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Bioinformatics Tool Review: Genome Variant Analysis Tools

Posted in Big Data, BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Cancer Informatics, Clinical Genomics, Computational Biology/Systems and Bioinformatics, Genome Biology, Personalized and Precision Medicine & Genomic Research, Uncategorized, tagged bioinformatic tools, Bioinformatics, Bioinformatics methodologies, disease variants, DNA, Ensembl, European Bioinformatics Institute, genetic variants, National Center for Biotechnology Information, NCBI, next gen sequencing (NGS), NGS tests, SNP, Whole genome sequencing on October 23, 2018| Leave a Comment »

Bioinformatics Tool Review: Genome Variant Analysis Tools

Curator: Stephen J. Williams, Ph.D.

Updated 02/07/2021

Updated 11/15/2018

The following post will be an ongoing curation of reviews of gene variant bioinformatic software.

The Ensembl Variant Effect Predictor.

McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GR, Thormann A, Flicek P, Cunningham F.

Genome Biol. 2016 Jun 6;17(1):122. doi: 10.1186/s13059-016-0974-4.

Author information

1

European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. wm2@ebi.ac.uk.

2

European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.

3

European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. fiona@ebi.ac.uk.

Abstract

The Ensembl Variant Effect Predictor is a powerful toolset for the analysis, annotation, and prioritization of genomic variants in coding and non-coding regions. It provides access to an extensive collection of genomic annotation, with a variety of interfaces to suit different requirements, and simple options for configuring and extending analysis. It is open source, free to use, and supports full reproducibility of results. The Ensembl Variant Effect Predictor can simplify and accelerate variant interpretation in a wide range of study designs.

Rare diseases can be difficult to diagnose due to low incidence and incomplete penetrance of implicated alleles however variant analysis of whole genome sequencing can identify underlying genetic events responsible for the disease (Nature, 2015).  However, a large cohort is required for many WGS association studies in order to produce enough statistical power for interpretation (see post and here).  To this effect major sequencing projects have been initiated worldwide including:

• Iceland
• UK (100,000 Genome Project)
• USA
• Genome 10K

A more thorough curation of sequencing projects can be seen in the following post:

Icelandic Population Genomic Study Results by deCODE Genetics come to Fruition: Curation of Current genomic studies

And although sequencing costs have dramatically been reduced over the years, the costs to determine the functional consequences of such variants remains high, as thorough basic research studies must be conducted to validate the interpretation of variant data with respect to the underlying disease, as only a small fraction of variants from a genome sequencing project will encode for a functional protein.  Correct annotation of sequences and variants, identification of correct corresponding reference genes or transcripts in GENCODE or RefSeq respectively offer compelling challenges to the proper identification of sequenced variants as potential functional variants.

To this effect, the authors developed the Ensembl Variant Effect Predictor (VEP), which is a software suite that performs annotations and analysis of most types of genomic variation in coding and non-coding regions of the genome.

Summary of Features

• Annotation: VEP can annotate two broad categories of genomic variants
  • Sequence variants with specific and defined changes: indels, base substitutions, SNVs, tandem repeats
  • Larger structural variants > 50 nucleotides
• Species and assembly/genomic database support: VEP can analyze data from any species with assembled genome sequence and annotated gene set. VEP supports chromosome assemblies such as the latest GRCh38, FASTA, as well as transcripts from RefSeq as well as user-derived sequences
• Transcript Annotation: VEP includes a wide variety of gene and transcript related information including NCBI Gene ID, Gene Symbol, Transcript ID, NCBI RefSeq ID, exon/intron information, and cross reference to other databases such as UniProt
• Protein Annotation: Protein-related fields include Protein ID, RefSeq ID, SwissProt, UniParc ID, reference codons and amino acids, SIFT pathogenicity score, protein domains
• Noncoding Annotation: VEP reports variants in noncoding regions including genomic regulatory regions, intronic regions, transcription binding motifs. Data from ENCODE, BLUEPRINT, and NIH Epigenetics RoadMap are used for primary annotation.  Plugins to the Perl coding are also available to link other databases which annotate noncoding sequence features.
• Frequency, phenotype, and citation annotation: VEP searches Ensembl databases containing a large amount of germline variant information and checks variants against the dbSNP single nucleotide polymorphism database. VEP integrates with mutational databases such as COSMIC, the Human Gene Mutation Database, and structural and copy number variants from Database of Genomic Variants.  Allele Frequencies are reported from 1000 Genomes and NHLBI and integrates with PubMed for literature annotation.  Phenotype information is from OMIM, Orphanet, GWAS and clinical information of variants from ClinVar.
• Flexible Input and Output Formats: VEP supports input data format called “variant call format” or VCP, a standard in next-gen sequencing. VEP has the ability to process variant identifiers from other database formats.  Output formats are tab deliminated and give the user choices in presentation of results (HTML or text based)
• Choice of user interface
  • Online tool (VEP Web): simple point and click; incorporates Instant VEP Functionality and copy and paste features. Results can be stored online in cloud storage on Ensembl.
  • VEP script: VEP is available as a downloadable PERL script (see below for link) and can process large amounts of data rapidly. This interface is powerfully flexible with the ability to integrate multiple plugins available from Ensembl and GitHub.  The ability to alter the PERL code and add plugins and code functions allows the flexibility to modify any feature of VEP.
  • VEP REST API: provides robust computational access to any programming language and returns basic variant annotation. Can make use of external plugins.

