Day 2: NGS for Drugs, Patients and Clinical Trials Conference


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Friday, March 22

8:20 Morning Coffeee

8:30 Discussion Groups: Diving Deeper into Drug Development

Grab a cup of coffee and join a discussion group. These are moderated discussions with brainstorming and interactive problem solving, allowing conference participants from diverse backgrounds to exchange ideas, experiences, and develop future collaborations around a focused topic.

Table 1: NGS Sequencing Entry into Clinical Diagnostics: When, Why and How

Seth D. Crosby, M.D., Director, Alliances and Partnerships, Department of Genetics, Washington University School of Medicine

•Technology

–Accuracy
–Heterogeneous Samples

•Interpretation

–Clinical significance of findings
–Translation to physician
–Continual reinterpretation
–Privacy, incidental findings, children

•Logistics

–Who are the payers in your country?
–How are your labs regulated?

Table 2: Resolving the Distinctions between Target Identification in Models, and Cell-Behavioral Modification in Human Disease

Raj K. Batra, M.D., Associate Professor, Medicine, Geffen School of Medicine, University of California, Los Angeles

•What approaches are currently being used to delineate specific "molecular mechanisms" of disease (especially cancer)?
•How have these approaches aided (or not) our understnading of disease processes?
•How do we get to adequate drug discovery and effective therapy?

Table 3: Where is Greatest Impact of NGS on Preclinical Drug Discovery and Where Are the Gaps?

Paul Rejto, Ph.D., Director, Computational Biology, Oncology Research Unit, Pfizer, Inc.

• New targets – have all the long-hanging fruit been picked?
• Model systems – what are the learnings?
• Biomarkers and patient selection – are we achieving the promise?
 

Harnessing NGS Technologies for Identification of Causal Variants and “Driver” Mechanisms in Disease 

9:30 Chairperson’s Opening Remarks

9:35 CASE STUDY: Approaches and Challenges to Identifying Rare Somatic Mutations in Patients with CRC or NSCLC Using Targeted Deep Sequencing

Brandon W. Higgs, Ph.D., Senior Scientist, Bioinformatics Lead, Pharmacogenomics Group, Translational Sciences, MedImmune, Inc.

Various well known mutations in genes such as EGFR and Kras have been shown to associate with outcomes to different treatments. This talk explores both the technological and biological challenges associated with calling somatic mutations - those with both high frequency and those that are very rare, using targeted deep sequencing. A case study in Chinese patients with NSCLC harboring low frequency mutations in both EGFR and Kras is presented.

10:05 Applications of Next-Generation Sequencing in Drug Discovery and Development

Matthew R. Nelson, Ph.D., Principal Scientific Investigator, Statistical Genetics, Quantitative Sciences, GlaxoSmithKline

Deep resequencing holds promise in dissecting genetic architecture of genes of interest to drug discovery and development. Genetic variants detected from deep resequencing can subsequently be used in Mendelian randomization studies to make effective decisions on validity of drug targets. They can also serve as powerful tools to investigate drug efficacy and safety. We will use some examples based on recently completed deep resequencing studies to demonstrate utility of deep resequencing in drug discovery and development.

10:35 Well-Characterized Whole Human Genomes: Forming Consensus Calls to Assess Performance of Clinical Sequencing

Justin Zook, Ph.D., Multiplexed Biomolecular Science Group, NIST Chemical Science and Technology Laboratory

Clinical laboratories need to have well-characterized whole genome Reference Materials to understand their sequencing performance. Hundreds of thousands of differences exist between human whole genome variant calls from different sequencing platforms and variant calling pipelines for the same genome, but the reasons for these differences are poorly understood. We have developed methods to characterize prospective whole genome Reference Materials by comparing and integrating 9 whole genome and 2 exome datasets for one genome. We form consensus genotype calls by using information about mapping, alignment, and sequencing biases of individual datasets. Based on a microarray, our consensus calls have a 4x to 8x lower false negative rate than any single dataset, with a similar or lower false positive rate. The resulting set of consensus genotypes for this prospective Reference Material (at 2.7 billion genomic positions) allows us (or any laboratory) to characterize and reduce biases and false positive/negative rates of any platform and bioinformatics approach, which is critical for clinical translation of genome sequencing. We have evaluated performance and biases of several common sequencing and bioinformatics pipelines, finding characteristics of systematic sequencing errors, alignment bias, and mapping bias that can be used to reduce false positive and false negative rates.

10:50 Coffee Break in the Exhibit Hall with Poster Viewing

11:15 The Genetic Landscape of Hepatocellular Carcinoma

Mao Mao, M.D., Ph.D., Research Fellow, Oncology Research, Pfizer, Inc.

Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide and yet the molecular basis of hepatocarcinogenesis remains largely unknown. NGS studies on HCCs have not only confirmed previously known mutations in CTNNB1 and TP53 in HCC, but also identified novel genetic alterations in HCC including mutations in genes involved in epigenetic regulation. These findings have started to depict a genetic landscape in HCC and will facilitate development of novel therapeutics for the treatment of this deadly disease.

