crystallographic and NMR methods and shows a high degree of structural conservation. We have carried out a series of extended (up to 10 μs long, ∼50 μs in total) molecular dynamics simulations to explore conformational stability and loop dynamics of this quadruplex. Unfolding no-salt simulations are consistent with a multi-pathway model of quadruplex folding and identify the single-nucleotide propeller loops as the most fragile part of the quadruplex. Thus, formation of propeller loops represents a peculiar atomistic aspect of quadruplex folding. Unbiased simulations reveal μs-scale transitions in the loops, which emphasizes the need for extended simulations in studies of quadruplex loops. We identify ion binding in the loops which may contribute to quadruplex stability. The long lateral-propeller loop is internally very stable but extensively fluctuates as a rigid entity. It creates a size-adaptable cleft between the loop and the stem, which can facilitate ligand binding. The stability gain by forming the internal network of GA base pairs and stacks of this loop may be dictating which of the many possible quadruplex topologies is observed in the ground state by this promoter quadruplex.

Molecular characterization of piebaldism in a Tunisian family

OBJECTIVE

The present study is aimed at performing the molecular characterization of a Tunisian family with piebaldism.

METHODS

As the proband and her mother showed a severe phenotype, we first chose to screen exons 10, 11, 12, 13, 16, 17 and 18 of the KIT proto-oncogene by direct sequencing.

RESULTS

Direct sequencing analysis showed a C to T substitution at 1939 in exon 13 (c.1939C>T) in heterozygous state in the patient and her mother. The mutation was not found in their unaffected family members or normal controls.

CONCLUSION

Our results provide additional support that mutations in the tyrosine kinase domain of the KIT gene are responsible for the severe form of piebaldism.

Inhibitory effects of Peg-IFNα 2b and imatinib, alone or in combination, on imatinib-resistant gastrointestinal stromal tumor cell line PC9/GR

AIM: To investigate the inhibition effects of polyethylene glycol interferon α 2b (Peg-IFNα 2b) and imatinib, alone or in combination, on imatinib-resistant gastrointestinal stromal tumor (GIST) cell line (GIST-R), and to explore the possible mechanism.

METHODS: GIST-R cells were exposed to Peg-IFNα 2b and imatinib, alone or in combination. The rate of reduced proliferation and the combination index (CI) in different treatment groups were detected by MTT assay. The apoptotic rate was detected by flow cytometry. The expression of phosphorylated mammalian target of rapamycin (p-mTOR) and Bcl-2 was analyzed by Western blot.

RESULTS: GIST-R cells presented remarkable resistance to imatinib, and the resistance index (RI) was 46.14. The rate of reduced proliferation in the combination group was significantly higher than those in the Peg-IFNα 2b or imatinib alone groups (P < 0.05). The calculated CI values in the combination group suggested a synergistic effect. Compared with non-treated GIST-R cells, the expression levels of p-mTOR and Bcl-2 proteins in GIST-R cells treated with Peg-IFNα 2b and imatinib significantly decreased (P < 0.01).

CONCLUSION: The combination of Peg-IFNα 2b and imatinib generates a synergistic effect in GIST-R cells. This effect may be related with apoptosis and the down-regulation of the expression of p-mTOR.

Massive intra-abdominal imatinib-resistant gastrointestinal stromal tumor in a 21-year-old male

Gastrointestinal stromal tumors (GISTs) in adolescence are far less common than adult GISTs and have varied GIST genotypes that present diagnostic and therapeutic challenges. Here, we discuss a 21-year-old male with diagnosis of unresectable, imatinib-resistant GIST. At initial evaluation, a neoadjuvant treatment approach was recommended. As such, the patient received imatinib over the course of one year. Unfortunately, the GIST increased in size, and a subsequent attempt at surgical resection was aborted fearing infiltration of major vascular structures. The patient was then referred to our institution, at which time imatinib therapy was discontinued. Surgical intervention was again considered and the patient underwent successful resection of massive intra-abdominal GIST with total gastrectomy and Roux-en-Y esophagojejunostomy. Since pediatric GISTs are typically resistant to imatinib, we performed genotype analysis of the operative specimen that revealed KIT mutations associated with imatinib sensitivity and resistance. Given the sequencing data and operative findings, the patient was started postoperatively on sunitinib. This case illustrates the importance of understanding both adult and pediatric GISTs when implementing appropriate treatment regimens. Since the genotype of GISTs dictates phenotypic behavior, mutational analysis is an important component of care especially for adolescents whose disease may mirror the pediatric or adult population.

