Healthcare analytics, AI solutions for biological big data, providing an AI platform for the biotech, life sciences, medical and pharmaceutical industries, as well as for related technological approaches, i.e., curation and text analysis with machine learning and other activities related to AI applications to these industries.
Genetic Testing in CVD and Precision Medicine, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)
In 2010, we introduced an approach to the evaluation of a personal genome in a clinical context (7). A patient with a family history of coronary artery disease (CAD) and sudden death was evaluated by a cardiac clinical team in conjunction with whole genome sequencing and interpretation. The genomic analysis revealed an increased genetic risk for myocardial infarction and type 2 diabetes. In addition, a pharmacogenomics analysis was performed to assess how the genetics of the patient might influence response to certain drugs, including lipid-lowering therapies and warfarin (7). This clinical assessment, which focused heavily on cardiovascular risk, suggested that whole genome sequencing might provide clinically relevant information for patients.
A 2011 joint statement from the Heart Rhythm Society and the European Heart Rhythm association recommended genetic testing as a class I indication for patients with a number of channelopathies and cardiomyopathies, including long QT syndrome (LQTS), arrhythmogenic right ventricular cardiomyopathy, familial dilated cardiomyopathy (DCM), and hypertrophic cardiomyopathy (HCM) (8). Similarly, a statement from the American Heart Association and the American College of Cardiology recommended genetic testing for HCM, DCM, and thoracic aortic aneurysms to facilitate familial cascade screening and deduce causative mutations 9, 10.
The diagnostic power of genetic testing is significant across the spectrum of CVDs, ranging from cardiomyopathies to life-threatening arrhythmias 10, 11, 12. In the clinic, genetic testing can:
1.
clarify disease diagnoses: genetic testing can help to clarify the diagnosis of diseases that cause similar clinical presentation (e.g., cardiac hypertrophy could be TTR amyloidosis, Fabry disease, or sarcomeric HCM);
2.
facilitate cascade screening: genetic testing can help to identify relatives at risk for CVD before disease symptoms manifest if a disease-associated variant is found in a proband and then screened for in relatives;
3.
direct more precise therapy: genetic testing can help physicians choose appropriate treatments and plan appropriate timing of those treatments. For example, inherited connective tissue disease due to variants in ACTA2, MYH11, or TGFBR2 might prompt consideration of surgical intervention at a smaller aortic aneurysm diameter (13); and
4.
identify patients for targeted therapies: targeted medical therapies, including antibody-based therapeutics, gene editing, and silencing technologies, are available or under development for several genetic diseases, including LQTS, Duchenne muscular dystrophy (DMD), TTR cardiac amyloidosis (14), and Fabry disease 13, 15.
8. Ackerman M.J., Priori S.G., Willems S. HRS/EHRA expert consensus statement on the state of genetic testing for the channelopathies and cardiomyopathies: this document was developed as a partnership between the Heart Rhythm Society (HRS) and the European Heart Rhythm Association (EHRA) Europace. 2011;13:1077–1109. [PubMed] [Google Scholar]
9. Gersh B.J., Maron B.J., Bonow R.O. 2011 ACCF/AHA guideline for the diagnosis and treatment of hypertrophic cardiomyopathy: executive summary: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2011;58:2703–2738. [PubMed] [Google Scholar]
10. Harper A.R., Parikh V.N., Goldfeder R.L., Caleshu C., Ashley E.A. Delivering clinical grade sequencing and genetic test interpretation for cardiovascular medicine. Circ Cardiovasc Genet. 2017;10(2) [PubMed] [Google Scholar]
11. Walsh R., Thomson K.L., Ware J.S. Reassessment of Mendelian gene pathogenicity using 7,855 cardiomyopathy cases and 60,706 reference samples. Genet Med. 2017;19:192–203. [PMC free article] [PubMed] [Google Scholar]
12. Sturm A.C., Hershberger R.E. Genetic testing in cardiovascular medicine: current landscape and future horizons. Curr Opin Cardiol. 2013;28:317–325. [PubMed] [Google Scholar]
13. Caleshu C., Ashley E. Genetic testing for cardiovascular conditions predisposing to sudden death. In: Wilson M.G., Drezner J., editors. IOC Manual of Sports Cardiology. Wiley & Sons, Ltd; Hoboken, NJ: 2016. pp. 175–186. [Google Scholar]
14. Benson M.D., Dasgupta N.R., Rissing S.M., Smith J., Feigenbaum H. Safety and efficacy of a TTR specific antisense oligonucleotide in patients with transthyretin amyloid cardiomyopathy. Amyloid. 2017;24:217–223. [PubMed] [Google Scholar]
15. Parikh V.N., Ashley E.A. Next-generation sequencing in cardiovascular disease: present clinical applications and the horizon of precision medicine. Circulation. 2017;135:406–409. [PMC free article] [PubMed] [Google Scholar]
Core member and chair of the faculty, Broad Institute of MIT and Harvard; director, Klarman Cell Observatory, Broad Institute of MIT and Harvard; professor of biology, MIT; investigator, Howard Hughes Medical Institute; founding co-chair, Human Cell Atlas.
millions of genome variants, tens of thousands of disease-associated genes, thousands of cell types and an almost unimaginable number of ways they can combine, we had to approximate a best starting point—choose one target, guess the cell, simplify the experiment.
In 2020, advances in polygenic risk scores, in understanding the cell and modules of action of genes through genome-wide association studies (GWAS), and in predicting the impact of combinations of interventions.
we need algorithms to make better computational predictions of experiments we have never performed in the lab or in clinical trials.
Human Cell Atlas and the International Common Disease Alliance—and in new experimental platforms: data platforms and algorithms. But we also need a broader ecosystem of partnerships in medicine that engages interaction between clinical experts and mathematicians, computer scientists and engineers
Feng Zhang, PhD
investigator, Howard Hughes Medical Institute; core member, Broad Institute of MIT and Harvard; James and Patricia Poitras Professor of Neuroscience, McGovern Institute for Brain Research, MIT.
fundamental shift in medicine away from treating symptoms of disease and toward treating disease at its genetic roots.
Gene therapy with clinical feasibility, improved delivery methods and the development of robust molecular technologies for gene editing in human cells, affordable genome sequencing has accelerated our ability to identify the genetic causes of disease.
1,000 clinical trials testing gene therapies are ongoing, and the pace of clinical development is likely to accelerate.
refine molecular technologies for gene editing, to push our understanding of gene function in health and disease forward, and to engage with all members of society
Elizabeth Jaffee, PhD
Dana and Albert “Cubby” Broccoli Professor of Oncology, Johns Hopkins School of Medicine; deputy director, Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins.
a single blood test could inform individuals of the diseases they are at risk of (diabetes, cancer, heart disease, etc.) and that safe interventions will be available.
developing cancer vaccines. Vaccines targeting the causative agents of cervical and hepatocellular cancers have already proven to be effective. With these technologies and the wealth of data that will become available as precision medicine becomes more routine, new discoveries identifying the earliest genetic and inflammatory changes occurring within a cell as it transitions into a pre-cancer can be expected. With these discoveries, the opportunities to develop vaccine approaches preventing cancers development will grow.
shape how the culture of research will develop over the next 25 years, a culture that cares more about what is achieved than how it is achieved.
building a creative, inclusive and open research culture will unleash greater discoveries with greater impact.
