Mapping combinatorial drug effects to DNA damage response kinase inhibitors.

One fundamental principle that underlies various cancer treatments, such as traditional chemotherapy and radiotherapy, involves the induction of catastrophic DNA damage, leading to the apoptosis of cancer cells. In our study, we conduct a comprehensive dose-response combination screening focused on inhibitors that target key kinases involved in the DNA damage response (DDR): ATR, ATM, and DNA-PK. This screening involves 87 anti-cancer agents, including six DDR inhibitors, and encompasses 62 different cell lines spanning 12 types of tumors, resulting in a total of 17,912 combination treatment experiments. Within these combinations, we analyze the most effective and synergistic drug pairs across all tested cell lines, considering the variations among cancers originating from different tissues. Our analysis reveals inhibitors of five DDR-related pathways (DNA topoisomerase, PLK1 kinase, p53-inducible ribonucleotide reductase, PARP, and cell cycle checkpoint proteins) that exhibit strong combinatorial efficacy and synergy when used alongside ATM/ATR/DNA-PK inhibitors.

Preclinical evidence for the effective use of TL-895, a highly selective and potent second-generation BTK inhibitor, for the treatment of B-cell malignancies.

TL-895 (formerly known as M7583) is a potent, highly selective, adenosine triphosphate (ATP)-competitive, second-generation, irreversible inhibitor of Bruton's tyrosine kinase (BTK). We characterized its biochemical and cellular effects in in vitro and in vivo models. TL-895 was evaluated preclinically for potency against BTK using IC50 concentration-response curves; selectivity using a 270-kinase panel; BTK phosphorylation in Ramos Burkitt's lymphoma cells by ProteinSimple Wes analysis of one study; anti-proliferative effects in primary chronic lymphocytic leukemia (CLL) blasts; cell viability effects in diffuse large B-cell lymphoma (DLBCL) and mantle-cell lymphoma (MCL) cell lines; effects on antibody-dependent cell-mediated cytotoxicity (ADCC) from Daudi cells and chromium-51 release from human tumor cell lines; and efficacy in vivo using four MCL xenograft model and 21 DLBCL patient-derived xenograft (PDX) models (subtypes: 9 ABC, 11 GCB, 1 Unclassified). TL-895 was active against recombinant BTK (average IC50 1.5 nM) and inhibited only three additional kinases with IC50 within tenfold of BTK activity. TL-895 inhibited BTK auto-phosphorylation at the Y223 phosphorylation site (IC50 1-10 nM). TL-895 inhibited the proliferation of primary CLL blasts in vitro and inhibited growth in a subset of activated DLBCL and MCL cell lines. TL-895 inhibited the ADCC mechanism of therapeutic antibodies only at supra-clinical exposure levels. TL-895 significantly inhibited tumor growth in the Mino MCL xenograft model and in 5/21 DLBCL PDX models relative to vehicle controls. These findings demonstrate the potency of TL-895 for BTK and its efficacy in models of B-cell lymphoma despite its refined selectivity.

RosettaSX: Reliable gene expression signature scoring of cancer models and patients.

Gene expression signatures have proven their potential to characterize important cancer phenomena like oncogenic signaling pathway activities, cellular origins of tumors, or immune cell infiltration into tumor tissues. Large collections of expression signatures provide the basis for their application to data sets, but the applicability of each signature in a new experimental context must be reassessed. We apply a methodology that utilizes the previously developed concept of coherent expression of genes in signatures to identify translatable signatures before scoring their activity in single tumors. We present a web interface (www.rosettasx.com) that applies our methodology to expression data from the Cancer Cell Line Encyclopaedia and The Cancer Genome Atlas. Configurable heat maps visualize per-cancer signature scores for 293 hand-curated literature-derived gene sets representing a wide range of cancer-relevant transcriptional modules and phenomena. The platform allows users to complement heatmaps of signature scores with molecular information on SNVs, CNVs, gene expression, gene dependency, and protein abundance or to analyze own signatures. Clustered heatmaps and further plots to drill-down results support users in studying oncological processes in cancer subtypes, thereby providing a rich resource to explore how mechanisms of cancer interact with each other as demonstrated by exemplary analyses of 2 cancer types.

Sensitive detection of viral transcripts in human tumor transcriptomes.

