2018 Archived Content
Track 12: Cancer Informatics

Track 12 explores the important technology and informatics trends and challenges of applying computational biology to cancer research and care. Themes that will be covered in expert-led presentations include collaboration and network models, data access/management/integration strategies, and applications of biological interpretation to aid in research at the bench side or care at the bedside.

Tuesday, May 15

7:00 am Workshop Registration Open (Commonwealth Hall) and Morning Coffee (Foyer)


8:0011:30 Recommended Morning Pre-Conference Workshops*

W3. D2K, Transforming Data to Knowledge with Cloud, IoT & Machine Learning (AI) – Part I


12:304:00 pm Recommended Afternoon Pre-Conference Workshops*

W11. Data Science Driving Better Informed Decisions


* Separate registration required.

2:006:30 Main Conference Registration Open (Commonwealth Hall)

4:00 PLENARY KEYNOTE SESSION (Amphitheater & Harborview 2)

5:007:00 Welcome Reception in the Exhibit Hall with Poster Viewing (Commonwealth Hall)

Wednesday, May 16

7:00 am Registration Open (Commonwealth Hall) and Morning Coffee (Foyer)

8:00 PLENARY KEYNOTE SESSION (Amphitheater & Harborview 2)

     

9:45 Coffee Break in the Exhibit Hall with Poster Viewing (Commonwealth Hall)

MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE IN CANCER INFORMATICS
Waterfront 2

10:50 Chairperson’s Remarks

Ted Slater, Global Head, Scientific AI & Analytics, Cray Inc.

11:00 KEYNOTE PRESENTATION: Can AI Beat Cancer?

Jay (Marty) Tenenbaum, PhD, Founder and Chairman, Cancer Commons

AI can beat go and drive cars, but can it beat cancer? Every year, many thousands of cancer patients die unnecessarily because their doctors do not know the optimal way to treat them with currently available therapies. Physicians and patients alike, struggle with information overload and conflicting expert opinions in making treatment decisions. Moreover, effective treatments increasingly involve intelligently designed, individually tailored, sequences and combinations, and there are far more plausible multi-drug regimens than can be efficiently tested in clinical trials. AI can help by connecting physicians and patients to the right information at the right time, and by planning and coordinating the thousands of formal and informal treatment experiments that take place daily in oncology, to optimize individual outcomes and maximize collective knowledge. We will describe a developing global collaboration to realize this bold vision, involving leading oncologists, cancer and data scientists, and AI experts from both academia and industry, and discuss opportunities for all to participate.

 IBM Logo12:00 pm Empowering Medical Practitioners to Train Models for AI

Srinivas Chitiveli, Offering Manager - PowerAI Vision, IBM Systems IBM

Medical practitioners are adopting AI embedded applications that diagnose diseases to ensure quick, accurate treatments for their patients. Current practices of developing AI applications can be complex and resource intensive. IBM PowerAI Vision, empowers Radiologists to label datasets, train and even deploy models to analyze images and videos helping pinpoint anomalies. Attend this session to see IBM PowerAI Vision firsthand.

12:30 Session Break

12:40 Luncheon Presentation (Sponsorship Opportunity Available) or Enjoy Lunch on Your Own

1:40 Session Break

SCALABILITY AND REPRODUCIBILITY
Amphitheater

1:50 Chairperson’s Remarks

Baisong Huang, Principal Statistical Analyst, Novartis Institutes for BioMedical Research

1:55 Scalable Visualization and Exploration Tool for Single-Cell Genomics Data

Marcin Tabaka, PhD, Postdoctoral Associate, Regev Lab, Broad Institute of MIT and Harvard

We developed an interactive tool for the visualization and exploratory analysis of massive single-cell omics data. Users can visualize a billion cells on a personal computer, show density of cells or gene expression values on a 2D embedding, plot gene expression profiles for selected groups of cells, visualize and annotate clusters of cells.

2:15 CanvasXpress: A Versatile Interactive High-Resolution Scientific Multi-Panel Visualization Toolkit

Baohong Zhang, Director of Clinical Bioinformatics, Precision Medicine, Pfizer, Inc.

CanvasXpress (http://canvasxpress.org) was developed as the core visualization component for bioinformatics and systems biology analysis at Bristol-Myers Squibb and further enhanced by scientists around the world and served as a key visualization engine for many popular bioinformatics tools. It offers a rich set of interactive plots to display scientific and genomics data, such as oncoprint of cancer mutations, heatmap, 3D scatter, violin, radar, and profile plots.