John Nkengasong, PhD
Director, Africa Centres for Disease Control and Prevention.
To meet its health challenges by 2050, the continent will have to be innovative in order to leapfrog toward solutions in public health.
Precision medicine will need to take center stage in a new public health order— whereby a more precise and targeted approach to screening, diagnosis, treatment and, potentially, cure is based on each patient’s unique genetic and biologic make-up.
Eric Topol, MD
Executive vice-president, Scripps Research Institute; founder and director, Scripps Research Translational Institute.
In 2045, a planetary health infrastructure based on deep, longitudinal, multimodal human data, ideally collected from and accessible to as many as possible of the 9+ billion people projected to then inhabit the Earth.
enhanced capabilities to perform functions that are not feasible now.
AI machines’ ability to ingest and process biomedical text at scale—such as the corpus of the up-to-date medical literature—will be used routinely by physicians and patients.
the concept of a learning health system will be redefined by AI.
Linda Partridge, PhD
Professor, Max Planck Institute for Biology of Ageing.
Geroprotective drugs, which target the underlying molecular mechanisms of ageing, are coming over the scientific and clinical horizons, and may help to prevent the most intractable age-related disease, dementia.
Trevor Mundel, MD
President of Global Health, Bill & Melinda Gates Foundation.
finding new ways to share clinical data that are as open as possible and as closed as necessary.
moving beyond drug donations toward a new era of corporate social responsibility that encourages biotechnology and pharmaceutical companies to offer their best minds and their most promising platforms.
working with governments and multilateral organizations much earlier in the product life cycle to finance the introduction of new interventions and to ensure the sustainable development of the health systems that will deliver them.
deliver on the promise of global health equity.
Josep Tabernero, MD, PhD
Vall d’Hebron Institute of Oncology (VHIO); president, European Society for Medical Oncology (2018–2019).
genomic-driven analysis will continue to broaden the impact of personalized medicine in healthcare globally.
Precision medicine will continue to deliver its new paradigm in cancer care and reach more patients.
Immunotherapy will deliver on its promise to dismantle cancer’s armory across tumor types.
AI will help guide the development of individually matched
genetic patient screenings
the promise of liquid biopsy policing of disease?
Pardis Sabeti, PhD
Professor, Harvard University & Harvard T.H. Chan School of Public Health and Broad Institute of MIT and Harvard; investigator, Howard Hughes Medical Institute.
the development and integration of tools into an early-warning system embedded into healthcare systems around the world could revolutionize infectious disease detection and response.
But this will only happen with a commitment from the global community.
Els Toreele, PhD
Executive director, Médecins Sans Frontières Access Campaign
we need a paradigm shift such that medicines are no longer lucrative market commodities but are global public health goods—available to all those who need them.
This will require members of the scientific community to go beyond their role as researchers and actively engage in R&D policy reform mandating health research in the public interest and ensuring that the results of their work benefit many more people.
The global research community can lead the way toward public-interest driven health innovation, by undertaking collaborative open science and piloting not-for-profit R&D strategies that positively impact people’s lives globally.
Complex rearrangements and oncogene amplification revealed by long-read DNA and RNA sequencing of a breast cancer cell line, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)
Complex rearrangements and oncogene amplification revealed by long-read DNA and RNA sequencing of a breast cancer cell line
Reporter: Stephen J. Williams, PhD
In a Genome Research report by Marie Nattestad et al. [1], the SK-BR-3 breast cancer cell line was sequenced using a long read single molecule sequencing protocol in order to develop one of the most detailed maps of structural variations in a cancer genome to date. The authors detected over 20,000 variants with this new sequencing modality, whereas most of these variants would have been missed by short read sequencing. In addition, a complex sequence of nested duplications and translocations occurred surrounding the ERBB2 (HER2) while full-length transcriptomic analysis revealed novel gene fusions within the nested genomic variants. The authors suggest that combining this long-read genome and transcriptome sequencing results in a more comprehensive coverage of tumor gene variants and “sheds new light on the complex mechanisms involved in cancer genome evolution.”
Genomic instability is a hallmark of cancer [2], which lead to numerous genetic variations such as:
Copy number variations
Chromosomal alterations
Gene fusions
Deletions
Gene duplications
Insertions
Translocations
Efforts such as the Cancer Genome Atlas [3], and the International Genome Consortium (2010) use short-read sequencing technology to detect and analyze thousands of commonly occurring mutations however short-read technology has a high false positive and negative rate for detecting less common genetic structural variations {as high as 50% [4]}. In addition, short reads cannot detect variations in close proximity to each other or on the same molecule, therefore underestimating the variation number.
Methods: The authors used a long-read sequencing technology from Pacific Biosciences (SMRT) to analyze the mutational and structural variation in the SK-BR-3 breast cancer cell line. A split read and within-read mapping approach was used to detect variants of different types and sizes. In general, long-reads have better alignment qualities than short reads, resulting in higher quality mapping. Transcriptomic analysis was performed using Iso-Seq.
Results: Using the SMRT long-read sequencing technology from Pacific Biosciences, the authors were able to obtain 71.9% sequencing coverage with average read length of 9.8 kb for the SK-BR-3 genome.
A few notes:
Most amplified regions (33.6 copies) around the locus spanning the ERBB2 oncogene and around MYC locus (38 copies), EGFR locus (7 copies) and BCAS1 (16.8 copies)
The locus 8q24.12 had the most amplifications (this locus contains the SNTB1 gene) at 69.2 copies
Long-read sequencing showed more insertions than deletions and suggests an underestimate of the lengths of low complexity regions in the human reference genome
Found 1,493 long read variants, 603 of which were between different chromosomes
Using Iso-Seq in conjunction with the long-read platform, they detected 1,692,379 isoforms (93%) mapping to the reference genome and 53 putative gene fusions (39 of which they found genomic evidence)
A table modified from the paper on the gene fusions is given below:
Table 1. Gene fusions with RNA evidence from Iso-Seq and DNA evidence from SMRT DNA sequencing where the genomic path is found using SplitThreader from Sniffles variant calls. Note link in table is GeneCard for each gene.
SplitThreader found two different paths for the RAD51B-SEMA6D gene fusion and for the LINC00536-PVT1 gene fusion. Number of Iso-Seq reads refers to full-length HQ-filtered reads. Alignments of SMRT DNA sequence reads supporting each of these gene fusions are shown in Supplemental Note S2.
References
Nattestad M, Goodwin S, Ng K, Baslan T, Sedlazeck FJ, Rescheneder P, Garvin T, Fang H, Gurtowski J, Hutton E et al: Complex rearrangements and oncogene amplifications revealed by long-read DNA and RNA sequencing of a breast cancer cell line.Genome research 2018, 28(8):1126-1135.
Kandoth C, McLellan MD, Vandin F, Ye K, Niu B, Lu C, Xie M, Zhang Q, McMichael JF, Wyczalkowski MA et al: Mutational landscape and significance across 12 major cancer types. Nature 2013, 502(7471):333-339.
