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Archive for the ‘BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceuticall R&D Informatics, Clinical Genomics, Cancer Informatics’ Category


Synopsis Track 7: NGS in Real Time @pharma_BI 2018 CHI’s BioIT World conference & Expo, May 15 – 17, 2018, Boston, MA – Seaport World Trade Center

http://www.bio-itworldexpo.com/

LPBI Group will cover Track 7: NGS in Real Time

@pharma_BI

@AVIVA1950

Aviva Lev-Ari, PhD, RN will be in attendance

 

 

 

 

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2018 Plenary Keynote Speakers

Mark BoguskiMark Boguski, MD, PhD
Executive Vice President and Chief Medical Officer, Liberty BioSecurity
tanya cashTanya Cashorali
Founder, TCB Analytics 
John ReyndersJohn Reynders, PhD
Vice President, Data Sciences, Genomics, and Bioinformatics, Alexion Pharmaceuticals, Inc.

 

Jerald SchindlerJerald Schindler, DrPH
Vice President, Biostatistics, Merck Research Laboratories (Retired)
Yu LihuaLihua Yu, PhD
Chief Data Science Officer, H3 Biomedicine

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TUESDAY, MAY 15

7:00 am Workshop Registration Open and Morning Coffee

 

8:00 – 11:30 Recommended Morning Pre-Conference Workshops*

W4. Introduction to Scalable and Reproducible RNA-Seq Data Processing, Analysis, and Result Reporting Using AWS, R, knitr, and LaTex

12:30 – 4:00 pm Recommended Afternoon Pre-Conference Workshops*

W13. Leveraging Cloud Technologies to Enable Large-Scale Integration of Human Genome and Clinical Outcomes Data

* Separate registration required.

2:00 – 6:30 Main Conference Registration Open

4:00 PLENARY KEYNOTE SESSION

Click here for detailed information

5:00 – 7:00 Welcome Reception in the Exhibit Hall with Poster Viewing

WEDNESDAY, MAY 16

7:00 am Registration Open and Morning Coffee

8:00 PLENARY KEYNOTE SESSION

Click here for detailed information

9:45 Coffee Break in the Exhibit Hall with Poster Viewing

LARGE-SCALE RNA-SEQ AND GENE EXPRESSION VARIABILITY

10:50 Chairperson’s Remarks

11:00 KEYNOTE PRESENTATION: RNA-Seq X: Look Back and Look Ahead

Shanrong Zhao, PhD, Director, Computational Biology and Bioinformatics, Pfizer, Inc.

Since Dr. Mortazavi published his groundbreaking research entitled “Mapping and Quantifying Mammalian Transcriptomes by RNA-Seq” in Nature Methods in 2008, RNA-seq has evolved rapidly and revolutionized biological research, drug development and clinical diagnostics. 2018 is the 10-year anniversary of RNA-seq, and it’s the right time to look back and look forward.

11:30 LCA: A Robust and Scalable Algorithm to Reveal Subtle Diversity in Large-Scale Single-Cell RNA Sequencing Data

Xiang Chen, PhD, Assistant Member, Department of Computational Biology, St. Jude Children’s Research Hospital

We developed Latent Cellular Analysis (LCA), a machine learning based single-cell RNA sequencing (scRNA-seq) analytical pipeline that combines similarity measurement by latent cellular states and a graph based clustering algorithm featuring dual-space model search for both the optimal number of subpopulations and the informative cellular states distinguishing them. LCA has proved to be robust, accurate and powerful by comparison to multiple state-of-the-art computational methods on large-scale real and simulated scRNA-seq data.

12:00 pm Presentation to be Announced

 

12:15 RSEQREP: An Open-Source Cloud-Enabled Framework for Reproducible RNA-Seq Data Processing, Analysis & Result Reporting

Johannes Goll, Director, Bioinformatics, The Emmes Corporation

RSEQREP (RNA-Seq Reports) is a new open-source cloud-enabled framework that allows researchers to execute start-to-end RNA-Seq analysis to characterize transcriptomics changes in human cells following treatment. It outputs dynamically generated reports using R and LaTeX. We provide results for a published RNA-Seq study to characterize transcriptomics changes following influenza vaccination.

12:30 Session Break

WuXi_Nextcode_notagline12:40 Luncheon Presentation I: Querying of 100k Genomes Using Google Cloud

Hákon Gudbjartsson, PhD, Chief Informatics Officer, WuXi NextCODE

Hákon Gudbjartsson will demonstrate the power of the GOR database in real time. GORdb is used to organize, mine and share massive genome datasets, providing a global architecture for the largest precision medicine efforts worldwide. It’s designed to enable fast, computationally-efficient use of sequence data, and allows for the query and application of data in the context of reference sets.