 

Watch Video on VES Instructional Webinar: https://youtu.be/7Fs7MHfXjWk

Watch Video on VES Web Version training on How to Analyze Your Sequence in VEP

Availability of data and materials

The dataset supporting the conclusions of this article is available from Illumina’s Platinum Genomes [93] and using the Ensembl release 75 gene set. Pre-built data sets are available for all Ensembl and Ensembl Genomes species [94]. They can also be downloaded automatically during set up whilst installing the VEP.

• Project name: Ensembl Variant Effect Predictor
• Project home page: http://www.ensembl.org/vep
• Archived version: https://github.com/Ensembl/ensembl-tools/archive/release/83.zip
• Zenodo deposit: https://zenodo.org/record/50492#.Vx9TJ5MrKEI
• Operating system: platform independent
• Programming language: Perl
• Other requirements: Perl 5.10 or higher and the DBI and DBD::mysql modules
• License: Apache 2.0
• Any restrictions to use by non-academics: none.

References

Large-scale discovery of novel genetic causes of developmental disorders.

Deciphering Developmental Disorders Study.

Nature. 2015 Mar 12;519(7542):223-8. doi: 10.1038/nature14135. PMID:25533962

Updated 11/15/2018

Research Points to Caution in Use of Variant Effect Prediction Bioinformatic Tools

Although we have the ability to use high throughput sequencing to identify allelic variants occurring in rare disease, correlation of these variants with the underlying disease is often difficult due to a few concerns:

• For rare sporadic diseases, classical gene/variant association studies have proven difficult to perform (Meyts et al. 2016)
• As Whole Exome Sequencing (WES) returns a considerable number of variants, how to differentiate the normal allelic variation found in the human population from disease-causing pathogenic alleles
• For rare diseases, pathogenic allele frequencies are generally low

Therefore, for these rare pathogenic alleles, the use of bioinformatics tools in order to predict the resulting changes in gene function may provide insight into disease etiology when validation of these allelic changes might be experimentally difficult.

In a 2017 Genes & Immunity paper, Line Lykke Andersen and Rune Hartmann tested the reliability of various bioinformatic software to predict the functional consequence of variants of six different genes involved in interferon induction and sixteen allelic variants of the IFNLR1 gene.  These variants were found in cohorts of patients presenting with herpes simplex encephalitis (HSE). Most of the adult population is seropositive for Herpes Simplex Virus (HSV) however a minor fraction (1 in 250,000 individuals per year) of HSV infected individuals will develop HSE (Hjalmarsson et al., 2007).  It has been suggested that HSE occurs in individuals with rare primary immunodeficiencies caused by gene defects affecting innate immunity through reduced production of interferons (IFN) (Zhang et al., Lim et al.).

References

Meyts I, Bosch B, Bolze A, Boisson B, Itan Y, Belkadi A, et al. Exome and genome sequencing for inborn errors of immunity. J Allergy Clin Immunol. 2016;138:957–69.

Hjalmarsson A, Blomqvist P, Skoldenberg B. Herpes simplex encephalitis in Sweden, 1990-2001: incidence, morbidity, and mortality. Clin Infect Dis. 2007;45:875–80.

Zhang SY, Jouanguy E, Ugolini S, Smahi A, Elain G, Romero P, et al. TLR3 deficiency in patients with herpes simplex encephalitis. Science. 2007;317:1522–7.

Lim HK, Seppanen M, Hautala T, Ciancanelli MJ, Itan Y, Lafaille FG, et al. TLR3 deficiency in herpes simplex encephalitis: high allelic heterogeneity and recurrence risk. Neurology. 2014;83:1888–97.

Genes Immun. 2017 Dec 4. doi: 10.1038/s41435-017-0002-z.

Frequently used bioinformatics tools overestimate the damaging effect of allelic variants.

Andersen LL¹, Terczyńska-Dyla E¹, Mørk N², Scavenius C¹, Enghild JJ¹, Höning K³, Hornung V^3,4, Christiansen M^5,6, Mogensen TH^2,6, Hartmann R⁷.

Abstract

We selected two sets of naturally occurring human missense allelic variants within innate immune genes. The first set represented eleven non-synonymous variants in six different genes involved in interferon (IFN) induction, present in a cohort of patients suffering from herpes simplex encephalitis (HSE) and the second set represented sixteen allelic variants of the IFNLR1 gene. We recreated the variants in vitro and tested their effect on protein function in a HEK293T cell based assay. We then used an array of 14 available bioinformatics tools to predict the effect of these variants upon protein function. To our surprise two of the most commonly used tools, CADD and SIFT, produced a high rate of false positives, whereas SNPs&GO exhibited the lowest rate of false positives in our test. As the problem in our test in general was false positive variants, inclusion of mutation significance cutoff (MSC) did not improve accuracy.