Sudmant PH, Rausch T, Gardner EJ, Handsaker RE, Abyzov A, Huddleston J, Zhang Y, Ye K, Jun G, Fritz MH et al: An integrated map of structural variation in 2,504 human genomes. Nature 2015, 526(7571):75-81.
Other articles on Cancer Genome Sequencing in this Open Access Journal Include:
scPopCorn: A New Computational Method for Subpopulation Detection and their Comparative Analysis Across Single-Cell Experiments
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
4.2.5 scPopCorn: A New Computational Method for Subpopulation Detection and their Comparative Analysis Across Single-Cell Experiments, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 4: Single Cell Genomics
Present day technological advances have facilitated unprecedented opportunities for studying biological systems at single-cell level resolution. For example, single-cell RNA sequencing (scRNA-seq) enables the measurement of transcriptomic information of thousands of individual cells in one experiment. Analyses of such data provide information that was not accessible using bulk sequencing, which can only assess average properties of cell populations. Single-cell measurements, however, can capture the heterogeneity of a population of cells. In particular, single-cell studies allow for the identification of novel cell types, states, and dynamics.
One of the most prominent uses of the scRNA-seq technology is the identification of subpopulations of cells present in a sample and comparing such subpopulations across samples. Such information is crucial for understanding the heterogeneity of cells in a sample and for comparative analysis of samples from different conditions, tissues, and species. A frequently used approach is to cluster every dataset separately, inspect marker genes for each cluster, and compare these clusters in an attempt to determine which cell types were shared between samples. This approach, however, relies on the existence of predefined or clearly identifiable marker genes and their consistent measurement across subpopulations.
Although the aligned data can then be clustered to reveal subpopulations and their correspondence, solving the subpopulation-mapping problem by performing global alignment first and clustering second overlooks the original information about subpopulations existing in each experiment. In contrast, an approach addressing this problem directly might represent a more suitable solution. So, keeping this in mind the researchers developed a computational method, single-cell subpopulations comparison (scPopCorn), that allows for comparative analysis of two or more single-cell populations.
The performance of scPopCorn was tested in three distinct settings. First, its potential was demonstrated in identifying and aligning subpopulations from single-cell data from human and mouse pancreatic single-cell data. Next, scPopCorn was applied to the task of aligning biological replicates of mouse kidney single-cell data. scPopCorn achieved the best performance over the previously published tools. Finally, it was applied to compare populations of cells from cancer and healthy brain tissues, revealing the relation of neoplastic cells to neural cells and astrocytes. Consequently, as a result of this integrative approach, scPopCorn provides a powerful tool for comparative analysis of single-cell populations.
This scPopCorn is basically a computational method for the identification of subpopulations of cells present within individual single-cell experiments and mapping of these subpopulations across these experiments. Different from other approaches, scPopCorn performs the tasks of population identification and mapping simultaneously by optimizing a function that combines both objectives. When applied to complex biological data, scPopCorn outperforms previous methods. However, it should be kept in mind that scPopCorn assumes the input single-cell data to consist of separable subpopulations and it is not designed to perform a comparative analysis of single cell trajectories datasets that do not fulfill this constraint.
Several innovations developed in this work contributed to the performance of scPopCorn. First, unifying the above-mentioned tasks into a single problem statement allowed for integrating the signal from different experiments while identifying subpopulations within each experiment. Such an incorporation aids the reduction of biological and experimental noise. The researchers believe that the ideas introduced in scPopCorn not only enabled the design of a highly accurate identification of subpopulations and mapping approach, but can also provide a stepping stone for other tools to interrogate the relationships between single cell experiments.
First Cost-Effectiveness Study of Multi-Gene Panel Sequencing in Advanced Non-Small Cell Lung Cancer Shows Moderate Cost-Effectiveness, Exposes Crucial Practice Gap, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)
First Cost-Effectiveness Study of Multi-Gene Panel Sequencing in Advanced Non-Small Cell Lung Cancer Shows Moderate Cost-Effectiveness, Exposes Crucial Practice Gap
WASHINGTON (June 27, 2019) — The results of the first economic modeling study to estimate the cost-effectiveness of “multi-gene panel sequencing” (MGPS) as compared to standard-of-care, single-gene tests for patients with advanced non-small cell lung cancer (aNSCLC) show that the MGPS tests are moderately cost-effective but could deliver more value if patients with test results identifying actionable genetic mutations consistently received genetically guided treatments. The results of the study, which was commissioned by the Personalized Medicine Coalition (PMC), underline the need to align clinical practices with an era of personalized medicine in which physicians can use diagnostic tests to identify specific biological markers that inform targeted prevention and treatment plans.
The study, which was published yesterday in JCO Clinical Cancer Informatics, analyzed the clinical and economic value of using MGPS testing to identify patients with tumors that over-express genetic mutations that could be targeted by available therapies designed to inhibit the function of those genes — a mainstay of modern care for aNSCLC patients. Using data provided by Flatiron Health, researchers examined clinical and cost information associated with the care of 5,688 patients with aNSCLC treated between 2011 – 2016, separating them into cohorts who received MGPS tests that assess at least 30 genetic mutations at once and those who received only “single-marker genetic testing” (SMGT) of less than 30 genes.
Compared to SMGT, the MGPS testing strategy, including downstream treatment and monitoring of disease, incurred costs equal to $148,478 for each year of life that it facilitated, a level suggesting that MGPS is moderately cost-effective compared to commonly cited thresholds in the U.S., which range from $50,000 to $200,000 per life year (LY) gained.
The authors of the study point out, however, that physicians only prescribed a targeted therapy to some of the patients whose MGPS test results revealed actionable mutations. MGPS tests can only improve downstream patient outcomes if actionable results are used to put the patient on a targeted treatment regimen that is more effective than the therapy they would otherwise have been prescribed. It is therefore impossible for the cost of an MGPS test to translate into additional LYs if actionable results do not result in the selection of a targeted treatment regimen.
Although MGPS testing revealed actionable mutations in 30.1 percent of the patients in the study cohort, only 21.4 percent of patients who underwent MGPS testing received a targeted treatment.
The study’s authors calculated that if all MGPS-tested patients with actionable mutations had received a targeted therapy, MGPS testing would deliver measurably better value ($110,000 per LY gained).
“This research underlines the importance of ensuring that clinical practices keep pace with scientific progress in personalized medicine so that we can maximize the benefits of diagnostic tests that can improve patient care and make the health system more efficient by ensuring that safe and effective targeted therapies are prescribed to those patients who will benefit,” said PMC President Edward Abrahams.
The study’s authors include Dr. Lotte Steuten, Vice President and Head of Consulting, The Office of Health Economics, London, U.K., and Affiliate Associate Faculty Member, Hutchinson Institute for Cancer Outcomes Research, Fred Hutchinson Cancer Research Center; Dr. Bernardo Goulart, Associate Faculty Member, Hutchinson Institute for Cancer Outcomes Research, Fred Hutchinson Cancer Research Center; Dr. Neal Meropol, Vice President, Research Oncology, Flatiron Health; Dr. Daryl Pritchard, Senior Vice President, Science Policy, Personalized Medicine Coalition; and Dr. Scott Ramsey, Director, Hutchinson Institute for Cancer Outcomes Research, Fred Hutchinson Cancer Research Center.
