High-throughput identification of genotype-specific cancer vulnerabilities in mixtures of barcoded tumor cell lines.

Hundreds of genetically characterized cell lines are available for the discovery of genotype-specific cancer vulnerabilities. However, screening large numbers of compounds against large numbers of cell lines is currently impractical, and such experiments are often difficult to control. Here we report a method called PRISM that allows pooled screening of mixtures of cancer cell lines by labeling each cell line with 24-nucleotide barcodes. PRISM revealed the expected patterns of cell killing seen in conventional (unpooled) assays. In a screen of 102 cell lines across 8,400 compounds, PRISM led to the identification of BRD-7880 as a potent and highly specific inhibitor of aurora kinases B and C. Cell line pools also efficiently formed tumors as xenografts, and PRISM recapitulated the expected pattern of erlotinib sensitivity in vivo

Aurora kinase A drives the evolution of resistance to third-generation EGFR inhibitors in lung cancer.

Although targeted therapies often elicit profound initial patient responses, these effects are transient due to residual disease leading to acquired resistance. How tumors transition between drug responsiveness, tolerance and resistance, especially in the absence of preexisting subclones, remains unclear. In epidermal growth factor receptor (EGFR)-mutant lung adenocarcinoma cells, we demonstrate that residual disease and acquired resistance in response to EGFR inhibitors requires Aurora kinase A (AURKA) activity. Nongenetic resistance through the activation of AURKA by its coactivator TPX2 emerges in response to chronic EGFR inhibition where it mitigates drug-induced apoptosis. Aurora kinase inhibitors suppress this adaptive survival program, increasing the magnitude and duration of EGFR inhibitor response in preclinical models. Treatment-induced activation of AURKA is associated with resistance to EGFR inhibitors in vitro, in vivo and in most individuals with EGFR-mutant lung adenocarcinoma. These findings delineate a molecular path whereby drug resistance emerges from drug-tolerant cells and unveils a synthetic lethal strategy for enhancing responses to EGFR inhibitors by suppressing AURKA-driven residual disease and acquired resistance

Predicting a small molecule-kinase interaction map: A machine learning approach

<p>Abstract</p> <p>Background</p> <p>We present a machine learning approach to the problem of protein ligand interaction prediction. We focus on a set of binding data obtained from 113 different protein kinases and 20 inhibitors. It was attained through ATP site-dependent binding competition assays and constitutes the first available dataset of this kind. We extract information about the investigated molecules from various data sources to obtain an informative set of features.</p> <p>Results</p> <p>A Support Vector Machine (SVM) as well as a decision tree algorithm (C5/See5) is used to learn models based on the available features which in turn can be used for the classification of new kinase-inhibitor pair test instances. We evaluate our approach using different feature sets and parameter settings for the employed classifiers. Moreover, the paper introduces a new way of evaluating predictions in such a setting, where different amounts of information about the binding partners can be assumed to be available for training. Results on an external test set are also provided.</p> <p>Conclusions</p> <p>In most of the cases, the presented approach clearly outperforms the baseline methods used for comparison. Experimental results indicate that the applied machine learning methods are able to detect a signal in the data and predict binding affinity to some extent. For SVMs, the binding prediction can be improved significantly by using features that describe the active site of a kinase. For C5, besides diversity in the feature set, alignment scores of conserved regions turned out to be very useful.</p

Vatalanib for metastatic gastrointestinal stromal tumour (GIST) resistant to imatinib: final results of a phase II study