11:45 An Approach to Discover Novel Targets and Rational Combinatorial Approaches to Treat Cancer

Raj K. Batra, M.D., Associate Professor, Medicine, Geffen School of Medicine, University of California, Los Angeles

We have developed an approach to study intratumoral heterogeneity, and to uncover the molecular basis of aggressive tumor cell phenotypes.  Some tumor cell subsets in an individual tumor are more competent than others in forming tumors, or to metastasize, or to resist therapy.  Collectively, these properties have been attributed to “cancer stem cells” or CSC.  We have developed novel processes and methods to extract CSC from clinical biospecimens, primarily culture them in vitro, and live-sort candidate CSC-subsets in order to jointly validate distinctive phenotypes and directly associate these properties with molecular signatures using OMICs.  This strategy will enable to both discover novel “behavior-specific” therapeutic targets, as well as rational combinatorial approaches to treat cancer. 

12:15 pm Luncheon Presentation (Sponsorship Opportunity Available) or Lunch on Your Own

 

Dissecting NGS Data for Actionable information 

1:30 Chairperson’s Remarks

1:35 Searching for Rare Molecular Markers Using Next-Generation Sequencing

Jadwiga Bienkowska, Ph.D., Head, Computational Biology, Principal Investigator, Patient Stratification, Translational Research, Biogen Idec

This talk will discuss search strategies for rare molecular markers using next-generation sequencing. I will also focus on unexpected challenges and limitations in NGS for this application.

2:05 Enabling Oncology Drug Discovery by Integrative Omics

Zhengyan ‘George’ Kan, Ph.D., Senior Principal Scientist, Oncology Computational Biology, Pfizer, Inc.

Cancer omics studies increasingly adopt the integrative approach, applying multiple assays on the same tumor specimens to identify multiple types of alterations that potentially drive the disease. Ambitious efforts spearheaded by the public sector such as TCGA and ICGC are generating cancer omics data by the petabytes. The Pfizer Oncology Research Unit (ORU) has also generated large-scale integrative omics data sets, spanning several cancer types. To enable drug discovery efforts at Pfizer, we have designed a data analysis and management system (OASIS) to analyze, manage and extract information from public and proprietary data sets. Based on tumor-specific alteration patterns, we aim to establish a comprehensive catalogue of cancer driver genes, identify novel drug targets and novel indication for existing drugs, and better define patient segments for future drug candidates.

2:35 Large-Scale Whole Genome Sequencing Data Analysis in Cloud: Evaluating the Association between RA Patients SNP and Their Response to SIMPONI® (Golimumab)

Shanrong Zhao, Ph.D, Senior Scientist, Informatics, Janssen Pharmaceutical Companies of Johnson & Johnson

SIMPONI® is a drug developed by Johnson & Johnson to treat RA (rheumatoid arthritis) disease. But not all RA patients respond to this drug very well. We sequenced 50 subjects (25 responders vs. 25 non-responders), and want to understand the association between SNP and drug response. For association study, a large sample size is desirable. However, WGS is expensive, and we cannot sequence too many subjects. To meet these challenges, we built a workflow in Amazon cloud for large-scale whole genome sequencing data analysis.

3:05 The ERCC Dashboard: Spike-in Controls Provide Quantitative Performance Metrics for Gene Expression Profiling

PodcastMarc Salit, Ph.D., Leader, Multiplexed Biomolecular Science Group, NIST Chemical Science and Technology Laboratory

The recently released NIST Standard Reference Material 2374 (DNA Library for External RNA Controls) underpins a reference set of exogenous RNA transcripts useful as external "spike-in" controls in gene expression experiments. A recently developed package of statistical analysis methods acts as a "dashboard" to report on the technical performance of such experiments, providing a path to confidence in results and science-based regulatory oversight. The use of the controls and the dashboard analysis will be presented and discussed

 

Integrating NGS Technologies into Biologics Development 

3:35 Molecular Characterization of Biotherapeutics by Next-Generation Sequencing

Dean A Regier, Senior Scientist, Protein Sciences, AbbVie

One of the major challenges in biologic drug development is product heterogeneity as a result of contamination, mutation or alternative splicing in transcription. Traditional methods such as cloning and Sanger sequencing, etc. are inefficient and even incompetent in detection of genetic variants, especially in low amount. NGS, a powerful tool in deciphering genetic information, was applied on the task and was demonstrated as a perfect fit in molecular characterization of biotherapeutics during drug development.Various tools are currently available for transcriptome assembly and analysis and some of these tools will be reviewed. Detecting differential expression, identifying SNPs, and performing annotation will be discussed using examples from the literature and data analysis performed in our lab.

4:05 Applications of NGS Technologies in the Discovery of microRNA Based Therapeutics

Vivek Kaimal, Ph.D., Scientist, Informatics, Regulus Therapeutics, Inc.

This talk will cover two areas of application of NGS in microRNA research. The first will highlight the use of RNA-Seq in determining the presence or absence of target engagement using miRNA antagonists. This includes tests for broad on-target effects as well as trying to identify specific PD markers. The second part will cover the use of dual miRNA-Seq& RNA-Seq profiles to test for correlation between miRNA and its targets (PD markers).

4:35 Conference Wrap-Up

4:45 Close of Conference



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