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Ido Bachelet, who was previously at Harvard’s Wyss Institute in Boston, Massachusetts and Israel’s Bar-Ilan University, intends to treat a patient who has been given six months to live. The patient is set to receive an injection of DNA nanocages designed to interact with and destroy leukemia cells without damaging healthy tissue. Speaking in December, he said: ‘Judging from what we saw in our tests, within a month that person is going to recover.

 

DNA nanocages can be programmed to independently recognize target cells and deliver payloads, such as cancer drugs, to these cells. 

George Church, who is involved in the research at the Wyss Institute explained the idea of the microscopic robots is to make a ‘cage’ that protects a fragile or toxic payload and ‘only releases it at the right moment.’

 

These nanostructures are built upon a single strand of DNA which is combined with short synthetic strands of DNA designed by the experts.  When mixed together, they self-assemble into a desired shape, which in this case looks a little like a barrel.

 

Dr Bachelet said: ‘The nanorobot we designed actually looks like an open-ended barrel, or clamshell that has two halves linked together by flexible DNA hinges and the entire structure is held shut by latches that are DNA double helixes.’

 

A complementary piece of DNA is attached to a payload, which enables it to bind to the inside of the biological barrel. The double helixes stay closed until specific molecules or proteins on the surface of cancer cells act as a ‘key’ to open the ‘barrel’ so the payload can be deployed.

 

‘The nanorobot is capable of recognizing a small population of target cells within a large healthy population,’ Dr Bachelet continued.

‘While all cells share the same drug target that we want to attack, only those target cells that express the proper set of keys open the nanorobot and therefore only they will be attacked by the nanorobot and by the drug.’

 

The team has tested its technique in animals as well as cell cultures and said the ‘nanorobot attacked these [targets] with almost zero collateral damage.’ The method has many advantages over invasive surgery and blasts of drugs, which can be ‘as painful and damaging to the body as the disease itself,’ the team added.

Source: www.dailymail.co.uk

See on Scoop.itCardiovascular Disease: PHARMACO-THERAPY


Future timeline, a timeline of humanity’s future, based on current trends, long-term environmental changes, advances in technology such as Moore’s Law, the latest medical advances, and the evolving geopolitical landscape.

 

10TB solid state drives may soon be possibleConsumer virtual reality will grow exponentially 200GB microSD card announced by SanDisk”The Vive” – new VR headset being developed by HTC and ValveTesco becomes first UK retailer to launch a Google Glass-enabled serviceLaying the foundations for 5G mobileClothes that can monitor and transmit biomedical info3-D haptic shapes can be seen and felt in mid-airAI software can identify objects in photos and videos at near-human levelsDARPA circuit achieves speed of 1 terahertz (THz)3D printer which is 10 times faster than current modelsCreating DNA-based electrical circuitsWi-Fi up to five times faster coming in 2015Long-distance virtual telepathy is demonstratedThe Internet of Things: A Trillion Dollar MarketBrain-like supercomputer the size of a postage stampProject Adam: a new deep-learning system

Source: www.futuretimeline.net

See on Scoop.itCardiovascular and vascular imaging


LIVE — April 23, 1:55PM – CLINICAL UTILITY OF GENOME VARIATION  @ Cambridge HealthTech Institute’s 14th Annual Meeting BioIT World – Conference & Expo ’15, April 21 – 23, 2015 @Seaport World Trade Center, Boston, MA

Dr. Aviva Lev-Ari will be in attendance on April 21, 22, 23

 

Leaders in Pharmaceutical Business Intelligence (LPBI) are Covering the Event in REALTIME using Social Media

http://pharmaceuticalintelligence.com

#biotech

#investment

@MassBio

@BostonBizJournl

@BioWorld

@BiotechNews

@bioitworld

CLINICAL UTILITY OF GENOME VARIATION 

1:55 Chairperson’s Remarks

Louis Fiore, M.D., MPH, Executive Director, MAVERIC, Research, Veterans Affairs Boston Healthcare System

450,000 Veterans DNA Sequenced on Illimina Chip customized with PTSD information at Veterans Affairs Boston Healthcare System, all SNPs recorded

CANCELLED — 2:00 Striking the Right Balance in Clinical Interpretation of Genomes

Elizabeth Worthey, Ph.D., Assistant Professor, Pediatrics; Director, Genomic Informatics, Human and Molecular Genetics Center, Medical College Wisconsin

I provide a review of the 2014 lessons learned in our molecular diagnostic lab and the challenges and opportunities for 2015. It focuses on the end-to-end solution for clinical genomics we have implemented, including platforms developed for genomic medicine clinical decision support. It also discusses how we integrate this data into the practice of medicine in our genomic medicine clinics, providing specific case examples.

2:30 Epigenetic Profiling of DNA Methylation to Identify Breast Tumor Aggressiveness

Adam Marsh, Ph.D., Professor, Center for Bioinformatics and Computational Biology, University of Delaware; CSO, Genome Profiling, LLC

Women with triple-negative genotypes (i.e., normal for the three common marker mutations for breast cancer) are still at risk for developing aggressive breast tumors. We identify a suite of differentially methylated CpG sites between normal and tumor breast tissues using NGS that indicate a high degree of epigenetic conservation among different triple-negative patients who have developed advanced-stage breast tumors. Subtle epigenetic shifts in methylation status may provide a key line of evidence for assessing tumor risk and informing therapy decisions between surgery or versus noninvasive treatments.