Therapeutic targeting of c-KIT in cancer

INTRODUCTION

Mutated forms of the receptor tyrosine kinase c-KIT are "drivers" in several cancers and are attractive targets for therapy. While benefits have been obtained from use of inhibitors of KIT kinase activity such as imatinib, especially in gastrointestinal stromal tumours (GIST), primary resistance occurs with certain oncogenic mutations. Furthermore, resistance frequently develops due to secondary mutations. Approaches to addressing both of these issues as well as combination therapies to optimise use of KIT kinase inhibitors are discussed.

AREAS COVERED

This review covers the occurrence of oncogenic KIT mutations in different cancers and the molecular basis of their action. The action of KIT kinase inhibitors, especially imatinib, sunitinib, dasatinib and PKC412, on different primary and secondary mutants is discussed. Outcomes of clinical trials in GIST, acute myeloid leukaemia (AML), systemic mastocytosis and melanoma and their implications for future directions are considered.

EXPERT OPINION

Analysis of KIT mutations in individual patients is an essential prerequisite to the use of kinase inhibitors for therapy, and monitoring for development of secondary mutations that confer drug resistance is necessary. However, it is unlikely that KIT inhibitors alone can lead to cure. KIT mutations alone do not seem to be sufficient for transformation; thus identification and co-targeting of synergistic oncogenic pathways should lead to improved outcomes.

The HSP90 inhibitor, AT13387, is effective against imatinib-sensitive and -resistant gastrointestinal stromal tumor models

The majority of gastrointestinal stromal tumors (GIST) are characterized by activating mutations of KIT, an HSP90 client protein. Further secondary resistance mutations within KIT limit clinical responses to tyrosine kinase inhibitors, such as imatinib. The dependence of KIT and its mutated forms on HSP90 suggests that HSP90 inhibition might be a valuable treatment option for GIST, which would be equally effective on imatinib-sensitive and -resistant clones. We investigated the activity of AT13387, a potent HSP90 inhibitor currently being evaluated in clinical trials, in both in vitro and in vivo GIST models. AT13387 inhibited the proliferation of imatinib-sensitive (GIST882, GIST-T1) and -resistant (GIST430, GIST48) cell lines, including those resistant to the geldanamycin analogue HSP90 inhibitor, 17-AAG. Treatment with AT13387 resulted in depletion of HSP90 client proteins, KIT and AKT, along with their phospho-forms in imatinib-sensitive and -resistant cell lines, irrespective of KIT mutation. KIT signaling was ablated, whereas HSP70, a marker of HSP90 inhibition, was induced. In vivo, antitumor activity of AT13387 was showed in both the imatinib-sensitive, GIST-PSW, xenograft model and a newly characterized imatinib-resistant, GIST430, xenograft model. Induction of HSP70, depletion of phospho-KIT and inhibition of KIT signaling were seen in tumors from both models after treatment with AT13387. A combination of imatinib and AT13387 treatment in the imatinib-resistant GIST430 model significantly enhanced tumor growth inhibition over either of the monotherapies. Importantly, the combination of AT13387 and imatinib was well tolerated. These results suggest AT13387 is an excellent candidate for clinical testing in GIST in combination with imatinib.

Gastrointestinal stromal tumors: Molecular pathogenesis and targeted therapy

The autophosphorylation of KIT protein, resulting from gain-of-function mutations of the c-kit or PDGFR gene, is the most important molecular mechanism involved in the pathogenesis of gastrointestinal stromal tumors (GISTs). Imatinib is a small molecule tyrosine kinase inhibitor and is effective in the treatment of GISTs. KIT is a convenient target in GISTs, and inhibition of this receptor with imatinib (Gleevec, STI571) in GISTs has shown dramatic efficacy. Unfortunately, resistance to imatinib is a significant clinical problem. Further understanding of the molecular pathogenesis of GISTs is therefore important and may lead to the identification of novel drug targets. This review will focus on recent advances in the understanding of molecular mechanisms involved in the pathogenesis of all types of GISTs. The molecular biological characteristics of each type of GISTs will also be discussed.