###
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.
Tweets and Re-Tweets by @Pharma_BIand @AVIVA1950 at 2019 Petrie-Flom Center Annual Conference: Consuming Genetics: Ethical and Legal Considerations of New Technologies, Friday, May 17, 2019 from 8:00 AM to 5:00 PM EDT @Harvard_Law
#DTCgenome@PetrieFlom@pharma_BI@AVIVA1950 Leila Jamal, NIAID Pre- Test Genetic Counseling – information and testing need, indication for testing Post-Test Informational Burden low vs high: Likely pathogenic, Pathogenic benign – natural history data potentially high impact
#DTCgenome@PetrieFlom@pharma_BI@AVIVA1950 Leila Jamal, NIAID benefit the patient, positive autonomy, benefiesence – how potentially impactful is the Test Information Nondirectiveness – Why? distance from eugenics + abortion politics persons and patient autonomy
#DTCgenome@PetrieFlom@pharma_BI@AVIVA1950 Natalie RamGenetic Genealogy and the Problem of Familial Forensic Identification Familial Forensic Identification – Privacy for information held by Telephone companies Involuntarily Identification by genetic data genetic markers
#DTCgenome@PetrieFlom@pharma_BI@AVIVA1950 Natalie Ram, Assistant Professor of Law, University of Baltimore School of Law – Genetic Genealogy and the Problem of Familial Forensic Identification Opt in to share genetic data on the platforms opt in national DB
#DTCgenome@PetrieFlom@pharma_BI@AVIVA1950 Natalie Ram, Univ of Baltimore School of Law Genetic relatedness is stickier than social relations Voluntary sharing of genetic information – no other party can protect genetic information of any person, thu, if shared voluntarily
#DTCgenomic @PetrieFlom @Pharma_BI @AVIVA1950 gene APO-E e-2, e-3, e-4 If e-4 variant risk AD is 40% 23andMe since 2011 rest for e-4 unlock result # copies of e-4 are present little clinical value post diagnosis recommendation do not depend on e-4
#DTCgenome@PetrieFlom@pharma_BI@AVIVA1950 Jonathan Kahn, http://neu.edu Precision Medicine and the Resurgence of Race in Genomic Medicineprecision medicine – classification of individuals into subpopulations that differ in their susceptability to a particular disease
#DTCgenome@PetrieFlom@pharma_BI@AVIVA1950 Kif Augustine-Adams, BYU Law School – Generational Failures of Law and Ethics: Rape, Mormon Orthodoxy, the Revelatory Power of Ancestry DNAComplex Sorrows: Anscestry DNA – 20 Millions records. Complete anonymity and privacy collapsed
#DTCgenome@PetrieFlom@pharma_BI@AVIVA1950 Regulating Consumer Genetic Technologies Conclusions: DTC policies are over the place, FDA poised to regulate Big Data, Human Genomics Somatic vs Germline are key distinctions NY Dept Health 3rd Party Certification in Genomics
#DTCgenome@PetrieFlom@pharma_BI@AVIVA1950 Scott Schweikart, Council on Ethical and Judicial Affairs, American Medical Association and Legal Editor, AMA Journal of Ethics – Human Gene Editing: An Ethical Analysis and Arguments for Regulatory Guidance National and Global Levels
#DTCgenome@PetrieFlom@pharma_BI@AVIVA1950 Catherine M. Sharkey, The Emerging Role of the FDA Genetic predisposition – BRCA I & II approved Testing Pharmaco-genetic Test authorization incorrect interpretation, incorrect action based on results False positive and False negative
#DTCgenome@PetrieFlom@pharma_BI@AVIVA1950 Scott Schweikart, AMA Ethical concernsTechnologiesCRISPR-Cas-9 Somatic vs GermlineAMA: Individual liberty (1) Autonomy & Gene Editing (2) Non-maleficence and Beneficence (3) Social Justice Treatment vs Enhancement National Regulations
#DTCgenome@PetrieFlom@pharma_BI@AVIVA1950 Patricia J. Zettler, Regulators can do: Promote self regulations vs restrict community labs Drugs: premarket approval by FDA 11/2017: any use of CRISPR is subjected to regulation Bio hacking materials are distributed outside channels
#DTCgenome@PetrieFlom@pharma_BI@AVIVA1950 Patricia J. Zettler, FDA agency – regulation can’t reach everything, Not seen wide range abuse, FDA encourage learning and information dissemination and Educate
#DTCgenome@PetrieFlom@pharma_BI@AVIVA1950 Maxwell J. Mehlman, Governing Non-Traditional Biology On-Line gene editing equipment CRISPR-Cas9 – IGEM – international Competition in community of Scientists Biological weapons – issues of Prior Art impeding patentability may come up
#DTCgenome@PetrieFlom@pharma_BI@AVIVA1950 Maxwell J. Mehlman Harm to subjects Biosafety Safety Phase I Gene drives in Human?? – Human gene editing: “Nanoparticle and liposomal delivery” and “Allelic drive using CRISPR”
#DTCgenome @PetrieFlom @Pharma_BI @AVIVA1950 regulatory options: liabilities, legal requirements industry restrictions on access to material community labs, NTB IRBs self-governing bodies FBI surveillance
#DTCgenome@PetrieFlom@pharma_BI@AVIVA1950 Barbara J. Evans Is it FDA duty on Cosmetic enhancement Genome is Software, US is not good in regulating software The Harm Principle, Legal Paternalism benevolent vs non-benevolent Legal Moralism – no body is harmed but it’s just wrong
#DTCgenome@PetrieFlom@pharma_BI@AVIVA1950 Barbara J. Evans Multiple Agencies: In the 80’s on Future Products of Biotechnology: EPA, FDA, USDA, OSHA, CPSC, NIH, NEPA, ESA, APA Skepticism that compulsory regulation for compliance with norms
#DTCgenome@PetrieFlom@pharma_BI@AVIVA1950 Barbara J. Evans Regulatory Challenges Citizen Science and DIY Bio democratization of science and medicine narrative, new frontiers for institutional science narrative nostalgia narrative, political narrative: “hacker” portrayals
#DTCgenome@PetrieFlom@pharma_BI@AVIVA1950 Seema Mohapatra, AAbolishing the Myth of “Anonymous” Gamete Donation in the Age of Direct-to-Consumer Genetic TestingAnonymous sperm donation Sell sperm $30 – $130 per sample – industry is thriving due to donor anonymity last 3 years,
#DTCgenome@PetrieFlom@pharma_BI@AVIVA1950 Seema Mohapatra, 2.6 million Ancestry DNA only to keep donor Anonymity Donor-Conceived Individuals at age 18 can identified the DonorLegal landscape ART – no federal laws regarding UT and WA [medical disclosure about the donor
#DTCgenome@PetrieFlom@pharma_BI@AVIVA1950 Nita Farahany, Professor Law, Philosophy Duke Law School need new Framework if anonymity is dead, most uses are diverted for medicinePrivacy is improving, ACA – protects from preexisting conditionsIndividual costs vs societal benefits
#DTCgenome@PetrieFlom@pharma_BI@AVIVA1950 Kayte Spector-Bagdady, Data coming into Academia – Genetic data partnerships Academia (41% NIH funding) and Industry: Use of existing private data, company performs analysisPatients: using data and specimens in ways they do not wish
#DTCgenome@PetrieFlom@pharma_BI@AVIVA1950 Kayte Spector-Bagdady, to secondary research: stay anonymousPublic health covers Informed consent forms – conceptualize for secondary research protocols Transparency In BioBank Research 67% commercialization of biospecimens agree
#DTCgenome@PetrieFlom@pharma_BI@AVIVA1950 Property and Health Data: Excludability, Alienability and Divisibility, Valuation and compensation, Unstewarded and Orphan data, duration, tracking Propertization of medical information effect on biomedical research
#DTCgenome@PetrieFlom@pharma_BI@AVIVA1950 direct consumer protection may get that by Claim of conversion – Common Law Genetic Testing companies are protected by three legal laws consumers as employees face genetic information been accessed by employers via Wellness Programs
#DTCgenome@PetrieFlom@pharma_BI@AVIVA1950 Courts shows a newfound openness to claims for genetic conversion claims will not stifle reaserch or create moral harms consumers genetics, claims for genetic conversion necessary to adequately protect people’s interests in their DNA
#DTCgenome @PetrieFlom @Pharma_BI @AVIVA1950 three new regulations of ownership of genetic test information ownership even Dx of breast cancer Insurance may not cover BRCA testing
#DTCgenome@PetrieFlom@pharma_BI@AVIVA1950 Family not in treatment relationship with the Researcher – Court rejected the claim family donated to research unfair benefir of the Hospital from the data and tissue donatedClaim of conversion – Common LawGene by Gene Family Tree DNA
#DTCgenome@PetrieFlom@pharma_BI@AVIVA1950 wellness Programs Test for preventing genetic conditions Like BRCA, Lynch syndrome, preventable – win/win proposition –>>> Healthier employees. Studies show shift of cost from employer to employee and employer have access to genetic i
In reviewing how @US_FDA reviewed various @23andMe tests, Catherine Sharkey @nyulaw discusses how some were reviewed through De Novo and others through 510(k) pathway and benefits and drawbacks of each.