Methodology

1. Identification of rare variants
2. Genomes of nineteen Dutch patients with a history of HSE sequenced by WES and identification of novel HSE causing variants determined by filtering the single nucleotide polymorphisms (SNPs) that had a frequency below 1% in the NHBLI Exome Sequencing Project Exome Variant Server and the 1000 Genomes Project and were present within 204 genes involved in the immune response to HSV.
3. Identified variants (204) manually evaluated for involvement of IFN induction based on IDBase and KEGG pathway database analysis.
4. In-silico predictions: Variants classified by the in silico variant pathogenicity prediction programs: SIFT, Mutation Assessor, FATHMM, PROVEAN, SNAP2, PolyPhen2, PhD-SNP, SNP&GO, FATHMM-MKL, MutationTaster2, PredictSNP, Condel, MetaSNP, and CADD. Each program returned prediction scores measuring likelihood of a variant either being ‘deleterious’ or ‘neutral’. Prediction accuracy measured as

ACC = (true positive+true negative)/(true positive+true negative+false positive+false negative)

1. Validation of prediction software/tools

In order to validate the predictive value of the software, HEK293T cells, deficient in IRF3, MAVS, and IKKe/TBK1, were cotransfected with the nine variants of the aforementioned genes and a luciferase reporter under control of the IFN-b promoter and luciferase activity measured as an indicator of IFN signaling function.  Western blot was performed to confirm the expression of the constructs.

Results

Table 2 Summary of the bioinformatic predictions	HSE variants						IFNLR1 variants						Overall ACC
	TN	TP	FN	FP	Total	ACC	TN	TP	FN	FP	Total	ACC
Uniform cutoff
SIFT	4	1	0	4	9	0.56	8	1	0	7	16	0.56	0.56
Mutation assessor	6	1	0	2	9	0.78	9	1	0	6	16	0.63	0.68
FATHMM	7	1	0	1	9	0.89							0.89
PROVEAN	8	1	0	0	9	1.00	11	1	0	4	16	0.75	0.84
SNAP2	5	1	0	3	9	0.67	8	0	1	7	16	0.50	0.56
PolyPhen2	6	1	0	2	9	0.78	12	1	0	3	16	0.81	0.80
PhD-SNP	7	1	0	1	9	0.89	11	1	0	4	16	0.75	0.80
SNPs&GO	8	1	0	0	9	1.00	14	1	0	1	16	0.94	0.96
FATHMM MKL	4	1	0	4	9	0.56	13	0	1	2	16	0.81	0.72
MutationTaster2	4	0	1	4	9	0.44	14	0	1	1	16	0.88	0.72
PredictSNP	6	1	0	2	9	0.78	11	1	0	4	16	0.75	0.76
Condel	6	1	0	2	9	0.78							0.78
Meta-SNP	8	1	0	0	9	1.00	11	1	0	4	16	0.75	0.84
CADD	2	1	0	6	9	0.33	8	0	1	7	16	0.50	0.44
MSC 95% cutoff
SIFT	5	1	0	3	9	0.67	8	1	0	8	16	0.50	0.56
PolyPhen2	6	1	0	2	9	0.78	13	1	0	3	16	0.81	0.80
CADD	4	1	0	4	9	0.56	7	0	1	9	16	0.44	0.48

Note: TN: true negative, TP: true positive, FN: false negative, FP: false positive, ACC: accuracy

Functional testing (data obtained from reporter construct experiments) were considered as the correct outcome.

Three prediction tools (PROVEAN, SNP&GO, and MetaSNP correctly predicted the effect of all nine variants tested.

Updated 02/07/2021

InMeRF: prediction of pathogenicity of missense variants by individual modeling for each amino acid substitution

Jun-Ichi Takeda , Kentaro Nanatsue , Ryosuke Yamagishi , Mikako Ito , Nobuhiko Haga ², Hiromi Hirata , Tomoo Ogi , Kinji Ohno in NAR Genomics and  Bioinformatics. 2020 May 26;2(2):lqaa038.doi: 10.1093/nargab/lqaa038. eCollection 2020 Jun.

