Implementing precision genomic medicine in the pediatric acute care setting has several challenges.  First, the diagnosis must be made quickly.  Second, the determination of pathogenicity must be accurate. Third, there must be a specific effective treatment for the child.  

At Rady Children's institute for Genomic Medicine we have pioneered extremely rapid sequencing, combined with natural language processing of the electronic medical record, and automated analysis tools to help speed time to diagnosis.  We have also begun rapid modeling of variants of uncertain significance in animal model systems in order to interrogate their pathogenicity.  Finally, we can utilize these animal model systems to test different drugs to either establish a therapeutic regimen for the patients when therapies exist or to discover or repurpose existing compounds where there are no therapies.

Learning Objectives: 

1. Understand challenges to implementing precision medicine in an acute care setting
2. Understand how model systems are critical for solving the 'VUS problem' 

Tumor heterogeneity is a hallmark of cancer and can have significant impact on identifying drivers, including those that may be therapeutically relevant. Although, the traditional sequencing of bulk tumor specimens provides invaluable information, sequencing of individual cells provides a true representation of the cellular heterogeneity that exists within a cancer. Novel technologies such as single cell copy number variation (scCNV) enable copy number profiling at a single cell resolution. Single Cell CNV generates genome wide sequencing libraries from single cells to reveal tumor heterogeneity and evolution of subclonal populations. 

In this study, we utilized a scCNV assay to assess heterogeneity within the 4T1 murine breast tumor model.  To accomplish this we have developed an end-to-end workflow including tissue dissociation, single cell library preparation, sequencing and data analysis. We injected 4T1 cells into the mammary fat pad of 4-6 week old BALB/c mice. Mice were sacrificed at 21 days and tumors were removed from the injection site and dissociated using Milteny’s Biotec Octo Dissociator. We utilized the 10X Genomics Chromium system to partition single cells and prepare sequencing libraries in parallel such that all DNA fragments produced within a partition are barcoded. Following library construction, the Illumina Novaseq 6000 system was used to sequence single cell libraries. Additionally, single cell libraries from a fraction of the 4T1 cells that were injected into the mammary fat and non-metastatic isogeneic cell line variants, 67NR and 168FARN, were also sequenced. 

We observed a pair of amplifications (>8 copies) on chromosome 9 and three segments of amplification on chromosome 15 that were conserved throughout the injection site tumor cells. Interestingly, the amplifications on chromosome 15 encompass one of the most commonly observed breast cancer oncogenes, Myc, and the cell migration regulator, Skp2. Further, we observed three unique cluster of tumors cells which suggests the possibility of detecting major and minor clonal populations using this technology. 

Our findings suggest scCNV will help us to improve characterizations of the entirety of a patient’s disease to uncover potential clones that are primed to induce metastasis, drug resistance, and relapse.

Learning Objectives: 

1. Apply single cell sequencing technologies to their ongoing projects that seek to develop precision therapies
2. Identify subclonal populations at a single cell resolution
 

Clopidogrel, an antiplatelet agent frequently used for secondary stroke prevention, is a prodrug that requires both sufficient intestinal absorption and hepatic modification to produce its active metabolite. An individual's ABCB1 alleles affect clopidogrel absorption, while one's CYP2C19 alleles affect activation of this prodrug. Herein, we explore the role of platelet function assays and genotyping of CYP2C19 and ABCB1 in terms of clopidogrel absorption and activation, and its efficacy in platelet inhibition through three patients who experienced recurrent ischemic stroke despite adherence to clopidogrel. 

Learning Objectives: 

1. Describe the role that platelet function phenotype may play in secondary stroke prevention antiplatelet selection in the future.
2. Describe the role that CYP2C19 and/or ABCB1 genotypes may play in secondary stroke prevention antiplatelet selection in the future.

This talk will review successful efforts at Washington University to employ novel reagents and informatics to the problem of rare allele detection. I will discuss a published bakeoff of in silico variant callers. I will describe rare variant detection in our lab using a novel “unlockable” blocked PCR primer developed at IDT. Finally I will describe a new method, developed at Washington University to leverage UMI to push detection down to 1 in 10,000.