1:10 Luncheon Presentation II (Sponsorship Opportunity Available) or Enjoy Lunch on Your Own

1:40 Session Break

OPTIMIZING GENE BASES WITH CODON USAGE

1:50 Chairperson’s Remarks

Leonard Lipovich, PhD, Associate Professor with Tenure, Center for Molecular Medicine and Genetics, Wayne State University

1:55 Analysis of Codon Optimized Therapeutic Proteins Using Ribosome Profiling

Chava Kimchi-Sarfaty, PhD, Research Chemist, Principal Investigator, OTAT Acting Deputy Associate Director for Research, Division of Plasma Protein Therapeutics, Office of Tissues and Advanced Therapies, FDA | CBER | OTAT

Codon optimization is a genetic engineering technique used to improve the yield of recombinant therapeutic proteins. Despite being used ubiquitously to increase protein expression, codon optimization requires widespread substitution of synonymous codons across the native expression sequence. This degree of genetic manipulation can carry consequences, including altered conformation of the recombinant product. These unforeseen modifications can have impacts on protein function and health outcomes, and are of high regulatory importance. To study these techniques, we have used ribosome profiling, a technique used to characterize the translation pattern of the ribosome across the mRNA transcript. In this technique, actively translating ribosomes are cross‐linked to mRNA and is followed by nuclease digestion of mRNA not protected by a ribosome, generating short mRNA fragments (called “ribosome footprints”). These fragments are sequenced and aligned to generate a differential coverage map across portions of the transcript. This technique provides insight into the relative translation efficiency in a given area of the transcript. We have analyzed the ribosome profiling data for relationships to codon usage. By identifying regions of differential ribosome profiling patterns between wild type and codon optimized transcripts, we aim to create a method of selecting regions to leave unmodified, allowing recombinant proteins to benefit from increased expression while maintaining the integrity and safety of the protein product. Codon optimization as a technique relies heavily on accurate codon usage statistics of the organism in question, to identify rare codons to be replaced with common codons for an increase in translation efficiency. However, previous databases containing this information were either outdated or limited in scope. To address this gap in knowledge, we constructed a new database containing codon usage tables for all the species in GenBank and RefSeq. We designed a program in Python to download, parse, and organize all the sequence data available in these two repositories, and in Javascript designed an accessible web portal available to the public to query the new database. The new HIVE‐CUTs database contains substantially more organisms and coding sequence data and is a dramatic improvement upon prior databases. This tool will aid in the effective implementation of codon optimization techniques and other areas of recombinant protein design.

  1. FDA approved 2011-2017 – Therapeutic Proteins by Drug Class: i.e., Oncology , Hematology
  2. Effort on Gene therapy is primarily in Cancer Disease
  3. Factor 9 and Hemophillia B
  4. Codon Optimization to increase Protein expression TECHNIQUES:
  5. Transcription
  6. mRNA
  7. Codon Usage Database – Tables open to the public -9606 (Homo Sapient)
  8. Evaluation of Codon Optimized Factor 9 – Altered Protein Structure Binding Affinity, protein folding
  9. Riboson profile: Protected Profile 20-22 or 24-27 nucleotides [PCR Sequence] Sites: A, P, E
  10. Analysis of Ribosome Profiling Data: Correlations F9 and ACTB and GAPDH – each against Codon Optimizer
  11. In Silico: translation kinetics based solely on calculated codon usage frequency
  12. CONCLUSION:
  • Ribosomal profiling data do not correlate with codon Optimization
  • Genetically engineered therapeutics – benefit from Codon Optimization

 

 

2:25 Multidimensional Global Proteogenomics Identifies Persistent Ribosomal In-Frame Mis-Translation of Stop Codons as Amino Acids in Multiple Open Reading Frames from a Human Breast Cancer Long Non-Coding RNA

Leonard Lipovich, PhD, Associate Professor with Tenure, Center for Molecular Medicine and Genetics, Wayne State University

Two-thirds of the ~60,000 human genes (www.gencodegenes.org) do not encode known proteins, and aside from long non-coding RNA (lncRNA) genes with recently characterized functions, the possibility that these poorly understood genes’ transcripts serve as de-facto unconventional messenger RNAs has not been formally excluded. Our group was the first to use direct evidence from protein mass spectrometry, preceding efforts that employed indirect evidence from ribosome profiling, to demonstrate that specific lncRNAs are recurrently and nonrandomly translated in human cells (Bánfai et al 2012, Genome Research 22:1646-1657). In our current study, we integrated RNAseq, ribosome profiling, and mass spectrometry to globally assess lncRNA translation in human estrogen receptor alpha positive MCF7 breast cancer cells. We identified 27 peptides, mapping to multiple sense-strand open reading frames (ORFs) of the lncRNA gene MMP24-AS1, united by a novel and highly unconventional property: the existence of these peptides can only be explained by stop-to-nonstop in-frame replacements of specific UAG and UGA (but not UAA) stop codons by amino acids. This result, validated by the absence of any genomic mutations, polymorphisms, and RNA editing events in genomic and cDNA targeted resequencing, represents an unprecedented apparent gene-specific violation of the Genetic Code in human breast cancer cells, and hints at a new mechanism enhancing the combinatorial complexity of the cancer proteome.
[Note 1: This work has been funded in its entirety by the NIH Director’s New Innovator Award 1DP2-CA196375 to LL.]
[Note 2: This project encompasses collaborations. A full listing of co-authors will be shown during the talk.]

