Predicting cancer drug mechanisms of action using molecular network signatures.
ABSTRACT Molecular signatures are a powerful approach to characterize novel small molecules and derivatized small molecule libraries. While new experimental techniques are being developed in diverse model systems, informatics approaches lag behind these exciting advances. We propose an analysis pipeline for signature based drug annotation. We develop an integrated strategy, utilizing supervised and unsupervised learning methodologies that are bridged by network based statistics. Using this approach we can: 1, predict new examples of drug mechanisms that we trained our model upon; 2, identify "New" mechanisms of action that do not belong to drug categories that our model was trained upon; and 3, update our training sets with these "New" mechanisms and accurately predict entirely distinct examples from these new categories. Thus, not only does our strategy provide statistical generalization but it also offers biological generalization. Additionally, we show that our approach is applicable to diverse types of data, and that distinct biological mechanisms characterize its resolution of categories across different data types. As particular examples, we find that our predictive resolution of drug mechanisms from mRNA expression studies relies upon the analog measurement of a cell stress-related transcriptional rheostat along with a transcriptional representation of cell cycle state; whereas, in contrast, drug mechanism resolution from functional RNAi studies rely upon more dichotomous (e.g., either enhances or inhibits) association with cell death states. We believe that our approach can facilitate molecular signature-based drug mechanism understanding from different technology platforms and across diverse biological phenomena.
SourceAvailable from: Tamas Korcsmaros[Show abstract] [Hide abstract]
ABSTRACT: Cancer is increasingly described as a systems-level, network phenomenon. Genetic methods, such as next generation sequencing and RNA interference uncovered the complexity tumor-specific mutation-induced effects and the identification of multiple target sets. Network analysis of cancer-specific metabolic and signaling pathways highlighted the structural features of cancer-related proteins and their complexes to develop next-generation protein kinase inhibitors, as well as the modulation of inflammatory and autophagic pathways in anti-cancer therapies. Importantly, malignant transformation can be described as a two-phase process, where an initial increase of system plasticity is followed by a decrease of plasticity at late stages of tumor development. Late-stage tumors should be attacked by an indirect network influence strategy. On the contrary, the attack of early-stage tumors may target central network nodes. Cancer stem cells need special diagnosis and targeting, since they potentially have an extremely high ability to change the rigidity/plasticity of their networks. The early warning signals of the activation of fast growing tumor cell clones are important in personalized diagnosis and therapy. Multi-target attacks are needed to perturb cancer-specific networks to exit from cancer attractors and re-enter a normal attractor. However, the dynamic non-genetic heterogeneity of cancer cell population induces the replenishment of the cancer attractor with surviving, non-responsive cells from neighboring abnormal attractors. The development of drug resistance is further complicated by interactions of tumor clones and their microenvironment. Network analysis of intercellular cooperation using game theory approaches may open new areas of understanding tumor complexity. In conclusion, the above applications of the network approach open up new, and highly promising avenues in anti-cancer drug design.Seminars in Cancer Biology 07/2013; DOI:10.1016/j.semcancer.2013.06.011 · 9.14 Impact Factor
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ABSTRACT: The cellular response evoked by antiproliferating osmium(VI) nitrido compounds of general formula OsN(N^N)Cl3 (N^N = 2,2'-bipyridine 1, 1,10-phenanthroline 2, 3,4,7,8-tetramethyl-1,10-phenanthroline 3, or 4,7-diphenyl-1,10-phenanthroline 4) can be tuned by subtle ligand modifications. Complex 2 induces DNA damage, resulting in activation of the p53 pathway, cell cycle arrest at the G2/M phase, and caspase-dependent apoptotic cell death. In contrast, 4 evokes endoplasmic reticulum (ER) stress leading to the upregulation of proteins of the unfolded protein response pathway, increase in ER size, and p53-independent apoptotic cell death. To the best of our knowledge, 4 is the first osmium compound to induce ER stress in cancer cells.Journal of the American Chemical Society 09/2013; 135(38). DOI:10.1021/ja4075375 · 11.44 Impact Factor
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ABSTRACT: Screening methods that use traditional genomic, transcriptional, proteomic and metabonomic signatures to characterize drug mechanisms are known. However, they are time consuming and require specialized equipment. Here, we present a high-throughput multichannel sensor platform that can profile the mechanisms of various chemotherapeutic drugs in minutes. The sensor consists of a gold nanoparticle complexed with three different fluorescent proteins that can sense drug-induced physicochemical changes on cell surfaces. In the presence of cells, fluorescent proteins are rapidly displaced from the gold nanoparticle surface and fluorescence is restored. Fluorescence 'turn on' of the fluorescent proteins depends on the drug-induced cell surface changes, generating patterns that identify specific mechanisms of cell death induced by drugs. The nanosensor is generalizable to different cell types and does not require processing steps before analysis, offering an effective way to expedite research in drug discovery, toxicology and cell-based sensing.Nature Nanotechnology 12/2014; 10(1). DOI:10.1038/nnano.2014.285 · 33.27 Impact Factor