Translational Bioinformatics: Biobanks in the Precision Medicine Era
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Integrative Analysis of Exogenous, Endogenous, Tumour and Immune
Gut Online First, published on February 6, 2018 as 10.1136/gutjnl-2017-315537 Recent advances in basic science Integrative analysis of exogenous, endogenous, Gut: first published as 10.1136/gutjnl-2017-315537 on 6 February 2018. Downloaded from tumour and immune factors for precision medicine Shuji Ogino,1,2,3,4 Jonathan A Nowak,1 Tsuyoshi Hamada,2 Amanda I Phipps,5,6 Ulrike Peters,5,6 Danny A Milner Jr,7 Edward L Giovannucci,3,8,9 Reiko Nishihara,1,3,4,8,10 Marios Giannakis,4,11,12 Wendy S Garrett,4,11,13 Mingyang Song8,14,15 For numbered affiliations see ABSTRACT including inflammatory and immune cells. A fraction end of article. Immunotherapy strategies targeting immune checkpoints of somatic mutations may result in the generation of such as the CTLA4 and CD274 (programmed cell death new antigens (neoantigens) that can be recognised as Correspondence to 1 ligand 1, PD-L1)/PDCD1 (programmed cell death 1, non-self by the immune system. During an individu- Dr Shuji Ogino, Program in MPE Molecular Pathological PD-1) T-cell coreceptor pathways are revolutionising al's life-course, cells may acquire somatic molecular Epidemiology, Brigham and oncology. The approval of pembrolizumab use for alterations, and some of these cells undergo clonal Women’s Hospital, Boston, MA solid tumours with high-level microsatellite instability expansion, displaying hallmarks of early neoplasia. 02215, USA; shuji_ ogino@ dfci. or mismatch repair deficiency by the US Food and Many of these cells are likely kept in check or killed harvard. edu Drug Administration highlights promise of precision by the host immune system before they can develop Received 24 October 2017 immuno-oncology. -
The Future of Precision Medicine : Potential Impacts for Health Technology Assessment
This is a repository copy of The Future of Precision Medicine : Potential Impacts for Health Technology Assessment. White Rose Research Online URL for this paper: https://eprints.whiterose.ac.uk/133069/ Version: Accepted Version Article: Love-Koh, James orcid.org/0000-0001-9009-5346, Peel, Alison, Rejon-Parilla, Juan Carlos et al. (6 more authors) (2018) The Future of Precision Medicine : Potential Impacts for Health Technology Assessment. Pharmacoeconomics. pp. 1439-1451. ISSN 1179-2027 https://doi.org/10.1007/s40273-018-0686-6 Reuse This article is distributed under the terms of the Creative Commons Attribution-NonCommercial (CC BY-NC) licence. This licence allows you to remix, tweak, and build upon this work non-commercially, and any new works must also acknowledge the authors and be non-commercial. You don’t have to license any derivative works on the same terms. More information and the full terms of the licence here: https://creativecommons.org/licenses/ Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing [email protected] including the URL of the record and the reason for the withdrawal request. [email protected] https://eprints.whiterose.ac.uk/ The Future of Precision Medicine: Potential Impacts for Health Technology Assessment Title: The Future of Precision Medicine: Potential Impacts for Health Technology Assessment Authors: James Love-Koh1,2, Alison Peel1, Juan Carlos Rejon-Parilla3, Kate Ennis1,4, Rosemary Lovett3, Andrea Manca2,5, Anastasia Chalkidou6, Hannah Wood1, Matthew Taylor1 1 YorK Health Economics Consortium 2 Centre for Health Economics, University of YorK 3 National Institute for Health and Care Excellence 4 Institute of Infection and Global Health, University of Liverpool 5 Luxembourg Institute of Health 6 Kings Technology Evaluation Centre Corresponding Author Details: Name: James Love-Koh Address: Centre for Health Economics, Alcuin A Block, University of York, Heslington, York, YO10 5DD, UK. -
What Are Precision Medicine and Personalized Medicine?