Talking about her team’s @Health_Affairs paper @KayteSB shows most patients want notification of commercial use of biospecimens, most are uncomfortable about profit from biospecimens, but feel better if reinvested in research. #DTCgenome @PetrieFlom
Excellent, powerful talk-made not weaker bec/no solution is in sight. People want anonymity as adoption “birth parents” for good personal reasons, & had justified expectations of it. Children have real interests in knowing. Genetic genealogy means it will (almost) always be known …
I asked Maxwell Mehlman how he envisioned biohackers could form an IRB-style review process. One suggestion was to engage with insitutional IRBs. Raise your hand if you think an establishment IRB would approve #communitybio enhancement experiments? (I don’t…) #DTCgenome
Gene editing has become cheaper, easier to do in community labs. Max Mehlman @cwru compares it to where Steve Jobs and Bill Gates began with the personal computer. But US gov has listed #CRISPR as a “weapon of mass destruction” #DTCgenome @PetrieFlom
Is @23andMe ‘s test for APOE associated with Alzheimer’s different ethically speaking from its other tests? @emily_a_largent @PennMedicine discuss @PetrieFlom #DTCgenome
“Diversity” means a LOT of different things–it’s very easy to slip back and forth (problematically) between molecular/genetic diversity and social constructs of race. Just using “diversity” elides and blurs important concepts. – Jonathan Kahn @ #DTCGenome
Audience member during Q&A calls the first group of talks “very very interesting — and terrifying.” That’s what we’re here for, folks. These issues are real and we’re happy you’re here to talk about them with us. @PetrieFlom#DTCgenome
#DTCgenomics Just FYI my research showed you cannot waive statutory nondiscrim rights (under GINA or others) but can waive right to judicial forum to decide if there has been a violation (2009 Pyett v 14 Penn Plaza decision-ie case after GINA-overturned 30 years of precedence)
“If I want to edit my genes and make my skin glow green, whose business is that?” Barbara Evans on paternalism issues in our views of regulating DIYbio #DTCgenome
Takeaways from Regulating #DTCGenome: Hazel: existing DTC genetic privacy policies are all over the place Sharkey: in an era of big data, FDA is poised to pose enhanced role as health information regulator Schweikart: in gene editing, somatic ≠ germline editing
I have been waiting so long for this and #dtcgenome is finally here!
Alex Pearlman 💡added,
Vardit Ravitsky@VarditRavitsky
Kicking off what I expect to be a fascinating evening of discussions of consumer genetics at the American Acedemy of Arts and Sciences @americanacad organized by @PetrieFlom@Harvard#DTCgenome kudos …
Many of the regulatory issues/possibilities raised by DIYbio will presented by @pzettler (co-authors @jsherkow and @cjguerrini) “What can we do with what we’ve already got?” #DTCgenome
Moderators summary: In today’s panel we heard: -Property law won’t work -Anonymity is dead -Data is being commercialized and we don’t realize it -May be need for publicity rights for DNA. But there is hope. Good things are being done with this data. @PetrieFlom#DTCgenome
Is @Madonna right, asks Vertinsky @EmoryLaw,to be worried about “genetic #Paparazzi” publishing of information derived from your genetic information (especially discarded DNA). Or a presidential candidate @potus ? What role for law? #DTCgenome @PetrieFlom
Interesting points by @pzettler: Because many biohacking materials exchanges may not take place in traditional commercial contexts, attempting to regulate the trade of materials could prove difficult for FDA. #DTCgenome
We have not seen much FDA involvement in “genetic biohacking” says @pzettler, but that might be a shame.Don’t need “harsh involvement” but “engagement” such as education — e.g., how long you can leave potato salad out at picnic, does not mean enforcement #dtcgenome@PetrieFlom
On genetic ownership and federalism. @contreraslegals discusses the 5 states that have protected genetic property and skeptical about how well thought out the common law property approach has been. #DTCgenome @PetrieFlom @UUtah
“When you’re doing something that’s really high risk and cutting edge, maybe you SHOULD experiment on yourself–maybe that’s the most ethical way.” Barbara Evans talks up self-experimentation (reffing previous Nobels) @ #DTCGenome
I feel simultanously very overwhelmed and very excited #DTCGenome
Alex Pearlman 💡added,
Vardit Ravitsky@VarditRavitsky
A very full house today (480 people registered!) for the Consuming Genetics conference @HarvardLaw@PetrieFlom. @CohenProf opening a day that promises to be fascinating. Kudos also @HankGreelyLSJU …
“Whatever the boundaries of FDA’s authority are [re: biohacking]…there are important questions about how it should use that authority.” @pzettler @ #DTCGenome
One person uploading info to a genetic database illuminates hundreds or thousands of other people–those people’s info isn’t “voluntarily” in datasets. Genetic databases familial searches aren’t voluntary. Natalie Ram @ #DTCGenome
DIY gene therapy, CRISPR, etc. – failures likely to cause more harm (inadvertent) than successes. Speaker at #DTCgenome analogizes to regulation of drones, beer, computer hacking many stakeholders with competing interests.