Learning Objectives: 

1. Will discuss bench versus bioinformatic approaches to sensitivity to rare alleles both in malignant tissue and in a so-called ”liquid biopsy".
2. Will discuss the method that proports maximum sensitivity (0.01)

In this presentation, Dr. Kothari will provide an overview of the Precision Medicine Initiative from NIH and how NGS technologies have helped the researchers to look deep inside the human transcriptome. Focusing on kinases, Dr. Kothari will discuss the sample centric versus gene centric approaches to analyze the transcriptome data to identify new drug targets. He will finally share his thoughts on how integrative omics approaches can help researchers find better drug targets and robust biomarkers of disease progression, that will help in achieving the goals of precision medicine initiative. 

Learning Objectives: 

1. To understand the merit of sample centric transcriptome analysis to identify new drug targets using NGS.
2. To understand the merit of gene centric transcriptome analysis to identify new drug targets using nGS.
 

The development of automated DNA sequencers utilizing Sanger sequencing and capillary electrophoresis made it possible to develop the first draft sequences of the human genome. The cost of doing this was hundreds of millions of dollars. However, the advent of technologies which could generate sequences for hundreds of thousands of DNA fragments simultaneously based up MPS heralded a revolution in sequence capability. All first generation MPS platforms utilize one of three approaches to amplify individual DNA molecules to a high copy number followed by sequence interrogation of the original short DNA molecules. The most successful platform for MPS was developed by the company Illumina and they have increased sequence output from 1 gigabase (Gb) to over 6 terrabases (Tbs) in less than 13 years. Currently the cost for generating sufficient DNA sequence for whole genome sequencing (WGS) of an individual human is just $375. However, the total cost for WGS is considerably higher when one factors in library preparation, sequencing, assembling and interpreting that genome sequence, and data storage. There is an alternative platform developed by Complete Genomics based upon a non-PCR based technology to amplify DNA templates. This platform is now being utilized by BGI and they have a machine which is capable of generating 7 Tbs of sequence data per run. On this platform the total cost for WGS is now just $600 and BGI is developing larger machines in the hope of bringing WGS total costs down to just $100. First generation MPS can now be utilized for WGS, but also for whole exome sequencing, targeted genome sequencing, transcriptome sequencing, methylation sequencing as well as metagenomic sequencing. All of these will completely transform research and its’ clinical translation. Second generation MPS is based upon the analysis of single unamplified DNA molecules and can generate DNA sequences that can be 100 kilobases in length or greater. In my talk I will discuss the history of first generation MPS and how this revolution represents an important technological singularity.

Learning Objectives: 

1. To understand the revolution that has occurred in DNA sequencing based upon technologies that utilize massively parallel sequencing
2. To understand how these technologies will completely transform clinical practice.

Recent advances in genomic technologies have revealed enormous complexities and uniqueness of human physiology. Although enormous efforts are being made to apply this knowledge to enhance the efficacy and safety of many marketed therapeutic agents, still the drug discovery process is still slowly evolving. In oncology, the concept of precision medicine is being utilized to accelerate the drug discovery process by patient/disease stratifications. Moreover, several drugs discoveries programs are gaining accelerated approval for rare/orphan diseases of unmet medical need. 

Using precision medicine many attempts are made to differentiate the responder from non-responders via genetic predictors. The success of patient stratification is solely based on genomic driver-based stratification. Additionally, precision medicine-based approaches with genomic analysis can reveal the mechanism of therapy failure. Although targeted therapies often elicit profound initial patient responses, these effects are transient due to residual disease leading to acquired resistance. How tumors transition between drug responsiveness, tolerance, and resistance, especially in the absence of pre-existing subclones, remains unclear. Moreover, using technologies that enable precision medicine we can elucidate rational combination therapy to alter tumor microenvironment and alter tumor resistance.