LIVE

How the Polar environment affect low metabolism (cold adaptation), epigenetics and tumor aggresivity in Breast Cancer

  1. Clinical Utility of Genome Variation
  • Human vs Monkey
  • Human vs Human
  • Intra – Human variation
  1. Epigenetic DNA Methylation: Phynotype diversity
  • Human Genome (Cytosine methylation)
  • Intra-individual variation

Epigenetic Profiling Platform

  • Triple Negative Breast Cancer (TNBC)
  • Negative on HER-2
  • Negative on Estrogen Receptor (ER)
  • Negative on Progesteron Receptor (PR)

5mC: Unique Quantification Metrics

  • Gardient MET score
  • Mixed state CpG

Analytic Comparison of methylation States

Unique Gene Scoring Strategy

Comparative Changes for functional

Blood Study – NGS gDNA Method Comparison

  1. Parkinson Samples
  2. Methylation Profile: Bisulfitet Oxidation vs GenPro Prep
  3. 3 early-onset Parkinson
  4. 3 Normal population
  5. Coriell BioBnk’Yale: preparation sequencing
  6. Cornell: Epigenomics
  7. Comparison of Mean Methylation by two Methods: GenPro and …..

Potential Epigenetic Blood Biomarkers: Parkinson vs Normal – DNA Methylation Patterns — different

TOP 10 KEGG Groups Parkinson vs Normal

  • lymphocytes
  • Neurodegenerative

Tumor Study  – Breast Tumor Profiling – Normal tissue vs Tumor Tissue – CpG SItes: Methylation gain, loss and no change = the majority

COMBINED PATIENT/SSAMPLE ANALYSES – TISSUE RESPONSE ACROSS PATIENTS

  • Genome Profiling: COMPARISONS ACROSS GENES AND PATHWAYS
  • Validation

Conclusions

In TNBC tumors – mapping methylation changes back to functional genes reveals large variation

SIgnificance

  • 8%-10 of masectomies are therapeutically necessary — but only via post-op pathology
  • Over diagnosis of Breast Cancer, ERplus

3:00 Establishing Clinical-Grade RNA Sequencing

Sheng Li, Ph.D., Instructor, Bioinformatics, Neurological Surgery, Weill Cornell Medical College

High-throughput sequencing drastically expands the potential for large-scale whole transcriptome profiling of clinical samples for disease monitoring and diagnosis. Here we established standard approach and analysis methods and benchmark datasets for evaluation of RNA-seq performance of different platforms, protocols and various qualities of input materials.

LIVE

Establishing Clinical-Grade RNA Sequencing – RNA and DIagnosis DIsease

  • HIV RNA Test
  • AlloMap — rejection of Heart transplantation
  • oncoTypeDX — one single assay
  • SOurce of the wiggles – Fetal Hypothulmus

INTEGRITY OF RNA SAMPLES – depends on many factors

  • aldorithms for alignment and assembly
  • rna fragmentation
  • technology bifurcation

NEED for RNA Standards

Phase 1: Inter-Performance: Error highly variable amonf platform and daligners

  • Samples A: Stragene vs Sample B
  • Asso of biomolecular Research Facilities – NGS Study
  • Nature Biotechnology and FDA Sequencing Quality Control (SeQC) COnsortium
  • Roche
  • Illimina — MOST RELIABLE for Isoforms
  • Ion Torrent
  • Pacific BioSciences –
    • highesr base efficient of junction detection efficiency is the highest for Pacific BioSciences

Inter-platform differential gene expression show 88%-97% agreement

Phase II InterProtocol  of Illumina 

Gene regions distribution varies betweenprotocols

  • Intergenic
  • Exon
  • Mean FPKM differences

HIGH EXPRESSOR: PolyA and RING – PolyA is higher

Phase III: Degraded RNA

Full length genebodt coverage varied between preparation methods

Illumina Ribo-depletion protocol: Degraded RNA

SUMMARY

Inter-platform performance

Consortia: NIST, ABRF, FDA, ENCODE – Standards set up for RNA Studies

3:30 The Department of Veterans Affairs Precision Oncology Program: The Crossroads of Clinical Care and Research

Louis Fiore, M.D., MPH, Executive Director, MAVERIC, Research, Veterans Affairs Boston Healthcare System

This presentation describes a model for creation of “Learning Healthcare Systems” through integration of a clinical precision oncology program with a tailored research program that leverages and augments the clinical investment. Databases and applications that support clinical trial matching, capture of patient reported outcomes, clinician collaboration and patient outcome prediction will be discussed.

LIVE

MAVERIC – founded in 1997 120 person VA System for 150 Hospitals – SINGLE PAYER (VA Ssytem)

  • Clinical – MDs
  • Hospital Adm
  • Clinical Research
  • Pharma

INTEGRATING EMR WITH CLINICAL OUTCOME

  • Point of Care
  • OMICS data at VA – at the POC – Actionable Results
  • Precision Medicine in the VA
  1. 350,000 enrolled for DNA Sequencing – Million Veteran

LUNG CANCER @ VA – What mutation is the driver – Sequence Tumor

PRECISION ONCOLOGY

Target genes for sequencing

Lung Adenocarcinoma – Clinical NSCLC Cases – 724 consecutive FFPE

  • UCSF designed Sequence data Tumor mutation, treatments, Outcomes in Knowledge Repository match to Clinical Trial.gov
  • application of the Process for Lung Cancer to other indications
  • Prediction regression inform Clinician – Stanford Medical Center engine

4:00 Conference Adjourns


LIVE — April 23, 10:30PM –  MODELING & ANALYTICS @Cambridge HealthTech Institute’s 14th Annual Meeting BioIT World – Conference & Expo ’15, April 21 – 23, 2015 @Seaport World Trade Center, Boston, MA

Dr. Aviva Lev-Ari will be in attendance on April 21, 22, 23

 