“We need to rethink our Informed Consent methods for our secondary research protocols” – given all the confusion arising among Patients, their Doctors and the Researchers working with the data specimens about the use of the data, says @KayteSB#DTCgenome – at Wasserstein Hall
How do we deal with Publicity Rights in DNA? Thought-provoking talk #DTCgenome by Professor Vertinsky of @EmoryLaw The “Genetic Paparazzi” conundrum – at Wasserstein Hall
@contreraslegals argues against recognizing Property Rights in personal health data: “A vast amount of ‘orphan Biomedical data’ is useless” – doesn’t help advance research in the field Other protections already available and more suitable #DTCgenome
Professor Kif Augustine-Adams of @BYULawSchool says that individual privacy settings on Consumer Genetics testing have limited power; total anonymity is a myth. It is only a matter of time before the relational nature of DNA makes all connections identifiable. #DTCgenome – at Wasserstein Hall
“Wellness Programs” by Employers or Insurance underwriters – how should they deal with collecting genetic data? @_anyaprince suggests Employers / Insurers only act as mediators between members and DTC genetic testing companies, and only get aggregate, anonymized data #DTCgenome – at Harvard Law School
Natalie Ram: there’s an idea of voluntariness re: searching & genetic information. THAT’S FICTION. Genetic relatedness is different–it’s sticky! “I could decline my aunt’s FaceBook request…but [she] can still serve as reliable-as-ever genetic informant on me.” #DTCGenome
A lot of thought-provoking posts this month from leading scholars in law, ethics, genetics. Get immersed in the issues before Friday’s #DTCgenome@petrieflom conference!
Alex Pearlman 💡added,
Petrie-Flom Center@PetrieFlom
We have a digital symposium running as a complement to our Annual Conference on #DTCgenome this week— Check it out! Posts include this one and many more! “Ethnic Identity and Genomics Research: Toward Creating Culturally Sensitive Policies and Practices” …
It’s not every day that a serious conference on #DTCgenome discusses Brad Pitt in a bath leaving behind sperm that later impregnates a woman and the legal challenges that emerge. Well done @NitaFarahany@CohenProf@profmohapatra – you managed to get everyone’s attention 🙂
Anguishing story told with elegance and grace. We are all utterly unprepared for generations of secrets unearthed by 26 million ++ #DTCGeneticTesting kits sold to date. @PetrieFlom#DTCGenome#ethics
Ronnie S Stangler, MD added,
I. Glenn Cohen@CohenProf
You can hear a pin drop in the auditorium as Kif Augustine @BYULawSchool tells a very personal tale about how #dtcgenome reveals a story of rape and a lost half sister. Secrets, lies, ancestry, DNA, and Mormon Orthodoxy in 1959 Utah. @PetrieFlom
“Civilized societies are nearby, believe it or not!” @VarditRavitsky explains how when #NIPT is implemented in Canada, it means the government pays for it. (We are all v jealous about your developed country to the north, Vardit) #DTCgenome
Yes! And we should continue to strive to have racial and ethnic representation to ensure that genomic research and policy doesn’t continue to exacerbate racial disparities #DTCGenome
Tala Berro added,
Nicholson Price@WNicholsonPrice
Terrific representation of women at #DTCGenome! Speakers: 14F, 6M Moderators: 4F, 2M…
Are celebs and politicians who have cleaning crews come in when they leave a place, like Madonna, paranoid or prophetic? It’s only a matter of time until we’re dealing with “genetic paparazzi” says Liza Vertinsky. #DTCgenome
Potential consequences are greater when editing germline compared with somatic cells, because its modification can allow for the generational transmission of altered genes. @scottschweikart laying out priciplist bioethical concerns of #geneediting#DTCgenome
Health information should not be treated as property to protect individuals, says @contreraslegals. Instead, we should continue to enhance existing regulatory and liability rules to safeguard individual privacy and data security. #DTCgenome
There has been a relunctance by courts to recognize information as property, but that could change drastically when it comes to genetic data. @contreraslegals#DTCgenome
Major disconnect with the ideas of ways to convert health data into what we have traditionally considered property-like rights. #dtcgenome @contreraslegals
FDA involvement with DTC tests hasn’t shut them down. Five have been approved, and FDA has been flexible in its approval pathway (4 de novo, 1 510(k)). – Catherine Sharkey @ #DTCgenome
Great turnout at @Harvard_Law@PetrieFlom DTC genomics conference today. Tour de force discussion of the issues facing the personal genomics industry and consumers today. #DTCgenome@SJQ_LABS
Jorge Contreras added,
I. Glenn Cohen@CohenProf
With over 400 registrants #dtcgenome @PetrieFlom is off to a great start. Follow that hashtag for our conference “Consuming Genetics: Ethical and Legal Considerations of New Technologies”
“Informed consent is a process” that should include: test’s purpose, possible results of the test, test’s limitations/consequences, confidentiality/privacy, risks of testing and familial implications, and voluntary participation. #DTCgenome@VeritasGenetics
The amazing @VarditRavitsky closes out the conference by discussing the ethics of non-invasive prenatal testing (NIPT), it’s ethical challenges, and how whole genome NIPT may make “the fetus transparent.” #DTCgenome @PetrieFlom
Ultrasound technology made the uterus transparent, so parents could see their child before it was born. In the future, #NIPT could make the fetus itself transparent, so parents can see the whole genome. Many associated ethical challenges, both pre- and post-birth #DTCgenome
The #GoldenStateKiller case opened our eyes to how law enforcement can use direct-to-consumer testing data. A recent article explores privacy and discrimination issues and loopholes in the era of direct-to-consumer genetic testing: http://bit.ly/2PJkfRS#GCchat
Johnathan on The Fall and Rise of Race in Genomics: – not a thing (2000) – a stepping stone to true targeting (2005) – useful to classify subpopulations (2011) – under-representation of ethnically diverse subpopulations are necessary for good data (2019) #DTCGenome
When #DTCGenome tests allow the breach of anonymity and privacy of relatives who don’t want to be known–including in cases of rape–what should we do? Answers aren’t easy. -Kif Augustine-Adams
Grateful for the opportunity to participate in the @PetrieFlom Annual Conference – thanks @CohenProf@NitaFarahany@HankGreelyLSJU@lexikon1 Carmel Sachar & Cristine Hutchison-Jones for a great line-up & planning- learned a lot & left with many more ?s to consider #dtcgenome
Center for Bioethics Retweeted AMA Journal of Ethics
#Regulations#GeneEditing & #PublicTrust: “The more jurisdictions that adopt a cautionary approach to their own regulations for genome editing (particularly heritable genome editing) the more likely negative world-wide consequences can be mitigated.” @PetrieFlom#Crispr
Center for Bioethics added,
AMA Journal of Ethics@JournalofEthics
Check out this blog post by AMA Journal of Ethics legal editor, @scottschweikart. Published as part of @PetrieFlom‘s 2019 conference, “Consuming Genetics: Ethical and Legal Considerations of New Technologies.” http://spr.ly/6013EiQ1S
There’s no prospect of potentially suing @23andme because of the disclaimers and forced arbitration put into agreements by the company @GaryMarchant1
#DTCgenome@_anyaprince explains the ways employer wellness programs is only a “theoretical win-win.” Minimal results come at the cost at privacy, and all of which can also show up in insurace realms as well. (Ex: Life insurers also implementing wellness policies)
Although increasing access to predictive/actionable genetic tests could theoretically be beneficial, we should be cautious about using third-parties, like life insurers, to disseminate these tests to their consumers without greater regulatory protections. #DTCgenome@_anyaprince
Property Conversion in Genetic Property Rights – who owns the rights? “Researchers need to be transparent and use adequate informed consent” – claims for generic conversion should not stifle research or create moral harms, suggests @jrobertsuhlc#DTCgenome – at Wasserstein Hall
Why is most insurance typically a state issue? FYI – Congress essentially “blessed” and preserved a state regulatory system of the insurance industry with passage of the McCarran-Ferguson Act of 1945. It makes it politically difficult to push this at federal level #DTCgenome
.@_anyaprince takeaway: Wellness programs aren’t necessarily bad, but question is what data goes to consumers, what data to employers and insurers, and what can they do with it? #DTCGenome
.@GaryMarchant1 takeaway: w/r/t liability, #DTCGenome companies are essentially immune because of disclaimers & arbitration clauses; doctors may be on the hook.
