{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T16:30:59Z","timestamp":1753893059292,"version":"3.41.2"},"reference-count":51,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,4,17]],"date-time":"2023-04-17T00:00:00Z","timestamp":1681689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Immuno-oncology (IO) therapies targeting the PD-1\/PD-L1 axis, such as immune checkpoint inhibitor (ICI) antibodies, have emerged as promising treatments for early-stage breast cancer (ESBC). Despite immunotherapy's clinical significance, the number of benefiting patients remains small, and the therapy can prompt severe immune-related events. Current pathologic and transcriptomic predictions of IO response are limited in terms of accuracy and rely on single-site biopsies, which cannot fully account for tumor heterogeneity. In addition, transcriptomic analyses are costly and time-consuming. We therefore constructed a computational biomarker coupling biophysical simulations and artificial intelligence-based tissue segmentation of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRIs), enabling IO response prediction across the entire tumor.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>By analyzing both single-cell and whole-tissue RNA-seq data from non-IO-treated ESBC patients, we associated gene expression levels of the PD-1\/PD-L1 axis with local tumor biology. PD-L1 expression was then linked to biophysical features derived from DCE-MRIs to generate spatially- and temporally-resolved atlases (virtual tumors) of tumor biology, as well as the <jats:italic>TumorIO<\/jats:italic> biomarker of IO response. We quantified <jats:italic>TumorIO<\/jats:italic> within patient virtual tumors (<jats:italic>n<\/jats:italic> = 63) using integrative modeling to train and develop a corresponding <jats:italic>TumorIO Score<\/jats:italic>.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We validated the <jats:italic>TumorIO<\/jats:italic> biomarker and <jats:italic>TumorIO Score<\/jats:italic> in a small, independent cohort of IO-treated patients (<jats:italic>n<\/jats:italic> = 17) and correctly predicted pathologic complete response (pCR) in 15\/17 individuals (88.2% accuracy), comprising 10\/12 in triple negative breast cancer (TNBC) and 5\/5 in HR+\/HER2- tumors. We applied the <jats:italic>TumorIO Score<\/jats:italic> in a virtual clinical trial (<jats:italic>n<\/jats:italic> = 292) simulating ICI administration in an IO-na\u00efve cohort that underwent standard chemotherapy. Using this approach, we predicted pCR rates of 67.1% for TNBC and 17.9% for HR+\/HER2- tumors with addition of IO therapy; comparing favorably to empiric pCR rates derived from published trials utilizing ICI in both cancer subtypes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The <jats:italic>TumorIO<\/jats:italic> biomarker and <jats:italic>TumorIO Score<\/jats:italic> represent a next generation approach using integrative biophysical analysis to assess cancer responsiveness to immunotherapy. This computational biomarker performs as well as PD-L1 transcript levels in identifying a patient's likelihood of pCR following anti-PD-1 IO therapy. The <jats:italic>TumorIO<\/jats:italic> biomarker allows for rapid IO profiling of tumors and may confer high clinical decision impact to further enable personalized oncologic care.<\/jats:p><\/jats:sec>","DOI":"10.3389\/frai.2023.1153083","type":"journal-article","created":{"date-parts":[[2023,4,17]],"date-time":"2023-04-17T04:45:15Z","timestamp":1681706715000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Next generation immuno-oncology tumor profiling using a rapid, non-invasive, computational biophysics biomarker in early-stage breast cancer"],"prefix":"10.3389","volume":"6","author":[{"given":"Daniel","family":"Cook","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthew","family":"Biancalana","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicole","family":"Liadis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dorys","family":"Lopez Ramos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Snehal","family":"Patel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joseph R.","family":"Peterson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John R.","family":"Pfeiffer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John A.","family":"Cole","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anuja K.","family":"Antony","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2023,4,17]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1694","DOI":"10.1016\/j.cell.2022.03.033","article-title":"Quiescent cancer cells resist T cell attack by forming an immunosuppressive niche","volume":"185","author":"Baldominos","year":"2022","journal-title":"Cell"},{"key":"B2","doi-asserted-by":"publisher","first-page":"1061176","DOI":"10.1080\/2162402X.2015.1061176","article-title":"Biological network-driven gene selection identifies a stromal immune module as a key determinant of triple-negative breast carcinoma prognosis","volume":"5","author":"Bonsang-Kitzis","year":"2016","journal-title":"Oncoimmunology"},{"key":"B3","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1038\/s41698-021-00197-w","article-title":"Bevacizumab improves tumor infiltration of mature dendritic cells and effector T-cells in triple-negative breast cancer patients","volume":"5","author":"Boucher","year":"2021","journal-title":"NPJ Precis. 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