Leaders in Pharmaceutical Business Intelligence (LPBI) are Covering the Event in REALTIME using Social Media

http://pharmaceuticalintelligence.com

#biotech

#investment

@MassBio

@BostonBizJournl

@BioWorld

@BiotechNews

@bioitworld

MODELING & ANALYTICS

April 23, 10:30 Chairperson’s Remarks

Yuriy Gankin, Ph.D., Chief Life Science Officer, EPAM Systems

10:40 Translational R&D Analytics: Delivering ‘Big Insights’ to Drive Translational Research

Kaushal Desai, Associate Director, Translational R&D Analytics and Decision-Support, Research Informatics & Automation, Bristol-Myers Squibb

The emergence of immunotherapy and a focus on systems approaches has led to an unprecedented surge in translational research opportunities for discovery and development of newer treatment paradigms. Organizations leading the race for scientific breakthroughs in patient treatment have accumulated overwhelming quantities of efficacy, survival, safety and biomarker data from decades of preclinical studies and clinical trials. Translational R&D organizations face the arduous task of mining this data to deliver insights that drive translational research. This session will explore case studies demonstrating how translational R&D analytics can inform patient stratification and trial design in early clinical and translational research. The talk will focus on the journey from a lack of discoverability for disjointed datasets to insights that drive key decisions in translational research. Challenges associated with delivering actionable information at the point of decision-making will be highlighted and opportunities to deliver business value will be outlined using real examples from multiple disease areas.

11:10 Integrated Genomics Platform: Putting Patients and Their Genomes into the Focus of Our Research

Nora Manstein, Ph.D., IT Project Manager, Bayer Business Services GmbH

The fast progress in the generation of genomic data has reached the patient. Especially the advent of next generation sequencing and high resolution microarrays enable accurate descriptions of diseases with a strong genetic component ultimately leading to novel therapeutic approaches. Application of these technologies, however, leads to large amounts of data in need of effective storage and analysis. As now several data types (mutation, expression, microRNAs) become available for each patient, patient-centric views and analyses become mandatory. Consistent data handling and storage is a scientific and technological challenge towards both the research organization and the IT infrastructure. We have established the Integrated Genomics Platform (IGP) as a central tool for genomics research in Cardiology, Oncology and Clinical Sciences. The platform supports advanced data analysis and is intended to simplify discovery processes, e.g. for novel therapeutic targets and genetic biomarkers. In this strategic project, we have overcome known bottlenecks and enabled true translational research by establishing a company-wide mandatory repository and toolbox for storage and analysis of genomics data as well as common standards for data annotation, privacy & security.

Bina Technologies

11:40 Building a Globally Distributed, Hybrid NGS Sequence Analysis and Integration Infrastructure for Oncology Discovery and Translational R&D

Justin H. Johnson, Principal Scientist, AstraZeneca

Next-Generation Sequencing is changing the way pharmaceutical companies develop drugs, perform patient stratification, and evaluate treatment efficacy. However, managing the massive amounts of NGS data has introduced fundamental IT challenges. Here we discuss the implementation of a fast, flexible, scalable and validated IT infrastructure that can streamline the upkeep of the NGS analysis workflow and the distribution of genomic information throughout an organization for translational discovery.

Translational Oncology @AstraZeneca

  • Disease biology
  • Patient selection
  • Diagnostics
  • EGFR mutations and NSCLC 60% have One single mutation
  • NGS allow identification of new mutation of circulating tumor DNA – progression plasma samples
  • Plasma Circulating Tumor DNA ctDNA Sequencing
  • NGS is CHanging – Drug development in Oncology
  • AZ Production Informatics Mission Statement
  • Patient Selection

INFRASTRUCTURE

  1. Liquid biopsy
  2. EXOME Seq 16 hrs to 45 min
  3. WGS 72 hrs to 6 hors
  4. CLOUD
  5. FAst and FLexibile

VENDORS; Bina, GMS vs AGXP, NIST, GIAB, Clarity, GetRM, Platinum Genomes

ICGC-TCGA DREAM Somatic Mutation Calling Challenge

  • AZ & Bina — AZ Active in BRCA Challenge

Reference Standard – SNP focus

  • SOmatic Calls – Horizon DIagnostics
  • Assay Performance
  • VarDict Key Features
  • COmplex Variants
  • handles 1M depth wihtout down-sampling
  • make accurate composite haplotype calls
  • accessing EGFR sequence at Exon 19 in a trial patient
  • ansumble approach to identify false positives
  • Variants and covered Profiles
  • Bina helped to make Bioinformatics to the Biologist Scientists
  • Open SOurce Framework for Data Analysis
  • Translational NoSQL MongoDB variant warehouse and API

Easy tio Use Modules and GUI

Bina AAiM

BinaRAVE

 

12:10 pm Session Break

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

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


LIVE — Plenary Session 2015 BioIT, April 23, 2015, 8:00 – 10:00AM – Cambridge HealthTech Institute’s 14th Annual Meeting BioIT World – Conference & Expo ’15, April 21 – 23, 2015 @Seaport World Trade Center, Boston, MA

Dr. Aviva Lev-Ari will be in attendance on April 21, 22, 23

 

Leaders in Pharmaceutical Business Intelligence (LPBI) are Covering the Event in REALTIME using Social Media

http://pharmaceuticalintelligence.com

#biotech

#investment

@MassBio

@BostonBizJournl

@BioWorld

@BiotechNews

@bioitworld

April 23, 2015 * 8:00 – 10:00 am

8:00 Chairperson’s Opening Remarks

Allison Byrum Profitt, Editorial Director, Bio-IT World & Clinical Informatics News

8:05 Rubik’s Cube Challenge

Play the Rubik’s Cube Challenge! Get your Rubik’s Cube from Intel at the Plenary Session. After the Plenary session, go to the Rubik’s Cube Challenge in the Exhibit Hall, try to solve it, and enter to win great prizes! See an Intel representative during the Plenary Session for complete rules.

