{"id":788855,"date":"2021-10-26T23:16:59","date_gmt":"2021-10-27T06:16:59","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-project&#038;p=788855"},"modified":"2021-11-09T20:39:13","modified_gmt":"2021-11-10T04:39:13","slug":"learning-to-decode-the-immune-system-to-diagnose-disease","status":"publish","type":"msr-project","link":"https:\/\/www.microsoft.com\/en-us\/research\/project\/learning-to-decode-the-immune-system-to-diagnose-disease\/","title":{"rendered":"Learning to Decode the Immune System to Diagnose Disease"},"content":{"rendered":"<section class=\"mb-3 moray-highlight\">\n\t<div class=\"card-img-overlay mx-lg-0\">\n\t\t<div class=\"card-background bg-gray-200 has-background- card-background--full-bleed\">\n\t\t\t\t\t<\/div>\n\t\t<!-- Foreground -->\n\t\t<div class=\"card-foreground d-flex mt-md-n5 my-lg-5 px-g px-lg-0\">\n\t\t\t<!-- Container -->\n\t\t\t<div class=\"container d-flex mt-md-n5 my-lg-5 align-self-center\">\n\t\t\t\t<!-- Card wrapper -->\n\t\t\t\t<div class=\"w-100 w-lg-col-5\">\n\t\t\t\t\t<!-- Card -->\n\t\t\t\t\t<div class=\"card material-md-card py-5 px-md-5\">\n\t\t\t\t\t\t<div class=\"card-body \">\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\n<h1 id=\"learning-to-decode-the-immune-system-to-diagnose-disease\">Learning to Decode the Immune System to Diagnose Disease<\/h1>\n\n\n\n<p><\/p>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/div>\n<\/section>\n\n\n\n\n\n<p>Adaptive published the original proof-of-concept for this project (Emerson, et al. Nat Genet. 2017), demonstrating that shared TCRs can be used as a basis for diagnosing viral infection. Since then, we have further demonstrated the applicability of this work in covid, Lyme and other diseases (see publications). Despite the enormous complexity of the immune response, the elegant simplicity of this approach shows how common immunological solutions to pathogens can be used to provide a window into what the immune system is actively targeting. In principle, this approach can be extended and augmented with antigen-specific binding data to provide diagnostics for cancers, infections and autoimmune diseases. To date, we have sequenced the T-cell repertoires of tens of thousands of individuals affected by one of the several specific diseases we are initially pursuing based on unmet clinical need and our understanding of the underlying immunology:<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>Oncology: Ovarian cancer, Pancreatic cancer.<\/li><li>Autoimmune disease: Type I diabetes, Celiac disease, Crohn\u2019s disease.<\/li><li>Infectious Disease: COVID-19, Lyme disease.<\/li><li>Other diseases with an unmet need for a blood-based diagnostic.<\/li><\/ul>\n\n\n","protected":false},"excerpt":{"rendered":"<p>Adaptive published the original proof-of-concept for this project (Emerson, et al. Nat Genet. 2017), demonstrating that shared TCRs can be used as a basis for diagnosing viral infection. Since then, we have further demonstrated the applicability of this work in covid, Lyme and other diseases (see publications). Despite the enormous complexity of the immune response, [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"research-area":[13556],"msr-locale":[268875],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-788855","msr-project","type-msr-project","status-publish","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us","msr-archive-status-active"],"msr_project_start":"","related-publications":[],"related-downloads":[],"related-videos":[],"related-groups":[],"related-events":[],"related-opportunities":[],"related-posts":[],"related-articles":[],"tab-content":[],"slides":[],"related-researchers":[{"type":"user_nicename","display_name":"Pan Deng","user_id":39865,"people_section":"Section name 0","alias":"paden"},{"type":"user_nicename","display_name":"Liang He","user_id":38505,"people_section":"Section name 0","alias":"lihe"},{"type":"user_nicename","display_name":"Tie-Yan Liu","user_id":34431,"people_section":"Section name 0","alias":"tyliu"},{"type":"user_nicename","display_name":"Bin Shao","user_id":31238,"people_section":"Section name 0","alias":"binshao"},{"type":"user_nicename","display_name":"Yingce Xia","user_id":37784,"people_section":"Section name 0","alias":"yinxia"}],"msr_research_lab":[199560],"msr_impact_theme":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/788855"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-project"}],"version-history":[{"count":1,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/788855\/revisions"}],"predecessor-version":[{"id":788858,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/788855\/revisions\/788858"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=788855"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=788855"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=788855"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=788855"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=788855"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}