Exploiting antitumor immunity to overcome relapse and improve remission duration

Cancer survivors often relapse due to evolving drug-resistant clones and repopulating tumor stem cells. Our preclinical study demonstrated that terminal cancer patient’s lymphocytes can be converted from tolerant bystanders in vivo into effective cytotoxic T-lymphocytes in vitro killing patient’s own tumor cells containing drug-resistant clones and tumor stem cells. We designed a clinical trial combining peginterferon α-2b with imatinib for treatment of stage III/IV gastrointestinal stromal tumor (GIST) with the rational that peginterferon α-2b serves as danger signals to promote antitumor immunity while imatinib’s effective tumor killing undermines tumor-induced tolerance and supply tumor-specific antigens in vivo without leukopenia, thus allowing for proper dendritic cell and cytotoxic T-lymphocyte differentiation toward Th1 response. Interim analysis of eight patients demonstrated significant induction of IFN-γ-producing-CD8⁺, -CD4⁺, -NK cell, and IFN-γ-producing-tumor-infiltrating-lymphocytes, signifying significant Th1 response and NK cell activation. After a median follow-up of 3.6 years, complete response (CR) + partial response (PR) = 100%, overall survival = 100%, one patient died of unrelated illness while in remission, six of seven evaluable patients are either in continuing PR/CR (5 patients) or have progression-free survival (PFS, 1 patient) exceeding the upper limit of the 95% confidence level of the genotype-specific-PFS of the phase III imatinib-monotherapy (CALGB150105/SWOGS0033), demonstrating highly promising clinical outcomes. The current trial is closed in preparation for a larger future trial. We conclude that combination of targeted therapy and immunotherapy is safe and induced significant Th1 response and NK cell activation and demonstrated highly promising clinical efficacy in GIST, thus warranting development in other tumor types.

MicroRNA-494 downregulates KIT and inhibits gastrointestinal stromal tumor cell proliferation

PURPOSE

Gain-of-function mutations and KIT overexpression are well-known tumorigenesis mechanisms in gastrointestinal stromal tumors (GIST). This study aimed to discover microRNAs (miRNA) that target KIT and reveal the relationship between the discovered miRNAs and KIT expression in GISTs.

EXPERIMENTAL DESIGN

Fresh-frozen GISTs from 31 patients were used to confirm the relationship between miR-494 and KIT expression using quantitative reverse transcription-PCR to assess miR-494 expression levels and Western blotting to assess KIT protein expression levels. A luciferase assay was conducted for the target evaluation. The functional effects of miR-494 on GIST882 cells (GIST cell line with activating KIT mutation) were validated by a cell proliferation assay and fluoresce-activated cell sorting analysis.

RESULTS

An inverse relationship was found between the expression levels of miR-494 and KIT in GISTs (r = -0.490, P = 0.005). The direct targeting of KIT by miR-494 was shown by the reduction in KIT expression after miR-494 overexpression and the increase in KIT expression after inhibiting endogenous miR-494 expression. We showed that miR-494 regulates KIT by binding two different seed match sites. Induced miR-494 overexpression in GIST882 reduced the expression of downstream molecules in KIT signaling transduction pathways, including phospho-AKT and phospho-STAT3. Finally, miR-494 overexpression provoked apoptosis and inhibited GIST cell growth, which were accompanied by changes in G(1) and S phase content.

CONCLUSION

Our findings indicate that miR-494 is a negative regulator of KIT in GISTs and overexpressing miR-494 in GISTs may be a promising approach to GIST treatment.

Tumour heterogeneity of mucosal melanomas during treatment with imatinib

A comparison of in vitro and in vivo characteristics of tumour cells derived from patients with mucosal melanoma treated with imatinib was performed with respect to KIT mutations. Three patients with mucosal melanoma were treated with imatinib. Patient-derived tumour material was used to establish melanoma cell cultures ex vivo. We evaluated tumour material and cell cultures for KIT protein expression and KIT mutation status. In addition, proliferation assays with melanoma cell cultures were performed with imatinib. Expression of KIT protein and KIT mutation was shown in two patients who responded to imatinib in vivo. Cells derived from a third patient who did not respond to imatinib did not express KIT and lacked a KIT mutation. Patient-derived melanoma cells did not show any KIT mutations, nor did they respond to imatinib in vitro. Our study underlines that melanoma consists of a heterogeneous cell population, making it imperative to use the mapping of involved activating tumour growth-driving pathways in order to improve response to therapy with kinase inhibitors.

Changing pathology with changing drugs: skin cancer

Today skin cancer is mainly treated by surgical interventions. New findings concerning molecular biology and the signaling pathways in epithelial skin cancers such as basal cell carcinoma, squamous cell carcinoma or melanoma, and mesenchymal skin cancers such as angiosarcoma and dermatofibrosarcoma protuberans (DFSP) have identified new molecular targets for a systemic or local treatment approach. For DFSP there is an opportunity already today to reduce the intensity of surgical procedures by pretreatment with targeted therapy. This article highlights important aspects in several skin cancer types.