  • LncRNA
  • ENCODE –
  • WG 6-frame tRanslation + mass spectrometric data {mass specriptom] = empirical redefinition of the genomic sequencing field
  • MMP24 maps – Breast Cancer
  • Mechanism: MisTranslation – Translation Infidelity: why are only UAG and UGA, never UAA, reference-genome stop codons are affected

 

2:55 CO-PRESENTATION: Workflow Optimization for NGS Discovery – How to Drive BIX Insights

Jack DiGiovanna, PhD, General Manager, NGS Applications and Services, Seven Bridges Genomics (->2009) 250 CS

Isaac M. Neuhaus, PhD, Director, Computational Genomics, Bristol Myers Squibb

  • Predict immuno-oncoogy outcomes
  • Biomarkers
  • Microsatellite Instability (MSI) – short tanden repeats of 1 to 6 base-pairs: Detections of MSI mutations in somatic variants, Profiling
  • Whole Exome gene data
  • Companion diagnostics
  • Colorectal adenocarcinomas with MSI status
  • Validating predictions
  • Tumor Heterogeneity: Clones have different potentials to metastasize
  • Heterogeneity (purity) work flow & Validation – Variant Allele Frequency
  • MSI sensor score: Benchmarking MSIsensor vs Tumor Purity
  • Clinical MSI data

 

3:25 Refreshment Break in the Exhibit Hall with Poster Viewing

NGS DATA ANALYSIS, INTEGRATION, INTERPRETATION, AND VISUALIZATION

4:00 Variant Query Tool: Drag & Drop for a Scalable, Server-Less, Web UI to Querying Annotated Variants

William Van Etten, Senior Scientific Consultant, BioTeam

It’s a challenge to build an environment that provides real-time querying of reads and annotated variants for genomics research, requiring significant human and computational resources. Whether tens or thousands of genomes, the barrier to entry can be high for the biologists/geneticist, who might not also be computer scientist. BioTeam has developed a simple tool that leverages several AWS services (S3, Athena, Lambda, Cognito, IAM, CloudWatch) to enable a biologists/geneticist to drag & drop VCF and BAM files onto an S3 bucket, then point their web browser at this bucket, to provide a scalable, server-less, web UI to querying the reads and annotated variants within these files. We aim to demonstrate, explain, and promote what we’ve learned from this proof of concept software development in the hope that others might benefit from our experience.

http://vqt.bioteam.net

  • Amazon Athena API Introduced – Variant Query Tool – Server-less

 

 4:30 Building a GXP Validated Platform for NGS Analysis Pipelines

Anthony Rowe, PhD, Business Technology Leader, R&D IT, Janssen R&D LLC

An NGS applications approach the clinic the bioinformatics pipelines used to analyze the data have to be validated to demonstrate their correctness. This talk will present Janssen approach to deploying validated NGS applications with specific focus in microbiome metagnomics.

  • IBD: UlcerativeColitis  (UC), Crohn’s DIsease (CD)
  • $11Bil – $28Billion – Cost burden in Health Care systems
  • J&J and Vedanta announced a collaboration DNAnexus 
  • Microbiome based approach for IBD: Gut Dysbiosis, beneficial microbes symbionts, Pathogenesis
  • Jeanssen developing a new platform for DIsease therapy
  • How to analyze microbiome data as drug?
  • Stelara Biotherapeutics – 27 microbes
  • PK PD of antigen therapy
  • Biotherapeutic product like VE202 –
  • Can VE202 be detected in stool
  • Real Time NGS – The Clinical Novel NSG  for Clinical Trials – Emerging Science meets Regulated Science
  • Emerging Science: Novel NGS Informatics
  • Tool Box for Microbiome: Biomathematicians carried workflow to Clinical Trials
  • Computational workflow: Step 1: alienment 1,2,3,4
  • 7 samples 4 tools: Run Time cost result quality
  • Quality Control: for Clinical NGS Platform:Manufacturing, Software QA,
  • current system Overview: sequencing Vendors prtnerships
  • Establish scientific ladscape and a structured drug development process

Sapio Sciences

 

5:00 LIMS or ELN, Which Do You Need? [Electronic Lab Notebook]

Kevin Cramer, CEO, Sapio Sciences (->2007)

Both Biotech and Pharma need Laboratory Information Management (LIMS) and Electronic Lab Notebook (ELN) capabilities. Sapio has eliminated the barriers between these two product areas by leveraging its more than decade of unique experience offering both LIMS and ELN solutions and combining the key features of each solution into one, best of breed, product: Exemplar ELN Pro.

