The greatest health epidemic of our time is cancer. Deaths from cancer worldwide outnumber the combined deaths from HIV/AIDS, tuberculosis and malaria by a wide margin. There are at least 100 000 new cases of cancer per year in South Africa ( 2009 numbers, most likely a gross underestimate). The people of Africa, and especially southern Africa, have the greatest degree of genetic diversity in the world. Yet nearly all cancer research occurs outside Africa on non-African patients. Over half of the approximately 8 million deaths from cancer in 2008 occurred in populations in the developing world and this is expected to increase to over two thirds in the next 20 years. As cancer is a molecular disease with a genetic etiology that differs significantly from individual to individual, its treatment has catalyzed the emergence of precision medicine.

Learning Objectives:
1.      Describe the importance of african genetic diversity in cancer diagnosis and care
2.      Describe the benefits are obtained of using "living biobanks" of organoids for drug screening 
 

A century and a half after we first probed heritability, we risk forgetting one of Mendel's own basic findings, in rushing to broaden clinical genomics to lifelong care for all. Embracing that key insight now -- as we long have in tracking the chemistry of health -- can help gird our health infrastructure for the long haul.

Learning objectives

• Conventionally, we clinically classify and report genomic variants.
• That habit -- rooted in early clinical genomics (reading few genes, in few people, mostly sick) -- presumes that disease cases trace mainly to strongly acting single variants...so sweeps widespread real impenetrance, for major diseases, under a vast rug.
• Classifying and reporting genotypes would more neatly and realistically track non-additive genomic interactions mined from emerging data, girding healthcare for broad longterm genomic utility.
•  The history of toxicology (and of early genetics too!) starkly highlights this lesson, and suggests simple but urgent steps that stakeholders (genomicists, informaticians, caregivers, and patients) should take now.

While next-generation sequencing has proven to be a very useful tool in basic research, two major hurdles remain for its broad adoption in the clinical research setting: lack of seamless workflow for routine clinical research use and truly relevant content to guide clinical research activities.
This webinar will discuss QIAGEN's approach to address these two issues. Our speaker, Dr. John Leamon, will share his experience developing and launching the industry’s first complete NGS workflow: The GeneReader System. The GeneReader offers a fully integrated solution, addressing all steps of the workflow from DNA extraction to bioinformatics analysis and interpretation. It is engineered specifically for any laboratory as a powerful tool for clinical cancer research, including those with limited resources or lacking highly specialized expertise. To this end, the QIAGEN proprietary Knowledge Base was used to design a tumor panel to identify aberrations of value for clinical research. The GeneReader NGS System was then used to deliver clinical research insights in a laboratory setting. Testing was conducted with a set of colorectal cancer samples, and results confirmed with alternative testing platforms.
The QIAGEN GeneReader NGS System is a fully integrated sequencing workflow, engineered specifically for clinical research purposes. This turnkey solution is purpose-built, focusing on the assays that matter most. 
 

Precision medicine requires understanding the mechanistic basis of complex disorders, and to precisely manipulate these mechanisms to better human health. This is partly enabled by the recent revolution in human genetic studies, which resulted in thousands of disease-associated regions. However, translating genetic results into mechanistic insights remains an unsolved challenge, partly because in 90% of cases, disease-associated common variants do not affect protein function directly, but instead alter genomic control elements that govern gene expression patterns. To systematically characterize these control elements and their circuitry, we generated epigenomic maps of 127 primary human tissues and cell types, and networks linking them to their target genes and their upstream regulators. We have used these maps and circuits to understand how human genetic variation contributes to disease and cancer, providing an unbiased view of disease genetics and sometimes re-shaping our understanding of common disorders. For example, we find evidence that genetic variants contributing to Alzheimer’s disease act primarily through immune processes, rather than neuronal processes. We also find that the strongest genetic association with obesity acts via a master switch controlling energy storage vs. energy dissipation in our adipocytes, rather than through the control of appetite in the brain. We have shown that we can manipulate these circuits by genome editing or gene targeting, opening up tissue-autonomous therapeutic avenues against the obesity epidemic. In addition to dissecting known disease-associated regions, we have combined genetic information with regulatory annotations and with epigenetic variation to discover new disease regions in cardiovascular disease, Alzheimer’s disease, and prostate cancer. These results span the spectrum of common, rare, and somatic variants, and illustrate the power and broad applicability of regulatory annotations and circuits for understanding human disease and cancer. 

