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The 4th Annual Clinical Operations in Oncology Trials East Coast was an amazing hit. Over 25 speakers challenged the 200 attendees on how- "WE" as an industry can use new tools and strategies to better our Clinical Trial Execution and Patient Enrollment. With only 3% of the patients in the USA participating in Cancer Trials- we have to do a better job finding ways to educate them about the benefits of clinical studies. The following tools are some of the new enhancements for better site and physician selection which can help find better results.

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ClinVar: Aggregating Data to Improve Variant Interpretation - Melissa Landrum

Human Variome Project

The rate of variant discovery continues to surpass the rate of clinicalgrade interpretation. This is a challenge for precision medicine, because fast, reliable access to variant interpretations is necessary to provide well-informed and timely interpretations of test results to patients. ClinVar is a public repository for interpretations of clinical significance and functional effects of variants in any gene and for any disease. Interpretations are submitted by many sources, including clinical testing laboratories, research laboratories, locus-specific databases, expert panels, practice guidelines, as well as OMIM® and GeneReviews™. Collecting variant interpretations in ClinVar depends on integrating data from these different sources, which has several benefits. First, data integration requires standardizing the data from each source. This improves the quality of the data in ClinVar as well as in each of the individual datasets. ClinVar staff validate HGVS expressions as a routine part of ClinVar submission processing. Submitters are encouraged to use standard terms in MedGen for diseases and phenotypes. Standard terms for clinical significance are used in ClinVar when available; for example, ClinVar uses the terms recommended by ACMG to classify variants for Mendelian diseases. Secondly, ClinVar aggregates all data for a variant defined by its genomic location. Therefore, HGVS descriptions on different transcripts or on different genomic sequences can be recognized as the same variant. Thirdly, integrating data from multiple submitters allows the evidence from all sources to be pooled together. This larger collection of evidence aids the re-evaluation of variant classifications, and is especially valuable for rare variants and novel gene-disease relationships. Fourthly, data integration means that variant interpretations from different sources can be viewed together and compared. Thus a ClinVar user has access to interpretations outside any internal system and knows when there is consensus in the interpretation or not. Submitting laboratories use reports of conflicting interpretations in ClinVar to prioritize variants that they should re-evaluate. ClinVar receives data from many data providers, and therefore provides clear attribution to each contributing group, including links to records in LSDBs. Each source may update their submission to ClinVar at any time. For example, a record may be updated when a variant is re-classified or when additional evidence is available to support the interpretation. Submitters may consider providing regular updates to ClinVar to prevent their interpretations from becoming out of date. Submissions to ClinVar describe variants that range in complexity from simple alleles with explicit sequence locations through copy number changes and cytogenetic rearrangements with fuzzy boundaries.

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Human Variome Project

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Human Variome Project

Background: New technologies and increased competition have, and will continue to improve the cost-effectiveness of genetic testing, making genetic analysis more accessible to medical practices worldwide. However, challenges remain to establishing the validity of such tests. Moreover many patients harbor rare or novel variants and classification is likely to remain a bottleneck in broader deployment of genetic medicine.

The PhenX Toolkit: Standard Measures for Collaborative Research - Wayne Huggins

Human Variome Project

Introduction and Background: The Web-based PhenX Toolkit (consensus measures for Phenotypes and eXposures, https://www. phenxtoolkit.org/) is a catalog of standard measures designed to facilitate collaborative biomedical research. PhenX measures help ensure that phenotypes from different studies are collected and represented in a consistent format. This consistency can enable data comparability across sites in large cohorts (e.g., Precision Medicine Initiative) and facilitates combining data to validate clinically actionable variants, increase statistical power (e.g., studies of rare genetic conditions or gene-enviroment interactions), or compare treatments and outcomes between patients.

Legal and regulatory challenges to data sharing for clinical genetics and ge...