What Are Precision Medicine and Personalized Medicine? Precision medicine, also known as personalized medicine, is a new frontier for healthcare combining genomics, big data analytics, and population health. Source: Thinkstock Since the beginning of recorded history, healthcare practitioners have striven to make their actions more effective for their patients by experimenting with different treatments, observing and sharing their results, and improving upon the efforts of previous generations. Becoming more accurate, precise, proactive, and impactful for each individual that comes under their care has always been the goal of all clinicians, no matter how basic the tools at their disposal. But now, modern physicians and scientists are now able to take this mission far, far beyond the reach of their ancestors with the help of electronic health records, genetic testing, big data analytics, and supercomputing – all the ingredients required to engage in what is quickly becoming truly precise and personalized medicine. Precision medicine, also commonly referred to as personalized medicine, is one of the most promising approaches to tackling diseases that have thus far eluded effective treatments or cures. Cancer, neurodegenerative diseases, and rare genetic conditions take an enormous toll on individuals, families and societies as a whole. Approximately 1.7 million new cancer cases were diagnosed in the United States in 2017. Around 600,000 deaths were expected during that year, according to the American Cancer Society. The Agency for Healthcare Research and Quality adds that the direct economic impact of cancer is around $80 billion per year – loss of productivity, wages, and caregiver needs sap billions more from the economy. -
Advancing Standards for Precision Medicine
Advancing Standards for Precision Medicine FINAL REPORT Prepared by: Audacious Inquiry on behalf of the Office of the National Coordinator for Health Information Technology under Contract No. HHSM-500-2017-000101 Task Order No. HHSP23320100013U January 2021 ONC Advancing Standards for Precision Medicine Table of Contents Executive Summary ...................................................................................................................................... 5 Standards Development and Demonstration Projects ............................................................................ 5 Mobile Health, Sensors, and Wearables ........................................................................................... 5 Social Determinants of Health (SDOH) ............................................................................................. 5 Findings and Lessons Learned .......................................................................................................... 6 Recommendations ........................................................................................................................................ 6 Introduction ................................................................................................................................................... 7 Background ................................................................................................................................................... 7 Project Purpose, Goals, and Objectives .................................................................................................. -
Molecular Pathological Epidemiology Gives Clues to Paradoxical Findings
Molecular Pathological Epidemiology Gives Clues to Paradoxical Findings The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Nishihara, Reiko, Tyler J. VanderWeele, Kenji Shibuya, Murray A. Mittleman, Molin Wang, Alison E. Field, Edward Giovannucci, Paul Lochhead, and Shuji Ogino. 2015. “Molecular Pathological Epidemiology Gives Clues to Paradoxical Findings.” European Journal of Epidemiology 30 (10): 1129–35. https://doi.org/10.1007/ s10654-015-0088-4. Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:41392032 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#OAP HHS Public Access Author manuscript Author Manuscript Author ManuscriptEur J Epidemiol Author Manuscript. Author Author Manuscript manuscript; available in PMC 2016 October 07. Published in final edited form as: Eur J Epidemiol. 2015 October ; 30(10): 1129–1135. doi:10.1007/s10654-015-0088-4. Molecular Pathological Epidemiology Gives Clues to Paradoxical Findings Reiko Nishiharaa,b,c, Tyler J. VanderWeeled,e, Kenji Shibuyac, Murray A. Mittlemand,f, Molin Wangd,e,g, Alison E. Fieldd,g,h,i, Edward Giovannuccia,d,g, Paul Lochheadi,j, and Shuji Oginob,d,k aDepartment of Nutrition, Harvard T.H. Chan School of Public Health, 655 Huntington Ave., Boston, Massachusetts 02115 USA bDepartment of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, 450 Brookline Ave., Boston, Massachusetts 02215 USA cDepartment of Global Health Policy, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, Japan dDepartment of Epidemiology, Harvard T.H. -