8:15 Plenary Session Introduction – Precision Medicine

Ketan Paranjape, General Manager Life Sciences, Intel Corp.

Multiple sources of Data: Medical and Technical

  • Patients – Safety — > Precision in one day, genome sequence , visit MD in 24 hours
  • Labs
  • Genomics
  • Clinical Trials
  • claims & tranactions
  • Payer
  • Provider – order Genetic Seq, analyze results, share pros and cons
  • IT – deploy scalable drive cost and ownership, data sharing, security and control of local data

Biopsy -> sequencing  -> Analysis -> Diagnosis ->Treatment -> Clinical Trial [Yes or No]

In a Hospital – AIOD:

AM:Imaging, genomics workflow

PM: Variants/Mutations – NOT only at Broad Institute, many other center have it

genomic workflow

optimized hardware clusters

Ecosystem to support OIOD:

– Education: MDs and Patient

_ Investment

– Policy

– Reimbursement

INTEL’S RESPONSE; TGen RNA-Seq Pipeline – from 7 days to 4 hours

  • Challenge
  • Solutions

http://www.intel.com/healthcare/optimizecodee

“All-in-one-day?” (AIOD)

  • Challenge for Charity
  • Solve the Cube

WHO is up for AIOD Challenge ????????

LIVE 8:25 Data Custodians, Patient Advocates

One of the greatest challenges for every branch of medical science is rounding up patients for studies, trials, and endless measurements. What if patients could offer up their own data for research that interests and engages them? These panelists are working where big data science meets patient empowerment, finding ways that modern health records, patient portals, new data sources like next gen sequencing and activity trackers, and other technology innovations can give everyone a stake in how data are used. They will discuss how they keep patients in the research loop, and how non-professionals can be a driving force behind the medical breakthroughs of the future — in sickness and in health.

Benjamin Heywood

Benjamin Heywood, Co-Founder and President, PatientsLikeMe, Inc.

  • ALS Therapy Development Institute
  • patientslikeme

Undertanding phynotypes

  1. Learn from others
  2. Connect e=with people
  3. track your health
  4. PATIENTS 320,000, 2300 DISEASES
  5. DATA – CODED TO ICD, SNOMED,

CLINICAL DEVELOMENT LEALERD SAY THEY DO

  • 76% Patients worry of sharing data
  • 94% are willing to share

SYSTEM MEASUREMENT with collaboration of other entities

  • The search for a Better Definition of Pain – Patients actively defined a Pain Scale
  • Item-level feedback user interface
  • Survey-level feedback user interfaces
  • Interest in clinical Trials
  • FDA PFDD – engage Patients: Fibromyalgia
  • MS Condition Severity vs Patient Attitude & Concern
  • LUPUS – Living with SLE: Severe Symptomology: PAIN, Fatigue, Flare, Rash
  • Toterability of Drugs: Methotrexate Side effects
  • Time on Prednisone and side effects
  • PatienLiikeMe – PLM: DID SYmptoms get better?
  • Sensors-based Research  – Wearable devices changes landscape of DIAGNOSTICS

Andreas KogelnikAndreas Kogelnik, M.D., Ph.D., Founder, Open Medicine Institute

Omics in the Era od Patient-driven data Collection

Challenge: Patient & Clinical Data + Lab/-omic Data = Optimized Research & Care

MDs are overhelmed  – >> for Patients and the Crowd — DATA IS POWER

Genomics Capacity is outstripping our capacity to store it: gel-based systems, capillary seq, massively parallel seq

Omics Data and Wearables

  1. Walking in SIlicon Valley  – 40 paparements are monitored across Runners – BP – Gene by Gene Impact of walking
  2. Google MashUp: better integration of data and ways to share are clear needs
  3. Open Med Net: Research — Clinical – Imaging – Pharmacy – Device – Insurane – ANALYTICS OF HEALTH AT THE PATIENT LEVEL – Speech recognition pattern — in Parkinsons and Omics data of the Patient

Genomics of Young Lung Study – FOundation Medicine – Lung Cancer

Pre-study- -> Pre screening –> Remote sites -Social Media — >Local Sites US,UK [remote consent protocol REMOTE registration] –> Epidemiology –> Science

Collaborative Community Approach to Chronic Fatigue, Lyme & Autism – Poorly understood Conditions

Longitudinal Genomic SUrveying: Data Collection of Patient generated data combined with molecular evaluation

300,000 protein data points od one patient, comapy in Sandiego

Large scale Screening: data-driven Pregnancy & Rare Diseases

  • 1/2 have a chronic disease – most medication are not for pregnent women

Quality of Data — >> Better action

Data-Driven Patient Testing and Care – Apply Omics DIgital Population health + genomics

Katherine WendelsdorfKatherine Wendelsdorf, Ph.D., Field Application Scientist – Ingenuity Systems, QIAGEN Bioinformatics; Spokesperson, Empowered Genome Community

Create Tools for big biological data sets and tools

  • genetic variation data analysis Omics to drive the translation
  • Personalize Medicine: Samples from Patients with and without the disease
  • Which of all variences

Ingenuity Variant Analysis – Tool to Filter Variants

  1. likely cause of disease
  2. identify pathways
  3. contentt from variety tools of Variant  – Sequence banks
  4. literature – knowledge base – overcome data complexity
  5. Association with case group with a disease
  6. all patients with mutation x
  7. none of the healthy control have the mutation x