From in silico target prediction to multi-target drug design: current databases, methods and applications

Given the tremendous growth of bioactivity databases, the use of computational tools to predict protein targets of small molecules has been gaining importance in recent years. Applications span a wide range, from the 'designed polypharmacology' of compounds to mode-of-action analysis. In this review, we firstly survey databases that can be used for ligand-based target prediction and which have grown tremendously in size in the past. We furthermore outline methods for target prediction that exist, both based on the knowledge of bioactivities from the ligand side and methods that can be applied in situations when a protein structure is known. Applications of successful in silico target identification attempts are discussed in detail, which were based partly or in whole on computational target predictions in the first instance. This includes the authors' own experience using target prediction tools, in this case considering phenotypic antibacterial screens and the analysis of high-throughput screening data. Finally, we will conclude with the prospective application of databases to not only predict, retrospectively, the protein targets of a small molecule, but also how to design ligands with desired polypharmacology in a prospective manner.

Molecular mechanisms of drug resistance in tyrosine kinases cAbl and cKit

The inhibition of protein kinases has gained general acceptance as an effective approach to treat a wide range of cancers. However, in many cases, prolonged administration of kinase inhibitors often leads to acquired resistance, and the therapeutic effect is subsequently diminished. The wealth of recent studies using biochemical, kinetic, and structural approaches have revealed the molecular basis for the clinically observed resistance. In this review, we highlight several of the most common molecular mechanisms that lead to acquired resistance to kinase inhibitors observed with the cAbl (cellular form of the Abelson leukemia virus tyrosine kinase) and the type III receptor tyrosine kinase cKit, including a newly identified mechanism resulting from accelerated kinase activation caused by mutations in the activation loop. Strategies to overcome the loss of drug sensitivity that represents a challenge currently facing the field and the emerging approaches to circumvent resistance are discussed.

Mechanisms of resistance to imatinib and sunitinib in gastrointestinal stromal tumor

Gastrointestinal stromal tumor (GIST), the most common mesenchymal neoplasm of the GI tract and one of the most common sarcomas, is dependent on the expression of the mutated KIT or platelet-derived growth factor receptor in most cases. Imatinib mesylate potently abrogates the effects of KIT signaling by directly binding into the ATP-binding pocket of the kinase. It is becoming increasingly apparent that the binding affinity of imatinib for the receptor is dependent on the type and location of mutation. Within KIT, patients whose tumor has an exon 9 mutation are treated by many clinicians with higher doses of imatinib than those patients with mutations within exon 11. Additionally, there are over 400 unique mutations within exon 11 that may have distinctly different binding affinity for imatinib as well as other kinases. Secondary KIT mutations generally occur at a codon where imatinib binds resulting in KIT reactivation and resistance. Sunitinib malate, a second-generation KIT inhibitor is active in imatinib-resistant disease and is FDA-approved for use in this setting. In this review, we describe the biology of the genes and gene mutations responsible for GIST and discuss known and potential clinical implications.

Motesanib inhibits Kit mutations associated with gastrointestinal stromal tumors

Background

Activating mutations in Kit receptor tyrosine kinase or the related platelet-derived growth factor receptor (PDGFR) play an important role in the pathogenesis of gastrointestinal stromal tumors (GIST).

Methods

This study investigated the activity of motesanib, an inhibitor of vascular endothelial growth factor receptors (VEGFR) 1, 2, and 3; PDGFR; and Kit, against primary activating Kit mutants and mutants associated with secondary resistance to imatinib. Single- and double-mutant isoforms of Kit were evaluated for their sensitivity to motesanib or imatinib in autophosphorylation assays and in Ba/F3 cell proliferation assays.

Results

Motesanib inhibited Kit autophosphorylation in CHO cell lines expressing primary activating mutations in exon 9 (AYins503-504, IC₅₀ = 18 nM) and exon 11 (V560 D, IC₅₀ = 5 nM; Δ552-559, IC₅₀ = 1 nM). Motesanib also demonstrated activity against kinase domain mutations conferring imatinib resistance (V560D/V654A, IC₅₀ = 77 nM; V560D/T670I, IC₅₀ = 277 nM; Y823 D, IC₅₀ = 64 nM) but failed to inhibit the imatinib-resistant D816V mutant (IC₅₀ > 3000 nM). Motesanib suppressed the proliferation of Ba/F3 cells expressing Kit mutants with IC₅₀ values in good agreement with those observed in the autophosphorylation assays.