Q by @laurahercher: What do we tell GCs/trainees when we get a DTC result that needs to be confirmed but insurance won’t pay for confirmation? Answer: not very clear, but might be liable if we do nothing. Yikes! #DTCgenome#GCChat
Major disconnect with the ideas of ways to convert health data into what we have traditionally considered property-like rights. #dtcgenome @contreraslegals
Great point from @contreraslegals about how to treat “control group” genetic data, from those without the indicative genetic information, in arguments for genetic ownership/remuneration arguments. #dtcgenome@PetrieFlom
The ethical debate about anonymity is MOOT. There is no anonymity for sperm donors, nor are there any federal laws regarding anonymity of sperm donors. (Some states address medical information/disclosure but not anonymity) #DTCgenome@profmohapatra
Three observations: 1. Biomedical data/samples are governed by method of procurement 2. Contributors care about use 3. Specimens/data procured differently end up being used similarly (lots of mixing between academia & industry). ==>TENSION. –@KayteSB @ #DTCGenome
Rights to privacy or publicity – What will the courts decide? Well, it’s unclear because there are gaps in the existing laws. Liza Vertinsky also looking at the underlying implications of the choices of legal pathways #DTCgenome
.@NitaFarahany is an active moderator! Asking excellent questions (including mine–how do we react to patients not ‘getting’ consent info?, and then @CohenProf‘s on right not to be a genetic parent! Need to think on your feet w/ Nita around!) #DTCgenome
Yeah that looks simple! Barbara Evans @UHouston on what the regulatory pathway issupposed to look like and what makes it challenging in the world of genetics using charts from @theNASEM 2017 reports. And an ode to the “pink golden retriever” we all want #DTCgenome@PetrieFlom
Barbara Evans: Peer-review is no longer the threshold for good science it once was – grant review is. But if research is not funded…those protections aren’t there #DIYBio#DTCgenome
How well are #DTCgenome companies doing in complying with the privacy principles they themselves signed on to? James Hazel talks about the work he and Chris Slobogin @VanderbiltU @vanderbiltlaw have done. @PetrieFlom
Excellent talk by Barbara Evans expressing skepticism about a top down regulatory approach on biohacking (“If I want to turn my skin bright green who’s the FDA to tell me I can’t?), citing Lisa Ikemoto’s excellent DIY Bio Hacking article #DTCgenome@PetrieFlom
Panel takeaways: * DTC privacy policies are all over the place, and Best practices are a good way forward. * FDA is poised to take an advanced role as a regulator in the field. * We must differentiate between germline and somatic editing for regulation #dtcgenome
Catherine Sharkey asks us to consider the FDA may play in managing the conceptual risk and regulatory model for DTC genetic testing especially given the complexities that AI, machine learning, and big data add to this industry #DTCgenome@PetrieFlom
You can hear a pin drop in the auditorium as Kif Augustine @BYULawSchool tells a very personal tale about how #dtcgenome reveals a story of rape and a lost half sister. Secrets, lies, ancestry, DNA, and Mormon Orthodoxy in 1959 Utah. @PetrieFlom
Health equity is due to structural and systemic racism in the field present from its beginnings. Seeking more diversity in the workforce will not solve this “health equity” issue. As Jonathan Khan notes, these d&i initiatives can be used to elide responsibility #DTCgenome
Tala Berro added,
Petrie-Flom Center@PetrieFlom
Jonathan Kahn: Geneticists need to recognize their responsibility in the case of underrepresentation of racial diversiy in genetic databases and research. #DTCgenome
Natalie Ram @ubaltlaw@UMDLaw uses her baby bump as the ultimate scholarly “flex” in showing the involuntary and immutable nature of informational revelation for the children we produce. How do these elements make the forensic use of that information different? #DTCgenome
Extracellular RNA and their carriers in disease diagnosis and therapy, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
RNA plays various roles in determining how the information in our genes drives cell behavior. One of its roles is to carry information encoded by our genes from the cell nucleus to the rest of the cell where it can be acted on by other cell components. Rresearchers have now defined how RNA also participates in transmitting information outside cells, known as extracellular RNA or exRNA. This new role of RNA in cell-to-cell communication has led to new discoveries of potential disease biomarkers and therapeutic targets. Cells using RNA to talk to each other is a significant shift in the general thought process about RNA biology.
Researchers explored basic exRNA biology, including how exRNA molecules and their transport packages (or carriers) were made, how they were expelled by producer cells and taken up by target cells, and what the exRNA molecules did when they got to their destination. They encountered surprising complexity both in the types of carriers that transport exRNA molecules between cells and in the different types of exRNA molecules associated with the carriers. The researchers had to be exceptionally creative in developing molecular and data-centric tools to begin making sense of the complexity, and found that the type of carrier affected how exRNA messages were sent and received.
As couriers of information between cells, exRNA molecules and their carriers give researchers an opportunity to intercept exRNA messages to see if they are associated with disease. If scientists could change or engineer designer exRNA messages, it may be a new way to treat disease. The researchers identified potential exRNA biomarkers for nearly 30 diseases including cardiovascular disease, diseases of the brain and central nervous system, pregnancy complications, glaucoma, diabetes, autoimmune diseases and multiple types of cancer.
As for example some researchers found that exRNA in urine showed promise as a biomarker of muscular dystrophy where current studies rely on markers obtained through painful muscle biopsies. Some other researchers laid the groundwork for exRNA as therapeutics with preliminary studies demonstrating how researchers might load exRNA molecules into suitable carriers and target carriers to intended recipient cells, and determining whether engineered carriers could have adverse side effects. Scientists engineered carriers with designer RNA messages to target lab-grown breast cancer cells displaying a certain protein on their surface. In an animal model of breast cancer with the cell surface protein, the researchers showed a reduction in tumor growth after engineered carriers deposited their RNA cargo.
