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      MMB-FOXM1-driven premature mitosis is required for CHK1 inhibitor sensitivity

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          SUMMARY

          To identify genes whose loss confers resistance to CHK1 inhibitors, we perform genome-wide CRISPR-Cas9 screens in non-small-cell lung cancer (NSCLC) cell lines treated with the CHK1 inhibitor prexasertib (CHK1i). Five of the top six hits of the screens, MYBL2 (B-MYB), LIN54, FOXM1, cyclin A2 (CCNA2), and CDC25B, are cell-cycle-regulated genes that contribute to entry into mitosis. Knockout of MMB-FOXM1 complex components LIN54 and FOXM1 reduce CHK1i-induced DNA replication stress markers and premature mitosis during Late S phase. Activation of a feedback loop between the MMB-FOXM1 complex and CDK1 is required for CHK1i-induced premature mitosis in Late S phase and subsequent replication catastrophe, indicating that dysregulation of the S to M transition is necessary for CHK1 inhibitor sensitivity. These findings provide mechanistic insights into small molecule inhibitors currently studied in clinical trials and provide rationale for combination therapies.

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          In brief

          Branigan et al., by using genome-wide CRISPR screens, identify the MMB-FOXM1 complex as being required for CHK1 inhibitor (CHK1i) sensitivity. Their study shows that CHK1i-induced premature activation of the G2/M transcriptional program by this complex triggers a breakdown in the separation of DNA synthesis and mitosis, leading to replication catastrophe.

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          Most cited references88

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            STAR: ultrafast universal RNA-seq aligner.

            Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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              Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

              Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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                Author and article information

                Journal
                101573691
                39703
                Cell Rep
                Cell Rep
                Cell reports
                2211-1247
                7 March 2021
                02 March 2021
                18 March 2021
                : 34
                : 9
                : 108808
                Affiliations
                [1 ]Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
                [2 ]Program in Virology, Graduate School of Arts and Sciences, Harvard University, Cambridge, MA, USA
                [3 ]Department of Radiation Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
                [4 ]Center for DNA Damage and Repair, Dana-Farber Cancer Institute, Boston, MA 02215, USA
                [5 ]Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
                [6 ]These authors contributed equally
                [7 ]Lead contact
                Author notes

                AUTHOR CONTRIBUTIONS

                Conceptualization, T.B.B., D.K., G.I.S., A.D.D., and J.A.D.; methodology, T.B.B., D.K., A.E.S., and P.D.; formal analysis, T.B.B., D.K., A.E.S., and P.D.; investigation, T.B.B., D.K., A.E.S., H.G.R., L.S., H.D.R., and P.D.; writing – original draft, T.B.B. and D.K.; writing – review & editing, T.B.B., D.K., A.E.S., and J.A.D.; visualization, T.B.B., D.K., A.E.S., and P.D.; supervision, D.K., A.D.D., and J.A.D.; funding acquisition, D.K. and J.A.D.

                Article
                NIHMS1679674
                10.1016/j.celrep.2021.108808
                7970065
                33657372
                0313c7b0-75e2-46a4-9fb4-48e4274606bc

                This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

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                Cell biology
                Cell biology

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