Conclusions

In conclusion, our data suggest that motesanib possesses inhibitory activity against primary Kit mutations and some imatinib-resistant secondary mutations.

Molecular basis and management of gastrointestinal stromal tumors

Molecularly targeted agents have dramatically impacted the management of several cancers. Targeting KIT has led to a new treatment paradigm in gastrointestinal stromal tumors (GISTs). KIT is a cell surface receptor with tyrosine kinases that, upon binding of its ligand, stem cell factor, activates various signaling pathways. Imatinib and sunitinib, both tyrosine kinase inhibitors directed to KIT, were approved for first- and second-line treatment of metastatic and unresectable GISTs. In this article, we will review the molecular pathogenesis of GISTs followed by a discussion of imatinib and sunitinib’s role in the treatment of GISTs. Finally, we will introduce novel therapeutic options for imatinib- and sunitinib-resistant GISTs.

Combination of KIT gene silencing and tocopherol succinate may offer improved therapeutic approaches for human mastocytosis

Gain-of-function mutations of kit tyrosine kinase receptor are associated with mastocytosis. Two subclones of the HMC1 mast leukaemia cell line were used; both express an identical KIT allele-specific regulatory type mutation (V560G), but differ in that one also expresses an enzymatic site type mutation (D816V) that confers on them resistance to imatinib mesylate tyrosine kinase inhibitor. In both cell lines, proliferation was suppressed and apoptosis induced by the combination of KIT gene silencing and alpha-tocopherol succinate (alpha-TOS), a derivate of alpha-tocopherol, also known as vitamin E. Furthermore, HMC1 cells with decreased kit levels by KIT silencing, failed to form tumours when xenotransplanted into immunocompromised mice and the animals were treated systemically with alpha-TOS. Targeting kit in the presence of alpha-TOS represents a new approach against proliferation of human mast leukaemia cell lines.

Current clinical strategy for imatinib-resistant gastrointestinal stromal tumors

Before the advent of imatinib, no effective agent was available for the treatment of unresectable or metastatic gastrointestinal stromal tumors (GISTs). However, the treatment strategy changed and the prognosis of patients with advanced or recurrent GIST improved remarkably after the development of imatinib. Despite the high rate of clinical benefit of imatinib in GIST patients, more than half of GIST patients eventually develop primary or secondary resistance to imatinib, resulting in the progression of the disease. It has also been reported that about 5% of patients are intolerant to imatinib. An effective treatment strategy for imatinib-resistant GIST is needed. The National Comprehensive Cancer Network guidelines recommend a multidisciplinary approach, but the therapeutic modalities are still limited, and none are able to achieve complete remission. In this review, we summarize the current understandings of the mechanism of imatinib resistance and the clinical strategies used against imatinib-resistant GIST, including surgical intervention, escalation of imatinib dose, and use of sunitinib or other agents. Understanding these issues may help in the development of a new treatment paradigm for GIST patients.

Novel approaches to gastrointestinal stromal tumors resistant to imatinib and sunitinib

Gastrointestinal stromal tumors (GIST) are rare tumors of mesenchymal origin that may arise anywhere along the gastrointestinal tract or in the peritoneum. In most cases, GIST harbor mutations of KIT or PDGFRA. Imatinib mesylate (IM), a small-molecule tyrosine kinase inhibitor developed for the treatment of chronic myeloid leukemia, has been shown to be active against these mutations and has significant activity in patients with metastatic GIST. However, resistance to IM emerges after a median of 24 months of treatment. Sunitinib malate (SU) has been approved for the treatment of patients with IM-resistant advanced GIST, but the median progression-free survival in this setting is only 6 months. This article reviews the current knowledge regarding IM and SU resistance in GIST, as well as the available options for the management of GIST resistant to IM and SU.

Primary and secondary kinase genotypes correlate with the biological and clinical activity of sunitinib in imatinib-resistant gastrointestinal stromal tumor

PURPOSE

Most gastrointestinal stromal tumors (GISTs) harbor mutant KIT or platelet-derived growth factor receptor alpha (PDGFRA) kinases, which are imatinib targets. Sunitinib, which targets KIT, PDGFRs, and several other kinases, has demonstrated efficacy in patients with GIST after they experience imatinib failure. We evaluated the impact of primary and secondary kinase genotype on sunitinib activity.