Molecular Epidemiology and Precision Medicine
Pharmacogenetics: Past, Present, and Future Brooke L. Fridley, PhD Associate Professor of Biostatistics University of Kansas Medical Center Site Director, K-INBRE Bioinformatics Core Director, Biostatistics and Informatics Shared Resource, University of Kansas Cancer Center What is Pharmacogenomics? Pharmacogenomics & Precision Medicine Aims to deliver the correct drug or treatment: • At the right time • To the right patient • At the right dose Can only be achieved when we have accurate clinical tests (BIOMARKER) and companion drugs at our disposal What is a Biomarker? • Biomarker: “A characteristic that is objectively measured and evaluated as an indicator of – normal biological processes, – pathogenic processes, or – pharmacologic responses to a therapeutic intervention.” Biomarkers Definitions Working Group. Biomarkers and surrogate end points: preferred definitions and conceptual framework. Clin Pharmacol Ther 2001;69:89–95 Candidate Gene Era 1950 – 1990 – Structure of 1980 – 1985 – HGP DNA TPMT PCR begins 1997 – 1977 – 1983 – First 1987 – NHGRI Sanger human P450 formed Sequencing disease supergene gene family mapped Candidate • Genotyping Variant & • RT-PCR Gene • Re-sequencing genes (exons) with Sanger Studies Sequencing PK and PD and * Alleles (haplotypes) PGx Effect = Gene*Drug Interaction Drug 1 Drug 2 Genome-wide Array Era 2003 – FDA 2002 – issues draft 1998 -Illumina HapMap guidelines for founded begins PGx 2001 – 1st 2003 – 2004 – 1st NGS version of Completion of platform human HGP genome completed Genome- • SNP arrays (~mid 2000s) wide • mRNA arrays (~mid 1990s) Association Studies • Methylation arrays (~2008-2010) Study of Genetic Association with Drug Response Phenotypes Cases: Non-responders Cases: Responders Genetic association studies look at the frequency of genetic changes to try to determine whether specific changes are associated with a phenotype. -
The Personalized Medicine Report
THE PERSONALIZED MEDICINE REPORT 2017 · Opportunity, Challenges, and the Future The Personalized Medicine Coalition gratefully acknowledges graduate students at Manchester University in North Manchester, Indiana, and at the University of Florida, who updated the appendix of this report under the guidance of David Kisor, Pharm.D., Director, Pharmacogenomics Education, Manchester University, and Stephan Schmidt, Ph.D., Associate Director, Pharmaceutics, University of Florida. The Coalition also acknowledges the contributions of its many members who offered insights and suggestions for the content in the report. CONTENTS INTRODUCTION 5 THE OPPORTUNITY 7 Benefits 9 Scientific Advancement 17 THE CHALLENGES 27 Regulatory Policy 29 Coverage and Payment Policy 35 Clinical Adoption 39 Health Information Technology 45 THE FUTURE 49 Conclusion 51 REFERENCES 53 APPENDIX 57 Selected Personalized Medicine Drugs and Relevant Biomarkers 57 HISTORICAL PRECEDENT For more than two millennia, medicine has maintained its aspiration of being personalized. In ancient times, Hippocrates combined an assessment of the four humors — blood, phlegm, yellow bile, and black bile — to determine the best course of treatment for each patient. Today, the sequence of the four chemical building blocks that comprise DNA, coupled with telltale proteins in the blood, enable more accurate medical predictions. The Personalized Medicine Report 5 INTRODUCTION When it comes to medicine, one size does not fit all. Treatments that help some patients are ineffective for others (Figure 1),1 and the same medicine may cause side effects in only certain patients. Yet, bound by the constructs of traditional disease, and, at the same time, increase the care delivery models, many of today’s doctors still efficiency of the health care system by improving prescribe therapies based on population averages. -
A Bioinformatics Approach for Precision Medicine Off-Label Drug
