Big Data for Personalized Medicine and Biomarker Discovery Banner


5:00 – 6:00pm Short Course Registration and Conference Pre-Registration

6:00 – 9:00 Dinner Short Course*

SC1: Fit-for-Purpose Biomarker Assay Development and Validation

*Separate registration required or all access

Tuesday, May 17

7:00 am Conference Registration and Morning Coffee

Opening Plenary Session

8:00 Chairperson’s Opening Remarks

Eric H. Rubin, M.D., Vice President and Therapeutic Area Head, Oncology Early Development, Merck Research Laboratories

8:10 Next Steps in Cancer Immunotherapy

Eric H. Rubin, M.D., Vice President and Therapeutic Area Head, Oncology Early Development, Merck Research Laboratories

The presentation will focus on important next steps in the development of cancer immunotherapies, including understanding mechanisms of resistance to anti-PD-1/PD-L1 treatments, identifying optimal combinations, and identifying individual patient predictors of response or lack of response. In addition, efforts to modify existing imaging-based response classifiers, accounting for the unique mechanism-of-action of immunotherapies, will be discussed.

8:35 Big Data and the Evolution of Precision (Personalized) Medicine

George Poste, Ph.D., Chief Scientist, Complex Adaptive Systems, Professor, Health Innovation, Arizona State University

The rise of precision medicine and data intensive medicine are inextricably linked. Academia, industry and healthcare providers are ill-prepared for this data deluge which will impose profound changes in research and clinical care.

9:00 Multi-Stakeholder Progress on Biomarker Qualification

John Wagner, M.D., Ph.D., Senior Vice President & Head, Clinical & Translational Sciences, Takeda Pharmaceuticals

9:25 Coffee Break in the Exhibit Hall with Poster Viewing

Big Data in Biomarker Discovery and Drug Development

10:10 Chairperson’s Opening Remarks

Marc D. Chioda, Pharm.D., Medical Director, Lung Cancer, Pfizer Oncology

10:15 Big Data in the Development of ALK Inhibitors and the Evolving Diagnostic Landscape of Biomarkers in Non-Small Cell Lung Cancer

Marc D. Chioda, Pharm.D., Medical Director, Lung Cancer, Pfizer Oncology

Biomarker discoveries are resulting in a paradigm shift in the treatment of patients living with non-small cell lung cancer (NSCLC). This presentation will discuss the development program of crizotinib (Xalkori) for the treatment of ALK+ metastatic NSCLC. This case-study underscores the importance of genetic profiling in this disease. Insights from the development of crizotinib as a first-in-class ALK-inhibitor are being applied to the development of next-generation ALK inhibitors such as lorlatinib.

10:40 Leverage Big Data to Generate Real World Evidence (RWE) on Biomarkers' Clinical and Economic Utility

Usman Iqbal, M.D., MPH, MBA, Senior Medical Affairs Leader, Global Medical Affairs, AstraZeneca

This presentation will discuss leveraging Big Data to generate Real World Evidence (RWE) on biomarkers’ clinical and economic utility: 1) assessing current barriers in adoption of precision medicine and need for generating evidence of clinical and economic utility, 2) role of RWE-based Big Data platforms and participatory medicine in influencing adoption of precision medicine and companion diagnostics, 3) methods and study design approaches to generate the right kind of real world evidence geared towards stakeholder requirements.

11:05 The Powers and Perils of Observational Data

Nicholas Tatonetti, Ph.D., Herbert Irving Assistant Professor of Biomedical Informatics; Director, Clinical Informatics, Herbert Irving Cancer Center, Departments of Biomedical Informatics, Systems Biology & Medicine, Columbia University

Observation is the starting point of discovery. Based on observations, scientists form hypotheses that are then tested. In the information, trillions of observations are being made and recorded every day – from online social interactions to the emergency room visit. With so much data available, generating hypotheses using a single scientist’s mind is no longer sufficient. Data mining is about training algorithms to recognize patterns in enormous sets of data and automatically identify new hypotheses. In this talk, I will discuss how we use data mining algorithms to identify unexpected effects of drugs used singly and in combination with other drugs. Using integrative informatics methods, we are able to discover drug-drug interactions that no one considered possible before. Finally, I will demonstrate how to use simple and efficient laboratory experiments to validate these hypotheses. In many cases these experiments can be executed in high-throughput by robotic systems, with the ultimate goal of automating the scientific method.