PATIENTS AND METHODS

Tumor responses were assessed radiologically in a phase I/II trial of sunitinib in 97 patients with metastatic, imatinib-resistant/intolerant GIST. KIT/PDGFRA mutational status was determined for 78 patients by using tumor specimens obtained before and after prior imatinib therapy. Kinase mutants were biochemically profiled for sunitinib and imatinib sensitivity.

RESULTS

Clinical benefit (partial response or stable disease for > or = 6 months) with sunitinib was observed for the three most common primary GIST genotypes: KIT exon 9 (58%), KIT exon 11 (34%), and wild-type KIT/PDGFRA (56%). Progression-free survival (PFS) was significantly longer for patients with primary KIT exon 9 mutations (P = .0005) or with a wild-type genotype (P = .0356) than for those with KIT exon 11 mutations. The same pattern was observed for overall survival (OS). PFS and OS were longer for patients with secondary KIT exon 13 or 14 mutations (which involve the KIT-adenosine triphosphate binding pocket) than for those with exon 17 or 18 mutations (which involve the KIT activation loop). Biochemical profiling studies confirmed the clinical results.

CONCLUSION

The clinical activity of sunitinib after imatinib failure is significantly influenced by both primary and secondary mutations in the predominant pathogenic kinases, which has implications for optimization of the treatment of patients with GIST.

Primary and secondary kinase genotypes correlate with the biological and clinical activity of sunitinib in imatinib-resistant gastrointestinal stromal tumor

PURPOSE

Most gastrointestinal stromal tumors (GISTs) harbor mutant KIT or platelet-derived growth factor receptor alpha (PDGFRA) kinases, which are imatinib targets. Sunitinib, which targets KIT, PDGFRs, and several other kinases, has demonstrated efficacy in patients with GIST after they experience imatinib failure. We evaluated the impact of primary and secondary kinase genotype on sunitinib activity.

PATIENTS AND METHODS

Tumor responses were assessed radiologically in a phase I/II trial of sunitinib in 97 patients with metastatic, imatinib-resistant/intolerant GIST. KIT/PDGFRA mutational status was determined for 78 patients by using tumor specimens obtained before and after prior imatinib therapy. Kinase mutants were biochemically profiled for sunitinib and imatinib sensitivity.

RESULTS

Clinical benefit (partial response or stable disease for > or = 6 months) with sunitinib was observed for the three most common primary GIST genotypes: KIT exon 9 (58%), KIT exon 11 (34%), and wild-type KIT/PDGFRA (56%). Progression-free survival (PFS) was significantly longer for patients with primary KIT exon 9 mutations (P = .0005) or with a wild-type genotype (P = .0356) than for those with KIT exon 11 mutations. The same pattern was observed for overall survival (OS). PFS and OS were longer for patients with secondary KIT exon 13 or 14 mutations (which involve the KIT-adenosine triphosphate binding pocket) than for those with exon 17 or 18 mutations (which involve the KIT activation loop). Biochemical profiling studies confirmed the clinical results.

CONCLUSION

The clinical activity of sunitinib after imatinib failure is significantly influenced by both primary and secondary mutations in the predominant pathogenic kinases, which has implications for optimization of the treatment of patients with GIST.

Computer simulations reveal a novel nucleotide-type binding orientation for ellipticine-based anticancer c-kit kinase inhibitors

Receptor tyrosine kinase (RTK) enzymes regulate cell signaling pathways and so are an important target for cancer chemotherapy. Current inhibitors of c-kit, a key RTK stem cell factor receptor, are inactive against the most common mutated variant Asp816Val, associated with highly malignant cancers. Recent combined experimental/simulation work has highlighted the utility of the ellipticine pharmacore in inhibiting mutant c-kit, and the present simulation study applies a combination of high-level simulation tools to probe further the binding of ellipticine-based derivatives to c-kit. We find a large preference for protonation of bound ellipticine, which stabilizes the negative protein residues that coordinated ADP.Mg (2+) in the native complex. The resulting ellipticine inhibitor binding mode resembles the native nucleotide complex and serves to explain some existing experimental data on binding specificities, indicating that functionalization at the C4/C5 sites of ellipticine derivatives may be important for the design of novel nucleotide analogues that inhibit mutant c-kit.

