www.hcltech.com E2ES to Accelerate Next-Generation Genome Analysis in Clinical Research whitepaper April 2015
TABLE OF CONTENTS Introduction 3 Challenges associated with NGS data analysis 3 HCL s NGS Solution for Data Analysis 4 HCL s NGS Solution for Integration 5 Benefits 5 Conclusion 6 References 6 authors 7 2015, HCL Technologies. Reproduction Prohibited. This document is protected under Copyright by the Author, all rights reserved. 2
Introduction The science of genomics is at the beginning of a new era of innovation. Today, a human genome can be sequenced in a few hours and for a few thousand dollars; a task that took 13 years and USD 2.7 billion to accomplish during the Human Genome Project. Next Generation Sequencing (NGS) has revolutionized the way we carry out biological research today. The applications of NGS seem endless; various applications of the technology in the fields of science such as cancer research, disease diagnosis, biofuels, marine sciences, livestock research, agricultural, and veterinary research drive the NGS market. Another important driver of the sector is to provide cheaper sequencing possibilities for needing patients in the provider universe. Advances in the next-generation sequencing technology have opened the door to leveraging genomics in personalized medicine and clinical trials. Still, the clinical research is mostly based on enrolling clinically similar participants to evaluate the efficacy and safety of new drugs. In many trials selection criteria based on genotypic standards are still unavailable. Performing clinical trials on such patient populations carries the risk of exposure to relatively smaller population and any statistically significant treatment benefit may occur in only a small proportion of patients. The inadequacy in clinical trial procedure leads to the drug being considered as a failure despite providing real benefit for some patients. Next generation genomics has the potential to transform how doctors diagnose and treat cancer as well as other diseases. According to McKinsey report, the economic impact of next-generation gene sequencing in health care, agriculture, and the production of substances such as biofuels could be USD 700 billion to USD 1.6 trillion a year by 2025. About 80 percent of this potential value would be realized through extending and enhancing lives through faster disease detection, more precise diagnoses, newer drugs, and more tailored disease treatments. 26 million annual deaths take place from cancer, cardiovascular disease or Type 2 diabetes, and the global health-care cost is USD 6.5 trillion. The paper discusses on how IT can enable NGS as its space gets bigger and bigger. Challenges associated with NGS data analysis Over the past decade, the improvement value in sequencing speed has exceeded Moore s law. The improvement in gene sequencing performance has been achieved by creating highly parallel systems that can sequence millions of DNA base pairs in a very short time. The major challenges of NGS technology are as follows: y The growth of technology has rapidly generated massive data and posed a fundamental problem of data management, workflow, and analysis. Data analysis is time consuming and may require special knowledge of bioinformatics to garner accurate information from sequence data. 2015, HCL Technologies. Reproduction Prohibited. This document is protected under Copyright by the Author, all rights reserved. 3
y NGS technology mandates extensive investments in networking speed, data storage facilities, and high-performance computing capability through hardware and software platforms. These platforms acquire, store, and analyze large datasets created by these instruments. y Data integration and Business Intelligence to retrieve, analyze, and integrate NGS data with patients /clinical trial participants data. The next-generation gene sequencing depends on improvements in computational power and big data analytics capability. HCL s NGS Solution for Data Analysis Analyzing NGS data is a challenging and time-consuming operation for scientists. Considering the pain points of data analysis and the challenges associated with them, HCL along with its partners proposes a Graphical Workflow Environment framework, which can automate the required set of workflows for NGS applications to populate analysis data. It has the following features and process: y A built-in data management system allows the import of large sequence data files directly from the sequencing instrument pipeline. This system also helps in cataloguing and storing these files for subsequent use. y Software specialized for next-generation sequence alignment and post-processing includes: 1. Align read 2. De novo assembly 3. Quality control 4. Single nucleotide polymorphism 5. Indels 6. Copy number variation 7. Annotation y It is more flexible for users to modify the workflow and develop any additional steps interfacing with other computational tools. y The output of each component is interlinked with the subsequent process to create graphical workflow pipelines. Several alternative workflows can run independently or get connected in a logical flow. 2015, HCL Technologies. Reproduction Prohibited. This document is protected under Copyright by the Author, all rights reserved. 4