• PMID: 33543123
• PMCID: PMC7671370
• DOI: 10.1093/nargab/lqaa03

Abstract

In predicting the pathogenicity of a nonsynonymous single-nucleotide variant (nsSNV), a radical change in amino acid properties is prone to be classified as being pathogenic. However, not all such nsSNVs are associated with human diseases. We generated random forest (RF) models individually for each amino acid substitution to differentiate pathogenic nsSNVs in the Human Gene Mutation Database and common nsSNVs in dbSNP. We named a set of our models ‘Individual Meta RF’ (InMeRF). Ten-fold cross-validation of InMeRF showed that the areas under the curves (AUCs) of receiver operating characteristic (ROC) and precision-recall curves were on average 0.941 and 0.957, respectively. To compare InMeRF with seven other tools, the eight tools were generated using the same training dataset, and were compared using the same three testing datasets. ROC-AUCs of InMeRF were ranked first in the eight tools. We applied InMeRF to 155 pathogenic and 125 common nsSNVs in seven major genes causing congenital myasthenic syndromes, as well as in VANGL1 causing spina bifida, and found that the sensitivity and specificity of InMeRF were 0.942 and 0.848, respectively. We made the InMeRF web service, and also made genome-wide InMeRF scores available online (https://www.med.nagoya-u.ac.jp/neurogenetics/InMeRF/).

Source: https://pubmed.ncbi.nlm.nih.gov/33543123/

ADDRESS: A database of disease-associated human variants incorporating protein structure and folding stabilities

Jaie Woodard , Chengxin Zhang , Yang Zhang in J Mol Biol. 2021 Feb 1;166840. doi: 10.1016/j.jmb.2021.166840.

• PMID: 33539887
• DOI: 10.1016/j.jmb.2021.166840

Abstract

Numerous human diseases are caused by mutations in genomic sequences. Since amino acid changes affect protein function through mechanisms often predictable from protein structure, the integration of structural and sequence data enables us to estimate with greater accuracy whether and how a given mutation will lead to disease. Publicly available annotated databases enable hypothesis assessment and benchmarking of prediction tools. However, the results are often presented as summary statistics or black box predictors, without providing full descriptive information. We developed a new semi-manually curated human variant database presenting information on the protein contact-map, sequence-to-structure mapping, amino acid identity change, and stability prediction for the popular UniProt database. We found that the profiles of pathogenic and benign missense polymorphisms can be effectively deduced using decision trees and comparative analyses based on the presented dataset. The database is made publicly available through https://zhanglab.ccmb.med.umich.edu/ADDRESS.

Source: https://pubmed.ncbi.nlm.nih.gov/33539887/

PopDel identifies medium-size deletions simultaneously in tens of thousands of genomes

Sebastian Niehus , Hákon Jónsson , Janina Schönberger  , Eythór Björnsson, Doruk Beyter , Hannes P Eggertsson , Patrick Sulem  Kári Stefánsson , Bjarni V Halldórsson Birte Kehr 

in Nature Communications. 2021 Feb 1;12(1):730.doi: 10.1038/s41467-020-20850-5.

• PMID: 33526789
• DOI: 10.1038/s41467-020-20850-5

Abstract

Thousands of genomic structural variants (SVs) segregate in the human population and can impact phenotypic traits and diseases. Their identification in whole-genome sequence data of large cohorts is a major computational challenge. Most current approaches identify SVs in single genomes and afterwards merge the identified variants into a joint call set across many genomes. We describe the approach PopDel, which directly identifies deletions of about 500 to at least 10,000 bp in length in data of many genomes jointly, eliminating the need for subsequent variant merging. PopDel scales to tens of thousands of genomes as we demonstrate in evaluations on up to 49,962 genomes. We show that PopDel reliably reports common, rare and de novo deletions. On genomes with available high-confidence reference call sets PopDel shows excellent recall and precision. Genotype inheritance patterns in up to 6794 trios indicate that genotypes predicted by PopDel are more reliable than those of previous SV callers. Furthermore, PopDel’s running time is competitive with the fastest tested previous tools. The demonstrated scalability and accuracy of PopDel enables routine scans for deletions in large-scale sequencing studies.

Source: https://pubmed.ncbi.nlm.nih.gov/33526789/

Other articles related to Genomics and Bioinformatics on this online Open Access Journal Include:

Finding the Genetic Links in Common Disease: Caveats of Whole Genome Sequencing Studies

Large-scale sequencing does not support the idea that lower-frequency variants have a major role in predisposition to type 2 diabetes

US Personalized Cancer Genome Sequencing Market Outlook 2018 –

Icelandic Population Genomic Study Results by deCODE Genetics come to Fruition: Curation of Current genomic studies

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LIVE eProceedings Day One – The 14th Annual Personalized Medicine Conference: PREPARING FOR THE NEW POSSIBLE, November 14, 2018, HMS, Boston

Posted in Conference Coverage with Social Media, Personalized and Precision Medicine & Genomic Research on July 18, 2018| Leave a Comment »

LIVE eProceedings Day One – The 14th Annual Personalized Medicine Conference: PREPARING FOR THE NEW POSSIBLE,

November 14, 2018, HMS, Boston

 

Real Time Press Coverage: Aviva Lev-Ari, PhD, RN

 

#PMConf

@pharma_BI

@AVIVA1950

 

PART I

The Infrastructure for Innovation

“We need to make the transition to a predict, prevent and protect health system.”