Other than the above research work the scientists also created a catalog of exRNA molecules found in human biofluids like plasma, saliva and urine. They analyzed over 50,000 samples from over 2000 donors, generating exRNA profiles for 13 biofluids. This included over 1000 exRNA profiles from healthy volunteers. The researchers found that exRNA profiles varied greatly among healthy individuals depending on characteristics like age and environmental factors like exercise. This means that exRNA profiles can give important and detailed information about health and disease, but careful comparisons need to be made with exRNA data generated from people with similar characteristics.
Next the researchers will develop tools to efficiently and reproducibly isolate, identify and analyze different carrier types and their exRNA cargos and allow analysis of one carrier and its cargo at a time. These tools will be shared with the research community to fill gaps in knowledge generated till now and to continue to move this field forward.
3.3.8 The 3rd STATONC Annual Symposium, April 25-27, 2019, Hilton Hartford, CT, 315 Trumbull St., Hartford, CT 06103, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 2: CRISPR for Gene Editing and DNA Repair
SYMPOSIUM OBJECTIVES
The three-day symposium aims to bring oncologists and statisticians together to share new research, discuss novel ideas, ask questions and provide solutions for cancer clinical trials. In the era of big data, precision medicine, and genomics and immune-based oncology, it is crucial to provide a platform for interdisciplinary dialogues among clinical and quantitative scientists. The Stat4Onc Annual Symposium serves as a venue for oncologists and statisticians to communicate their views on trial design and conduct, drug development, and translations to patient care. To be discussed includes big data and genomics for oncology clinical trials, novel dose-finding designs, drug combinations, immune oncology clinical trials, and umbrella/basket oncology trials. An important aspect of Stat4Onc is the participation of researchers across academia, industry, and regulatory agency.
Meeting Agenda will be announced coming soon. For Updated Agenda and Program Speakers please CLICK HERE
An industry news titled ‘Direct-to-Consumer (DTC) Genetic Testing Market to hit USD 2.5 Bn by 2024’ by Global Market Insights is relevant to your esteemed website https://pharmaceuticalintelligence.com/ . This email is a suggestion to publish this news (content attached in word format or can be picked from link mentioned below) on your website with an objective to share the information with your audiences.
Single-cell Genomics: Directions in Computational and Systems Biology – Contributions of Prof. Aviv Regev @Broad Institute of MIT and Harvard, Cochair, the Human Cell Atlas Organizing Committee with Sarah Teichmann of the Wellcome Trust Sanger Institute
Curator: Aviva Lev-Ari, PhD, RN
4.1.3 Single-cell Genomics: Directions in Computational and Systems Biology – Contributions of Prof. Aviv Regev @Broad Institute of MIT and Harvard, Cochair, the Human Cell Atlas Organizing Committee with Sarah Teichmann of the Wellcome Trust Sanger Institute, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 4: Single Cell Genomics
Dana Pe’er, PhD, now chair of computational and systems biology at the Sloan Kettering Institute at the Memorial Sloan Kettering Cancer Center and a member of the Human Cell Atlas Organizing Committee,
what really sets Regev apart is the elegance of her work. Regev, says Pe’er, “has a rare, innate ability of seeing complex biology and simplifying it and formalizing it into beautiful, abstract, describable principles.”
Dr. Aviv Regev, an MIT biology professor who is also chair of the faculty of the Broad and director of its Klarman Cell Observatory and Cell Circuits Program, was reviewing a newly published white paper detailing how the Human Cell Atlas is expected to change the way we diagnose, monitor, and treat disease at a gathering of international scientists at Israel’s Weizmann Institute of Science, 10/2017.
For Regev, the importance of the Human Cell Atlas goes beyond its promise to revolutionize biology and medicine. As she once put it, without an atlas of our cells, “we don’t really know what we’re made of.”
Regev, turned to a technique known as RNA interference (she now uses CRISPR), which allowed her to systematically shut genes down. Then she looked at which genes were expressed to determine how the cells’ response changed in each case. Her team singled out 100 different genes that were involved in regulating the response to the pathogens—some of which weren’t previously known to be involved in immune function. The study, published in Science, generated headlines.
The project, the Human Cell Atlas, aims to create a reference map that categorizes all the approximately 37 trillion cells that make up a human. The Human Cell Atlas is often compared to the Human Genome Project, the monumental scientific collaboration that gave us a complete readout of human DNA, or what might be considered the unabridged cookbook for human life. In a sense, the atlas is a continuation of that project’s work. But while the same DNA cookbook is found in every cell, each cell type reads only some of the recipes—that is, it expresses only certain genes, following their DNA instructions to produce the proteins that carry out a cell’s activities. The promise of the Human Cell Atlas is to reveal which specific genes are expressed in every cell type, and where the cells expressing those genes can be found.
Regev says,
The final product, will amount to nothing less than a “periodic table of our cells,” a tool that is designed not to answer one specific question but to make countless new discoveries possible.
Sequencing the RNA of the cells she’s studying can tell her only so much. To understand how the circuits change under different circumstances, Regev subjects cells to different stimuli, such as hormones or pathogens, to see how the resulting protein signals change.
“the modeling step”—creating algorithms that try to decipher the most likely sequence of molecular events following a stimulus. And just as someone might study a computer by cutting out circuits and seeing how that changes the machine’s operation, Regev tests her model by seeing if it can predict what will happen when she silences specific genes and then exposes the cells to the same stimulus.
By sequencing the RNA of individual cancer cells in recent years—“Every cell is an experiment now,” she says—she has found remarkable differences between the cells of a single tumor, even when they have the same mutations. (Last year that work led to Memorial Sloan Kettering’s Paul Marks Prize for Cancer Research.) She found that while some cancers are thought to develop resistance to therapy, a subset of melanoma cells were resistant from the start. And she discovered that two types of brain cancer, oligodendroglioma and astrocytoma, harbor the same cancer stem cells, which could have important implications for how they’re treated.
As a 2017 overview of the Human Cell Atlas by the project’s organizing committee noted, an atlas “is a map that aims to show the relationships among its elements.” Just as corresponding coastlines seen in an atlas of Earth offer visual evidence of continental drift, compiling all the data about our cells in one place could reveal relationships among cells, tissues, and organs, including some that are entirely unexpected. And just as the periodic table made it possible to predict the existence of elements yet to be observed, the Human Cell Atlas, Regev says, could help us predict the existence of cells that haven’t been found.
This year alone it will fund 85 Human Cell Atlas grants. Early results are already pouring in.
In March, Swedish researchers working on cells related to human development announced they had sequenced 250,000 individual cells.
In May, a team at the Broad made a data set of more than 500,000 immune cells available on a preview site.
The goal, Regev says, is for researchers everywhere to be able to use the open-source platform of the Human Cell Atlas to perform joint analyses.
Eric Lander, PhD, the founding director and president of the Broad Institute and a member of the Human Cell Atlas Organizing Committee, likens it to genomics.
“People thought at the beginning they might use genomics for this application or that application,” he says. “Nothing has failed to be transformed by genomics, and nothing will fail to be transformed by having a cell atlas.”
“How did we ever imagine we were going to solve a problem without single-cell resolution?”
NIH to Award Up to $12M to Fund DNA, RNA Sequencing Research: single-cell genomics, sample preparation, transcriptomics and epigenomics, and genome-wide functional analysis.
2012pharmaceutical
sjwilliamspa