In excess of 12% of human cancer incidents have a viral cofactor. Epidemiological studies of idiopathic human cancers indicate that additional tumor viruses remain to be discovered. Recent advances in sequencing technology have enabled systematic screenings of human tumor transcriptomes for viral transcripts. However, technical problems such as low abundances of viral transcripts in large volumes of sequencing data, viral sequence divergence, and homology between viral and human factors significantly confound identification of tumor viruses. We have developed a novel computational approach for detecting viral transcripts in human cancers that takes the aforementioned confounding factors into account and is applicable to a wide variety of viruses and tumors. We apply the approach to conducting the first systematic search for viruses in neuroblastoma, the most common cancer in infancy. The diverse clinical progression of this disease as well as related epidemiological and virological findings are highly suggestive of a pathogenic cofactor. However, a viral etiology of neuroblastoma is currently contested. We mapped 14 transcriptomes of neuroblastoma as well as positive and negative controls to the human and all known viral genomes in order to detect both known and unknown viruses. Analysis of controls, comparisons with related methods, and statistical estimates demonstrate the high sensitivity of our approach. Detailed investigation of putative viral transcripts within neuroblastoma samples did not provide evidence for the existence of any known human viruses. Likewise, de-novo assembly and analysis of chimeric transcripts did not result in expression signatures associated with novel human pathogens. While confounding factors such as sample dilution or viral clearance in progressed tumors may mask viral cofactors in the data, in principle, this is rendered less likely by the high sensitivity of our approach and the number of biological replicates analyzed. Therefore, our results suggest that frequent viral cofactors of metastatic neuroblastoma are unlikely.

Deep sequencing reveals mutagenic effects of ribavirin during monotherapy of hepatitis C virus genotype 1-infected patients.

The preeminent mode of action of the broad-spectrum antiviral nucleoside ribavirin in the therapy of chronic hepatitis C is currently unresolved. Particularly under contest are possible mutagenic effects of ribavirin that may lead to viral extinction by lethal mutagenesis of the hepatitis C virus (HCV) genome. We applied ultradeep sequencing to determine ribavirin-induced sequence changes in the HCV coding region (nucleotides [nt] 330 to 9351) of patients treated with 6-week ribavirin monotherapy (n = 6) in comparison to placebo (n = 6). Baseline HCV RNA levels maximally declined on average by -0.8 or -0.1 log10 IU/ml in ribavirin- versus placebo-treated patients. No general increase in rates of nucleotide substitutions in ribavirin-treated patients was observed. However, more HCV genome positions with high G-to-A and C-to-U transition rates were detected between baseline and treatment week 6 in ribavirin-treated patients in comparison to placebo-treated patients (rate of 0.0041 transitions per base pair versus rate of 0.0022 transitions per base pair; P = 0.049). Similarly, the sensitive detection of low-frequency minority variants by statistical filtering indicated significantly more positions with G-to-A and C-to-U transitions in ribavirin-treated patients than in placebo-treated patients (rate of 0.0331 transitions versus rate of 0.0186 transitions per G/C-containing position at baseline; P = 0.018). In contrast, non-ribavirin-associated A-to-G and U-to-C transitions were not enriched in the ribavirin group (P = 0.152). We conclude that ribavirin exerts a mutagenic effect on the virus in patients with chronic hepatitis C by facilitating G-to-A and C-to-U nucleotide transitions.

Inferring physical protein contacts from large-scale purification data of protein complexes.

Recent large-scale data sets of protein complex purifications have provided unprecedented insights into the organization of cellular protein complexes. Several computational methods have been developed to detect co-complexed proteins in these data sets. Their common aim is the identification of biologically relevant protein complexes. However, much less is known about the network of direct physical protein contacts within the detected protein complexes. Therefore, our work investigates whether direct physical contacts can be computationally derived by combining raw data of large-scale protein complex purifications. We assess four established scoring schemes and introduce a new scoring approach that is specifically devised to infer direct physical protein contacts from protein complex purifications. The physical contacts identified by the five methods are comprehensively benchmarked against different reference sets that provide evidence for true physical contacts. Our results show that raw purification data can indeed be exploited to determine high-confidence physical protein contacts within protein complexes. In particular, our new method outperforms competing approaches at discovering physical contacts involving proteins that have been screened multiple times in purification experiments. It also excels in the analysis of recent protein purification screens of molecular chaperones and protein kinases. In contrast to previous findings, we observe that physical contacts inferred from purification experiments of protein complexes can be qualitatively comparable to binary protein interactions measured by experimental high-throughput assays such as yeast two-hybrid. This suggests that computationally derived physical contacts might complement binary protein interaction assays and guide large-scale interactome mapping projects by prioritizing putative physical contacts for further experimental screens.