The advent of the microarray technology in 2000 has paved the way for advanced translational research methods that use molecular markers such as microRNA, proteins, metabolites and copy number data. The popularity of next generation sequencing (NGS) grew exponentially in 2007 when a faster, more accurate and affordable sequencing throughput became a reality. Since then, the size and complexity of genomic data has increased many fold, making its analysis, management and integration increasingly challenging. To drive hypothesis generation and validation of molecular markers for biologists and researchers, it would be convenient to have a “one–stop” system that can handle omics and de-identified clinical data in one location.In response to this need, we have developed a software research platform called G-DOC Plus. G-DOC Plus is an enhanced web platform that uses cloud computing and other advanced computational tools to handle NGS and medical images so that they can be analyzed in the full context of other omics and clinical information to drive personalized medicine research. G-DOC Plus tools have been leveraged in the cancer and non-cancer realms for hypothesis generation in precision medicine and translational research.  With the goal of improving overall health outcomes through advanced genomics research, G-DOC Plus, enables the integrative analysis of multiple a variety of data types to understand mechanisms of cancer and non-cancer diseases to drive new hypothesis for precision medicine.  G-DOC Plus allows researchers to explore data one patient at a time, as a sub-cohort of samples; or as a population as a whole, providing the user with a comprehensive view of the data.

Learning Objectives: 

1. Learn how to use a publicly available translational research software platform
2. Learn how to combine omics and clinical data to generate novel hypothesis for cancer studies
3. Learn about application of Bioinformatics to discover prognostic biomarkers in stage II colorectal cancers

Cell death is involved in diseases such as cancer and neurodegeneration, and also has a natural role in the development of multicellular organisms. Although apoptosis has been well defined, alternative forms of cell death have been less explored. In my lab, we performed a global analysis of pharmacologically accessible cell death mechanisms using a strategy we refer to as modulatory profiling. Using this approach, we found compounds that induce apoptosis, necrosis, and a novel form of regulated cell death. These novel compounds induce a non-apoptotic, iron-dependent oxidative form of cell death that we refer to as ferroptosis. We have found that this form of cell death is distinct from apoptosis and necrosis on the basis of morphological, biochemical, genetic and inhibitor sensitivity data. In addition, we have used these compounds to elucidate the key mechanisms regulating ferroptosis, and to probe the relevance of ferroptosis to a variety of diseases and therapeutic applications. Finally, we have defined biomarkers that predict sensitivity and resistance to ferroptosis, opening the possibility of precision medicines that induce ferroptosis for treating a variety of cancers.

Learning Objectives:
 
1. Design experiments to test the mechanism of cell death occurring in a specific context
2. Predict whether a specific patient will be sensitive to a drug that activates a specific type of cell death

 

Every day, vast amounts of healthcare data are collected from clinical trials as well as real world medical visits on patient treatment regimens and subsequent clinical outcomes. This big data raw material provides a rich asset to investigate for understanding therapeutic effectiveness and patient care. Datasets range from genomics and other ‘omic data to clinical to medical and pharmacy claims to electronic medical records to registries and beyond, and analysis can discover and predict biomarkers, drivers of disease, novel interventions, mechanisms of action, drug combinations, disease models, portfolio optimization, and personalized care/treatment algorithms. Key to leveraging this data and uncovering which treatments and interventions specifically improve a patient’s health, are powerful analytic approaches. Utilizing causal mathematics and machine learning to create in silico disease networks directly from data has been a successful approach to identify predictive and causal mechanistic associations. Simulations of resultant models unlock the knowledge within complex data, enabling personalized, actionable predictions and precision targeting of interventions. Fully realizing the power of precision medicine to identify and predict patient outcomes will significantly increase the ability of healthcare leaders and professionals to make better decisions to improve patient care.