2:35 MJFF Initiative for Open Source PD Research and Data Integration

Luba Smolensky, Director, Data Science & Analytics, The Michael J. Fox Foundation

As the world’s largest nonprofit funder of Parkinson’s research, The Michael J. Fox Foundation (MJFF) is dedicated to accelerating a cure for Parkinson’s disease and improved therapies for those living with the condition today. MJFF is leading a Parkinson’s research data curation and standardization effort that will accelerate insights into the disease. The goal is to provide access to curated datasets across platforms for all researchers across academia, public institutions, and industry.

Schrodinger2:55 Collaborative Drug Discovery with LiveDesign: Integrated Computational Chemistry and Cheminformatics

Melissa Landon, PhD, Regional Director, Applications Science, Northeast; Director, Education, Schrödinger

Drug Discovery has become increasingly dependent upon a plethora of computational tools and data, requiring collaboration across computational and medicinal chemistry project teams for ideation, querying, and project management. Herein we present LiveDesign, a highly-collaborative web-based platform for workflow management by bringing computational modeling alongside experimental data and informatics, presented with real-world examples.

3:25 Refreshment Break in the Exhibit Hall with Poster Viewing (Commonwealth Hall)

APPLICATIONS OF GENOME ANALYSIS AND MACHINE LEARNING IN CANCER
Harborview 1

4:00 Building Integrated Pipeline for Cancer Genome Analysis: Role of Mobile Genetic Elements in Cancers

Kamal Rawal, PhD, Senior Assistant Professor, Biotechnology and Bioinformatics, Jaypee Institute of Information Technology (JIIT)

Transposable elements (TEs), or mobile genetic elements (MGEs), are the most abundant elements in mammalian genomes and found in nearly 100 cases of diseases including cancers. Here, we present a new method (software pipeline) to determine exact position and insertion mechanism of transposable elements in cancer genomes using machine learning method. We also show new techniques to integrate somatic mutation data with results from genome-wide characterizations experiments and clinical data elements.

4:30 Analysis of Cancer Genome Variation from Long DNA and RNA Sequencing  

Jeffrey Rosenfeld, PhD, Manager, Biomedical Informatics Shared Resource and Assistant Professor of Pathology and Laboratory Medicine, Rutgers Cancer Institute of New Jersey

Cancer genomes are full of rearrangements which drive the oncogenesis. Using long reads, we have been able to accurately determine the structure of important cancer genes in breast cancer including BRCA1 and HER2. The variation in these genes cannot be accurately determined with short reads due to their repetitive structure. In addition, we have used RNA sequencing to determine the full sequence of kinase fusions in cancer. These fusions play a critical role in the progression of many types of cancer and understanding their full structure will lead to greater insight and improved therapies.

5:00 Ontology Learning and Personalization

Anna Lyubetskaya, Data Scientist, Engineering, Copyright Clearance Center

In this talk, we’ll discuss a framework that enables learning of ontologies in a semi-supervised manner through the best machine learning and distributed computing approaches. We discuss issues inherent to data science: input data filtering and enrichment, robust iterative learning, cross-validation, rapid prototyping, and transition between prototyping and production.

5:30 Best of Show Awards Reception in the Exhibit Hall with Poster Viewing (Commonwealth Hall)

 

7:0010:00 Bio-IT World After Hours @Lawn on D
 **Conference Registration Required. Please bring your conference badge, wristband, and photo ID for entry.   




Thursday, May 17

7:30 am Registration Open (Commonwealth Hall) and Morning Coffee (Foyer)

8:00 PLENARY KEYNOTE SESSION & AWARDS PROGRAM (Amphitheater & Harborview 2)

9:45 Coffee Break in the Exhibit Hall and Poster Competition Winners Announced (Commonwealth Hall)

APPLICATION OF NGS TO ONCOLOGY, IMMUNOLOGY, DIAGNOSTICS, AND THERAPEUTIC DEVELOPMENT
Cambridge

10:30 Chairperson’s Remarks

Bruce Press, Executive Vice President, Business Development & Strategy, Seven Bridges Genomics

10:40 Instantiating a Single Point of Truth for Genomic Reference Data

David Herzig, Scientist, Research Informatics, Roche Pharmaceuticals

This talk will exemplify how expression and mutation data were made actionable by consolidating a scattered landscape of genomic reference data into a real SPoT.