View metadata, citation and similar papers at core.ac.uk brought to you by CORE Cheng L, et al. J Am Med Inform Assoc 2016;23:741–749. doi:10.1093/jamia/ocw004, Research and Applications provided by IUPUIScholarWorks RECEIVED 5 September 2015 A bioinformatics approach for precision REVISED 10 December 2015 ACCEPTED 10 January 2016 medicine off-label drug selection among PUBLISHED ONLINE FIRST 23 April 2016 triple negative breast cancer patients Lijun Cheng,1,2 Bryan P Schneider,3,4 and Lang Li1,2,4 ABSTRACT .................................................................................................................................................... Background Cancer has been extensively characterized on the basis of genomics. The integration of genetic information about cancers with data on how the cancers respond to target based therapy to help to optimum cancer treatment. Objective The increasing usage of sequencing technology in cancer research and clinical practice has enormously advanced our understanding of cancer RESEARCH AND APPLICATIONS mechanisms. The cancer precision medicine is becoming a reality. Although off-label drug usage is a common practice in treating cancer, it suffers from the lack of knowledge base for proper cancer drug selections. This eminent need has become even more apparent considering the upcoming genomics data. Methods In this paper, a personalized medicine knowledge base is constructed by integrating various cancer drugs, drug-target database, and knowl- edge sources for the proper cancer drugs and their target selections. Based on the knowledge base, a bioinformatics approach for cancer drugs selec- tion in precision medicine is developed. It integrates personal molecular profile data, including copy number variation, mutation, and gene expression. Results By analyzing the 85 triple negative breast cancer (TNBC) patient data in the Cancer Genome Altar, we have shown that 71.7% of the TNBC patients have FDA approved drug targets, and 51.7% of the patients have more than one drug target. -
Integration of Molecular Pathology, Epidemiology and Social Science for Global Precision Medicine
Expert Review of Molecular Diagnostics ISSN: 1473-7159 (Print) 1744-8352 (Online) Journal homepage: http://www.tandfonline.com/loi/iero20 Integration of molecular pathology, epidemiology and social science for global precision medicine Akihiro Nishi, Danny A Milner Jr, Edward L Giovannucci, Reiko Nishihara, Andy S Tan, Ichiro Kawachi & Shuji Ogino To cite this article: Akihiro Nishi, Danny A Milner Jr, Edward L Giovannucci, Reiko Nishihara, Andy S Tan, Ichiro Kawachi & Shuji Ogino (2015): Integration of molecular pathology, epidemiology and social science for global precision medicine, Expert Review of Molecular Diagnostics, DOI: 10.1586/14737159.2016.1115346 To link to this article: http://dx.doi.org/10.1586/14737159.2016.1115346 Published online: 04 Dec 2015. Submit your article to this journal Article views: 82 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=iero20 Download by: [University of California, San Francisco] Date: 31 December 2015, At: 13:21 Perspectives Integration of molecular pathology, epidemiology and social science for global precision medicine Expert Rev. Mol. Diagn. Early online, 1–13 (2015) Akihiro Nishi1,2, The precision medicine concept and the unique disease principle imply that each patient has Danny A Milner Jr3,4, unique pathogenic processes resulting from heterogeneous cellular genetic and epigenetic Edward L alterations and interactions between cells (including immune cells) and exposures, including Giovannucci5,6,7, dietary, environmental, microbial and lifestyle factors. As a core method field in population health science and medicine, epidemiology is a growing scientific discipline that can analyze Reiko Nishihara5,6,8,9, 9,10 disease risk factors and develop statistical methodologies to maximize utilization of big data Andy S Tan , on populations and disease pathology. -
Systems Biology and Experimental Model Systems of Cancer