Clinical significance of oncogenic KIT and PDGFRA mutations in gastrointestinal stromal tumours

Gastrointestinal stromal tumours (GISTs) are the most common mesenchymal neoplasms of the gastrointestinal tract. Despite clinicopathological differences, GISTs share oncogenic KIT or platelet-derived growth factor-alpha (PDGFRA) mutations. Imatinib, KIT and PDGFRA inhibitor, has been successfully used in the treatment of metastatic GISTs. There are primary KIT or PDGFRA mutations diagnosed before imatinib treatment, linked to GIST pathogenesis, and secondary mutations detected during treatment, causing drug resistance. KIT exon 11 mutations are the most common. Gastric GISTs with exon 11 deletions are more aggressive than those with substitutions. KIT exon 11 mutants respond well to imatinib. Less common KIT exon 9 Ala502_Tyr503dup mutants occur predominantly in intestinal GISTs and are less sensitive to imatinib. An Asp842Val substitution in exon 18 is the most common PDGFRA mutation. GISTs with such mutation are resistant to imatinib. PDGFRA mutations are associated with gastric GISTs, epithelioid morphology and a less malignant course of disease. GISTs in neurofibromatosis 1, Carney triad and paediatric tumours generally lack KIT and PDGFRA mutations. Secondary KIT mutations affect exons 13-17. GISTs with secondary mutations in exon 13 and 14 are sensitive to sunitinib, another tyrosine kinase inhibitor. KIT and PDGFRA genotyping is important for GIST diagnosis and assessment of sensitivity to tyrosine kinase inhibitors.

Secondary mutations in the kinase domain of the KIT gene are predominant in imatinib-resistant gastrointestinal stromal tumor

Although imatinib showed high activity for advanced gastrointestinal stromal tumor (GIST) and improved the prognosis of GIST patients, resistance to the drug appears with prolonged use. Mechanisms of acquired resistance are still under investigation. In the present study, we carried out histologic and genetic analysis of 45 secondary resistant lesions obtained from 25 Japanese GIST patients treated with imatinib. All resistant lesions showed viable tumor cells expressing KIT protein, whereas imatinib-sensitive lesions did not. All pre-imatinib samples have KIT mutations either in exon 9 (n = 3) or exon 11 (n = 22), identified in the KIT gene of corresponding resistant tumors. In addition to primary mutations, 33 out of 45 tumors (73%) showed secondary KIT mutations in the kinase domain of the KIT gene. Secondary mutations are missense mutations and are mostly located in the kinase domains of the same allele to the primary mutations (cis-position). Resistant lesions showed monoclonal development of tumor cells. Taken together, additional cis-positioned mutations in the kinase domains are a major cause of secondary resistance to imatinib in Japanese GIST patients.

Molecular analysis of secondary kinase mutations in imatinib-resistant gastrointestinal stromal tumors

Most gastrointestinal stromal tumors (GISTs) are associated with activating kinase mutation in KIT or platelet-derived growth factor receptor alpha (PDGFRA) gene, and imatinib has revolutionized the care of advanced GISTs. However, most patients gradually developed resistance to imatinib. We intend to identify the secondary kinase mutations in imatinib-resistant GISTs and to study the relationship between secondary kinase mutations and the clinical response to imatinib. Twelve advanced GIST patients, who have developed resistance to imatinib were included in this study. Paraffin-embedded pretreatment GIST specimens and progression lesions of the tumors after resistance to imatinib were analyzed for kinase mutations in exons 9, 11, 13, and 17 of KIT gene and exons of 10, 12, 14, and 18 of PDGFRA gene. Primary KIT mutations have been found in all but one of the primary tumors including one case harboring de novo double KIT exon 11 mutations. Secondary kinase mutations in KIT and PDGFRA were found in seven and 1 of 12 patients, respectively. Two patients harbored more than one secondary KIT mutations in different progression sites, and there are four types of clonal or polyclonal evolution being observed. The secondary PDGFRA exon 14 mutation H687Y is a novel mutation that has never been reported before. Acquired secondary kinase mutations are the most important cause of secondary imatinib resistance in advanced GISTs. The identification of secondary kinase mutations is important in the development of new therapeutic strategies.