BACKGROUND: Vatalanib (PTK787/ZK 222584) inhibits a few tyrosine kinases including KIT, platelet-derived growth factor receptors (PDGFRs) and vascular endothelial growth factor receptors (VEGFRs). We report efficacy and safety results of vatalanib in advanced gastrointestinal stromal tumour (GIST) resistant to imatinib or both imatinib and sunitinib. PATIENTS AND METHODS: Forty-five patients whose metastatic GIST had progressed on imatinib were enrolled. Nineteen (42.2%) patients had received also prior sunitinib. Vatalanib 1250 mg was administered orally daily. RESULTS: Eighteen patients (40.0%; 95% confidence interval (CI), 25.7-54.3%) had clinical benefit including 2 (4.4%) confirmed partial remissions (PR; duration, 9.6 and 39.4 months) and 16 (35.6%) stabilised diseases (SDs; median duration, 12.5 months; range, 6.0-35.6+ months). Twelve (46.2%) out of the 26 patients who had received prior imatinib only achieved either PR or SD compared with 6 (31.6%, all SDs) out of the 19 patients who had received prior imatinib and sunitinib (P = 0.324). The median time to progression was 5.8 months (95% CI, 2.9-9.5 months) in the subset without prior sunitinib and 3.2 (95% CI, 2.1-6.0) months among those with prior imatinib and sunitinib (P = 0.992). Vatalanib was generally well tolerated. CONCLUSION: Vatalanib is active despite its narrow kinome interaction spectrum in patients diagnosed with imatinib-resistant GIST or with imatinib and sunitinib-resistant GIST

Dose-Levels and First Signs of Efficacy in Contemporary Oncology Phase 1 Clinical Trials

PURPOSE: Phase 1 trials play a crucial role in oncology by translating laboratory science into efficient therapies. Molecular targeted agents (MTA) differ from traditional cytotoxics in terms of both efficacy and toxicity profiles. Recent reports suggest that higher doses are not essential to produce the optimal anti-tumor effect. This study aimed to assess if MTA could achieve clinical benefit at much lower dose than traditional cytotoxics in dose seeking phase 1 trials. PATIENTS AND METHODS: We reviewed 317 recent phase 1 oncology trials reported in the literature between January 1997 and January 2009. First sign of efficacy, maximum tolerated dose (MTD) and their associated dose level were recorded in each trial. RESULTS: Trials investigating conventional cytotoxics alone, MTA alone and combination of both represented respectively 63.0% (201/317), 23.3% (74/317) and 13.7% (42/317) of all trials. The MTD was reached in 65.9% (209/317) of all trials and was mostly observed at the fifth dose level. First sign of efficacy was less frequently observed at the first three dose-levels for MTA as compared to conventional cytotoxics or combinations regimens (48.3% versus 63.2% and 61.3%). Sign of efficacy was observed in the same proportion whatever the treatment type (73-82%). MTD was less frequently established in trials investigating MTA alone (51.3%) or combinations (42.8%) as compared to conventional cytotoxic agents (75.6%). CONCLUSION: First sign of efficacy was less frequently reported at the early dose-levels and MTD was less frequently reached in trials investigating molecular targeted therapy alone. Similar proportion of trials reported clinical benefit

Prediction of specificity-determining residues for small-molecule kinase inhibitors

<p>Abstract</p> <p>Background</p> <p>Designing small-molecule kinase inhibitors with desirable selectivity profiles is a major challenge in drug discovery. A high-throughput screen for inhibitors of a given kinase will typically yield many compounds that inhibit more than one kinase. A series of chemical modifications are usually required before a compound exhibits an acceptable selectivity profile. Rationalizing the selectivity profile for a small-molecule inhibitor in terms of the specificity-determining kinase residues for that molecule can be an important step toward the goal of developing selective kinase inhibitors.</p> <p>Results</p> <p>Here we describe S-Filter, a method that combines sequence and structural information to predict specificity-determining residues for a small molecule and its kinase selectivity profile. Analysis was performed on seven selective kinase inhibitors where a structural basis for selectivity is known. S-Filter correctly predicts specificity determinants that were described by independent groups. S-Filter also predicts a number of novel specificity determinants that can often be justified by further structural comparison.</p> <p>Conclusion</p> <p>S-Filter is a valuable tool for analyzing kinase selectivity profiles. The method identifies potential specificity determinants that are not readily apparent, and provokes further investigation at the structural level.</p