Genomic Community

  1. share with all researcher 23andMe data for free — for research purposes
  2. users submit Exon, Variant,
  3. Sequencees – share Genetic data Broadly or Privately – subset with a specific researcher
  4. Researchers: benefit by definition of Phynotypes: Genomics of women under 40 with gene BRCA 53???
  5. Filter varients against AFC frequencies
  6. get in contact with individuals who have a specific variant/varient set of interest
  7. Patient —>> Scientists — give Patients discoveries & treatment go back to the Patient

PANEL DISCUSSION

Value creation for Patients:

  • Results, give patients a window to the research process
  • Actionable, cultural differences, Patients want to have data but do not want to know if no therapy is available
  • Privacy is a Risk factor – hiding Medical Information Privacy vs Sharing
  • EMR – Interfaces efforts helps, data does not fit Interoperability issues within the Clinics
  • Claims data vs EMR
  • Gemone Data — who own the analysis?? vs owner owns the genome data
  • Exporting Pt Variant Data Sample — Pt control, Pt must agree for sharing, Uploading Variant Data is blind, remaind blind UNLESS THE PT.SIGNS UP TO SHARE THE DATA
  • Sharing DIscoveries, inside the DB Pt need to give permission to look at the data
  • Participatory Studies
  • Environmental Dat Dental data Medical Devicecs data
  • Proteomics, clinical data — PatientLikeMe
  • Blood drawn in the Home of the Patient
  • CBC in EMR – no analysis of CBC
  • Reimbursement of Order Sequencing – Insurance contracts are foe years coming for renewal – no interest in Long Term (LT) Wellness of Patients, Employees are interested in LT

LIVE  April 22, 1:50PM – BIG DATA, DIGITAL TOOLS AND BIOINFORMATICS ACROSS MULTIPLE RESEARCH INITIATIVES @Cambridge HealthTech Institute’s 14th Annual Meeting BioIT World – Conference & Expo ’15, April 21 – 23, 2015 @Seaport World Trade Center, Boston, MA

Dr. Aviva Lev-Ari will be in attendance on April 21, 22, 23

 

Leaders in Pharmaceutical Business Intelligence (LPBI) are Covering the Event in REALTIME using Social Media

http://pharmaceuticalintelligence.com

#biotech

#investment

@MassBio

@BostonBizJournl

@BioWorld

@BiotechNews

@bioitworld

 

BIG DATA, DIGITAL TOOLS AND BIOINFORMATICS ACROSS MULTIPLE RESEARCH INITIATIVES

April 22, 1:50 Chairperson’s Remarks
Michael Liebman, Ph.D., Managing Director, IPQ Analytics, LLC

Disease and Disease Processes

Data

Information

Knowledge

Medical Utility – Slow to get

150,000Biomarkers in Research 100 approved only 20 lead to treatment

Research too focus and the Big picture is never been reached.

1:55 Metabolic Biomarkers in Duchenne Muscular Dystrophy

Simina Boca, Ph.D., Assistant Professor, Innovation Center for Biomedical Informatics, Georgetown University Medical Center

Duchenne Muscular Dystrophy (DMD) is a devastating degenerative X-linked disorder which affects approximately 1 in 5,000 newborn males and results in muscle degeneration, eventual loss of ambulation around the age of 9, and a life expectance of around 25 years of age. We considered serum metabolomic profiling of 51 DMD patients and 22 age-matched healthy volunteers in order to find novel serum circulating metabolites for DMD, with the ultimate goal of discovering molecular surrogate markers associated with disease progression, which can be used in future clinical trials. The DMD patients had a minimum age of 4, a maximum age of 28.7, and a median age of 11.4 years, while the healthy controls had a minimum age of 6, a maximum age of 17.8, and a median age of 13.7 years. 22 of the 51 DMD patients were non-ambulatory at the time of serum collection. As expected, age and ambulation status were strongly correlated in the DMD group, where patients with ages between 4 and 17.8 years, with a median of 6.8 years, were ambulatory, while patients between 11.4 and 28.7 years, with a median of 18 years, had lost ambulation. Liquid chromatography – mass spectrometry (LC-MS) techniques were used to process the serum of the study participants, with the XCMS analysis tool detecting a total of 246 peaks in negative mode and 1676 peaks in positive mode. Metabolite values were further log2 transformed, then normalized using internal standards for both modes. A two-class comparison using a two-sample t-test identified 46 peaks associated with disease status at a false discovery rate (FDR) threshold of 0.05, employing a Benjamini-Hochberg correction. A similar comparison was performed for the DMD cases, comparing ambulatory and non-ambulatory individuals, leading to 154 significant peaks at an FDR threshold of 0.05. After the analyses are finalized, significant peaks will be annotated, in order to match the m/z values to metabolite identities. One particular challenge in interpreting these results is eliminating metabolites which are not associated with disease mechanism from further consideration, such as those associated with drugs or dietary supplements used by certain patients. A bioinformatics platform for metabolic data interpretation has been developed and tested to identify DMD-associated biomarkers and will be made available on GitHub once validation is complete. This platform will be presented along with another use case from a breast cancer metabolomics study.
Contributors/Authors: Simina M. Boca1,2, Maki Nishida1, Michael Harris1, Shruti Rao1, Amrita K. Cheema2,3, Kirandeep Gill2, Haeri Seol4, Eric Hoffman4, Erik Henricson5, Craig McDonald5, Yetrib Hathout4 and Subha Madhavan1,2 1Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, D.C.; 2Department of Oncology, Georgetown University Medical Center, Washington, DC; 3Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington, D.C.; 4Children’s National Medical Center and the George Washington University, Washington, D.C., 5 Department of Physical Medicine and Rehabilitation, University of California, Davis School of Medicine, Davis, CA.

