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      Personalized In Vitro and In Vivo Cancer Models to Guide Precision Medicine

      research-article
      1 , 2 , 10 , 5 , 6 , 7 , 6 , 1 , 4 , 5 , 1 , 2 , 1 , 5 , 1 , 7 , 1 , 1 , 1 , 1 , 1 , 2 , 1 , 2 , 1 , 2 , 1 , 2 , 1 , 3 , 5 , 9 , 7 , 1 , 4 , 5 , 1 , 3 , 1 , 6 , 8 , 7 , 5 , 1 , 2 , 5
      Cancer discovery
      precision cancer care, whole exome sequencing, tumor organoids, patient derived xenograft, high throughput drug screening
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          Abstract

          Precision Medicine is an approach that takes into account the influence of individuals' genes, environment and lifestyle exposures to tailor interventions. Here, we describe the development of a robust precision cancer care platform, which integrates whole exome sequencing (WES) with a living biobank that enables high throughput drug screens on patient-derived tumor organoids. To date, 56 tumor-derived organoid cultures, and 19 patient-derived xenograft (PDX) models have been established from the 769 patients enrolled in an IRB approved clinical trial. Because genomics alone was insufficient to identify therapeutic options for the majority of patients with advanced disease, we used high throughput drug screening effective strategies. Analysis of tumor derived cells from four cases, two uterine malignancies and two colon cancers, identified effective drugs and drug combinations that were subsequently validated using 3D cultures and PDX models. This platform thereby promotes the discovery of novel therapeutic approaches that can be assessed in clinical trials and provides personalized therapeutic options for individual patients where standard clinical options have been exhausted.

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          Most cited references43

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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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            Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples

            Detection of somatic point substitutions is a key step in characterizing the cancer genome. Mutations in cancer are rare (0.1–100/Mb) and often occur only in a subset of the sequenced cells, either due to contamination by normal cells or due to tumor heterogeneity. Consequently, mutation calling methods need to be both specific, avoiding false positives, and sensitive to detect clonal and sub-clonal mutations. The decreased sensitivity of existing methods for low allelic fraction mutations highlights the pressing need for improved and systematically evaluated mutation detection methods. Here we present MuTect, a method based on a Bayesian classifier designed to detect somatic mutations with very low allele-fractions, requiring only a few supporting reads, followed by a set of carefully tuned filters that ensure high specificity. We also describe novel benchmarking approaches, which use real sequencing data to evaluate the sensitivity and specificity as a function of sequencing depth, base quality and allelic fraction. Compared with other methods, MuTect has higher sensitivity with similar specificity, especially for mutations with allelic fractions as low as 0.1 and below, making MuTect particularly useful for studying cancer subclones and their evolution in standard exome and genome sequencing data.
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              Mutational heterogeneity in cancer and the search for new cancer genes

              Major international projects are now underway aimed at creating a comprehensive catalog of all genes responsible for the initiation and progression of cancer. These studies involve sequencing of matched tumor–normal samples followed by mathematical analysis to identify those genes in which mutations occur more frequently than expected by random chance. Here, we describe a fundamental problem with cancer genome studies: as the sample size increases, the list of putatively significant genes produced by current analytical methods burgeons into the hundreds. The list includes many implausible genes (such as those encoding olfactory receptors and the muscle protein titin), suggesting extensive false positive findings that overshadow true driver events. Here, we show that this problem stems largely from mutational heterogeneity and provide a novel analytical methodology, MutSigCV, for resolving the problem. We apply MutSigCV to exome sequences from 3,083 tumor-normal pairs and discover extraordinary variation in (i) mutation frequency and spectrum within cancer types, which shed light on mutational processes and disease etiology, and (ii) mutation frequency across the genome, which is strongly correlated with DNA replication timing and also with transcriptional activity. By incorporating mutational heterogeneity into the analyses, MutSigCV is able to eliminate most of the apparent artefactual findings and allow true cancer genes to rise to attention.
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                Author and article information

                Journal
                101561693
                39259
                Cancer Discov
                Cancer Discov
                Cancer discovery
                2159-8274
                2159-8290
                10 February 2017
                22 March 2017
                May 2017
                01 May 2018
                : 7
                : 5
                : 462-477
                Affiliations
                [1 ]Englander Institute for Precision Medicine, Weill Cornell Medicine-New York Presbyterian Hospital. New York, NY 10021, USA
                [2 ]Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10021, USA
                [3 ]Department of Medicine, Division of Hematology and Medical Oncology, Weill Cornell Medicine, New York, NY 10021, USA
                [4 ]Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA
                [5 ]Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10021, USA
                [6 ]Centre of Integrative Biology, University of Trento, Trento 38121, Italy, Europe
                [7 ]Cure First and SEngine Precision Medicine, Seattle, WA 98109, USA
                [8 ]Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
                [9 ]Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
                [10 ]Institute of Pathology and Molecular Pathology, University Hospital Zurich, 8091 Zurich, Switzerland, Europe
                Author notes
                Corresponding author: Mark A. Rubin, MD, Director, Englander Institute for Precision Medicine, Weill Cornell Medicine-New York Presbyterian Hospital, 413 E 69 th St, 14 th Floor, New York, NY 10021, Tel: 646- 746-6326, Fax: 646-962-0576, rubinma@ 123456med.cornell.edu
                [†]

                These authors contributed equally to this work.

                Article
                PMC5413423 PMC5413423 5413423 nihpa849941
                10.1158/2159-8290.CD-16-1154
                5413423
                28331002
                c392473c-3da7-4925-ac99-450674a9f84c
                History
                Categories
                Article

                precision cancer care,whole exome sequencing,tumor organoids,patient derived xenograft,high throughput drug screening

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