  • Configurable data model for LIMS Platform
  • LIMS 1.0 Configure Data Model
  • LIMS 2.0 Workflow enginw for tracking complex processes
  • NGS – 2008 Celexa,
  • ELN – 2015 Exemplar support ad Hoc experimentation, clean sheet design
  1. create RQS for Samples
  2. Assign processes
  3. Track progres
  4. register Samples
  5. Register plates Aliquoting Define Storage
  6. Graphical Assignment
  7. register consumables
  • ELN: Spreadsheets, Office Integration, Drag & Drop experiment items, Curve fitting, R Stat, Charting/visualization
  • Author/witness/Reviewer/Approver Accept/reject attach instrument design
  • ELN, LIMS, ELN Pro[fessional]
  • ELN Pro: ELN plus LIMS properties
  • Exemplar ELN Pro: storage mgm

Sapio Sciences – Exemplar ELN Pro

  • Integrate collaboration,charting tools
  • global repository
  • Chemaxon Integration
  • Prebuilt NGS pipelines out of the box

 

 

5:15 Sponsored Presentation (Opportunity Available)

5:30 Best of Show Awards Reception in the Exhibit Hall with Poster Viewing

7:00 – 10:00 Bio-IT World After Hours @Lawn on D

THURSDAY, MAY 17

7:30 am Registration Open and Morning Coffee

8:00 PLENARY KEYNOTE SESSION & AWARDS PROGRAM

Click here for detailed information

9:45 Coffee Break in the Exhibit Hall and Poster Competition Winners Announced

 

APPLICATION OF NGS TO ONCOLOGY, IMMUNOLOGY, DIAGNOSTICS, AND THERAPEUTIC DEVELOPMENT

10:30 Chairperson’s Remarks, Bruce Press, EVP Seven Bridges Genomics

10:40 Instantiating a Single Point of Truth for Genomic Reference Data

David Herzig, Scientist, Research Informatics, Roche Pharmaceuticals

This talk will exemplify how expression and mutation data were made actionable by consolidating a scattered landscape of genomic reference data into a real SPoT.

  • Common System Landscapes
  1. Data Sources
  2. Silo Solutions
  3. API
  4. Consumers: Bioinformatics, Data Science, JBrowser
  • Single Source of Truth
  1. Moving away from Silo solutions
  2. Roche Data Commons

Physical HW

File SYstem & Workflow

Single Point of Truth SSOT

Integration Data Mart

UI

All data goes into Data Storage API as input and Output from the data storage

Evaluation

  • inhouse development
  • customization of open source
  • Ensembl
  1. Genomics Reference Data manu species
  2. Multi species DB [stable ID] MySQL DB is been used
  3. API & SW: REST, Tools, Web Code
  4. Modules – Variation, Funcgen, other features CORE – genome annotation
  5. SOLUTION – 150 hours = Homo-sapiens Variation is the most time consuming Ensebl REST API Endpoints
  6. Customization: NCBI: DOwnload, Unzip data – Import data – Ensembl PERL Script goes into SPoT (SSoT)
  7. Into CORE  – only species of interestLoading Log – UPDATE META TABLE (Roche Data from BioInformatics Dept)
  8. SSOT & Arvados – 2 updates a year, 5 versions are available in parallel: Portal Page, latest version: http://genomes.roce.com:3091 http://genomes.roche.com/latest 
  9. USE CASES: Comparative Genomics

 

11:10 A Network-Based Approach to Understanding Drug Toxicity

Yue Webster, PhD, Principal Research Scientist, Informatics Capabilities, Research IT, Eli Lilly and Company

Despite investment in toxicogenomics, nonclinical safety studies are still used to predict clinical liabilities for new drug candidates. Network-based approaches for genomic analysis help overcome challenges with whole-genome transcriptional profiling using limited numbers of treatments for phenotypes of interest. Herein, we apply co-expression network analysis to safety assessment using rat liver gene expression data to define 415 modules, exhibiting unique transcriptional control, organized in a visual representation of the transcriptome. Compared to gene-level analysis alone, the network approach identifies significantly more phenotype-gene associations, including established and novel biomarkers of liver injury.

