BTEP Seminar Series

There is an urgent need to take what we have learned in our new data-driven era of medicine, and use it to create a new system of precision medicine, delivering the best, safest, cost-effective preventative or therapeutic intervention at the right time, for the right patients.  Dr. Butte's teams at the University of California build and apply tools that convert trillions of points of molecular, clinical, and epidemiological data -- measured by researchers and clinicians over the past decade and now commonly termed “big data” -- into diagnostics, therapeutics, and new insights into disease.  Dr. Butte, a computer scientist and pediatrician, will highlight his center’s recent work on integrating electronic health records data from over 8 million patients across the entire University of California, and how analytics on this “real world data” can lead to new evidence for drug efficacy, new savings from better medication choices, and new methods to teach intelligence – real and artificial – to more precisely practice medicine.

The Trapnell Lab at the University of Washington's Department of Genome Sciences studies how genomes encode the program of vertebrate development and how that program goes awry in disease. We build new tools, technologies, and software for decoding this program from large-scale single-cell experiments.

While there is a positive correlation between cancer and aging, the mechanisms underlying this relationship remain unclear. Clonal hematopoiesis, a benign condition that is both associated with aging and predisposes to increased risk of development of blood cancers, presents an opportunity to understand the connection between cancer and aging. This seminar will discuss emerging discoveries of mechanisms acting within the hematopoietic stem cells as well as alterations in the bone marrow microenvironment that promote clonal hematopoiesis and transformation to blood cancers.

The Bhattacharya Lab at the UCSF Parnassus Campus is focused on the functional role of monocyte-derived macrophages in the onset and persistence of fibrosis in the lung. We are addressing the following major questions, with a goal of discovering new targets for therapy for acute lung injury and fibrosis:

• What molecules released by monocyte-derived macrophages and other immune cells signal to and activate pro-fibrotic programs in parenchymal cell types such as fibroblasts and epithelial cells?
• What reciprocal signals derive from these parenchymal cells to modify the immune response?
• How can this pathologic crosstalk be reversed to combat fibrosis and restore lung health?

CellTypist was first developed as a platform for exploring tissue adaptation of cell types using scRNA-seq semi-automatic annotations. Now it's an open source tool for automated cell type annotations as well as a working group in charge of curating models and ontologies.

Single-cell technologies, such as single-cell RNA sequencing (scRNA-seq), have increased the resolution achieved in the study of cellular phenotypes, allowing measurements of thousands of different genes in thousands of individual cells. This has created an opportunity to begin understanding the dynamics of the prime biological processes undergone by cells, while requiring unique computational tools. In our lab, we develop novel and innovative computational methods for single-cell data analysis. - Theis Lab

Azimuth is a web application that uses an annotated reference dataset to automate the processing, analysis, and interpretation of a new single-cell RNA-seq experiment. Azimuth leverages a 'reference-based mapping' pipeline that inputs a counts matrix of gene expression in single cells, and performs normalization, visualization, cell annotation, and differential expression (biomarker discovery). All results can be explored within the app, and easily downloaded for additional downstream analysis. - Satija Lab

The development of Azimuth is led by the New York Genome Center Mapping Component as part of the NIH Human Biomolecular Atlas Project (HuBMAP).

This webinar will be recorded and made available on the BTEP web site: https://bioinformatics.ccr.cancer.gov/btep/btep-video-archive-of-past-classes/ within 48 hours after the event ends. 

AI Models of Cancer and Precision Medicine: Building a Mind for Cancer

The long-term objective of the Ideker Lab is to create artificially intelligent, mechanistic models of cancer and neurodegenerative diseases for translation of patient data to precision diagnosis and treatment. We seek to advance this goal by addressing fundamental questions in the field: What are the genetic and molecular networks that promote disease, and how do we best chart these? How do we use knowledge of these networks in intelligent systems for predicting the effects of genotype on phenotype? – Ideker Lab, https://idekerlab.ucsd.edu/research/cancer/

This webinar will be recorded and made available on the BTEP web site: https://bioinformatics.ccr.cancer.gov/btep/btep-video-archive-of-past-classes/ within 48 hours after the event ends. 

The avalanche of easy-to-create genomics data has impacted almost all areas of medicine and science, from cancer patients and microbial diagnostics to molecular monitoring for astronauts in space. In this lecture, new discoveries from RNA- and DNA-sequencing with the FDA’s SEQC study show the ability of single-molecule methods to reveal rare alleles and provide more comprehensive epigenomics maps of patients and cancers. Also, recent technologies and algorithms from our laboratory and others demonstrate that an integrative, cross-kingdom view of patients (precision metagenomics) holds unprecedented biomedical potential to discern risk, improve diagnostic accuracy, and to map both genetic and epigenetic states, as well as clonal changes in mutations with clonal hematopoiesis. Finally, these methods and molecular tools work together to guide comprehensive, longitudinal, multi-omic views of human astronaut physiology and biology in the NASA Twins Study and several other missions with SpaceX and Axiom, which lay the foundation for future, long-duration spaceflight, including sequencing, quantifying, and engineering genomes to survive on other planets over the next 500 years (https://mitpress.mit.edu/books/next-500-years).

The efforts of our laboratory are split evenly between experimental and computational biology.  We develop new experimental methods to sequence single cells and isolate rare subpopulations and develop new analytical approaches to detect variants and apply statistical methods to these data sets.  We focus mainly on breast cancer to understand the role of clonal diversity in the evolution of invasion, metastasis and response to chemotherapy.  We are also using these tools to study rare tumor cell subpopulations including circulating tumor cells and cancer stem cells.  Our goal is to understand the role of clonal diversity in tumor evolution so that we can exploit this diversity for therapeutic vulnerabilities and improve diagnostic tools and the early detection of cancer.  We fully expect that applying these tools to human patients will lead to reduced morbidity in breast cancer.

Sarah Teichmann is co-founder and principal leader of the Human Cell Atlas (HCA) international consortium. The International Human Cell Atlas initiative aims to create comprehensive reference maps of all human cells to further understand health and disease.

The 37 trillion cells of the human body have a remarkable array of specialized functions, and must cooperate and collaborate in time and space to construct a functioning human. In this talk I will describe my lab’s efforts to understand this cellular diversity through a programme of cell atlasing. Harnessing cutting edge single cell genomics, imaging and computational technologies, we investigate development, homeostasis and disease states, at scale and in 3D, with a particular focus on immunity. I will illustrate the relevance of cell atlas-ing for engineering organoids and regenerative medicine, and will share new results providing insights into pacemaker cells from the sinoatrial node of this heart. Overall I hope to illustrate the power of single cell approaches in unlocking fundamental knowledge about the human body.

Making data reusable for discovery and shared analytics across domains is a laborious, specific-skill requiring task that most data providers do not have the resources, expertise, or perspective to perform. Equally challenged are the data re-users, who function in a landscape of bespoke schemas, formats, and coding – when they can get past understanding the licensing and access control issues. Making the most of our collective data requires partnerships between basic researchers, clinicians, patients, and informaticians, as well as sophisticated strategies to address a myriad of interoperability issues. This talk will review different communities endeavors towards these ends from across the translational spectrum.

Our goal is to understand how cellular heterogeneity encodes the molecular structure, function, and regulation of complex biological systems. Primarily using single cell genomics, we analyze systems by profiling their most fundamental units individually – a ‘bottom-up’ approach that allows us to study how diverse groups of cells work together to drive biological processes and behaviors. – Satija Lab

Since Dr. Satija will be presenting unpublished work in this webinar, it will not be recorded or distributed.