— JOSHUA OFMAN, M.D., M.S.H.S.
Senior Vice President, Global Value, Access and Policy, Amgen

Keynote Speakers David King, J.D. Chairman, CEO LabCorp Elizabeth Nabel, M.D. President Brigham and Women’s Hospital Health IT at Partners and Mayo Clinic is the largest investment at comparable levels  – Digital Health: Payers, patient engagement is $233Billion projected for 2020 AI & Machine Learning, partnership with GE – Response to normal dose, automate disease prediction, treatment pathways, clinical trial eligibility assessment, prophylaxis plans, diagnosis and medical imaging 4 TRENDS in Academic Medicine: Consolidation of Health Systems in MA – efficiency of large care (EMR), increasing market care in relations with suppliers Outcomes of consolidations: reduced readmissions, collaborations MGH and BWH – 1.7 Billion of sponsored research, 17Billion assets under investment 3700 faculty appointed at HMS 452 research and clinical departments – Spaulding, MEEI, McLean Infrastructure for innovations: PARTNERS Healthcare Innovations: Patient Engagement & Education  – PM Opportunities AllofUs – precision Initiative BIOBANK – Partners Health Providers – Payer: Why Work together? 7% fee paid to BlueCross BlueShield to administer claims. – New company acquired to handle claims and save 7% paid to BC/BS – recovery ALWAYS HealthCare – marketed Retail Pharmacy – CVS Health – Aetna, OPTUM DeVita FUTURE partnerships: Amazon, Berkshire Hathaway and JP Morgan PATIENTS CHOICES are promoted while drug prices in the US is highest in the World Medicare Part D – accepts price does not compete Partners subsidize Genomics-based therapy [Millenial Physician-Scientists pushing ML and Genomics], mental health and primary care Daniel O’Day CEO Roche Pharmaceuticals

7:00 a.m.

Registration and Continental Breakfast

Joseph B. Martin Conference Center at Harvard Medical School
77 Avenue Louis Pasteur, Boston, MA 02115

8:00 a.m.

Opening Remarks

SPEAKER | Edward Abrahams, Ph.D., President, Personalized Medicine Coalition

• Evidence-based to interact with providers
• Future of Health care: Infrastructure of innovations, technologies and business aspects for efficient Health care delivery
• Strategies to stimulate Personalized Medicicne
• DIagnostics regulations for patient protection
• cost of drugs, therapy costs 7 figures
• clinical trial design
• Prevention of illness
• clinical and economic utilities of genome sequences
• moving health care to a better future
• Advisory committee contributed vastly in topic selection for the agenda for the two days conference
• Sponsores Intermountain Genomics

8:10 a.m.

Setting the Stage: Exploring the Promise of Personalized Medicine — A Keynote Address

SPEAKER | Elizabeth Nabel, M.D., President, Brigham and Women’s Hospital

8:55 a.m.

Shifting Systems: Identifying the Common Challenges and Notable Achievements of Government Efforts to Advance Personalized Medicine

Government executives have an enormous influence over the direction of health systems and can therefore play a role in developing personalized medicine — but they need to know what works and what doesn’t if their respective efforts to promote personalized medicine are going to succeed. With that in mind, government representatives from around the globe will discuss the competitive advantages various countries have in personalized medicine and explore the common challenges and notable achievements of government initiatives to advance the field during this panel discussion.

MODERATOR | Antonio L. Andreu, M.D., Ph.D., Scientific Director, EATRIS European Infrastructure for Translational Medicine, Amsterdam

• General Medicine: Metabolomics, biological systems vs GENOMICS
• Medical care – due to genomic testing, MDs will call to tell patients bad news

Ora Dar, Ph.D., Senior Expert, Medical Sciences, consultant to the Israel Innovation Authority

• $300Miliion  R&D sponsored clinical data on genomics, MDs are trained to place genomics data on EMR, epidemiology, sequencing of genetic diseases
• Genetic testing early implementation in Israel due to marriage of 3rd degree of kin,
• Private insurance vs Public Insurance for genomic testing, Healthcare Basket coverage of genomics is not complete
• 2nd largest HMO collect data from Patients arriving to their Annual Exam will be genomically sequenced = Healthy and ill Patients – data gathered

Tom Fowler, Ph.D., Deputy Chief Scientist, Genomics England

• Building infrastructure, education, future, National approach to genomic testing, built in a National lab, scaling research
• three areas: Technical Operations, samples from Urban Hospitals to rural areas, population Health and Population system, Patients need to be brought about, do they want genomic testing?
• rapid change in knowledge been flexible for genomic testing
• Fooled not to come on board for Genomics in Medicine
• Stories of patients that had success with Genomic-based therapy

Marc LePage, President, CEO, Genome Canada

• Social impact, adoption systems for focusing on rare diseases, following UK and US trends, 10 sites in Canada, aggregate the date at the National level, extract clinical data securely implementation and expertise needs transfer for having it in each hospital
• better health care at a lower cost can be achieved if genomics will become part of the equation – Open science
• Optimism and hope

Liisa-Maria Voipio-Pulkki, M.D., Ph.D., Director General, Chief Medical Officer, Ministry of Social Affairs and Health, Finland

• Public sector is the majority of Health care systems, Expertise is as high as can be, entrepreneurship is on the rise in search for opportunities
• Brought to Parliament decision on funding a Center for Genomics Research as an independent institute for experimental therapies
• Barrier in Health Care system National Screening and communication with other parts of the Health Care system
• TRUST: validation of algorithms, insurance disclosure, ethics of AI,
• Train new generation of CLinical Trial
• Evaluation of Health care systems – new methods are needed for that task
• pharmacogenomics – recommended for the Health system, ICU cost is very high will not change due to evidence from a genomic test which is preventative in nature 

10:10 a.m.