LIVE

Disease Progration – DMD – Duchenne – Rare Disease

Metabolite in Blood (Serum), urine Tissue (Tumor)

Metabolomics – assessment of small molecule

2:25 Personalized Medicine: Moving from Correlation to Causality in Breast Cancer

Michael Liebman, Ph.D., Managing Director, IPQ Analytics, LLC

Sabrina Molinaro, Ph.D., Institute for Clinical Physiology, National Research Council, Italy

We have developed a fundamental model of the disease process for breast cancer, from pre-disease through early detection, treatment and outcome, and apply a multi-scalar approach across the risk assessment-enhanced diagnosis-therapeutic decision axis and will present the modeling methodologies.

LIVE

Disease is a Process not a State: Direction (high dimensionality, Progression (Stage), Velocity (rate of progression)

Risk to get a disease : Environmental exposure, genetic make up,

life style events, cumulative history of other diseases

Risk Factor for Breast Cancer – Calculator:

  • life style: smoke, alcohol, weigh
  • not having children

Correlation vs Causality

NCI – Gail Model:

  • One Biopsy with Histological Atypical — PRIMARY FACTOR
  • Relative had BR

Invasive Breast Cancer – NCCR

Pathologists agree in Early and in Late NOT IN THE MIDDLE

Breast Pathology:

CO-Concurrence Matrix

Difference in Pre and post menapausal in aggressiveness – How subtypes progress in different time

CAUSALITY: Stages of development, complexity of the disease,Biomarker, Meta analysis – Data accuracy not there

2:55 Streamline R&D and Catalyze Drug Repositioning by Identifying Expert Networks and Expertise

Xavier Pornain, Vice President, Sales & Alliances, Sinequa

Finding networks of experts with similar or complementary expertise on a given subject helps avoid costly redundant research, shed light on a complex research problem from different angles, foster cooperation, facilitate drug repositioning, and accelerate time to market. This session will delve into the benefits pharmaceutical companies are seeing by employing Search & Analytics technology to: “link” researchers and teams with one another, create internal “journals of science” to share internal results and snippets, access “breaking science”, with alerts and spotting trends across all scientific information. We show solutions for dealing with scientific vocabulary, detecting “synonyms” as well as “similar” and “complementary” notions, e.g. brand names for drugs, scientific names for the active ingredients, and even descriptions of molecules using a standard description language. In addition, we analyze vast quantities (200 to 500 million) of highly technical documents and data (billions of records), such as internal and external publications, patent filings, lab reports, clinical test reports, trade databases, etc.

LIVE

Data reside as Web data, Proprietary data, Cloud data

Big data IT Architecture

Project that Profiled search by Scientists

  • R&D Search
  • R&D KOLs

3:10 Cloud-Based Solutions for Population-Scale, Whole Human Genome and Exome Analysis

George Asimenos, Ph.D., Director, Science & Clinical Solutions, DNAnexus

Thanks to advances in sequencing technology, the size and scope of DNA sequencing projects is rapidly moving towards an era of thousands of whole genomes and tens of thousands of exomes per year. Learn how certain field-leading institutes are using a cloud-based bioinformatics platform to manage their big data deluge across multiple initiatives.

LIVE

DNAnexus, based in Mountainview, CA

  • Client: Kinghorn Center for Clinical Genomic — Australia

Large-Scale Whole Genomic Clinical Diagnosis

  • Clients: Regeneron, NY and Geisinger, PA (3Million Pt)  – USE DNAnexus CLOUD architecture & Management of Cloud Resources

Challenges for Cloud Computing – DNAnexus

  • Security and compliance
  1. Input
  2. ANALYSIS
  3. Distribution
  • Control Data & Collaborator Access
  • BioInformatics Expertise: Download a subset of a BAM fie without transcoding

4:00 Using Games as Data Analytical Tools

Melanie Stegman, Ph.D., Owner, Molecular Jig Games, LLC.; Director, Science Game Center

Immune Defense is a video game, but it is also a molecular level simulation of the immune system. Individual data points tell us very specific details about cells, and a large database of these details should tell us a more complete story. But do we have enough data yet to tell the story of one cell, facing one bacterium? It has been a challenge gathering the knowledge to create this small story. Part of Immune Defense game development is the creation of a “game level editor.” We can make new molecules, give them new binding partners, assign their affinities for each partner, increase or decrease their relative concentrations and give our enzymes activity… We have created a “medium data” analysis chamber–that is, not Big Data, but more data than one person can hold in their head. We are planning to build up our level editor as a tool for biochemists to analyze their data with much more perspective than ever before. We will also have a tool for scientists, students, public and game developers to use to create realistic scenarios for various purposes, from science fairs to testing to video game development. Play Immune Defense at http://www.MolecularJig.com/demo.

4:30 A Rigorous Methodology for Non-Randomized & Observational Study in Healthcare Testing

Gil Weigand, Ph.D., Director, Strategic Projects, Oak Ridge National Laboratory

Healthcare R&D or innovation trials have for more than a decade experienced an acceleration of the application of non-randomized study (NRS), including observational or pragmatic methods. Driven by a demand for rapid translation and patient centeredness using a randomized controlled trial—todays acknowledged “gold standard” for testing in healthcare—may not be practical or desirable when there is a need for flexibility, responsiveness, or timeliness. The challenge for researchers and clinicians using NRS testing is getting sufficient rigor in the scientific evaluation to assure data and study veracity, particularly as complexity and heterogeneity increase in innovation trials. IDAMS-HC achieves the state-of-the-art available today with regard to rigor, technology, and science-based in evaluation of NRS and it supersedes today’s ad hoc methodologies. Moreover it increases external validity. In this presentation, we present an advanced rigorous science-based evaluation methodology for evaluation in healthcare testing. The methodology extends today’s general practice, rapid cycle evaluation, by introducing in silico methods of big data and modeling & simulation and tightly integrating the methods within a knowledge discovery infrastructure. An ACO intervention trial provides initial experience with IDAMS-HC.
Contributors/Authors: Gil Weigand, PhD, Director, Strategic Projects, Computer and Computational Sciences, Oak Ridge National Laboratory (ORNL); Mallikarjun Shankar, PhD, Senior Research Scientist, Computer and Computational Sciences, ORNL; C. Edward McBride, III, MD, MBA, VP, Clinical Services, Summit Medical Group (SMG); Kimberley Kauffman, VP, Value-Based Care, SMG; and Suzanne Kieltyka, Manager, Health Education, SMG