HCL s NGS Solution for Integration Ability to integrate and mine public and proprietary datasets from microarray and next-gen technologies is central to present-day biology. HCL and its partners have developed a Big Data analytics and BI solution for NGS data with the following features: y Tight integration of the participant s genomics data with clinical trial data and pathway information. y Clinical trial participants can be segmented based on genomic profiles. y Gene expression signature can be predicted based on pharmacogenomics activity. y Disease subtype or progression stages can be analyzed based on genomics data. y Prediction of new complex genetic signature by accessing literature and public database. Researcher/Clinical Convert the Sample data and meta data into Sample Sheet Receive input from customer Genomic Core Facility Interface for Data Entry L I M S Report Sequencing details and Workflow management using LIMS Data warehouse Cloud Services Sequence Align Analyzed using Indels Pipelines SNP CNV Annotation * Integration of Omics data with Patient data * BI Analysis HPC Environment 1 Benefits y Automatic analysis pipeline helps reduce analysis time. The reduction in time removes manual data transfer at every step, rendering data preparation to be painless and simple for implementation, analysis, and storage. y Rational selection of patients for clinical trials based on their predicted sensitivity to the drug will increase the response rates, shorten the duration and cut the cost of the clinical drug development. y Observed therapeutic response helps discover potential biomarkers, develop diagnostic assays and higher the potential for drug efficacy with fewer adverse events. 2015, HCL Technologies. Reproduction Prohibited. This document is protected under Copyright by the Author, all rights reserved. 5
Conclusion NGS has numerous opportunities to be explored and leveraged in many fields, and it has its own set of challenges. One of the major challenges is the preparation of data that can be addressed by having the HCL-proposed Graphical Workflow Environment. If leveraged during clinical trials, NGS will improve patient selection, translate base pair reads to bedside applications, and better the understanding of disease condition itself. The most pressing challenge to NGS technologies is the need to expedite the use of NGS data in the clinical practice of medicine. Analysis of large sequencing datasets mandates an advanced computational infrastructure dedicated to preserving, processing, and analyzing NGS data. HCL has associated with a partner to provide High Performance Computing services for NGS analysis and develop its capability in data analysis and data integration to facilitate clinical action. References 1. McKinsey on Personalized Medicine, March 2013. 2. Disruptive technologies: Advances that will transform life, business and the global company. McKinsey Global Institute May 2013. 3. Bioinformatics Market By Sector (Molecular Medicine, Agriculture, Research & Forensic), Segment (Sequencing Platforms, Knowledge Management Tools & Data Analysis Services) & Application (Genomics, Proteomics & Drug Design) Global Forecasts to 2017, MarketsandMarkets, 2013. 4. Next Generation Sequencing (NGS) Market [Platforms (Illumina HiSeq, MiSeq, Life Technologies Ion Proton/PGM, 454 Roche), Bioinformatics (RNA-Seq, ChIP-Seq), (Pyrosequencing, SBS, SMRT), (Diagnostics, Personalized Medicine)] Global Forecast to 2017, MarketsandMarkets, 2013. 2015, HCL Technologies. Reproduction Prohibited. This document is protected under Copyright by the Author, all rights reserved. 6
HCL LIFE SCIENCES & HEALTHCARE HCL is a leading provider of Life Sciences and Healthcare Business and Technology services. We are the chosen service provider for enabling new growth drivers for our clients, providing them with industry leading best practices, taking care of their compliance needs and ensuring goldstandard process cycle times. Our clientele includes seven of the top ten global pharmaceutical companies, seven of the top ten medical devices companies, six of the top ten health plans, three of the top five CRO s and two of the top three data providers. Equipped with certified technology experts and domain specialists, HCL offers services in critical areas of the life sciences and healthcare eco system such as drug discovery, clinical development, drug safety, regulatory compliance, manufacturing and plant automation, commercial, Healthcare analytics, Population Health Management [PHM], mhealth, member experience management [MEM], fraud, waste and abuse management [FWA]. Let s connect Please feel free to write to us at contact.lsh@hcl.com authors Dr. OmPraba joined HCL s family in August 2010. She is a trained and certified professional of Veeva Vault Admin, Accelrys Insight II, and Cerius2 products. She has an extensive knowledge and hands-on experience in operating various applications used in drug design and development. She has published 10 papers in reputed national and international journals and has given oral presentations in international conferences. She is currently working as a lead consultant with Life Science Vertical at HCL technologies. As a business analyst and a domain consultant, she has been involved in various development projects for leading pharmaceutical clients in the pharmaceutical R&D space. She is a part of HCL Training Academy and an SME for pharmaceutical business process trainings for various accounts at HCL. She has a doctorate degree in the field of Biochemistry and Biophysics from the University of Madras. She has an extensive research experience in the field of computer-aided drug design, molecular modeling, molecular biology/biotechnology and crystallography. 2015, HCL Technologies. Reproduction Prohibited. This document is protected under Copyright by the Author, all rights reserved. 7
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