SC7: OMIC TECHNOLOGY FOR CANCER IMMUNOLOGY
Room Location:  152 

Sunday, March 10 | 2:00-5:00pm

ABOUT THIS COURSE:

This short course will be amn introduction to cover strategies for integrating genomic and proteomic data for drug discovery and target validation for cancer immunotherapy.

Part 1: IMPORTANT: BRING YOUR COMPUTER TO WORK ALONG SIDE THE INSTRUCTOR:

The mutation associated neoantigen functional expansion of specific T cells (MANAFEST) assays have recently emerged as an appealing assay to detect and monitor anti-tumor T cell responses, particularly in cancer patients treated with checkpoint blockade. This T cell receptor sequencing (TCRseq)-based approach generates a molecular barcode of antigen-specific T cells that can then be tracked in any biological compartment from which DNA is available. With the wealth of data that is generated from this assay comes the added burden of analysis and interpretation by immunologists or other professionals who may not have a bioinformatics background. During this educational session, we will review the basic experimental procedure and pros and cons of the MANAFEST approach. We will go through a detailed step-by-step walkthrough of the analysis, with the option of participants running the analysis on their personal laptops with a test dataset, and the speaker will give detailed steps and advice on interpreting MANAFEST results. This session will focus on analysis of complex TCRseq files in the context of the MANAFEST assay and will be tailored to the non-bioinformatician.

• Brief overview of the MANAFEST assay
• Origin & rationale
• Assay optimization
• Brief overview of final optimized experimental protocol
• Examples of real-world use and application of findings
• Overview of TCRseq data and format
• Pieces of information that are vital to MANAFEST analysis
• Demonstration of data analysis from start to finish
• Walkthrough of analysis, including web app functionality
• Explanation of analysis output files
• Figure generation
• Considerations when interpreting results
• Identifying positive “hits”
• Filtering out the “noise”

Part 2: Mass spectrometry-based Proteomics: Opening the Black Box

Proteomics has an unmatched ability to go beyond a "parts list" of a cell of interest, but to show how cellular processes unfold across many dimensions.  Unlike genomic sequencing technologies, proteomics methods indicate which proteins physically interact, where they are located within a cell, and how they are post-translationally modified.  These strengths make proteomics methods -- often employing a combination of liquid chromatography and tandem mass spectrometry (LC-MS/MS) -- extremely valuable tools for deciphering the complex network of immune interactions within and between cells and body systems.  Also unlike genomic sequencing technologies, proteins cannot be amplified to improve their ability to be characterized, creating fundamental obstacles for simply transferring mature technologies developed for genome analysis to proteomics.  In this talk, I will describe fundamental aspects of proteomic analysis, touching upon the most widely used technologies and methods to achieve them.  Specifically, I will touch upon

1) The general workflow for proteomic analysis, including sample preparation and instrumentation

2) Methods for computational analysis of mass spectrometry data

3) Strategies for quantifying proteins using mass spectrometry-based experiments

4) Some allied / emerging mass spectrometry methods 

COURSE AGENDA:

2:00 Course introduction and discussion

3:15 Coffee Break

3:45 Course discussion and Q&A

5:00 Close of course

INSTRUCTOR:
Justina Caushi, Graduate Student, Johns Hopkins University 
To be announced.

Joshua Eric Elias-Merriman, PhD, Assistant Professor, Chemical and Systems Biology, Stanford University

Dr. Joshua Elias is a Cape Cod native who received his undergraduate degree from Cornell in Biology in Biology (1998), and his Ph.D. from Harvard Medical School in Cell Biology with Stephen Gygi (2006). Perhaps best-known for the ubiquitous "target-decoy" search strategy for controlling error in proteomics experiments, he has long been interested in bringing proteomics workflows in sync with the kinds of robust methods used in genomics and allied fields. Since 2009, Dr. Elias has directed his laboratory at Stanford University towards solving three extraordinary challenges in proteomics: identifying disease relevant antigens presented on MHC complexes; characterizing the biologically relevant proteins that mediate host and microbiome inter-and intra-organism interactions, and computational tools that surmount the obstacles both experiment types pose. As a step towards meeting the first experimental goal, his team has been building a MHC analysis platform that has earned support from the NIH, W.M.Keck and Gates foundations to study antigen presentation in lymphoma, head and neck cancer, allergy, and infection.  In parallel, the Elias lab developed a method they termed “host-centric” proteomic analysis of stool, which we have shown enriches the precise kinds of proteins one would desirably measure when studying host-microbiome relationships. To solve the problem of low protein identification rates associated with both types of experiments, his lab developed a new spectrum interpretation tool called TagGraph (under review), which is capable of unbiased, rapid, and accurate PTM discovery.  It can also identify proteins without a priori knowledge of their source organism. This strategy stems from our efforts to develop and apply de novo search methods for characterizing proteomes when the source genome is ill-defined or completely absent, as is the case with both gut microbiota and MHC-presented antigens. The lab’s long-term goal is to unify these efforts, to better understand and control how our diets and commensal microbes influence our immune health. 

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