DASMI: exchanging, annotating and assessing molecular interaction data.

MOTIVATION: Ever increasing amounts of biological interaction data are being accumulated worldwide, but they are currently not readily accessible to the biologist at a single site. New techniques are required for retrieving, sharing and presenting data spread over the Internet. RESULTS: We introduce the DASMI system for the dynamic exchange, annotation and assessment of molecular interaction data. DASMI is based on the widely used Distributed Annotation System (DAS) and consists of a data exchange specification, web servers for providing the interaction data and clients for data integration and visualization. The decentralized architecture of DASMI affords the online retrieval of the most recent data from distributed sources and databases. DASMI can also be extended easily by adding new data sources and clients. We describe all DASMI components and demonstrate their use for protein and domain interactions. AVAILABILITY: The DASMI tools are available at http://www.dasmi.de/ and http://ipfam.sanger.ac.uk/graph. The DAS registry and the DAS 1.53E specification is found at http://www.dasregistry.org/.

An integrative approach for predicting interactions of protein regions.

MOTIVATION: Protein-protein interactions are commonly mediated by the physical contact of distinct protein regions. Computational identification of interacting protein regions aids in the detailed understanding of protein networks and supports the prediction of novel protein interactions and the reconstruction of protein complexes. RESULTS: We introduce an integrative approach for predicting protein region interactions using a probabilistic model fitted to an observed protein network. In particular, we consider globular domains, short linear motifs and coiled-coil regions as potential protein-binding regions. Possible cooperations between multiple regions within the same protein are taken into account. A.negrained confidence system allows for varying the impact of specific protein interactions and region annotations on the modeling process. We apply our prediction approach to a large training set using a maximum likelihood method, compare different scoring functions for region interactions and validate the predicted interactions against a collection of experimentally observed interactions. In addition, we analyze prediction performance with respect to the inclusion of different region types, the incorporation of confidence values for training data and the utilization of predicted protein interactions. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Computing topological parameters of biological networks.

UNLABELLED: Rapidly increasing amounts of molecular interaction data are being produced by various experimental techniques and computational prediction methods. In order to gain insight into the organization and structure of the resultant large complex networks formed by the interacting molecules, we have developed the versatile Cytoscape plugin NetworkAnalyzer. It computes and displays a comprehensive set of topological parameters, which includes the number of nodes, edges, and connected components, the network diameter, radius, density, centralization, heterogeneity, and clustering coefficient, the characteristic path length, and the distributions of node degrees, neighborhood connectivities, average clustering coefficients, and shortest path lengths. NetworkAnalyzer can be applied to both directed and undirected networks and also contains extra functionality to construct the intersection or union of two networks. It is an interactive and highly customizable application that requires no expert knowledge in graph theory from the user. AVAILABILITY: NetworkAnalyzer can be downloaded via the Cytoscape web site: http://www.cytoscape.org

Transformational and derivational strategies in analogical problem solving.

Analogical problem solving is mostly described as transfer of a source solution to a target problem based on the structural correspondences (mapping) between source and target. Derivational analogy (Carbonell, Machine learning: an artificial intelligence approach Los Altos. Morgan Kaufmann, 1986) proposes an alternative view: a target problem is solved by replaying a remembered problem-solving episode. Thus, the experience with the source problem is used to guide the search for the target solution by applying the same solution technique rather than by transferring the complete solution. We report an empirical study using the path finding problems presented in Novick and Hmelo (J Exp Psychol Learn Mem Cogn 20:1296-1321, 1994) as material. We show that both transformational and derivational analogy are problem-solving strategies realized by human problem solvers. Which strategy is evoked in a given problem-solving context depends on the constraints guiding object-to-object mapping between source and target problem. Specifically, if constraints facilitating mapping are available, subjects are more likely to employ a transformational strategy, otherwise they are more likely to use a derivational strategy.