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      Translating genomic medicine to the clinic: challenges and opportunities

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Editorial summary

          Genomic medicine has considerable potential to provide novel diagnostic and therapeutic solutions for patients who have molecularly complex diseases and who are not responding to existing therapies. To bridge the gap between genomic medicine and clinical practice, integration of various data types, resources, and joint international initiatives will be required.

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          Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning

          Navneet Narula, Theodore Sakellaropoulos, Paolo Ocampo … (2018)
          Visual inspection of histopathology slides is one of the main methods used by pathologists to assess the stage, type and subtype of lung tumors. Adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) are the most prevalent subtypes of lung cancer, and their distinction requires visual inspection by an experienced pathologist. In this study, we trained a deep convolutional neural network (inception v3) on whole-slide images obtained from The Cancer Genome Atlas to accurately and automatically classify them into LUAD, LUSC or normal lung tissue. The performance of our method is comparable to that of pathologists, with an average area under the curve (AUC) of 0.97. Our model was validated on independent datasets of frozen tissues, formalin-fixed paraffin-embedded tissues and biopsies. Furthermore, we trained the network to predict the ten most commonly mutated genes in LUAD. We found that six of them-STK11, EGFR, FAT1, SETBP1, KRAS and TP53-can be predicted from pathology images, with AUCs from 0.733 to 0.856 as measured on a held-out population. These findings suggest that deep-learning models can assist pathologists in the detection of cancer subtype or gene mutations. Our approach can be applied to any cancer type, and the code is available at https://github.com/ncoudray/DeepPATH .
          Article 0 comments Cited 847 times – based on 0 reviews     Review now
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            Molecularly targeted therapy based on tumour molecular profiling versus conventional therapy for advanced cancer (SHIVA): a multicentre, open-label, proof-of-concept, randomised, controlled phase 2 trial.

            Christophe Le Tourneau, Jean-Pierre Delord, Anthony Goncalves … (2015)
            Molecularly targeted agents have been reported to have anti-tumour activity for patients whose tumours harbour the matching molecular alteration. These results have led to increased off-label use of molecularly targeted agents on the basis of identified molecular alterations. We assessed the efficacy of several molecularly targeted agents marketed in France, which were chosen on the basis of tumour molecular profiling but used outside their indications, in patients with advanced cancer for whom standard-of-care therapy had failed.
            Article 0 comments Cited 388 times – based on 0 reviews     Review now
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              Functional precision cancer medicine—moving beyond pure genomics

              Anthony Letai (2017)
              Anthony Letai proposes wider adoption of functional assays in efforts to match the right drug to the right patient and discusses why these assays might be complementary to existing genomics-based approaches.
              Article 0 comments Cited 132 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Huan Zhang: huan.zhang@liu.se
                Mikael Benson: mikael.benson@liu.se
                Journal
                Journal ID (nlm-ta): Genome Med
                Journal ID (iso-abbrev): Genome Med
                Title: Genome Medicine
                Publisher: BioMed Central (London )
                ISSN (Electronic): 1756-994X
                Publication date (Electronic): 22 February 2019
                Publication date PMC-release: 22 February 2019
                Publication date Collection: 2019
                Volume: 11
                Electronic Location Identifier: 9
                Affiliations
                [1 ]ISNI 0000 0001 2162 9922, GRID grid.5640.7, Centre for Personalised Medicine, , Linköping University, ; Linköping, Sweden
                [2 ]Crown Princess Victoria Children’s Hospital, Linköping, Sweden
                [3 ]Rheumatology Unit, Department of Medicine, Karolinska Institutet, Karolinska University Hospital (Solna), 171 76 Stockholm, Sweden
                [4 ]ISNI 0000 0000 9241 5705, GRID grid.24381.3c, Karolinska University Hospital Laboratory, ; Stockholm, Sweden
                [5 ]Hopp Children’s Cancer Center Heidelberg (KiTZ), Heidelberg, Germany
                [6 ]ISNI 0000 0004 0492 0584, GRID grid.7497.d, German Cancer Research Center (DKFZ), ; Heidelberg, Germany
                [7 ]ISNI 0000 0004 0492 0584, GRID grid.7497.d, German Cancer Consortium (DKTK), Partner Site Heidelberg, ; Heidelberg, Germany
                [8 ]ISNI 0000 0001 0328 4908, GRID grid.5253.1, Department of Pediatric Oncology, Hematology and Immunology, , Heidelberg University Hospital, ; Heidelberg, Germany
                Article
                Publisher ID: 622
                DOI: 10.1186/s13073-019-0622-1
                PMC ID: 6385380
                PubMed ID: 30795816
                SO-VID: c2bf15c5-ba4c-4f1c-865a-24d9dfba93b2
                Copyright © © The Author(s). 2019
                License:

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.

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                ScienceOpen disciplines: Molecular medicine
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                ScienceOpen disciplines: Molecular medicine

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