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      A living biobank of canine mammary tumor organoids as a comparative model for human breast cancer

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          Abstract

          Mammary tumors in dogs hold great potential as naturally occurring breast cancer models in translational oncology, as they share the same environmental risk factors, key histological features, hormone receptor expression patterns, prognostic factors, and genetic characteristics as their human counterparts. We aimed to develop in vitro tools that allow functional analysis of canine mammary tumors (CMT), as we have a poor understanding of the underlying biology that drives the growth of these heterogeneous tumors. We established the long-term culture of 24 organoid lines from 16 dogs, including organoids derived from normal mammary epithelium or benign lesions. CMT organoids recapitulated key morphological and immunohistological features of the primary tissue from which they were derived, including hormone receptor status. Furthermore, genetic characteristics (driver gene mutations, DNA copy number variations, and single-nucleotide variants) were conserved within tumor-organoid pairs. We show how CMT organoids are a suitable model for in vitro drug assays and can be used to investigate whether specific mutations predict therapy outcomes. Specifically, certain CMT subtypes, such as PIK3CA mutated, estrogen receptor-positive simple carcinomas, can be valuable in setting up a preclinical model highly relevant to human breast cancer research. In addition, we could genetically modify the CMT organoids and use them to perform pooled CRISPR/Cas9 screening, where library representation was accurately maintained. In summary, we present a robust 3D in vitro preclinical model that can be used in translational research, where organoids from normal, benign as well as malignant mammary tissues can be propagated from the same animal to study tumorigenesis.

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          Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

          This article provides an update on the global cancer burden using the GLOBOCAN 2020 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer. Worldwide, an estimated 19.3 million new cancer cases (18.1 million excluding nonmelanoma skin cancer) and almost 10.0 million cancer deaths (9.9 million excluding nonmelanoma skin cancer) occurred in 2020. Female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), followed by lung (11.4%), colorectal (10.0 %), prostate (7.3%), and stomach (5.6%) cancers. Lung cancer remained the leading cause of cancer death, with an estimated 1.8 million deaths (18%), followed by colorectal (9.4%), liver (8.3%), stomach (7.7%), and female breast (6.9%) cancers. Overall incidence was from 2-fold to 3-fold higher in transitioned versus transitioning countries for both sexes, whereas mortality varied <2-fold for men and little for women. Death rates for female breast and cervical cancers, however, were considerably higher in transitioning versus transitioned countries (15.0 vs 12.8 per 100,000 and 12.4 vs 5.2 per 100,000, respectively). The global cancer burden is expected to be 28.4 million cases in 2040, a 47% rise from 2020, with a larger increase in transitioning (64% to 95%) versus transitioned (32% to 56%) countries due to demographic changes, although this may be further exacerbated by increasing risk factors associated with globalization and a growing economy. Efforts to build a sustainable infrastructure for the dissemination of cancer prevention measures and provision of cancer care in transitioning countries is critical for global cancer control.
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            Second-generation PLINK: rising to the challenge of larger and richer datasets

            PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for even faster and more scalable implementations of key functions. In addition, GWAS and population-genetic data now frequently contain probabilistic calls, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1's primary data format. To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, O(sqrt(n))-time/constant-space Hardy-Weinberg equilibrium and Fisher's exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. This will be followed by PLINK 2.0, which will introduce (a) a new data format capable of efficiently representing probabilities, phase, and multiallelic variants, and (b) extensions of many functions to account for the new types of information. The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.
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              QuPath: Open source software for digital pathology image analysis

              QuPath is new bioimage analysis software designed to meet the growing need for a user-friendly, extensible, open-source solution for digital pathology and whole slide image analysis. In addition to offering a comprehensive panel of tumor identification and high-throughput biomarker evaluation tools, QuPath provides researchers with powerful batch-processing and scripting functionality, and an extensible platform with which to develop and share new algorithms to analyze complex tissue images. Furthermore, QuPath’s flexible design makes it suitable for a wide range of additional image analysis applications across biomedical research.
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                Author and article information

                Contributors
                sven.rottenberg@vetsuisse.unibe.ch
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                27 October 2022
                27 October 2022
                2022
                : 12
                : 18051
                Affiliations
                [1 ]GRID grid.5734.5, ISNI 0000 0001 0726 5157, Institute of Animal Pathology, Vetsuisse Faculty, , University of Bern, ; Bern, Switzerland
                [2 ]GRID grid.5734.5, ISNI 0000 0001 0726 5157, Graduate School for Cellular and Biomedical Sciences, , University of Bern, ; Bern, Switzerland
                [3 ]GRID grid.5734.5, ISNI 0000 0001 0726 5157, Institute of Genetics, Vetsuisse Faculty, , University of Bern, ; Bern, Switzerland
                [4 ]GRID grid.66859.34, ISNI 0000 0004 0546 1623, Genetic Perturbation Platform, , Broad Institute of MIT and Harvard, ; Cambridge, USA
                [5 ]GRID grid.5335.0, ISNI 0000000121885934, Early Cancer Institute, Hutchison Research Centre, , University of Cambridge, ; Cambridge, UK
                [6 ]GRID grid.5734.5, ISNI 0000 0001 0726 5157, Bern Center for Precision Medicine, , University of Bern, ; Bern, Switzerland
                [7 ]GRID grid.5734.5, ISNI 0000 0001 0726 5157, Cancer Therapy Resistance Cluster, Department for BioMedical Research, , University of Bern, ; Bern, Switzerland
                [8 ]Present Address: Vetscope Pathologie Dettwiler, Lörracherstrasse 50, 4125 Riehen, Switzerland
                [9 ]GRID grid.5734.5, ISNI 0000 0001 0726 5157, Institute of Animal Pathology, COMPATH, , University of Bern, ; Bern, Switzerland
                [10 ]GRID grid.5335.0, ISNI 0000000121885934, Academic Department of Medical Genetics, , University of Cambridge, ; Cambridge, UK
                Article
                21706
                10.1038/s41598-022-21706-2
                9614008
                36302863
                6743f6b3-046c-4d2d-b9e4-12829861ee76
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 16 June 2022
                : 30 September 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001711, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung;
                Award ID: 310030_179360
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000781, European Research Council;
                Award ID: CoG-681572
                Award Recipient :
                Categories
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                © The Author(s) 2022

                Uncategorized
                cancer,molecular biology
                Uncategorized
                cancer, molecular biology

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