Networking Break

Light refreshments provided.

10:40 a.m.

Evaluating Patients’ Priorities: Understanding Perspectives on Personalized Medicine — A Fireside Chat

MODERATOR | Susan McClure, Founder, Publisher, Genome magazine; Board Member, Personalized Medicine Coalition

Emily Kramer-Golinkoff, Co-Founder, Emily’s Entourage, cystic fibrosis patient

• Better advocate for a Patient is the Patient him/herself

Bryce Olson, Global Marketing Director, Health and Life Sciences Group, Intel Corporation; stage IV prostate cancer patient

• Patient engage in their care, involvement in interpretation og ONES OWN Genome sequence is ate most engaging

11:10 a.m.

Automating Actionable: How Artificial Intelligence May Chart a Course for Personalized Medicine

Artificial intelligence may help inform personalized medicine in the future by perceiving which genes, proteins and other biological characteristics contribute to human disease. During this session, a diverse panel will discuss how artificial intelligence may accelerate drug development, improve clinical decision support and drive patient outcomes — and what that might mean for the future of health care.

MODERATOR | Colin Hill, Chairman, CEO, Co-Founder, GNS Healthcare

• Data, right data
• Investment is it in data acquisition or in AI
• AI is not taking over, data is critical
• Predictionds for 2025

Tom Miller, Managing Partner, GreyBird Ventures LLC

• algorithms are behind the firewall of the Hospital, for privacy. the patient’s identity is not of central point, privacy is the key
• Meta ontologies –  not ready yet
• Validation of AI algorithms
• India shortage of Radiologists
• China is a huge market – AI will do functions of MDs
• Oncology will lead the way in innovations

Gregg Talbert, Ph.D., Global Head of Digital and Personalized Health Care Partnering, Roche

• AI applied to capture unusual movement allowing to detect a forthcoming neurological event.
• Data is enough to get started on mutations
• Data falls short on Patient follow up (longitudinal data on Patients) curation of EMR IS NOT AN EASY OR AUTOMATED PROCESS, IMAGING SPACE: CURATION OF IMAGES is quite a problem,
• lack of productivity in the Healthcare system: records on transfer of patient to hospitals
• Clinical Trial data for drug approval, also for future use of the analyzed data
• In developing markets – solutions needs to be not a replication of the Western World medicine
• Prediction: Data problem will be resolved in some areas, drugs developed by synthethic biologytaget to diseases drugs developed by AI
• Patients will drive processes in Healthcare
• Roche participates in data sharing and transfer protocols

Darrell M. West, Ph.D., Vice President of Governance Studies and Director of Center for Technology Innovation, Douglas Dillon Chair in Governance Studies, The Brookings Institution

• Interest in AI and in particular: Health care large part of the economy Technologies:
• Radiology: SW used in detection of disease
• Personalized Medicine and AI
• Data mining of Text using AI
• Vital signs monitoring – providers can spot Arrhythmias earlier
• Data centers have rich data , data analysis is important in Financial institutions for upselling instruments. In healthcare there is room as well
• Future of Work book authored by Dr. West – augmentation of skills not replacement of
• NY, Cold Spring: Local vs National, local progressive regulation is worrisome, pattern will migrate from NY to MA, to CA, OR, WA.
• Mobile technology in Africa is advance: Global money, smart phones
• 2015: AI will transform other technologies: Communications, smart phones,

12:25 p.m.

Seated Luncheon

1:40 p.m.

The Lay of the Lab: Exploring the State of the Clinical Laboratory Testing Industry— A Keynote Address

SPEAKER | David King, J.D., Chairman, CEO, LabCorp

• Clinical outcomes at reduced cost
• PM – field with success
• challenges faced by PM
• BRCA  gene at Breast Cancer patient – drug approved by FDA for this cases only
• Genetic conseling assist physicians
• What is diagnostics: NOT ONLY a test,
• NGS is part of diagnostics strategy – enormous potential vs biopsy
• Genetic testing improve outcomes
• Insurance covers BRCA I AND BRCAII – ONLY
• COVERAGE OF GENETIC TESTING : RANDOMIZED, CONTROLLED TRIALS, published in peer reviewed literature and the test will prolong life – Test do not prolong life, therefore, the test does not improve outcomes – drugs do !!!
• PM individualized treatment for a specific disease
• drug improve outcomes
• Validity od data is fundamental, credibility of testing
• Right drug to right patient
• Migraine drug to be used
• Non respondent – further researched
• MDs understanding, confidence of results
• PM Promise: close education GAP,
• convene on VALUE for individual cases not populations
• assess value among initiatives
• PM – beyond Oncology
• Regulation of diagnostics

2:25 p.m.

