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This study applies spatial transcriptomic profiling to FFPE tissues from treatment-naive women spanning good and poor prognosis cohorts. Using GeoMX Digital Spatial Profiler, segmented tumor and stromal regions are analyzed with transcriptome-wide probing. Differential and integrative analyses identify transcriptional distinctions, immune versus mesenchymal/fibrotic programs, and prognostic epithelial gene signatures for diagnostic classification.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/spatially-distinct-cellular-and-molecular-landscapes-define-prognosis-in-triple-negative-breast-cancer/371273/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/spatially-distinct-cellular-and-molecular-landscapes-define-prognosis-in-triple-negative-breast-cancer/371273.png","ImageObject",300,407,{"name":92,"@type":93},"Noah","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-03","2026-09-23",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What biological differences distinguish good vs poor prognosis in triple-negative breast cancer?","Question",{"text":112,"@type":113},"The study reports transcriptionally distinct epithelia and tumor microenvironment states between the cohorts. Good prognosis samples show increased immune transcripts and immune cell presence, while poor prognosis samples show greater metabolic/translational activity and a more mesenchymal/fibrotic microenvironment.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which spatial technology and workflow were used to profile the tissues?",{"text":117,"@type":113},"Spatial transcriptomic profiling was performed on FFPE tissues using GeoMX Digital Spatial Profiler. Regions of interest were segmented on pan-cytokeratin, then tumor and stromal components were analyzed with a GeoMx human whole transcriptome atlas panel.",{"name":119,"@type":110,"acceptedAnswer":120},"How were differential gene signatures linked to prognosis and diagnosis?",{"text":121,"@type":113},"After quality control, normalization, and differential analysis, additional computational steps (including cell-type deconvolution, spatial entropy, functional enrichment, TF-target/ligand-receptor analysis, and convolution neural networks) were used to identify gene signatures. These signatures can classify samples at diagnosis with high accuracy and may inform therapy.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},371273,1790960883,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},8796095462418,"https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780","bioRxiv preprint doi: [https://doi.org/10.1101/2025.02.10.637503](https://doi.org/10.1101/2025.02.10.637503); this version posted February 12, 2025. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made  \navailable under aCC-BY-NC-ND 4.0 International license.  \nSpatially distinct cellular and molecular landscapes define prognosis in triple negative breast cancer  \nKavitha Mukund 1, Darya Veraksa 1, David Frankhouser2, Lixin Yang2, Jerneja Tomsic2, Raju Pillai2, Srijan Atti3, Zahra Mesrizadeh 1, Daniel Schmolze2, Xiao-Cheng Wu4, Mary-Anne LeBlanc4, Lucio Miele4, Augusto Ochoa4, Victoria Seewaldt2 and Shankar Subramaniam 1,5*  \n1 Department of Bioengineering, UC San Diego, Gilman Drive, La Jolla, CA 92093, USA  \n2 City of Hope Comprehensive Cancer Center, 1500 East Duarte Road, Duarte, CA 91010, USA  \n3 Del Norte High School, San Diego, CA 92127, USA  \n4 LSU Health Sciences Center, School of Medicine, New Orleans, LA 70112, USA  \n5 Departments of Cellular & Molecular Medicine, Computer Science & Engineering, and Data Science, UC San Diego, Gilman Drive, La Jolla, CA 92093, USA  \n* Corresponding author: S.S: [shankar@ucsd.edu](shankar@ucsd.edu)  \nABSTRACT  \nBackground-Triple-negative breast cancer is a prevalent breast cancer subtype with the lowest 5-year survival. Several factors contribute to its treatment response, but the inherent molecular and cellular tumor heterogeneity are increasingly acknowledged as crucial determinants.  \nMethods-Spatial transcriptomic profiling was performed on FFPE tissues from a retrospective, treatment-naive group of women with differential prognoses (17 with >15 years survival-good prognosis (GPx) and 15 with \u003C3 years survival-poor prognosis (PPx)) using GeoMX® Digital Spatial Profiler. Regions of interest were segmented on pan-cytokeratin and analyzed for tumor and stromal components, probed using GeoMx human whole transcriptome atlas (WTA) panel. Data quality control, normalization, and differential analysis was performed in R using GeomxTools and linear mixed models. Additional analyses including cell-type deconvolution, spatial entropy, functional enrichment, TF-target / ligand-receptor analysis and convolution neural networks were employed to identify significant gene signatures contributing to differential prognosis.  \nResults- Here we report on the spatial and molecular heterogeneity underlying differential prognosis. We observe that the state of the epithelia and its microenvironment (TME) are transcriptionally distinct between the two groups. Invasive epithelia in GPx show a significant increase in immune transcripts with the TME exhibiting increased immune cell presence (via IF), while in PPx they are more metabolically and translationally active, with the TME being more mesenchymal/fibrotic. Specifically, pre-cancerous epithelia in PPx display a prescience of aggressiveness as evidenced by increased EMT-signaling. We identify distinct epithelial gene signatures for PPx and GPx, that can, with high accuracy, classify samples at the time of diagnosis and likely inform therapy.  \nbioRxiv preprint doi: [https://doi.org/10.1101/2025.02.10.637503](https://doi.org/10.1101/2025.02.10.637503); this version posted February 12, 2025. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made  \navailable under aCC-BY-NC-ND 4.0 International license.  \nConclusions- To the best of our knowledge, this is the first study to leverage spatial transcriptomics for an in-depth delineation of the cellular and molecular underpinnings of differential prognosis in TNBC. Our study highlights the potential of spatial transcriptomics to not only uncover the molecular drivers of differential prognosis in TNBC but also to pave the way for precision diagnostics","cbCaij7nRYJUDtvW","https://ap.wps.com/l/cbCaij7nRYJUDtvW","pdf",6804255,37,"English","# Abstract\n## Background\n## Methods\n## Results\n## Conclusions\n# Introduction","[{\"question\":\"What biological differences distinguish good vs poor prognosis in triple-negative breast cancer?\",\"answer\":\"The study reports transcriptionally distinct epithelia and tumor microenvironment states between the cohorts. Good prognosis samples show increased immune transcripts and immune cell presence, while poor prognosis samples show greater metabolic/translational activity and a more mesenchymal/fibrotic microenvironment.\"},{\"question\":\"Which spatial technology and workflow were used to profile the tissues?\",\"answer\":\"Spatial transcriptomic profiling was performed on FFPE tissues using GeoMX Digital Spatial Profiler. Regions of interest were segmented on pan-cytokeratin, then tumor and stromal components were analyzed with a GeoMx human whole transcriptome atlas panel.\"},{\"question\":\"How were differential gene signatures linked to prognosis and diagnosis?\",\"answer\":\"After quality control, normalization, and differential analysis, additional computational steps (including cell-type deconvolution, spatial entropy, functional enrichment, TF-target/ligand-receptor analysis, and convolution neural networks) were used to identify gene signatures. These signatures can classify samples at diagnosis with high accuracy and may inform therapy.\"}]","Spatially distinct cellular and molecular landscapes define prognosis in triple negative breast cancer | PDF",1790191673,93]