11:10 A Network-Based Approach to Understanding Drug Toxicity

Yue Webster, PhD, Principal Research Scientist, Informatics Capabilities, Research IT, Eli Lilly and Company

Despite investment in toxicogenomics, nonclinical safety studies are still used to predict clinical liabilities for new drug candidates. Network-based approaches for genomic analysis help overcome challenges with whole-genome transcriptional profiling using limited numbers of treatments for phenotypes of interest. Herein, we apply co-expression network analysis to safety assessment using rat liver gene expression data to define 415 modules, exhibiting unique transcriptional control, organized in a visual representation of the transcriptome. Compared to gene-level analysis alone, the network approach identifies significantly more phenotype-gene associations, including established and novel biomarkers of liver injury.

11:40 Advancing Clinical NGS Test Development Using Thousands of Pediatric Cancer Samples on St. Jude Cloud
Michael Rusch, Director of Bioinformatics Research Development, St. Jude Children's Research Hospital

12:10 pm Session Break

IDBS 12:20 Luncheon Presentation: Developing A Digital Transformation Roadmap to Future-Proof Your R&D Organization And Fuel Scientific Innovation

Scott Weiss, PhD, Vice President, Product Strategy, IDBS

Join Dr. Scott Weiss for this session as he illustrates the ways digital is transforming R&D; how it delivers value in scientific organizations, and what you need to do to prepare for this transformation in your organization. Learn how digital capabilities can be applied across R&D to enable future success, advance innovation and ensure compliance.

1:20 Dessert Refreshment Break in the Exhibit Hall with Poster Viewing (Commonwealth Hall)

DATA MINING FOR DISEASE CLASSIFICATION
Cityview 1

1:55 Chairperson’s Remarks

John Methot, Director, Health Informatics Architecture, Dana-Farber Cancer Institute

2:00 Disease Classification in the Era of Data-Intensive Medicine

Kanix Wang, PhD, Research Professional, Booth School of Business, Institute for Genomics & Systems Biology, University of Chicago

We used insurance claims for over one-third of the U.S. population to create a subset of 128,989 families (481,657 unique individuals). Using these data, we estimated the heritability and familial environmental patterns of 149 diseases. We then computed the environmental and genetic disease classifications for a set of 29 complex diseases after inferring their pairwise genetic and environmental correlations.

2:30 Enviro-Geno-Pheno State Approach and State-Based Biomarkers for Differentiation, Prognosis, Subtypes, and Staging

Lei Xu, PhD, Director, Centre for Cognitive Machines and Computational Health; Zhiyuan Chair Professor, Department of Computer Science and Engineering, Shanghai Jiao Tong University

In the joint space of geno-measures, pheno-measures, and enviro-measures, one point represents a bio-system behavior and a subset of points that locate adjacently and share a common system status represents a ‘state’. The system is characterized by such states learned from samples. This enviro-geno-pheno state is considered a biomarker, indicating ‘health/normal’ versus ‘risk/abnormal’ together with its associated enviro-geno-pheno condition.

3:00 PANEL DISCUSSION: Can We Improve Breast Cancer Patient Outcomes through Artificial Intelligence?

Maya Said, ScD, President & CEO, Outcomes4me, Inc. (Moderator)

 

Panelists:
Regina Barzilay, PhD, MacArthur Fellow and Delta Electronics Professor, Massachusetts Institute of Technology (MIT) Department of Electrical Engineering and Computer Science; Member, Computer Science and Artificial Intelligence Laboratory, MIT

 

Kevin Hughes, MD, Co-Director, Avon Breast Evaluation Program, Massachusetts General Hospital; Associate Professor of Surgery, Harvard Medical School; Medical Director, Bermuda Cancer Genetics Risk Assessment Clinic

 

Osama Rahma, MD, Assistant Professor of Medicine, Center For Immuno-Oncology, Dana-Farber Cancer Institute

Newly diagnosed cancer patients attempting to understand their treatment options face the overwhelming task of filtering an information deluge, much of which is irrelevant, outdated and occasionally inaccurate. Additionally, matching their diagnosis to best-in-class treatments or potential clinical trials, while simultaneously learning to navigate an extremely complex healthcare system is daunting, even for the most highly trained physicians. We will explore various platforms aimed at improving patient outcomes by leveraging technology to help educate, track, and connect patients with personalized resources while simultaneously working to improve the care continuum and the development of new treatments. We will explore the nexus of healthcare networks and their IT systems, clinical decision-making and delivery, R&D, and patients, for whom we all create our innovation solutions. Attendees will be interested to understand how various groups are working to increase value across the entire system by bringing laboratory, clinical and pharmaceutical science, real-world evidence and patient-reported data together with technology and artificial intelligence to solve health challenges. These approaches offer the opportunity to generate deeper insights into how therapies perform in the real world and harness that understanding to improve efficiency, effectiveness, value, and ultimately, patient care.

4:00 Conference Adjourns