The seminar will focus on the following learning objectives: [1] case studies detailing the data and experimental design that have yielded success and [2] key actionable insights generated from models and analytics that can be leveraged in drug discovery and development all the way to healthcare patient setting.
 

On January 20, 2015, President Obama announced the Precision Medicine Initiative® (PMI) (link is external) in his State of the Union address. Through advances in research, technology and policies that empower patients, the PMI will enable a new era of medicine in which researchers, providers and patients work together to develop individualized care.  This presentation will introduce the President’s PMI and provide an in-depth discussion of its key research component: the PMI Cohort Program.   With its aim to enroll 1 million or more U.S. volunteers representing the rich diversity of America, and to longitudinally collect EHR, environmental, genetic, and behavioral data, the PMI Cohort promises to change the face of precision medicine and generate results that will transform prevention and treatment for millions of Americans. 
 
Learning objective:

• Participants will understand the goals of the President’s Precision Medicine Initiative® (PMI) and implementation for the PMI Cohort Program.

One of the primary goals of precision medicine is the aggregation and interpretation of deep, longitudinal patient-specific data in the context of the digital universe of information, using advanced predictive analytics to better diagnose and treat patients, even down to tailoring individualized treatments to patients as happens today in areas such as oncology with some types of immunotherapy (novel vaccine development targeted to individual tumors). The rapid advance of not only panomic technologies such as next generation DNA sequencing and medical imaging technologies, but of the vast array of wearable and implantable sensors and sensors in the environment, all linked to personal smart devices, are transforming the scale, scope, and depth of data we can generate on patients. With these extensive data generated on individuals in a longitudinal fashion, the potential exists to characterize the health of an individual at a far deeper level than has been possible before. Better characterization of an individual’s health will lead to more precise diagnoses, treatments, and preventative strategies for disease at a highly personalized level. Towards this end, we have developed a predictive, multiscale framework that enables us to understand the health of an individual at the molecular, cellular, organ, organism and community scales so that we can better diagnose, treat, and prevent disease.
 
Learning Objectives for Schadt Talk: 

• Aggregating high-dimensional, large-scale molecular, cellular, imaging, and clinical data across populations of individuals to construct predictive models of disease can lead to better diagnoses and treatments for common and rare human diseases
• With predictive network models we can carry out biology in silico to understand the key drivers of disease and their mechanisms
• Predictive network models provide a path for prioritizing targets, stratifying patient populations, and matching patient-specific subtypes to most appropriate therapeutic interventions

A number of recently publications have heralded the advent of Precision Medicine in Oncology, where next generation sequencing technology, other ’omics technologies, serial biopsies, sophisticated treatment algorithms and access to databases detailing response to therapy by molecular subtype can be applied to an individual cancer patient’s genomic profile to generate a specific “matching” cancer treatment. This matching treatment can subsequently be modified, with the help of additional biopsies and further analysis, to continue to match the cancer’s specific genomic characteristics upon disease progression.
 
Many cancers with poor prognoses are characterized by high genomic heterogeneity and rapid escape from therapy. Outcomes improvements in patients suffering from these and other similar cancers will likely continue to be very difficult to achieve unless Precision Medicine approaches are applied. However, in order for the practice of Precision Medicine in Oncology to advance several major challenges must be overcome:

1. Development of detailed standards for the identification and reporting of actionable somatic genomic variants in cancer patient samples
2. Establishment of data repositories, adhering to actionable somatic genomic variant calling standards, for recording cancer patient responses to therapy and matching the responses to their genomic profiles
3. Clinical validation of the algorithm generation approach
4. Development of frameworks to allow companies to collaborate and to give treating oncologists access to a broad range of experimental medicines, allowing them to treat patients with optimal combinations
5. Clinical programs and regulatory frameworks supporting these approaches, allowing successful approaches to be commercialized 

Many of these challenges require broad collaborations across company and specialization boundaries to involve academics, treating clinicians, pathologists, regulators and diagnostic, pharma and IT companies. My presentation will focus on the drug development challenges which will likely follow the application of precision medicine approaches in the field of oncology.

