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      Off-target toxicity is a common mechanism of action of cancer drugs undergoing clinical trials

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          Abstract

          Ninety-seven percent of drug-indication pairs that are tested in clinical trials in oncology never advance to receive U.S. Food and Drug Administration approval. While lack of efficacy and dose-limiting toxicities are the most common causes of trial failure, the reason(s) why so many new drugs encounter these problems is not well understood. Using CRISPR-Cas9 mutagenesis, we investigated a set of cancer drugs and drug targets in various stages of clinical testing. We show that—contrary to previous reports obtained predominantly with RNA interference and small-molecule inhibitors—the proteins ostensibly targeted by these drugs are nonessential for cancer cell proliferation. Moreover, the efficacy of each drug that we tested was unaffected by the loss of its putative target, indicating that these compounds kill cells via off-target effects. By applying a genetic target-deconvolution strategy, we found that the mischaracterized anticancer agent OTS964 is actually a potent inhibitor of the cyclin-dependent kinase CDK11 and that multiple cancer types are addicted to CDK11 expression. We suggest that stringent genetic validation of the mechanism of action of cancer drugs in the preclinical setting may decrease the number of therapies tested in human patients that fail to provide any clinical benefit.

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          Expression profiling reveals off-target gene regulation by RNAi.

          RNA interference is thought to require near-identity between the small interfering RNA (siRNA) and its cognate mRNA. Here, we used gene expression profiling to characterize the specificity of gene silencing by siRNAs in cultured human cells. Transcript profiles revealed siRNA-specific rather than target-specific signatures, including direct silencing of nontargeted genes containing as few as eleven contiguous nucleotides of identity to the siRNA. These results demonstrate that siRNAs may cross-react with targets of limited sequence similarity.
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            Is Open Access

            Estimation of clinical trial success rates and related parameters

            SUMMARY Previous estimates of drug development success rates rely on relatively small samples from databases curated by the pharmaceutical industry and are subject to potential selection biases. Using a sample of 406 038 entries of clinical trial data for over 21 143 compounds from January 1, 2000 to October 31, 2015, we estimate aggregate clinical trial success rates and durations. We also compute disaggregated estimates across several trial features including disease type, clinical phase, industry or academic sponsor, biomarker presence, lead indication status, and time. In several cases, our results differ significantly in detail from widely cited statistics. For example, oncology has a 3.4% success rate in our sample vs. 5.1% in prior studies. However, after declining to 1.7% in 2012, this rate has improved to 2.5% and 8.3% in 2014 and 2015, respectively. In addition, trials that use biomarkers in patient-selection have higher overall success probabilities than trials without biomarkers.
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              Genetic compensation triggered by mutant mRNA degradation

              Genetic robustness, or the ability of an organism to maintain fitness in the presence of mutations, can be achieved via protein feedback loops. Recent evidence suggests that organisms may also respond to mutations by upregulating related gene(s) independently of protein feedback loops, a phenomenon called transcriptional adaptation. However, the prevalence of transcriptional adaptation and its underlying molecular mechanisms are unknown. Here, by analyzing several models of transcriptional adaptation in zebrafish and mouse, we show a requirement for mRNA degradation. Alleles that fail to transcribe the mutated gene do not display transcriptional adaptation and exhibit more severe phenotypes than alleles displaying mutant mRNA decay. Transcriptome analysis reveals the upregulation of a substantial proportion of the genes that exhibit sequence similarity with the mutated gene’s mRNA, suggesting a sequence dependent mechanism. Besides implications for our understanding of disease-causing mutations, these findings will help design mutant alleles with minimal transcriptional adaptation-derived compensation.
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                Author and article information

                Journal
                Science Translational Medicine
                Sci. Transl. Med.
                American Association for the Advancement of Science (AAAS)
                1946-6234
                1946-6242
                September 11 2019
                September 11 2019
                September 11 2019
                September 11 2019
                : 11
                : 509
                : eaaw8412
                Article
                10.1126/scitranslmed.aaw8412
                7717492
                31511426
                dbd5768d-b334-4707-9dba-716a1f925f5d
                © 2019

                http://www.sciencemag.org/about/science-licenses-journal-article-reuse

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