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Prediction of individualized therapeutic vulnerabilities in cancer from genomic profiles

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TLDR
Genetic profiling will in the future provide a promising basis for network pharmacology of epistatic vulnerabilities as a promising therapeutic strategy in cancer, and up to 44% of vulnerabilities can be targeted with at least one Food and Drug Administration-approved drug.
Abstract
Motivation: Somatic homozygous deletions of chromosomal regions in cancer, while not necessarily oncogenic, may lead to therapeutic vulnerabilities specific to cancer cells compared with normal cells. A recently reported example is the loss of one of the two isoenzymes in glioblastoma cancer cells such that the use of a specific inhibitor selectively inhibited growth of the cancer cells, which had become fully dependent on the second isoenzyme. We have now made use of the unprecedented conjunction of large-scale cancer genomics profiling of tumor samples in The Cancer Genome Atlas (TCGA) and of tumorderived cell lines in the Cancer Cell Line Encyclopedia, as well as the availability of integrated pathway information systems, such as Pathway Commons, to systematically search for a comprehensive set of such epistatic vulnerabilities. Results: Based on homozygous deletions affecting metabolic enzymes in 16 TCGA cancer studies and 972 cancer cell lines, we identified 4104 candidate metabolic vulnerabilities present in 1019 tumor samples and 482 cell lines. Up to 44% of these vulnerabilities can be targeted with at least one Food and Drug Administration-approved drug. We suggest focused experiments to test these vulnerabilities and clinical trials based on personalized genomic profiles of those that pass preclinical filters. We conclude that genomic profiling will in the future provide a promising basis for network pharmacology of epistatic vulnerabilities as a promising therapeutic strategy.

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Defining a Cancer Dependency Map

TL;DR: DEMETER, an analytical framework that segregates on- from off-target effects of RNAi, demonstrates the basis behind one such predictive model linking hypermethylation of the UBB ubiquitin gene to a dependency on UBC and provides a foundation for a cancer dependency map that facilitates the prioritization of therapeutic targets.
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Network pharmacology of cancer: From understanding of complex interactomes to the design of multi-target specific therapeutics from nature.

TL;DR: This review provides an overview of the current state of knowledge on network pharmacology, focuses on different technical approaches and implications for cancer therapy, and illustrates the therapeutic potential with selected examples green tea polyphenolics and Eleutherococcus senticosus.
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Collateral Lethality: A new therapeutic strategy in oncology.

TL;DR: This work reviews the novel concept of "collateral lethality", which has served to identify cancer-specific therapeutic vulnerabilities resulting from co-deletion of passenger genes neighboring TSG and offers novel opportunities for the development of personalized anti-neoplastic therapies.
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Lollipops in the Clinic: Information Dense Mutation Plots for Precision Medicine.

TL;DR: The Lollipops software creates information-dense, publication-quality mutation plots for automated pipelines and high-throughput workflows in precision medicine and helps visualization heuristics concisely present knowledge with minimal user configuration.
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Mathematical modeling and computational prediction of cancer drug resistance.

TL;DR: The current progress of experimentally revealed resistance mechanisms of targeted therapy, including genetic mechanisms, epigenetic mechanisms, posttranslational mechanisms, cellular mechanisms, microenvironmental mechanisms and pharmacokinetic mechanisms are summarized and some state-of-the-art computational methods used in drug resistance studies are introduced.
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