  • Phase III Clinical Trials fail due to Drug Toxicity – TXG – MAP
  • Food preservative BHA
  • antibiotic TB patient Trecator – like Tunicamycin
  • blood thinner – Ticlid
  • n-dimensional problem space for Toxicity:Gene Expression COmplexity DYnamic COmplexity, pathophysiology complexity
  • TRANSLATION: from Animal to Human Clinical Trials – Failure of Clinical Trial equals to failure of the TRANSLATION
  • Modules in DNA and RNA
  • Protein structure
  • Reduce dimensionality of the information space
  • Changes of patterns – risk assessment of the confidence in translation
  • Gene vs System-level View – Tunicamycin – image TXG – MAP = unsupervised approach to convert a table of data into ONE image
  • How to build TXG – MAP: Genotype and Phenotype – for Predictions of Untargeted Effects, recomendations
  1. Data Input
  2. Training set – DrugMatrix
  3. Algorithm – 415 co-expression module
  4. Interpretation (Gene ontology)
  • use TXG – MAP for adaptive response: Measure changes in Biological processes usinf eigengene scores
  • TXG – MAP to compare Treatments: Apoptosis post treatment with Tunicamycin Red Induced expression Green supressed expression
  • Hypetrophy caused by antibiotic Tunicamycin
  • Building TXG – MAP – for sharing with Scientific Community across species to be used in Translation Research for preservation across cell lines, across species and for translation to Humans

11:40 Michael Rusch , Dir Bioinformatics, St. Jude Cloud

  • 2017 Genomic Test is ORDERABLE >400 patients as of May 2018
  • 300 approved access requests globally – PCGP Data Sharing – 8 attempts to download have failed once
  • Solution: 2015 Cloud: Secure, sustainable, expandable
  • SW development Partner DNAnexus on Microsoft ADURE in 2017, St.Jude CLoud
  • 3000 pediatric cancer survivors – Optimize therapy to improve quality of life
  • Simple Data Access Procedure : data securely into private cloud
  • Gene fusions – Turnaround Time Challenge – Assay – 42 days – Leukemia RNA Seq workflow 15 days to get seq done
  • Cost $5-$10 per sample running 5-8 days seq, manual Review and Reporting 20 Minutes
  • most runs completed in 12 hours
  • Variant annotation, pathogenicity: Germ line Mutations and Pediatric Cancer NEJM, Journal Pediatric Oncology
  • Recan PIE – Pathogenicity Information Exchange (PIE) for SNV/Indel Classification
  • Results overview Variant page: Gene info Protein Paint, Gene ingo
  • damage prediction algorithm – ACMG classification Tool: Variant page
  • 100 Registered users
  • 425 jobs
  • 340,000 variants
  • VisualizationProtein paint, PCGP Mutation: SOmatic and Germ line Pathogenic and Likely Pathogenic Variants

https://stjude.cloud

 

11:40 Sponsored Presentation (Opportunity Available)

12:10 pm Session Break

12:20 Luncheon Presentation (Sponsorship Opportunity Available) or Enjoy Lunch on Your Own

1:20 Dessert Refreshment Break in the Exhibit Hall with Poster Viewing

DATA MINING FOR DISEASE CLASSIFICATION – CITYVIEW I

1:55 Chairperson’s Remarks

John Methot, Director, Health Informatics Architecture, Dana-Farber Cancer Institute

2:00 Disease Classification in the Era of Data-Intensive Medicine

Kanix Wang, PhD, Research Professional, Booth School of Business, Institute for Genomics & Systems Biology, University of Chicago

We used insurance claims for over one-third of the U.S. population to create a subset of 128,989 families (481,657 unique individuals). Using these data, we estimated the heritability and familial environmental patterns of 149 diseases. We then computed the environmental and genetic disease classifications for a set of 29 complex diseases after inferring their pairwise genetic and environmental correlations.

2:30 Enviro-Geno-Pheno State Approach and State-Based Biomarkers for Differentiation, Prognosis, Subtypes, and Staging

Lei Xu, PhD, Director, Centre for Cognitive Machines and Computational Health; Zhiyuan Chair Professor, Department of Computer Science and Engineering, Shanghai Jiao Tong University

In the joint space of geno-measures, pheno-measures, and enviro-measures, one point represents a bio-system behavior and a subset of points that locate adjacently and share a common system status represents a ‘state’. The system is characterized by such states learned from samples. This enviro-geno-pheno state is considered a biomarker, indicating ‘health/normal’ versus ‘risk/abnormal’ together with its associated enviro-geno-pheno condition.

3:00 PANEL DISCUSSION: Can We Improve Breast Cancer Patient Outcomes through Artificial Intelligence?

