\u201cThe goal is to pull those out so we can look at their cell identity,\u201d he said. “Now, we don\u2019t know which B cells we should be trying to induce. We want to understand which B cells’ identity is optimal for a durable response so we can better tailor vaccines to select for that type of B cell.”<\/p>\n
This is Glass\u2019 first project where he is interactively coding with AI. <\/p>\n
\u201cInstead of making a plot showing the composition of cells at different timepoints, I tell Claude what I want to accomplish and I edit it, make decisions about the results and decide what to do next. It is an incredibly effective autocomplete for me. I can offload more of the coding mindset to AI so I can spend more time thinking like a scientist.\u201d<\/p>\n
Using AI to make predictions<\/p>\n
Liu<\/a>, a statistical programmer and postdoctoral researcher in Ruth Etzioni\u2019s lab<\/a>\u00a0and Michael Haffner\u2019s lab<\/a>, is trying to determine if it\u2019s possible to use AI to help treat prostate cancer. Etzioni holds the Rosalie and Harold Rea Brown Endowed Chair.\u00a0<\/p>\n \u201cWe\u2019re at the very beginning stage of using AI to help guide cancer treatment,\u201d he said.\u00a0<\/p>\n Liu\u2019s work starts by considering pathology images from tumor biopsy slides. Physicians examine pathology images of tumor specimens to determine if patients have cancer and the grade of the cancer. But to determine whether a patient might benefit from certain treatments including immunotherapy<\/a>, providers currently need to send the patients\u2019 tumor tissue for molecular testing to look for biomarkers such as MSI-high (microsatellite instability-high) alterations, which are associated with treatment response.<\/p>\n This process is expensive, considering only 3% of prostate cancer<\/a> patients have MSI-H tumors, which struggle to repair DNA errors. Patients with MSI-high tumors have a great response rate to immunotherapy. <\/p>\n \u201cIt\u2019s too expensive to do that testing for every patient for only about 3% who have MSI-high tumors.\u201d Liu said. \u201cEspecially in low-resourced settings, no one wants to do that testing. But without it, we may miss patients who could benefit from the treatment.\u201d\u00a0<\/p>\n Liu has built an AI tool to predict an MSI-high profile from a pathology image for prostate cancer. Working with researchers at Fred Hutch and other institutions, he has tested the tool on multiple datasets and found its performance \u201cpromising.\u201d<\/p>\n \u201cWe need to continue to collect data to do further validation and move it closer to clinical use,\u201d he said. \u201cThe next step is to see if AI can directly predict treatment response to immunotherapy. Our goal is to use AI to make precision oncology more scalable, accessible, and equitable.”<\/p>\n Doing the limbo<\/p>\n Huang<\/a>, a graduate student in Manu Setty\u2019s lab<\/a>, relied on her computational background to pitch a plan that uses AI to study head and neck squamous cell carcinoma<\/a> recurrence. The disease is particularly challenging because it is often diagnosed at an advanced stage. The first-line treatment is surgery, including removal of a margin of tissue around the tumor to reduce the likelihood that cancer cells remain.\u00a0<\/p>\n Yet up to 50% of patients experience recurrence within a year. Why does the disease relapse so quickly if the tumor has been removed and no residual tumor is left? A previous study that Huang was involved in found an emergent cell population in tissue adjacent to the tumor that is not quite normal and not quite tumor.\u00a0<\/p>\n \u201cYou can think of those cells as being in a state of limbo,\u201d she said.\u00a0<\/p>\n Huang hypothesizes that signaling from the tumor may have changed nearby cells, pushing them into this in-between state and possibly contributing to the cancer\u2019s return.\u00a0<\/p>\n Huang is using the power of AI to characterize these \u201climbo\u201d cells, examining whether the way they communicate with nearby cells is linked to recurrence and whether they have a recognizable appearance under the microscope that could eventually help surgeons more precisely identify high-risk tissue.\u00a0<\/p>\n The full list of 2026 Fast Pitch AI competition winners:<\/p>\n Mechanisms, Functional Genomics, and Disease Biology<\/b><\/p>\n Translational Data Science Integrated Research Center Attendee Favorite ($10,000): Erin Barnett<\/p>\n Internal Advisory Board Favorite ($10,000): David Glass<\/p>\n Best Science\/Innovation ($5,000): Sarah Becker<\/p>\n Best Pitch\/Scientific Communication ($5,000): Betty Yu<\/p>\n Best Translational Impact ($5,000): David Sokolov<\/p>\n AI, Computational Modeling, and Clinical Translation<\/b><\/p>\n Translational Data Science Integrated Research Center Attendee Favorite ($10,000): Lucas Liu<\/p>\n Internal Advisory Board Favorite ($10,000): Sarah Huang<\/p>\n Best Science\/Innovation ($5,000): Isabelle DuPlessis<\/p>\n Best Pitch\/Scientific Communication ($5,000): Weston Hanson<\/p>\n Best Translational Impact ($5,000): Patrick McDeed<\/p>\n","protected":false},"excerpt":{"rendered":"\u201cThe goal is to pull those out so we can look at their cell identity,\u201d he said. “Now,…\n","protected":false},"author":3,"featured_media":982737,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_share_on_mastodon":"0"},"categories":[30],"tags":[394428,15634,394420,394427,171,394422,394424,394423,394426,95796,394421,394417,394425,50600,146559,53661,394418,394419,173,67,132,68,6456],"class_list":["post-982736","post","type-post","status-publish","format-standard","has-post-thumbnail","category-tv","tag-blanco-melo-lab","tag-breast-cancer","tag-cellular-immunotherapy","tag-daniel-blanco-melo","tag-entertainment","tag-etzioni-lab","tag-evan-newell","tag-hiv-vaccine-trials-network-hvtn","tag-hvtn","tag-immunology","tag-manu-setty","tag-michael-haffner","tag-newell-lab","tag-prostate-cancer","tag-ruth-etzioni","tag-squamous-cell-carcinoma","tag-translational-data-science-integrated-research-center","tag-translational-data-science-integrated-research-center-tds-irc","tag-tv","tag-united-states","tag-unitedstates","tag-us","tag-virology"],"share_on_mastodon":{"url":"","error":"Validation failed: Text character limit of 500 exceeded"},"_links":{"self":[{"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/posts\/982736","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/comments?post=982736"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/posts\/982736\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/media\/982737"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/media?parent=982736"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/categories?post=982736"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/tags?post=982736"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}