The Diagnostics Discussion: Evaluating the Extent to Which the Regulatory and Reimbursement Environment for Diagnostic Tests May Help or Hinder Personalized Medicine

The U.S. Food and Drug Administration and the U.S. Centers for Medicare and Medicaid Services have been working for over a decade to develop regulatory and reimbursement pathways that promote the development of innovative diagnostic tests. Many observers, however, believe the current regulatory and reimbursement landscape still limits the field. This panel of business leaders will discuss the extent to which the existing frameworks and proposed policies may help or hinder personalized medicine.

MODERATOR | Joseph V. Ferrara, CEO, Boston Healthcare Associates

• Regulatory action for reimbursement of test
• new categories of tests: new payment if Innovation, PLA codes, 45 months approval, CPT codes

3. Michael Doherty, Senior Vice President, Head of Product Development, Head of Research & Development, Foundation Medicine – ex Genentech/Roche

• Operate in regulated environment, how to establish a company for long term
• companion diagnostics
• payment collection is the burden of the company
• challenge for future products, investment process changes

Julie Khani, President, American Clinical Laboratory Association

• FDA will establish a center for Diagnostics, proposal for pre-certification like in Medical devices
• congress is involved in the decision making
• Labs are prohibited from providing the data

2. Kimberly Popovits, Chairman of the Board, CEO, President, Genomic Health – Oncology, Breast cancer molecular diagnostics

• Genomic testing saved the Health care System billions of dollars
• Genomic testing will not be placebo, 12 years study controlled arms
• proprietary test vs. test offered by all labs — different markets
• Utility agreed upon like “MedicalDevicesInnovation.org”
• demonstrate a pathway of product development that was already followed

4. Mark P. Stevenson, Executive Vice President, Chief Operating Officer, Thermo Fisher Scientific

3:25 p.m.

Networking Break

Light refreshments provided.

Sponsored By

3:55 p.m.

Examining Policies: Exploring How Emerging U.S. Regulatory Approaches May Help Facilitate Personalized Care Regimens

The U.S. Food and Drug Administration (FDA) remains firmly committed to regulatory strategies designed to advance personalized medicine. During this wide-ranging fireside chat, two senior leaders from government and industry will discuss the agency’s latest actions impacting the oversight of personalized medicine products and services. The discussion will cover topics including but not limited to next-generation sequencing, diagnostic test regulation, digital health, and real-world evidence.

MODERATOR | Cynthia A. Bens, Senior Vice President, Public Policy, Personalized Medicine Coalition

Jess Berlin, Sc.D., Vice President and Global Head of epidemiology, J&J

• Proposal for protocol, comments from FDA several cycles – makes science better
• data source caused variability in results – challenge, primarily if one data source
• City patient involvement – Patient preference study
• Patient advocacy teach approach – ProjectLead teaching advocacy methods
• Devices: ecosystem test feasibility NIST,
• • breath and depth of data vs completeness (change in employer, data disappears)
  • consent patient to access EMR  – Patient access to own data

 Lauren Silvis, J.D., Chief of Staff, Immediate Office of the Commissioner, FDA

• FDA listen to stack holders in the diagnostics from the medical community for regulation development
• Pre-market review for test review
• Pre-certification
• Product reviews in 2019
• Digital Health – FDA develops
• Diagnostics – development with the industry participants

4:55 p.m.

Engaging Everyone: Leveraging Diversity and Facilitating Equitable Access to Personalized Care

Advancing a medical paradigm that focuses on the unique characteristics of each patient will require, by definition, that patients from diverse backgrounds participate in the medical studies that advance our understanding of disease. Also critical is the need to ensure that those patients have access to personalized care informed by those studies. During this session, four panelists will discuss the effort to ensure that all patient populations benefit equally from personalized medicine.

MODERATOR | Donna R. Cryer, J.D., President, CEO, Global Liver Institute

• diversity of the Genome initiative for inclusion and generalizability

Vence L. Bonham, Jr., J.D., Senior Advisor, Director on Genomics and Health Disparities, U.S. National Human Genome Research Institute

• Genomics data is of European dissents no diversity
• minority populations not represented
• sland populations not represented
• hispanics not represented proportionally to their share in the population

Alex J. Carlisle, Ph.D., Chairman, CEO, National Alliance Against Disparities in Patient Health

• PM with focus on disparities, racial biologic, socio ecological
• Patient centered – raise health education
• Translation for interpretation Physicians and Patients
• Cancer as a disorde,

Adolph P. Falcón, Executive Vice President, National Alliance for Hispanic Health

• community based organization 50 million
• improving healthcare access
• improve inclusion in science, no advancement in 45 years
• Hard to reach through – academic language of research protocols is not understood by patients
• common language, inclusion, diffusion to the field of the models that were developed to work
• PROTOCOL TO BE IN ENGLISH AND IN SPANISH
• OPERATES IN 24 CITIES
• Funding needed to find which clinical trials support inclusion “data not available”
• Community work must be married with Health initiatives

Edward Tepporn, Executive Vice President, Asian & Pacific Islander American Health Forum

• since 1985 minorities education
• all surveys conducted in English, Asian American
• access to affordable health care
• services to accommodate services for communities of Asian American
• encourage NIH to conduct studies on Asian American

5:55 p.m.

