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      MYC is sufficient to generate mid-life high-grade serous ovarian and uterine serous carcinomas in a p53-R270H mouse model

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          Abstract

          Genetically engineered mouse models (GEMM) have fundamentally changed how ovarian cancer etiology, early detection, and treatment is understood. However, previous GEMMs of high-grade serous ovarian cancer (HGSOC) have had to utilize genetics rarely or never found in human HGSOC to yield ovarian cancer within the lifespan of a mouse. MYC, an oncogene, is amongst the most amplified genes in HGSOC, but it has not previously been utilized to drive HGSOC GEMMs. We coupled Myc and dominant negative mutant p53-R270H with a fallopian tube epithelium-specific promoter Ovgp1 to generate a new GEMM of HGSOC. Female mice developed lethal cancer at an average of 15.1 months. Histopathological examination of mice revealed HGSOC characteristics including nuclear p53 and nuclear MYC in clusters of cells within the fallopian tube epithelium and ovarian surface epithelium. Unexpectedly, nuclear p53 and MYC clustered cell expression was also identified in the uterine luminal epithelium, possibly from intraepithelial metastasis from the fallopian tube epithelium (FTE). Extracted tumor cells exhibited strong loss of heterozygosity at the p53 locus, leaving the mutant allele. Copy number alterations in these cancer cells were prevalent, disrupting a large fraction of genes. Transcriptome profiles most closely matched human HGSOC and serous endometrial cancer. Taken together, these results demonstrate the Myc and Trp53-R270H transgene was able to recapitulate many phenotypic hallmarks of HGSOC through the utilization of strictly human-mimetic genetic hallmarks of HGSOC. This new mouse model enables further exploration of ovarian cancer pathogenesis, particularly in the 50% of HGSOC which lack homology directed repair mutations. Histological and transcriptomic findings are consistent with the hypothesis that uterine serous cancer may originate from the fallopian tube epithelium.

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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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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              featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

              Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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                Author and article information

                Journal
                bioRxiv
                BIORXIV
                bioRxiv
                Cold Spring Harbor Laboratory
                29 January 2024
                : 2024.01.24.576924
                Affiliations
                [1 ]Medical University of South Carolina
                Author notes
                [*]

                Equal contributions

                Author contributions statement

                All authors were involved in writing the paper and had final approval of the submitted versions.

                [# ]Correspondence to: JR Delaney, Department of Biochemistry and Molecular Biology, Medical University of South Carolina, 173 Ashley Ave, Charleston, SC 29425, USA. delaneyj@ 123456musc.edu
                Author information
                http://orcid.org/0000-0002-8978-5961
                Article
                10.1101/2024.01.24.576924
                10862747
                38352443
                f1654c62-8ea6-4b28-a392-01d12eafd3b2

                This work is licensed under a Creative Commons Attribution 4.0 International License, which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.

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                Categories
                Article

                ovarian carcinoma,high-grade serous ovarian cancer,uterine serous cancer,p53,myc,oncogenesis,genetically engineered mouse model,spontaneous cancer,syngeneic cancer model

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