Human Variome Project

There are many factors that impede genomic variant sharing in the UK, despite it becoming a necessary part of clinical care. These include the lack of a designated infrastructure or mechanism aggravated by the complexity of laws that apply, and fragmented and variable advice from local ‘Caldicott guardians’ who guide NHS trusts on their responsibilities concerning data protection and confidentiality. Since the legitimacy of data sharing in the UK is framed in terms of ‘personal data’ being shared for ‘direct care’ (subject to legal exceptions), the blurred boundaries between clinical care and research, and the spectrum of identifiability of data also lead to differing interpretations resulting in inconsistent practices. In a multidisciplinary collaboration, the PHG Foundation and the UK’s Association for Clinical Genetic Science co-hosted a workshop to examine the clinical necessity for sharing variant data and associated phenotypic information, the technical feasibility and the legal and regulatory impediments to such sharing. Delegates included clinicians, laboratory scientists, and key policy makers, including the National Data Guardian for Health and Care and representatives from the 100,000 Genomes Project, a pioneering research project which promises to build a legacy for future genomics services in the UK. The key finding from our work was that current arrangements for sharing genomic variants within the NHS are unsatisfactory and inconsistent practices are compromising safety and quality. Our workshop report [1] highlights the urgent need for (i) national agreement to optimise sharing within the NHS and develop consensus on the legitimacy of data sharing, (ii) standardised operational processes, including a designated sustainable database or mechanism for sharing, and (iii) strong leadership by the multiple relevant health organisations to demonstrate the benefits and risks associated with sharing and not sharing data. Since publication of the workshop report, the NHS Consortium (operating within the DECIPHER database) has reported a 120% increase in the number of cases shared, the 100,000 Genomes Project and associated data embassy have got underway and the EU Data Protection Regulation has been finalised. However research highlights continuing public reservations about some aspects of data sharing including commercial access and misgivings around secondary uses of data. Publication of the National Data Guardian’s long-awaited review of consent and security provisions to provide guidance on a new consent and opt-out model for sharing patient information in the NHS, has been delayed pending the results of the EU referendum being known. Against this backdrop, the imperative to develop robust, proportionate policies for genomic data sharing becomes increasingly acute. Funding from the PHG Foundation and the Association for Clinical Genetic Science.

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Human Variome Project

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Human Variome Project

Numerous databases containing information about DNA, RNA and protein variations are available. Gene-specific variant databases (locus specific variation databases, LSDBs) are typically curated and maintained for single genes or groups of genes for a certain disease(s). These databases are widely considered as the most reliable information source for a particular gene/protein/disease, but it should also be made clear they may have widely varying contents, infrastructure, and quality. Quality is very important to evaluate because these databases may affect health decision-making, research and clinical practice. The Human Variome Project (HVP) established a Working Group for Variant Database Quality Assessment. The basic principle was to develop a simple system that nevertheless provides a good overview of the quality of a database [1]. The HVP quality evaluation criteria that resulted are divided into four main components: data quality, technical quality, accessibility, and timeliness. Instructions are available for the developed quality criteria and how implementation of the quality scheme can be achieved ([1], http://www.humanvariomeproject.org/finish/19/255.html). Examples are provided for the current status of the quality items in two different databases, BTKbase, an LSDB, and ClinVar, a central archive of submissions about variants and their clinical significance. Reference: [1] Vihinen, M., Hancock, J. M., Maglott, D. R., Landrum, M. J., Schaafsma, C. P., Taschner, P. Human Variome Project quality assessment scheme for variation databases. Hum. Mutat. (in press).

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Human Variome Project

GENETIC HETEROGENEITY OF MITOCHONDRIAL DISORDERS - Agnès Rötig

Human Variome Project

Mitochondrial diseases are characterized by a high clinical and genetic heterogeneity and a growing number of genes of mitochondrial disease has been identified. Mitochondrial diseases follow any mode of inheritance, due to the twofold genetic origin of RC components (nuclear DNA and mitochondrial DNA). 1 000 to 1 500 nuclear genes encode mitochondrial proteins. Approximately 250 of these genes have been reported as disease causing. These genes not only encode the various subunits of each respiratory chain complex, but also the ancillary proteins involved in the different stages of holoenzyme biogenesis, transcription, translation, chaperoning, addition of prosthetic groups and assembly of proteins, as well as the various enzymes involved in mtDNA maintenance. Some of these genes are associated with well defined syndromes but more and more are specific to one patient or family only, hampering to establish genotype-phenotype correlations. The clinical heterogeneity of these disorders makes the diagnosis difficult especially in the first years of the clinical course and other genetic diseases can present an overlapping phenotype. Therefore only the identification of the disease causing mutation allows to certainly establish the diagnosis of mitochondrial disease. Dr. Rötig (PhD) is the head of the group working on mitochondrial diseases in Necker Hospital (Paris). This group has initially settled and integrated platform of clinic, biochemistry and molecular analysis to investigate patients with OXPHOS disease. The scientific field of this group is the identification of genes involved in mitochondrial disorders and the investigation of their pathophysiology. They have described the first non-neuromuscular presentation of mitochondrial diseases and characterized the very first mutations in nuclear genes resulting in defects of Krebs’s cycle or the respiratory chain.