Learning Objectives:
1. Understanding the impact that Next Gen. Sequencing will have on Oncology Drug Development
2. Understanding the opportunities and challenges which are likely to arise as  consequence of the implementation of Precision Medicine approaches in Oncology.
 

Understanding disease at a deeper level so that it can be targeted more precisely is the essence of precision medicine. Rapid technological advance, particularly in genomics and sensor-based continuous measurement, has facilitated the advance towards this new era. In this talk, I will give examples of precision medicine, show studies on sensor based monitoring including on Apple’s ResearchKit platform, and present a new framework for the integration of this data with the Electronic Medical Record.
 

Precision medicine promises a more effective approach to disease treatment and management. It is based on analyzing mutations of disease samples to unlock mechanisms of disease development and progression, and resistance to effective therapies. Recent advances in NGS have made it the ideal approach for the comprehensive analysis of the mutational landscape of samples. Specifically, the ability to enrich certain genomic targets known, or suspected, to harbor driver mutations prior to NGS is allowing researchers to achieve very deep levels of sequencing coverage, levels that cannot be achieved by either WGA or WES, thereby increasing the sensitivity of NGS assays. The ability to enrich genomic targets has also facilitated the use of liquid biopsies for the discovery of mutations that contribute to drug resistance. Several challenges remain though, mainly the specificity and uniformity of target enrichment approaches, the amount of required DNA, and the turnaround time needed to go from sample to insight. Attend this seminar to learn what solutions QIAGEN scientists have developed to overcome these challenges and how these solutions can be used to analyze mutations in a wide range of samples such as FFPE, cfDNA, and CTCs, thereby enabling researchers to use solid and liquid biopsies for disease management and, ultimately, precision medicine.

In December 2012 the UK Prime Minister announced the 100,000 genomes project to introduce whole genome sequencing for treatment into the UK National Health Service (NHS) on a large scale. Since then more than 5,000 whole genomes have been sequenced through pilots organised by Genomics England, the body set up to deliver the project. In addition major components to deliver the main project have been put in place: 13 NHS Genome Medicine Centres have been setup across England involving ~90 hospitals which will recruit patients and collect samples for sequencing and associated clinical data. Illumina was announced as the partner to deliver the whole genome sequences. Several companies have been contracted to provide initial genome interpretation services. Finally Genomics England has invited applications from UK researchers and NHS Clinicians to join its new Clinical Interpretation Partnership to analyse the data generated from the project. I will introduce the project and discuss the challenges of handling clinical grade whole genome sequence at scale to deliver both timely and usable summary reports to clinicians and a secure environment for research
 
Learning objectives:
 
* An understanding of the UK 100,000 Genomes Project
* How diagnostic results will be generated and fed back to clinicians and patients
* How genome and clinical data will be used enable scientific discovery and medical insight
 

Personalised Healthcare is essential to AstraZeneca’s approach to drug development and is adopted by more than 80% of drug products in clinical development. Selection of the right patient population using companion diagnostics is integral to the way in which AstraZeneca focuses on quality and innovation. We will discuss examples where Personalised Healthcare has transformed the delivery of new therapies to patients. We will examine how emerging diagnostic science has the potential to improve patient outcomes; including circulating tumour DNA, next generation sequencing and expanding the benefits of Personalised Healthcare across all therapy areas. Finally we will consider the role of partnerships to delivering Personalised Healthcare into clinical practice. We believe that Personalised Healthcare is the future of drug development; it epitomizes what science can do.
 
Learning objectives:

• Describe how Personalised Healthcare been used to help transform AstraZeneca’s approach to drug development
• Understand examples of the use of Personalised Healthcare to develop novel therapies
• Evaluate the role of emerging diagnostic science to deliver benefit to patients