Maya Said, ScD, President & CEO, Outcomes4me, Inc. (Moderator)

Panelists:
Regina Barzilay, PhD, MacArthur Fellow and Delta Electronics Professor, Massachusetts Institute of Technology (MIT) Department of Electrical Engineering and Computer Science; Member, Computer Science and Artificial Intelligence Laboratory, MIT

Kevin Hughes, MD, Co-Director, Avon Breast Evaluation Program, Massachusetts General Hospital; Associate Professor of Surgery, Harvard Medical School; Medical Director, Bermuda Cancer Genetics Risk Assessment Clinic

Newly diagnosed cancer patients attempting to understand their treatment options face the overwhelming task of filtering an information deluge, much of which is irrelevant, outdated and occasionally inaccurate. Additionally, matching their diagnosis to best-in-class treatments or potential clinical trials, while simultaneously learning to navigate an extremely complex healthcare system is daunting, even for the most highly trained physicians. We will explore various platforms aimed at improving patient outcomes by leveraging technology to help educate, track, and connect patients with personalized resources while simultaneously working to improve the care continuum and the development of new treatments. We will explore the nexus of healthcare networks and their IT systems, clinical decision-making and delivery, R&D, and patients, for whom we all create our innovation solutions. Attendees will be interested to understand how various groups are working to increase value across the entire system by bringing laboratory, clinical and pharmaceutical science, real-world evidence and patient-reported data together with technology and artificial intelligence to solve health challenges. These approaches offer the opportunity to generate deeper insights into how therapies perform in the real world and harness that understanding to improve efficiency, effectiveness, value, and ultimately, patient care.

  • Targeted Therapy in Breast Cancer more than another diseases

4:00 Conference Adjourns

SOURCE

http://www.bio-itworldexpo.com/next-gen-sequencing-informatics/

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International Award for Human Genome Project

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

The Thai royal family awarded its annual prizes in Bangkok, Thailand, in late January 2018 in recognition of advances in public health and medicine – through the Prince Mahidol Award Foundation under the Royal Patronage. This foundation was established in 1992 to honor the late Prince Mahidol of Songkla, the Royal Father of His Majesty King Bhumibol Adulyadej of Thailand and the Royal Grandfather of the present King. Prince Mahidol is celebrated worldwide as the father of modern medicine and public health in Thailand.

 

The Human Genome Project has been awarded the 2017 Prince Mahidol Award for revolutionary advances in the field of medicine. The Human Genome Project was completed in 2003. It was an international, collaborative research program aimed at the complete mapping and sequencing of the human genome. Its final goal was to provide researchers with fundamental information about the human genome and powerful tools for understanding the genetic factors in human disease, paving the way for new strategies for disease diagnosis, treatment and prevention.

 

The resulting human genome sequence has provided a foundation on which researchers and clinicians now tackle increasingly complex problems, transforming the study of human biology and disease. Particularly it is satisfying that it has given the researchers the ability to begin using genomics to improve approaches for diagnosing and treating human disease thereby beginning the era of genomic medicine.

 

National Human Genome Research Institute (NHGRI) is devoted to advancing health through genome research. The institute led National Institutes of Health’s (NIH’s) contribution to the Human Genome Project, which was successfully completed in 2003 ahead of schedule and under budget. NIH, is USA’s national medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases.

 

Building on the foundation laid by the sequencing of the human genome, NHGRI’s work now encompasses a broad range of research aimed at expanding understanding of human biology and improving human health. In addition, a critical part of NHGRI’s mission continues to be the study of the ethical, legal and social implications of genome research.

 

References:

 

https://www.nih.gov/news-events/news-releases/human-genome-project-awarded-thai-2017-prince-mahidol-award-field-medicine

 

http://www.mfa.go.th/main/en/news3/6886/83875-Announcement-of-the-Prince-Mahidol-Laureates-2017.html

 

http://www.thaiembassy.org/london/en/news/7519/83884-Announcement-of-the-Prince-Mahidol-Laureates-2017.html

 

http://englishnews.thaipbs.or.th/us-human-genome-project-influenza-researchers-win-prince-mahidol-award-2017/

 

http://genomesequencing.com/the-human-genome-project-is-awarded-the-thai-2017-prince-mahidol-award-for-the-field-of-medicine-national-institutes-of-health-press-release/

 

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Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

A mutated gene called RAS gives rise to a signalling protein Ral which is involved in tumour growth in the bladder. Many researchers tried and failed to target and stop this wayward gene. Signalling proteins such as Ral usually shift between active and inactive states.

 

So, researchers next tried to stop Ral to get into active state. In inacvtive state Ral exposes a pocket which gets closed when active. After five years, the researchers found a small molecule dubbed BQU57 that can wedge itself into the pocket to prevent Ral from closing and becoming active. Now, BQU57 has been licensed for further development.

 

Researchers have a growing genetic data on bladder cancer, some of which threaten to overturn the supposed causes of bladder cancer. Genetics has also allowed bladder cancer to be reclassified from two categories into five distinct subtypes, each with different characteristics and weak spots. All these advances bode well for drug development and for improved diagnosis and prognosis.

 

Among the groups studying the genetics of bladder cancer are two large international teams: Uromol (named for urology and molecular biology), which is based at Aarhus University Hospital in Denmark, and The Cancer Genome Atlas (TCGA), based at institutions in Texas and Boston. Each team tackled a different type of cancer, based on the traditional classification of whether or not a tumour has grown into the muscle wall of the bladder. Uromol worked on the more common, earlier form, non-muscle-invasive bladder cancer, whereas TCGA is looking at muscle-invasive bladder cancer, which has a lower survival rate.