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Human Variome Project

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Human Variome Project

Use of open, curated variant databases: ethics? Liability? - Bartha Knoppers

Human Variome Project

Translation of genomics into medicine and drug development requires comprehensive, high-quality, genomic variant databases. To support translation, there is a movement towards sharing clinical annotations of variants (e.g., benign, unknown, pathogenic) internationally via open access. Despite the growing popularity of variant databases, ethical issues and liability risks have received scant attention. Ethical priorities for variant databases include 1) competence – ensuring that data is responsibly managed, curated, and used; 2) confidentiality – ensuring appropriate safeguards for patient data; 3) communication – clearly describing the purpose, quality standards, and data handling practices to contributing patients and potential users; and 4) continuous oversight to adapt database governance in a rapidly evolving environment. How can database managers fulfill these obligations when these responsibilities are increasingly distributed along the clinical pipeline? Legal issues include medical liability based on potential harm to patients; liability based on third-party intellectual property or privacy rights in the data; and regulatory risks as variant data is integrated into genetic tests or devices. Can these risks can be managed through appropriate governance structures – including adequate consents, access processes, contributor agreements, and disclaimers – while still facilitating sharing and clinical use?

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Human Variome Project

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Human Variome Project

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Human Variome Project

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Human Variome Project

We have investigated compliance with guidelines regarding HGVS nomenclature and/or submission of variants to public databases in the author instructions of several genetics and genomics journals. For this, we used a list of genetics and genomics journals created by the Human Variome Project (HVP) office (See http://www.humanvariomeproject.org/resources/genetics-and-genomics-journals.html). The HVP aims to work towards open and free sharing of high quality information on genetic variation and its effect on human health. The HVP Gene and Disease-specific Database Council has created example instructions for authors for genetics and genomic journals containing guidelines regarding HGVS nomenclature and submission of variants to public databases. The HVP office has sent this document to journal editors asking them to include similar requirements in their instructions for authors. The rationale is that better instructions will help improving the quality of variant descriptions in manuscripts and access to variant information in databases. We have used this list to select journals including both requirements. We have investigated the January 2016 issue of several of them. A group of students has checked the publications first for the basic requirements: mentions of the reference sequence used to describe variants and the presence of the variants in public databases. The next step was to check variant and phenotype descriptions with specific attention for predicted protein effects. Our preliminary results suggest that predicted protein effects in publications cannot be verified in case of altered splice sites without supporting RNA-level evidence or in case of insertions of unspecified nucleotides. Although the underlying variants are likely to have disease-causing effects, lack of supporting evidence disqualifies this information for diagnostic use. In several cases, the RNA-level information was specified in the gene variant database submission, indicating that submission of variants to databases prior to manuscript acceptance might improve the quality of publications. We have observed that authors include statements suggesting variants have been submitted to databases, but the variants were not found. Reviewers and journal editors could help improving manuscript quality by insisting on compliance and enforcing the existing guidelines.

Pathogenicity Decision Pathway in Tuberous Sclerosis Complex - Rosemary Ekong

Human Variome Project

Inactivating mutations in TSC1 and TSC2 cause tuberous sclerosis complex (TSC). This disease is variable in severity and classically causes seizures, intellectual disability, behavioural difficulties, brain tumours, heart tumours, renal tumours and a facial rash. It occurs worldwide and is found in approximately one in 10,000 live births. It can be inherited as an autosomal dominant disorder, but about 70% of cases are new in the family and there is a significant risk of recurrence in a second child because of gonadal mosaicism in one of the parents. In some cases, mildly affected individuals are not recognised till the birth of a severely affected child. Genetic testing is needed both for diagnosis and for reproductive decisions, but in at least 10% of tests the causative change is not found. There are also many TSC variants of uncertain significance which are often unique. The TSC1 and TSC2 databases (www.lovd.nl/TSC1 and www.lovd.nl/TSC2) attempt to record all TSC variants which have been reported, both in the contest of genetic testing for TSC and in other clinical conditions. Classification of the pathogenicity of these TSC variants in the TSC1 and TSC2 databases refers to their ability to cause TSC. Data from August 2015 show 889 different small variants for TSC1 of which 66% were pathological and 9% were not. For TSC2, there were 2522 different small variants of which 50% were pathological and 9% were not. All others were to some extent uncertain. Recent advances in the quantity and quality in NGS, and the access to enormous amounts of population data have produced many challenging opportunities to improve variant classification. We have now formalised our decision-making on the pathogenicity of variants and this uses type and position, likely effect of variant, confidence in diagnosis, frequency of variant in different patients, family details including de novo reports, co-occurrence with known harmful variant, and where appropriate, an in vitro function assay. We now also screen our database automatically for duplicates in very large cohort datasets, most of which do not have extensive phenotype data. We will present our current practice for interpretation and would welcome comments. The TSC variation databases are funded by the TSA and the TS Alliance.