5:00 Service-Oriented Bioinformatics – the CDC Influenza Sequence Data Management System
John M. Greene, Ph.D., CSM, Senior Director, Bioinformatics, Bioinformatics Solutions and Support, SRA International, Inc.
Next-Generation Sequencing technologies have opened enormous opportunities for improvements in the surveillance of infectious diseases such as influenza. However, effective use of such sequencing information depends on a robust system to store, manage, analyze, and interpret sequence data. The Influenza Sequence Data Management System (ISDMS) at the Centers for Disease Control and Prevention (CDC)’s Influenza Division in Atlanta fills this role using a service-based approach developed by SRA International that we refer to as ‘service-oriented bioinformatics’. Services are small programs that are coordinated by an enterprise service bus, in this case Apache ServiceMix, based on the service-oriented architecture (SOA) model. Services can be written in different languages and act as modular components of the system, providing individual functionality, such as searching, annotation display, and location standardization. These services underpin data loading, data annotation, and data display, and services can be combined to implement new features and reused to speed development.


LIVE  April 22, 10:50AM – GENOMICS: CLINICAL CHALLENGES AND MEDICAL OPPORTUNITIES @ Cambridge HealthTech Institute’s 14th Annual Meeting BioIT World – Conference & Expo ’15, April 21 – 23, 2015 @Seaport World Trade Center, Boston, MA

Dr. Aviva Lev-Ari will be in attendance on April 21, 22, 23

 

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GENOMICS: CLINICAL CHALLENGES AND MEDICAL OPPORTUNITIES  

April 22, 10:50 Chairperson’s Opening Remarks
Scott Kahn, Ph.D., Vice President, Commercial Enterprise Informatics, Illumina, Inc.

LIVE from Illumina Point of View – Sequencing Activity – GLOBAL

Genomics and MEDICINE – Validation – NGS rapidly to the Clivic penetration: – WHat do we do with the data

Health WOrkFLow and Challenges

  • equip MDs with tools
  • Translational Research and 13 Cancer Center in Holland — Biomarker discovery
  • Rate of illumina growth 450,000 genomes are sequenced per year

11:00 FEATURED PRESENTATION: CHALLENGES AND OPPORTUNITIES IN ESTABLISHING IT SUPPORT FOR CONTINUOUS LEARNING IN HEALTHCARE: THE POTENTIAL FOR APPLYING LESSONS LEARNED FROM CLINICAL GENOMIC IT SUPPORT TO BROADER CONTINUOUS LEARNING CHALLENGES

Samuel (Sandy) Aronson, Executive Director, IT, Partners HealthCare Center for Personalized Genetic Medicine

Continuously updated knowledge bases will be required to enable a true continuous learning healthcare environment. However, modern healthcare pressures make their maintenance difficult. The clinical genomic IT community has been wrestling with this issue for some time. We present lessons learned from supporting clinical genomic IT processes that may be generalizable to broader development of IT support for continuous learning healthcare processes.

LIVE – reposition Research and Care by CONTINUOUS LEARNING IN HEALTHCARE –

  • what datapoint can be measures
  • likelihood of efficacy of treatment scenarios
  • architectural change in IT
  • System in Partners that is related to Genetics
  • Clinical Improvement

GeneInsight is licensed by Partners

Healthcare IT – delivering data into HIT

Rules generated Outside of Clinical Flow

Engine Checks Transactions but not

ALTERNATIVE APPROACH

CONTINUOUS LEARNING IN HEALTHCARE — CONTINUOUS LEARNING ARCHITECTURE – Knowledge base

  • data association in the knowledgebase
  • mailine transaction processing

Clinical Genomics

Variant found – Discovery of the gene – lab pick up the test – CONTINUOUS LEARNING Process need to be enabled

Genesight Report Drafting ENgine

GenInsight Infrastructure:

Identified Variants, order information,auth-drafted Reports for Geneti ist Pathologist Knowledge base has information on genes and on variance.

  • rule — all reports must update the knowledge base

GeneInsight Clinic – Surfacing Alerts

Diagnostic process – genetic Report

  • Introduction of the infrastructure – TIME SAVED and improve quality
  • Time spent to update knowledgebase autodrFTING REPORT – TIVE SAVED IN ASSESSING VARIANCE
  • NETWORKS CAN HELP – linking Labs and CLinicls and Labs to one another
  • ClinVar – sharing data on Gene Variants

Improvement worth the investment

11:30 FEATURED PRESENTATION: THE PENETRANCE OF INCIDENTAL FINDINGS IN GENOMIC MEDICINE

Robert C. Green, M.D., MPH, Director, G2P Research Program; Associate Director, Research, Partners Personalized Medicine, Division of Genetics, Department of Medicine, Brigham & Women’s Hospital and Harvard Medical School

Much of the controversy surrounding the implementation of incidental findings in clinical sequencing is due to uncertainty about the penetrance of such findings in persons unselected for clinical features or family history. This uncertainty also influences the question of genomic population screening, i.e., whether actionable sequence variants should be sought and reported in ostensibly healthy individuals. In this talk, new data will be presented estimating the penetrance of actionable incidental findings.