11:30 Informatics Approaches to the Identification of Cancer Mutations and Genes for Drug Repurposing

Zhongming Zhao, Ph.D., Dr. Doris L. Ross Professor, Biomedical Informatics, The University of Texas Health Science Center at Houston

Currently, how to effectively identify driver mutations and genes in cancer genomes, especially those with the potential for druggable targets for the development of molecularly targeted cancer therapies, remains a major challenge. In this talk, I will present informatics approaches for identifying cancer mutations and genes from a large amount of somatic mutation data and our integrative network-based framework for identifying new druggable targets and anticancer indications from existing drugs.

12:00 pm Luncheon Presentation (Sponsorship Opportunity Available) or Lunch on Your Own

Big Data to Advance Personalized Medicine

2:00 Chairperson’s Remarks

Robert A. Beckman, M.D., Professor, Oncology, and Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center

2:05 Dynamic Precision Medicine: A New Approach for Increased Survival and Cure Rates

Robert A. Beckman, M.D., Professor, Oncology, and Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center

Although tumors have branching subclonal structure and continuously evolve, current precision medicine focuses primarily on the current, static and average properties of bulk samples, matching therapies to molecular subtypes and treating until relapse or progression. This presentation discusses dynamic precision medicine, which explicitly considers subclonal structure and evolutionary dynamics, adjusts therapy frequently, and plans ahead for the entire disease course. Dynamic precision medicine may significantly impact survival and cure rates.

2:30 Development and Clinical Application of an Integrative Genomic Approach to Personalized Cancer Therapy

Rong Chen, Ph.D., Director, Clinical Genome Informatics, Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai

We implemented a personalized cancer therapy (PCT) program in a clinical setting, applying an integrative genomics approach to maximally elucidate the complexity of each tumor. For patients in the study, we performed whole exome sequencing and SNP microarrays on tumor and patient-matched normal samples, as well as RNA sequencing on available frozen samples, to identify somatic mutations, copy number alterations, gene fusions, and gene expression alterations in the tumor. Ion AmpliSeq Cancer Hotspot Panel v2 (CHPv2) was used to ensure high sensitivity in cancer mutation hotspots. Genomics results were integrated with cancer knowledge databases and for each cancer type a specific workflow was developed to optimize data interpretation.

2:55 The Role of Artificial Intelligence in Precision Medicine

Jason H. Moore, Ph.D., Edward Rose Professor of Informatics, Director, Institute for Biomedical Informatics, The Perelman School of Medicine, University of Pennsylvania

The goal of precision medicine is to target diagnostic, treatment and prevention strategies to individuals based on their biological and clinical profile. We present here an artificial intelligence (AI) approach for identifying subgroups of subjects defined by genotypes from multiple genetic variants. This approach is capable of generating good genetic models and, importantly, learning how to generate those models using expert knowledge about disease pathobiology. We demonstrate the ability of our AI method to tackle genome-wide data to generate new hypotheses about drug targets.

3:20 Refreshment Break in the Exhibit Hall with Poster Viewing

4:10 Multiple Angles on Utilizing Big Data for Personalized Medicine and Biomarker Discovery

Bin Li, Ph.D., Associate Director, Computational Biology, Takeda

Big data includes various data types and it is critical to capture the right features for biomarker and precision medicine efforts. We first created a disease/drug map using high-level information to cover various therapeutic areas and disease types. Then, enrichment analysis was done upon ~800 canonical pathways to infer a disease/drug’s MOA. When molecular profiling data are available, several data driven machine learning approaches were developed to identify drug sensitivity biomarkers.