HVP Country Node: The Netherlands - Marielle van Gijn

Human Variome Project

The ClinGen Sequence Variant Interpretation Working Group: Refining Criteria ...

Human Variome Project

A key barrier to the efficient clinical utilization of genome sequence data is the lack of a systematic approach for interpreting the pathogenicity of genetic variants, with resultant discordance among laboratories and researchers in classification. The ClinGen (Clinical Genome) Project has been funded by the United States National Institutes of Health with a goal of maximizing the clinical relevance of results from genetic testing. ClinGen has established a Sequence Variant Interpretation (SVI) Working Group to refine and standardize the approach to pathogenicity classification. Recently the American College of Medical Genetics and Genomics (ACMG) published guidelines [1] that emerged from a workgroup that represented the expert opinion of clinical laboratory directors and genetics clinicians. These guidelines were developed to help clinical laboratories that report results from sequencing of single genes, panels, exomes, and genomes. The ClinGen SVI has set short term and long term objectives for advancing the field of pathogenicity interpretation using the ACMG framework as a starting point. There is consensus within the field that correct classification of variants requires integrating multiple lines of evidence, including, clinico-pathologic, epidemiologic, bioinformatics (in silico), and in vitro data. How best to combine them is unclear. The ACMG framework described different categories of evidence and assigned preliminary assessments of what comprises weak or strong evidence favoring a variant’s pathogenicity or neutrality and preliminary rule- based algorithms of how to combine evidence. In the short term, the ClinGen SVI has set up sub-committees to apply more precision in defining criteria for pathogenicity and what comprises strong and weak evidence. In the long term, the ClinGen SVI looks to transition from qualitative descriptions to a quantitative system that can assign an empirically derived probability of pathogenicity for each variant. Preliminary analyses suggest that there will be general rules that apply to all genes, as well as specific approaches for different genetic disorders. Well characterized databases of variants for each genetic disorder will be critical to the process. Funded by the National Human Genome Research Institute through the following three grants: U41 HG006834-01A1, U01 HG007437- 01, U01 HG007436-01, and by the National Cancer Institute through contract HHSN261200800001E. Reference: 1. Richards S, Aziz N, Bale S, et al. Standards and Guidelines for the Interpretation of Sequence Variants: A Joint Consensus Recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genetics in Medicine2015;17(5):405-424.

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1. SESSION VI HVP Country Nodes Thursday 2nd June Argentina/South America Angela R. Solano, Presenting a Project on course: Ministry of Health with the participation of other Ministry and Scientific Societies

2. Argentina: Dr. Angela R. Solano, PhD in Medicine INBIOMED, Dto. de Bioquímica Humana, Facultad de Medicina, UBA/ CONICET and Genotipificación y Cáncer Hereditario, Dto. De Análisis Clínicos, Dto. de Investigación CEMIC All data is regarding Genetic analysis by sequencing (in hereditary cancers, most of them in BRCA1/2, although -large rearregements it is in a single laboratory) • Clinical laboratories: 6 (90% of the analysis in one) • Clinicians: 55 • Genetic Counselors: 12 • Genetic testing & genetic healthcare services: Test are funded about 90% by private healthcare and less than 10% public • National Genetics Society and National Human Genetics Society: Argentinean Society of Human Genetics, October (at AMA)

3. HVP Country Node: on Progress • Year established: expected by end of 2016 • Structure: Steering committee, advisory group and education committee • Funding, expected from: National government through Ministry/s and National Research Council (CONICET) and applications for external grants. • Links with NHGS: Yet, no formal links, although the presenter is a member of CIMBA, ENIGMA, GA4GH and HVS • Ethical framework, privacy, consent: Already on use approved are all the requirements for genetic testing

4. HVP Country Node: Technical • National data repository: on construction, ready at CEMIC with more than 6000 variants (from more than 1200 probands analyzed) for BRCA1/2 • Data available nationally: the web page at CEMIC will be released at the end of july 2016 • Data available internationally: sharing data is in process • Available: – Data Collection Policy: in process – Collection Agreement: at the moment from CEMIC only since it has more than the 95% of the data – Data Access Policy: free (upon registration) – Data Ownership Policy: CEMIC

5. Progress, Plans and Problems • Progress in the past 12 months: – Organization for the Node • Plan for the next 12 months: – All pending from previous slides – Interaction with Ministrys • Problems/barriers we face: – Collection of data from the all country, without support for the activity, as it was up to 2015 – Grants to support the expectations

HVP Country Node: Argentina - Angela Solano

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