LIVE — FROM THE BRIGHAM

Integrating sequencing

The REVEAL Study – single risk variant

Using Genome Sequencing for Undiagnosed DIseases: Exome Seq. and Diagnostic CLinical : Secondary and Incidental Findings – Standardized Analogy Incidental Findings – What is the right Analogy – VCOntextualization will take place in the MDs Offices

Genomics – RIsk factors requiring Contextual — whole body MRI was found to be NOT needed.

Patient-driven research JAMA Incidental findings are incidental NOT EXCEPTIONAL.

Secondary finding to be reported

GENOMIC SCREENING

Opportunistics Screening – infrastruction is in place

  • Seq ordered is relatively cost neutral, Recommendation are in existance
  • Follows medical model

Population Screening

  • Public Health

Study MedSeq Project @Brigham – Cardiomyopathy and a Group of Health volunteers – Genome Report

Follow up with intense Medical Record monitoring

GINA Genetic Discremination and Genomic Medicine

  • Medical
  • Behavioral

Carrier Variation in 91%

21% had patogenic variance

2% Incidental monogenic ACMG genes, only

Penetrance of Actionable Incidental Findings in the Framingham Heart Study

465 patient — 8 pathogenic variance in dominant conditions

Phynotype – Blindly scored 5 out of 8 had a phynotype construed wiht the mutation

Aggregate Penetrance across Phynotype –  Positive on INCREASE RISKS phynotopical

  • Cancer
  • Cardiovascular

BabySeq Project – Indication-based Report 779 genes (IBGR)

  • Indication risk report
  • carrier
  • blood type
  • pharmacogenomics
  • risk for onset in childhood

Whole Genome Seq

genome2people.org

Elsevier

12:00 pm Census of the Apoptosis Pathway

Philip L. Lorenzi, Ph.D., Department of Bioinformatics and Computational Biology & the Proteomics and Metabolomics Core Facility, MD Anderson Cancer Center

We recently compared several different “omic” approaches to constructing the autophagy pathway de novo, including siRNA screening, mass spectrometry-based proteomics, and three different pathway analysis software packages. Unexpectedly, although merging all of the validated data sets yielded 739 autophagy-modulating genes, each individual approach alone yielded sparse coverage of the autophagy pathway. The best individual siRNA screen, for example, yielded only 169 of the 739 (23%) genes. Nevertheless, text mining-based pathway analysis with Pathway Studio in conjunction with manual curation provided the most comprehensive coverage, yielding 417 targets (56% of the pathway). Here, we explored the generalizability of those findings by examining a more well-characterized pathway—apoptosis. We compiled apoptosis-modulating genes from 12 published siRNA screens and two pathway analysis software packages—Ingenuity Pathway Analysis (IPA) and Pathway Studio. The resulting inventory of 6,882 proteins consisted of 215 targets identified by siRNA screening, 3,378 targets by IPA, and 6,381 targets by Pathway Studio. The extensive coverage (93%) of the apoptosis pathway provided by text mining with Pathway Studio can likely be attributed to recent upgrades in the software, including an expanded database and collection of full-text articles. Together with our previous autophagy pathway analysis, the new apoptosis results support the generalizable conclusions that: 1) siRNA screening has a large false negative rate (i.e., fails to identify many true “hits”), and 2) text mining-based pathway analysis using Pathway Studio provides the most comprehensive pathway coverage.

LIVE

Drug discovery & Development Process – Pathway Analysis – Drive drug discovery

  • Part One – Pathway Analysis Strategies: Autophagy and Optosis
  1. Ingenuity PathAnalysis
  2. MetaCore – Thomsom
  3. GeneSpring
  4. David
  5. Pathway Studio Elsevier – MedScan – Text analysis and mining

AUTOPHAGY

Cargo needed to be discharged

Proteome

UNION of multiple technologies

Pathway Studio: False positive rates  19% reduced by curation – change direction

False negatives

ATF4 assign unknown

BEST APPROACH: Pathway Studio wiht manual CUration

Apply Pathways to Clinical Use in Drug DIscovery

  • mTOR/autophagy stimulator – Combined mTOR and autophagy Inhibition
  • mitophagy
  • Part Two – validate on big data

AKT – Pathwat Studio

EDFR – no serious concern

MEK Inhibitor – No serious concern

PARP1 Inhibitor – concerns

TEXT MINING to be used in drug discovery – VERY importanr

GLS Inhibitors

12:30 Session Break

Molecular Health12:40 Luncheon Presentation I: Computational Enablement of the Hippocratic Oath in a Clinical Oncology Setting

David B. Jackson, Ph.D., Chief Innovation Officer, Molecular Health, Gmbh

The clinical response of cancer patients to oncolytic agents is influenced by three major classes of molecular determinant; tumor intrinsic factors (e.g. tumor biomarkers); patient intrinsic factors (e.g. polymorphisms) and patient extrinsic factors (e.g. co-medications). In my talk, I will present a novel computational technology and associated treatment decision support process that was designed to provide this knowledge-driven approach to clinical care in oncology.

illumina NEW1:10 Luncheon Presentation II: A High Performance Application Development Platform for Collaborative Genomics Research

Paul Flook, Ph.D., Senior Director, Enterprise Informatics, Illumina Inc.

Collaborative research among groups working with genomic data presents major logistical challenges. Transferring huge volumes of data can be prohibitively expensive for projects utilizing WGS data sets. Illumina has addressed this challenge by building a platform that enables collaborators to not only share data in a secure multitenant environment, but to develop and deploy their own applications close to the data.

1:40 Session Break

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