Learning Objectives: 

1. Use cases of precision medicine in the drug discovery process
2. Accelerating the drug discovery process through precision combinatorial therapy to modulate therapy resistance

The NIH put Precision Medicine on the map as a revolutionary way to manage disease, delivering the right treatment, to the right patient, at the right time. But what does Precision Medicine really look like in practice? This lecture shares an MD perspective from 20+ years on the frontline of academia and personalized health-care delivery. With case studies, this presentation takes a critical look at the literature, and the practicalities of integrating and interpreting N-of-1 omics data, evidence-based medicine, and precision technologies, to detect, predict, and reverse disorders of aging and optimize an individual’s health-span. 

Learning Objectives: 

1. Discover the practical application of Precision Health technologies in the clinic.
2. Learn the value of N-of-1, ‘Big Little Data’ to practically scale a personalized approach.

Most cancer therapies have highly variable activity from one patient to another, with only a fraction of patients’ cancers responding to a given treatment. In many types of cancer, combination therapies can improve the probability of response. Since 1961 it was known that combinations of individually active therapies can be expected to improve response rate to some degree simply by increasing each patient’s chance of receiving at least one personally effective therapy. Here I describe recent analyses of contemporary combination therapies showing that this phenomenon of patient-to-patient variability in single-drug activity accounts for the clinical activity of many approved combination cancer therapies. These results highlight widespread opportunities to make more precise use of cancer therapies both individually and in combinations.

Learning Objectives: 

1. How patient-to-patient variability in response to individual cancer therapies affects responses to combinations of cancer therapies, and the implications for precision medicine in oncology.
2. How to calculate the 'independent effect' Progression Free Survival distribution for a combination of cancer therapies, which can be used to analyze clinical trial results, and to predict the activity of combinations of independently acting drugs.

Virtually all tumors are genetically heterogeneous, containing subclonal populations of cells that are defined by distinct mutations. Subclones can have unique phenotypes that influence disease progression, but these phenotypes are difficult to characterize: subclones usually cannot be physically purified, and bulk gene expression measurements obscure interclonal differences. Single-cell RNA-sequencing has revealed transcriptional heterogeneity within a variety of tumor types, but it is unclear how this expression heterogeneity relates to subclonal genetic events – for example, whether particular expression clusters correspond to mutationally defined subclones. To address this question, we developed an approach that integrates enhanced whole genome sequencing (eWGS) with the 10x Genomics Chromium Single Cell 5’ Gene Expression workflow (scRNA-seq) to directly link expressed mutations with transcriptional profiles at single cell resolution. Using bone marrow samples from five cases of primary human Acute Myeloid Leukemia (AML), we generated WGS and scRNA-seq data for each case. Duplicate single cell libraries representing a median of 20,474 cells per case were generated from the bone marrow of each patient. Although the libraries were 5’ biased, we detected expressed mutations in cDNAs at distances up to 10 kbp from the 5’ ends of well-expressed genes, allowing us to identify hundreds to thousands of cells with AML-specific somatic mutations in every case. This data made it possible to distinguish AML cells (including normal-karyotype AML cells) from surrounding normal cells, to study tumor differentiation and intratumoral expression heterogeneity, to identify expression signatures associated with subclonal mutations, and to find cell surface markers that could be used to purify subclones for further study. The data also revealed transcriptional heterogeneity that occurred independently of subclonal mutations, suggesting that additional factors drive epigenetic heterogeneity. This integrative approach for connecting genotype to phenotype in AML cells is broadly applicable for analysis of any sample that is phenotypically and genetically heterogeneous.

Learning Objectives: 

1. Detection of SNVs from RNA-seq in single-cells is possible 
2. SNVs drive subclonal expression signatures in AML

It has been noted by many in the community that for Precision Medicine to become a transformative reality, the underlying DNA and RNA sequence data have to become more precise. In my talk, I will highlight several examples how long and accurate sequence reads using SMRT Sequencing have been utilized in clinical research to uniquely inform about certain genetic dispositions, and how it has helped elucidate the underlying cause of certain diseases that had previously eluded their determination. 

Learning Objectives: 

1. Understand the concept of generating long and accurate sequence reads using PacBio technology
2. Familiarize with examples for which long and accurate reads have helped elucidate genetic conditions that were previously undetermined