Role of c-kit/SCF in cause and treatment of gastrointestinal stromal tumors (GIST)

c-Kit encodes for the receptor tyrosine kinase (RTK) and belongs to type III receptor family. This includes platelet derived growth factor (PDGF) alpha and beta and macrophage colony stimulating factor (mCSF) apart from others. Their characteristic features are the presence of five immunologlobulin like domains in the extracellular region and 70-100 residues long kinase insert domain in the cytoplasmic region. The RTKs activate several signaling pathways within the cells leading to cell proliferation, differentiation, migration or metabolic changes. The Kit ligand-stem cell factor (SCF) induces a rapid and complete receptor dimerization resulting in activation by autophosphorylation of the catalytic tyrosine kinase and generation of signal transduction leading to regulation of cell growth. Various mutations in c-kit such as insertions and deletions (without affecting reading frame) and point mutations in the inhibitory juxtamembrane (JM) domain encoded by exon 11 have been reported in gastrointestinal stromal tumors (GISTs). Thus, c-kit signaling is believed to play a role in tumorigenesis. Efforts are being made to control and treat these tumors by blocking kit signaling using Imatinib with varying degrees of success. This review deals with the features of c-kit, its ligand and roles in gastrointestinal stromal tumors.

Resistance to c-KIT kinase inhibitors conferred by V654A mutation

Certain mutations within c-KIT cause constitutive activation of the receptor and have been associated with several human malignancies. These include gastrointestinal stromal tumors (GIST), mastocytosis, acute myelogenous leukemia, and germ cell tumors. The kinase inhibitor imatinib potently inhibits c-KIT and is approved for treatment of GIST. However, secondary point mutations can develop within the kinase domain to confer resistance to imatinib and cause drug-resistant relapse. A common mutation, which results in a V654A substitution, has been documented in imatinib-resistant GIST patients. We expressed c-KIT cDNA constructs encoding the V654A substitution alone and in combination with a typical activating exon 11 mutation characteristic of GIST, V560G, in factor-dependent FDC-P1 cells. The V654A substitution alone resulted in enhanced proliferation in c-KIT ligand (stem cell factor) but not factor independence. Cells expressing the double mutant were, like those expressing single V560G mutant c-KIT, factor independent. Analysis of cellular proliferation in the presence of imatinib showed that the V654A substitution alone conferred resistance. The difference in sensitivity was especially pronounced for cells expressing single mutant V560G c-KIT compared with double mutant V560G/V654A c-KIT. The findings were supported by studies of c-KIT phosphorylation. Analysis of the crystal structure of imatinib in complex with the kinase domain of c-KIT predicts that the V654A substitution directly affects the binding of imatinib to the receptor. Alternative c-KIT inhibitors, nilotinib (AMN107) and PKC412, were also less active on V560G/V654A c-KIT than on the V560G single mutant; however, nilotinib, like imatinib, potently inhibited the V560G mutant. PKC412 strongly inhibited imatinib-resistant D816V c-KIT.

KIT and PDGFRA mutations in gastrointestinal stromal tumors (GISTs)

Mutually exclusive KIT and PDGFRA mutations are central events in GIST pathogenesis, and their understanding is becoming increasingly important, because specific treatment targeting oncogenic KIT and PDGFRA activation (especially imatinib mesylate) has become available. KIT mutations in GIST are clustered in four exons. Most common are exon 11 (juxtamembrane domain) mutations that include deletions, point mutations (affecting a few codons), and duplications (mostly in the 3' region). The latter mutations most often occur in gastric GISTs. Among gastric GISTs, tumors with deletions are more aggressive than those with point mutations; this does not seem to hold true in small intestinal GISTs. Exon 9 mutations (5-10%) usually are 2-codon 502-503 duplications, and these occur predominantly in intestinal versus gastric GISTs. Lesser imatinib sensitivity of these tumors has been noted. Kinase domain mutations are very rare; GISTs with such mutations are variably sensitive to imatinib. PDGFRA mutations usually occur in gastric GISTs, especially in the epithelioid variants; their overall frequency is approximately 30% to 40% of KIT mutation negative GISTs. Most common is exon 18 mutation leading Asp842Val at the protein level. This mutation causes imatinib resistance. Exon 12 and 14 mutations are rare. Most mutations are somatic (in tumor tissue only), but patients with familial GIST syndrome have consitutitonal KIT/PDGFRA mutations; >10 families have been reported worldwide with mutations generally similar to those in sporadic GISTs. GISTs in neurofibromatosis 1 patients, children, and Carney triad seem to lack GIST-specific KIT and PDGFRA mutations and may have a different disease mechanism. Secondary mutations usually occur in KIT kinase domains in patients after imatinib treatment resulting in resistance to this drug. Mutation genotyping is a tool in GIST diagnosis and in assessment of sensitivity to kinase inhibitors. This is a US government work. There are no restrictions on its use.