Closing Remarks

SPEAKER | Amy Abernethy, M.D., Ph.D., Chief Medical Officer, Chief Scientific Officer, Senior Vice President, Oncology, Flatiron Health

• Science is better than ever
• Technologies: AI, Countries with platforms
• Regulatory framework, reproducibility of results
• Taking care of people, standardization of data analysis
• Inclusion, populations that historically were excluded

6:10 p.m.

Departure for the Museum of Fine Arts, Boston

6:30 p.m.

Welcome Reception at the Museum of Fine Arts, Boston

Avenue of the Arts
465 Huntington Avenue
Boston, MA 02115

Sponsored by

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New York Times vs. Personalized Medicine? PMC President: Times’ Critique of Streamlined Regulatory Approval for Personalized Treatments ‘Ignores Promising Implications’ of Field

Posted in Personalized and Precision Medicine & Genomic Research, Personalized Medicine Coalition on June 13, 2018| Leave a Comment »

Contact: Christopher J. Wells Personalized Medicine Coalition cwells@personalizedmedicinecoalition.org 202-589-1770 FOR IMMEDIATE RELEASE   New York Times vs. Personalized Medicine? PMC President: Times’ Critique of Streamlined Regulatory Approval for Personalized Treatments ‘Ignores Promising Implications’ of Field WASHINGTON (June 13, 2018) — In response to an editorial published on June 9 by the New York Times titled “Easier Drug Approval, at What Price?,” Personalized Medicine Coalition (PMC) President Edward Abrahams today defends a series of decisions by the U.S. Food and Drug Administration (FDA) over the last several years that have streamlined the regulatory review process for personalized medicines. “Unlike FDA, which has been an engine for innovation under the direction of Scott Gottlieb and his predecessors, ‘Easier Drug Approval, at What Price?’ ignores the promising implications of reforms in regulatory science that FDA has put in place to facilitate a new appreciation of how different individuals respond to selected treatments,” Abrahams said. As PMC underlined earlier this year in a report titled Personalized Medicine at FDA: 2017 Progress Report, personalized medicines now account for one of every four drugs the agency approves. The Times’ editorial, which was also published online under the headline “Easier Drug Approval Isn’t Cutting Drug Prices,” contends that “it’s not clear that people, as opposed to drug companies, are feeling much benefit” from the streamlined regulatory review pathways that bring personalized treatments to market faster. Abrahams disagrees, noting that in non-small cell lung cancer, for example, a disease that was nearly untreatable 20 years ago, there are now multiple drugs on the market that target a patient’s particular tumor. As indicated by the U.S. National Cancer Institute (NCI), these treatments “improve the survival of subsets of patients with metastatic disease.” Furthermore, because targeted therapies zero in on specific cancerous mutations, doctors can use diagnostic tests to identify with much greater certainty the patients who will likely benefit from them, sparing expenses and side effects for those will not. That logic underpins FDA’s decision to streamline its regulatory processes, to ensure that patients who will benefit from promising targeted therapies — many of whom have few remaining options — can access those treatments without unnecessary delay. “By putting in place smarter policies to encourage the efficient development of personalized drugs whose safety and efficacy profiles are often higher than one-size-fits-all, trial-and-error treatments, FDA serves the interests not only of patients but also the health system, which spends too much money on ineffective treatments,” Abrahams said. To evaluate the American public’s interest in personalized medicine, PMC and GenomeWeb recently commissioned Public Perspectives on Personalized Medicine: A Survey of U.S. Public Opinion, which was published in May. Two-thirds of Americans indicated in the survey that they appreciate personalized medicine’s potential, and the majority expressed concerns about whether they will have access to personalized tests and treatments in the future. ### About the Personalized Medicine Coalition: The Personalized Medicine Coalition, representing innovators, scientists, patients, providers and payers, promotes the understanding and adoption of personalized medicine concepts, services and products to benefit patients and the health system. For more information, please visit www.personalizedmedicinecoalition.org.

SOURCE

From: “Christopher Wells (PMC)” <cwells@personalizedmedicinecoalition.org>

Date: Wednesday, June 13, 2018 at 3:15 PM

To: Aviva Lev-Ari <AvivaLev-Ari@alum.berkeley.edu>

Subject: Re: New York Times vs. Personalized Medicine?

 

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