Dosage Regulation of the Active X Chromosome in Human Triploid Cells

In mammals, dosage compensation is achieved by doubling expression of X-linked genes in both sexes, together with X inactivation in females. Up-regulation of the active X chromosome may be controlled by DNA sequence–based and/or epigenetic mechanisms that double the X output potentially in response to autosomal factor(s). To determine whether X expression is adjusted depending on ploidy, we used expression arrays to compare X-linked and autosomal gene expression in human triploid cells. While the average X:autosome expression ratio was about 1 in normal diploid cells, this ratio was lower (0.81–0.84) in triploid cells with one active X and higher (1.32–1.4) in triploid cells with two active X's. Thus, overall X-linked gene expression in triploid cells does not strictly respond to an autosomal factor, nor is it adjusted to achieve a perfect balance. The unbalanced X:autosome expression ratios that we observed could contribute to the abnormal phenotypes associated with triploidy. Absolute autosomal expression levels per gene copy were similar in triploid versus diploid cells, indicating no apparent global effect on autosomal expression. In triploid cells with two active X's our data support a basic doubling of X-linked gene expression. However, in triploid cells with a single active X, X-linked gene expression is adjusted upward presumably by an epigenetic mechanism that senses the ratio between the number of active X chromosomes and autosomal sets. Such a mechanism may act on a subset of genes whose expression dosage in relation to autosomal expression may be critical. Indeed, we found that there was a range of individual X-linked gene expression in relation to ploidy and that a small subset (∼7%) of genes had expression levels apparently proportional to the number of autosomal sets

Combinatorial Clustering of Residue Position Subsets Predicts Inhibitor Affinity across the Human Kinome

The protein kinases are a large family of enzymes that play fundamental roles in propagating signals within the cell. Because of the high degree of binding site similarity shared among protein kinases, designing drug compounds with high specificity among the kinases has proven difficult. However, computational approaches to comparing the 3-dimensional geometry and physicochemical properties of key binding site residue positions have been shown to be informative of inhibitor selectivity. The Combinatorial Clustering Of Residue Position Subsets (CCORPS) method, introduced here, provides a semi-supervised learning approach for identifying structural features that are correlated with a given set of annotation labels. Here, CCORPS is applied to the problem of identifying structural features of the kinase ATP binding site that are informative of inhibitor binding. CCORPS is demonstrated to make perfect or near-perfect predictions for the binding affinity profile of 8 of the 38 kinase inhibitors studied, while only having overall poor predictive ability for 1 of the 38 compounds. Additionally, CCORPS is shown to identify shared structural features across phylogenetically diverse groups of kinases that are correlated with binding affinity for particular inhibitors; such instances of structural similarity among phylogenetically diverse kinases are also shown to not be rare among kinases. Finally, these function-specific structural features may serve as potential starting points for the development of highly specific kinase inhibitors

Structure-guided selection of specificity determining positions in the human kinome

Background: The human kinome contains many important drug targets. It is well-known that inhibitors of protein kinases bind with very different selectivity profiles. This is also the case for inhibitors of many other protein families. The increased availability of protein 3D structures has provided much information on the structural variation within a given protein family. However, the relationship between structural variations and binding specificity is complex and incompletely understood. We have developed a structural bioinformatics approach which provides an analysis of key determinants of binding selectivity as a tool to enhance the rational design of drugs with a specific selectivity profile. Results: We propose a greedy algorithm that computes a subset of residue positions in a multiple sequence alignment such that structural and chemical variation in those positions helps explain known binding affinities. By providing this information, the main purpose of the algorithm is to provide experimentalists with possible insights into how the selectivity profile of certain inhibitors is achieved, which is useful for lead optimization. In addition, the algorithm can also be used to predict binding affinities for structures whose affinity for a given inhibitor is unknown. The algorithm’s performance is demonstrated using an extensive dataset for the human kinome. Conclusion: We show that the binding affinity of 38 different kinase inhibitors can be explained with consistently high precision and accuracy using the variation of at most six residue positions in the kinome binding site. We show for several inhibitors that we are able to identify residues that are known to be functionally important