4:35 The Road to Personalized Medicine Goes through Big Data: From Omics to Sensing and through to Clinical Utility

Iris Grossman, Ph.D., Vice President and Head, Personalized & Predictive Medicine and Big Data Analytics, Global R&D, Teva Pharmaceuticals

This presentation will cover: i) increasing the probability of success throughout the pipeline via big data analytics and deep insights into complex biology, and ii) Big Data as an engine for repurposing existing molecules for novel unmet medical needs.

5:00 Welcome Reception in the Exhibit Hall with Poster Viewing

5:30 Short Course Registration

6:00-9:00 pm Dinner Short Course*

SC2: Circulating Tumor Cells: Biomarkers for Personalized Oncology

*Separate registration required or all access

Wednesday, May 18

7:30 am Breakfast Presentation (Sponsorship Opportunity Available) or Morning Coffee

Genomic Data Analysis and Interpretation

8:25 Chairperson’s Remarks

Jean C. Zenklusen, Ph.D., Director, The Cancer Genome Atlas Center for Cancer Genomics, National Cancer Institute, National Institutes of Health

8:30 OASIS: A Web-Based Platform for Exploring Cancer Multi-Omics Data

Zhengyan Kan, Ph.D., Senior Principal Scientist, Pfizer

There is an ever-increasing demand for broadly available informatics tools integrated with comprehensive, well-organized datasets that enable scientists to perform data mining and analyses. We have publicly released OASIS, the central repository and web-based analytical platform for cancer multi-omics data at Pfizer Oncology Research. By combining one of the largest repositories of multi-omics datasets on the web with intuitive analytics, OASIS provided a powerful resource to the cancer research community.

8:55 The NCI Genomics Data Commons: Democratizing Access to Large-Scale Data

Jean C. Zenklusen, Ph.D., Director, The Cancer Genome Atlas Center for Cancer Genomics, National Cancer Institute, National Institutes of Health

Although The Cancer Genome Atlas (TCGA) has produced an unprecedented amount of data molecularly characterizing thirty-two tumor types, the usability of the data has been restricted by download model. The Genomics Data Commons (GDC) is a new database for retrieval and query of all NCI genomic-level data, allowing the user to identify areas of interest and download the information on just those segments (BAM slicing). All the data contained in the GDC will be mapped to the genome (aligned) and variations on the sequence called in a uniform fashion. In future releases, the GDC will incorporate a variety of analytical tools, obviating the need to download the raw data in order to analyze it.

9:20 Finding a Needle in a Haystack: New Approaches to Identify the Single Disease-Causing Mutation in a Patient’s Genome Sequencing Data

Yuval Itan, Ph.D., Research Associate, Human Genetics of Infectious Diseases, The Rockefeller University

This presentation provides an insight into a new state-of-the-art gene-level metrics, tackling a crucial question in medicine genomics: how to identify the relevance of a mutated gene to a disease. We identified for the first time the accumulated mutational damage for each human gene and the biological distance between all human genes, and showed that both approaches are particularly powerful for the identification of disease genes in patients’ high-throughput data.

9:50 Coffee Break in the Exhibit Hall with Poster Viewing

10:45 Facilitating Precision Oncology through MSK-IMPACT and Enterprise-Level Data Sharing

Ahmet Zehir, Ph.D., Director, Clinical Bioinformatics, Molecular Diagnostics Service, Department of Pathology, Memorial Sloan Kettering Cancer Center

11:10 Complexities in Analysis of Exome Data in Pediatric Disorders

Avni Santani, Ph.D., Assistant Professor, Clinical Pathology, University of Pennsylvania School of Medicine

Analysis of exome data often involves multi-system review of phenotype and development of a comprehensive interpretation process. This process is complex, often requiring multidisciplinary teams and access to information from multiple databases. This talk will present several case reports that highlight the utility and challenges associated with interpretation of complex genomic tests such as exome sequencing.

11:35 Close of Conference