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      Patient-derived organoid (PDO) platforms to facilitate clinical decision making

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          Abstract

          Based on recent advances in organoid research as well as the need to find more accurate models for drug screening in cancer research, patient-derived organoids have emerged as an effective in vitro model system to study cancer. Showing numerous advantages over 2D cell lines, 3D cell lines, and primary cell culture, organoids have been applied in drug screening to demonstrate the correlation between genetic mutations and sensitivity to targeted therapy. Organoids have also been used in co-clinical trials to compare drug responses in organoids to clinical responses in the corresponding patients. Numerous studies have reported the successful use of organoids to predict therapy response in cancer patients. Recently, organoids have been adopted to predict treatment response to radiotherapy and immunotherapy. The development of high throughput drug screening and organoids-on-a-chip technology can advance the use of patient-derived organoids in clinical practice and facilitate therapeutic decision-making.

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          The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity

          The systematic translation of cancer genomic data into knowledge of tumor biology and therapeutic avenues remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacologic annotation is available 1 . Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number, and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacologic profiles for 24 anticancer drugs across 479 of the lines, this collection allowed identification of genetic, lineage, and gene expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Altogether, our results suggest that large, annotated cell line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of “personalized” therapeutic regimens 2 .
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            Next-generation characterization of the Cancer Cell Line Encyclopedia

            Large panels of comprehensively characterized human cancer models, including the Cancer Cell Line Encyclopedia (CCLE), have provided a rigorous backbone upon which to study genetic variants, candidate targets, small molecule and biological therapeutics and to identify new marker-driven cancer dependencies. To improve our understanding of the molecular features that contribute to cancer phenotypes including drug responses, here we have expanded the characterizations of cancer cell lines to include genetic, RNA splicing, DNA methylation, histone H3 modification, microRNA expression and reverse-phase protein array data for 1,072 cell lines from various lineages and ethnicities. Integrating these data with functional characterizations such as drug-sensitivity data, short hairpin RNA knockdown and CRISPR–Cas9 knockout data reveals potential targets for cancer drugs and associated biomarkers. Together, this dataset and an accompanying public data portal provide a resource to accelerate cancer research using model cancer cell lines.
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              Prospective derivation of a living organoid biobank of colorectal cancer patients.

              In Rspondin-based 3D cultures, Lgr5 stem cells from multiple organs form ever-expanding epithelial organoids that retain their tissue identity. We report the establishment of tumor organoid cultures from 20 consecutive colorectal carcinoma (CRC) patients. For most, organoids were also generated from adjacent normal tissue. Organoids closely recapitulate several properties of the original tumor. The spectrum of genetic changes within the "living biobank" agrees well with previous large-scale mutational analyses of CRC. Gene expression analysis indicates that the major CRC molecular subtypes are represented. Tumor organoids are amenable to high-throughput drug screens allowing detection of gene-drug associations. As an example, a single organoid culture was exquisitely sensitive to Wnt secretion (porcupine) inhibitors and carried a mutation in the negative Wnt feedback regulator RNF43, rather than in APC. Organoid technology may fill the gap between cancer genetics and patient trials, complement cell-line- and xenograft-based drug studies, and allow personalized therapy design. PAPERCLIP.
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                Author and article information

                Contributors
                lisa96liu@gmail.com
                pony8980@163.com
                Journal
                J Transl Med
                J Transl Med
                Journal of Translational Medicine
                BioMed Central (London )
                1479-5876
                21 January 2021
                21 January 2021
                2021
                : 19
                : 40
                Affiliations
                [1 ]GRID grid.263488.3, ISNI 0000 0001 0472 9649, Department of Urology, Shenzhen Second People’s Hospital, The First Affiliated Hospital of Shenzhen University, International Cancer Center, , Shenzhen University School of Medicine, ; Shenzhen, 518039 China
                [2 ]GRID grid.263488.3, ISNI 0000 0001 0472 9649, International Cancer Center, , Shenzhen University School of Medicine, ; Shenzhen, China
                [3 ]Guangdong Key Laboratory of Systems Biology and Synthetic Biology for Urogenital Tumors, Shenzhen, 518035 China
                Author information
                http://orcid.org/0000-0001-7521-9777
                Article
                2677
                10.1186/s12967-020-02677-2
                7821720
                33478472
                cbe55d0a-048b-4d87-a634-841d473eef1f
                © The Author(s) 2021

                Open AccessThis 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/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 6 March 2020
                : 12 December 2020
                Funding
                Funded by: National Key R&D Program of China
                Award ID: 2019YFA0906000
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 81772737
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100003453, Natural Science Foundation of Guangdong Province;
                Award ID: 2017B030301015
                Award Recipient :
                Funded by: The Shenzhen Municipal Government of China
                Award ID: JCYJ20170413161749433
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100012151, Sanming Project of Medicine in Shenzhen;
                Award ID: SZSM201412018
                Award ID: SZSM201512037
                Award Recipient :
                Categories
                Review
                Custom metadata
                © The Author(s) 2021

                Medicine
                cancer,drug screening,patient-derived organoids,clinical decision making
                Medicine
                cancer, drug screening, patient-derived organoids, clinical decision making

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