 

The Uromol team sought to identify people whose non-invasive tumours might return after treatment, becoming invasive or even metastatic. Bladder cancer has a high risk of recurrence, so people whose non-invasive cancer has been treated need to be monitored for many years, undergoing cystoscopy every few months. They looked for predictive genetic footprints in the transcriptome of the cancer, which contains all of a cell’s RNA and can tell researchers which genes are turned on or off.

 

They found three subgroups with distinct basal and luminal features, as proposed by other groups, each with different clinical outcomes in early-stage bladder cancer. These features sort bladder cancer into genetic categories that can help predict whether the cancer will return. The researchers also identified mutations that are linked to tumour progression. Mutations in the so-called APOBEC genes, which code for enzymes that modify RNA or DNA molecules. This effect could lead to cancer and cause it to be aggressive.

 

The second major research group, TCGA, led by the National Cancer Institute and the National Human Genome Research Institute, that involves thousands of researchers across USA. The project has already mapped genomic changes in 33 cancer types, including breast, skin and lung cancers. The TCGA researchers, who study muscle-invasive bladder cancer, have looked at tumours that were already identified as fast-growing and invasive.

 

The work by Uromol, TCGA and other labs has provided a clearer view of the genetic landscape of early- and late-stage bladder cancer. There are five subtypes for the muscle-invasive form: luminal, luminal–papillary, luminal–infiltrated, basal–squamous, and neuronal, each of which is genetically distinct and might require different therapeutic approaches.

 

Bladder cancer has the third-highest mutation rate of any cancer, behind only lung cancer and melanoma. The TCGA team has confirmed Uromol research showing that most bladder-cancer mutations occur in the APOBEC genes. It is not yet clear why APOBEC mutations are so common in bladder cancer, but studies of the mutations have yielded one startling implication. The APOBEC enzyme causes mutations early during the development of bladder cancer, and independent of cigarette smoke or other known exposures.

 

The TCGA researchers found a subset of bladder-cancer patients, those with the greatest number of APOBEC mutations, had an extremely high five-year survival rate of about 75%. Other patients with fewer APOBEC mutations fared less well which is pretty surprising.

 

This detailed knowledge of bladder-cancer genetics may help to pinpoint the specific vulnerabilities of cancer cells in different people. Over the past decade, Broad Institute researchers have identified more than 760 genes that cancer needs to grow and survive. Their genetic map might take another ten years to finish, but it will list every genetic vulnerability that can be exploited. The goal of cancer precision medicine is to take the patient’s tumour and decode the genetics, so the clinician can make a decision based on that information.

 

References:

 

https://www.ncbi.nlm.nih.gov/pubmed/29117162

 

https://www.ncbi.nlm.nih.gov/pubmed/27321955

 

https://www.ncbi.nlm.nih.gov/pubmed/28583312

 

https://www.ncbi.nlm.nih.gov/pubmed/24476821

 

https://www.ncbi.nlm.nih.gov/pubmed/28988769

 

https://www.ncbi.nlm.nih.gov/pubmed/28753430

 

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Cracking the Genome – Inside the Race to Unlock Human DNA – quotes in newspapers

Reporter: Aviva Lev-Ari, PhD, RN

 

Cracking the Genome

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Cracking the Genome

Inside the Race to Unlock Human DNA

In 1953, James Watson and Francis Crick unveiled the double helix structure of DNA. The discovery was a profound moment in the history of science, but solving the structure of the genetic material did not reveal what the human genome sequence actually was, or what it says about who we are. Cracking the code of life would take another half a century.

In 2001, two rival teams of scientists shared the acclaim for sequencing the human genome. Kevin Davies, founding editor of Nature Genetics, has relentlessly followed the story as it unfolded week by week since the dawn of the Human Genome Project in 1990. Here, in rich human and scientific detail, is the compelling story of one of the greatest scientific feats ever accomplished: the sequencing of the human genome.

In brilliant, accessible prose, Davies captures the drama of this momentous achievement, drawing on his own genetics expertise and on interviews with the key scientists. Davies details the fraught rivalry between the public consortium, chaperoned by Francis Collins, and Celera Genomics, directed by sequencer J. Craig Venter. And in this newly updated edition, Davies sheds light on the secrets of the sequence, highlighting the myriad ways in which genomics will impact human health for the generations to come.

Cracking the Genome is the definitive, balanced account of how the code that holds the answer to the origin of life, the evolution of humanity, and the future of medicine was finally broken.

Kevin Davies is the founding editor of Nature Genetics and is currently editor-in-chief of Bio•IT World. He graduated from Oxford University and holds a Ph.D. in genetics from the University of London.

“For an up-to-the-minute account of one of the most dramatic periods in present-day science, Cracking the Genome is an essential read.”