Journal of Personalized Medicine Review Systems Biology and Experimental Model Systems of Cancer Gizem Damla Yalcin y , Nurseda Danisik y, Rana Can Baygin y and Ahmet Acar * Department of Biological Sciences, Middle East Technical University, Universiteler Mah. Dumlupınar Bulvarı 1, Çankaya, Ankara 06800, Turkey; [email protected] (G.D.Y.); [email protected] (N.D.); [email protected] (R.C.B.) * Correspondence: [email protected] These authors contributed equally. y Received: 7 September 2020; Accepted: 16 October 2020; Published: 19 October 2020 Abstract: Over the past decade, we have witnessed an increasing number of large-scale studies that have provided multi-omics data by high-throughput sequencing approaches. This has particularly helped with identifying key (epi)genetic alterations in cancers. Importantly, aberrations that lead to the activation of signaling networks through the disruption of normal cellular homeostasis is seen both in cancer cells and also in the neighboring tumor microenvironment. Cancer systems biology approaches have enabled the efficient integration of experimental data with computational algorithms and the implementation of actionable targeted therapies, as the exceptions, for the treatment of cancer. Comprehensive multi-omics data obtained through the sequencing of tumor samples and experimental model systems will be important in implementing novel cancer systems biology approaches and increasing their efficacy for tailoring novel personalized treatment modalities in cancer. In this review, we discuss emerging cancer systems biology approaches based on multi-omics data derived from bulk and single-cell genomics studies in addition to existing experimental model systems that play a critical role in understanding (epi)genetic heterogeneity and therapy resistance in cancer. -
UNDERSTANDING the LANDSCAPE and INTEGRATION of PATHOLOGY with the COMMUNITY CANCER CARE TEAM Table of Contents
UNDERSTANDING THE LANDSCAPE AND INTEGRATION OF PATHOLOGY WITH THE COMMUNITY CANCER CARE TEAM Table of Contents Introduction ..................................................................................................................... 2 Central Topics ................................................................................................................. 3 Cancer Biomarker Testing and Molecular Pathology ...................................................... 3 Cancer Diagnosis and Treatment Discussions ................................................................. 7 Hospital Tumor Boards, Case Conferences, and Cancer Committee ............................. 9 Clinical Practice Guideline Collaborations .................................................................... 10 Evolving Themes and Future Direction ......................................................................... 11 Summary ........................................................................................................................ 11 Publication Abstracts ..................................................................................................... 11 References ..................................................................................................................... 25 Introduction In an era of precision medicine and rapidly advancing cancer therapeutics, the role of pathology in the diagnosis and management of cancer is evolving. Pathologists are uniquely positioned at the intersection of multiple points along the cancer -
Integration of Pharmacology, Molecular Pathology, and Population Data Science to Support Precision Gastrointestinal Oncology
www.nature.com/npjprecisiononcology REVIEW ARTICLE OPEN Integration of pharmacology, molecular pathology, and population data science to support precision gastrointestinal oncology Shuji Ogino1,2,3, Iny Jhun1, Douglas A. Mata1, Thing Rinda Soong1, Tsuyoshi Hamada3, Li Liu3,4, Reiko Nishihara1,2,3,4,5,6, Marios Giannakis6,7,8, Yin Cao4,9,10, JoAnn E. Manson2,11, Jonathan A. Nowak1,3 and Andrew T. Chan6,9,10,12 Precision medicine has a goal of customizing disease prevention and treatment strategies. Under the precision medicine paradigm, each patient has unique pathologic processes resulting from cellular genomic, epigenomic, proteomic, and metabolomic alterations, which are influenced by pharmacological, environmental, microbial, dietary, and lifestyle factors. Hence, to realize the promise of precision medicine, multi-level research methods that can comprehensively analyze many of these variables are needed. In order to address this gap, the integrative field of molecular pathology and population data science (i.e., molecular pathological epidemiology) has been developed to enable such multi-level analyses, especially in gastrointestinal cancer research. Further integration of pharmacology can improve our understanding of drug effects, and inform decision-making of drug use at both the individual and population levels. Such integrative research demonstrated potential benefits of aspirin in colorectal carcinoma with PIK3CA mutations, providing the basis for new clinical trials. Evidence also suggests that HPGD (15-PDGH) expression levels