“A superb job… A tantalizing glimpse of the ethical perils and technological possibilities awaiting humanity.”

“A rollicking good tale about an enduring intellectual monument.”

“The race is over, and Davies was there, all along, providing the running commentary—and there, too, at the finish line. In Cracking the Genome, he hands out the prizes.”

“Davies has tracked one of the most important stories ever to unfold. Davies helps readers understand how the deciphering of our genetic code will revolutionize our lives while posing serious ethical dilemmas.”

“An impressive job of contextualizing the science within a political, economic, and social framework, creating a lively tale as accessible to non—specialists as it is to scientists.”

“Investors and others looking for a quick primer on the science and business of biotechnology will find this a useful guide.”

“In Davies’ prose, this story of molecular biology and the Human Genome Project is as compelling as any Arthurian legend. In a fast-moving approachable style, Davies captures the uncovering of biology’s Holy Grail, relying on his own expertise in genetics and interviews with key players such as Collins and Venter.”

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SNP-based Study on high BMI exposure confirms CVD and DM Risks – no associations with Stroke

Reporter: Aviva Lev-Ari, PhD, RN

Genes Affirm: High BMI Carries Weighty Heart, Diabetes Risk – Mendelian randomization study adds to ‘burgeoning evidence’

by Crystal Phend, Senior Associate Editor, MedPage Today, July 05, 2017

 

The “genetically instrumented” measure of high BMI exposure — calculated based on 93 single-nucleotide polymorphisms associated with BMI in prior genome-wide association studies — was associated with the following risks (odds ratios given per standard deviation higher BMI):

  • Hypertension (OR 1.64, 95% CI 1.48-1.83)
  • Coronary heart disease (CHD; OR 1.35, 95% CI 1.09-1.69)
  • Type 2 diabetes (OR 2.53, 95% CI 2.04-3.13)
  • Systolic blood pressure (β 1.65 mm Hg, 95% CI 0.78-2.52 mm Hg)
  • Diastolic blood pressure (β 1.37 mm Hg, 95% CI 0.88-1.85 mm Hg)

However, there were no associations with stroke, Donald Lyall, PhD, of the University of Glasgow, and colleagues reported online in JAMA Cardiology.

The associations independent of age, sex, Townsend deprivation scores, alcohol intake, and smoking history were found in baseline data from 119,859 participants in the population-based U.K. Biobank who had complete medical, sociodemographic, and genetic data.

“The main advantage of an MR approach is that certain types of study bias can be minimized,” the team noted. “Because DNA is stable and randomly inherited, which helps to mitigate errors from reverse causality and confounding, genetic variation can be used as a proxy for lifetime BMI to overcome limitations such as reverse causality and confounding, a process that hampers observational analyses of obesity and its consequences.”

 

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Genomic Diagnostics: Three Techniques to Perform Single Cell Gene Expression and Genome Sequencing Single Molecule DNA Sequencing

Curator: Aviva Lev-Ari, PhD, RN

 

This article presents Three Techniques to Perform Single Cell Gene Expression and Genome Sequencing Single molecule DNA sequencing

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The BioPharma Industry’s Unrealized Wealth of Data, by Ben Szekely, Vice President, Cambridge Semantics

Reporter: Aviva Lev-Ari, PhD, RN

 

 

The BioPharma Industry’s Unrealized Wealth of Data

by Ben Szekely, Vice President of Solutions and Pre-sales, Cambridge Semantics

 

Solving the great medical challenges of our time reside within patient data. Clinical trial data, real-world evidence, patient feedback, genetic data, wearables data and adverse event reports contain signals to target medicines at the right patient populations, improve overall safety, and uncover the next blockbuster therapy for unmet medical needs.

However, data sources are large, diverse, multi-structured, messy and highly regulated presenting numerous challenges. As result, extracting value from data are slow to come and require manual work or long-poll dependencies on IT and Data Science teams.

Fortunately, there are new ways being adopted to take better advantage of the ever-growing volumes of patient data.  Called ‘Smart’ Patient Data Lakes (SPDL), these tools create an Enterprise Knowledge Graph built upon foundational and open Semantic Web technology standards, providing rich descriptions of data and flexibility end-to-end.  With the SPDL, biopharma researchers can:

  • Quickly on-board new data without requiring up-front modeling or mapping, ingesting data from any source versus months or weeks of preparation
  • Dynamically map and prepare data at analytics time
  • Horizontally scale in cloud or on-prem infrastructure to 100’s of nodes – allowing billions of facts to be analyzed, queried and explored in real-time   

The world’s BioPharma and research institutions are sitting on a wealth of highly differentiating and life-saving data and should begin to realize its value via Smart Patient Data Lakes (SPDL).

 

 

CONTACT: Nadia Haidar

Global Results Communications ∙ 949-278-7328 ∙ nhaidar@globalresultspr.com

 

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