[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-329090-105":59,"doc-detail-329090-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","single-cell-transcriptomes-identify-patient-tailored-therapies-for-selective-co-inhibition-of-cancer-clones","Single-cell transcriptomes identify patient-tailored therapies for selective co-inhibition of cancer clones","","Intratumoral cellular heterogeneity drives therapy resistance and limits the ability to select patient-specific drug regimens that selectively co-inhibit malignant cell populations. A machine learning framework, scTherapy, uses single-cell transcriptomic profiles to prioritize multi-targeting treatment options for individual patients with hematological cancers or solid tumors. Across acute myeloid leukemia and high-grade serous ovarian carcinoma, predicted therapies show selective efficacy or synergy in 96% of cases and low toxicity to normal cells in 83%, with shared and patient-specific selections varying across tumor types.",{"@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":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/single-cell-transcriptomes-identify-patient-tailored-therapies-for-selective-co-inhibition-of-cancer-clones/329090/",{"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/single-cell-transcriptomes-identify-patient-tailored-therapies-for-selective-co-inhibition-of-cancer-clones/329090.png","ImageObject",300,407,{"name":92,"@type":93},"Rowan","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-23","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does scTherapy address in cancer treatment selection?","Question",{"text":112,"@type":113},"It tackles the difficulty of systematically identifying patient-specific therapies that selectively co-inhibit cancer cell populations despite the combinatorial explosion of drug-dose possibilities.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does scTherapy use single-cell data?",{"text":117,"@type":113},"It leverages single-cell transcriptomic profiles to prioritize multi-targeting treatment options tailored to individual patients.",{"name":119,"@type":110,"acceptedAnswer":120},"What validation results are reported for the predicted multi-targeting treatments?",{"text":121,"@type":113},"Experimental validation indicates 96% of multi-targeting treatments show selective efficacy or synergy, and 83% demonstrate low toxicity to normal cells.","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},329090,1789996716,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":29,"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":36,"preview_page_count":4},1099514067415,"https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502","Article [https://doi.org/10.1038/s41467-024-52980-5](https://doi.org/10.1038/s41467-024-52980-5)  \nSingle-cell transcriptomes identify patienttailored therapies for selective co-inhibition of cancer clones  \nReceived: 15 August 2023  \n\n| Accepted: 27 September 2024 |\n| --- |\n| |\n| Check for updates |\n\nAleksandr Ianevski 1,10, Kristen Nader1,10, KyriakiDriva2, Wojciech Senkowski 2, Daria Bulanova1,2, Lidia Moyano-Galceran 2, Tanja Ruokoranta 1,3,  \nHeikki Kuusanmäki1,2,4, Nemo Ikonen 1, Philipp Sergeev1, Markus Vähä-Koskela1, Anil K. Giri 1,4, Anna Vähärautio 4,5,6, Mika Kontro1,3,4, Kimmo Porkka 3,6, Esa Pitkänen 1,6,7, Caroline A. Heckman 1,6, Krister Wennerberg 2 &  \nTero Aittokallio 1,6,8,9   \nIntratumoral cellular heterogeneity necessitates multi-targeting therapies for improved clinical benefits in advanced malignancies. However, systematic identification of patient-specific treatments that selectively co-inhibit cancerous cell populations poses a combinatorial challenge, since the number of possible drug-dose combinations vastly exceeds what could be tested inpatient cells. Here, we describe a machine learning approach, scTherapy, which leverages single-cell transcriptomic profiles to prioritize multi-targeting treatment options for individual patients with hematological cancers or solid tumors. Patient-specific treatments reveal a wide spectrum of co-inhibitors of multiple biological pathways predicted for primary cells from heterogenous cohorts of patients with acute myeloid leukemia and high-grade serous ovarian carcinoma, each with unique resistance patterns and synergy mechanisms. Experimental validations confirm that 96% of the multi-targeting treatments exhibit selective efficacy or synergy, and 83% demonstrate low toxicity to normal cells, highlighting their potential for therapeutic efficacy and safety. Ina pan-cancer analysis across five cancer types, 25% of the predicted treatments are shared among the patients of the same tumor type, while 19% of the treatments are patient-specific. Our approach provides a widely-applicable strategy to identify personalized treatment regimens that selectively co-inhibit malignant cells and avoid inhibition of non-cancerous cells, thereby increasing their likelihood for clinical success.  \nHigh intratumoral cellular heterogeneity and clonal evolution of cancer cell populations are major drivers of therapy resistance both in hematological malignancies and solid tumors1–5. In acute myeloid leukemia (AML), several single-cell genomic analyses have mapped the clonal evolutionary processes of disease progression and therapy resistance at the cell subpopulation level, as well as deciphered cellular  \nhierarchy and reprogramming among the leukemic cell subpopulations involved in chemoresistance, relapse and clinical outcomes6–9. Similarly in solid tumors, intratumoral and interpatient heterogeneity are significant medical challenges both for disease diagnosis and treatment optimization. The highly heterogeneous tumor ecosystem contains not only malignant cells but also other cell types, such as  \nA full list of affiliations appears at the end of the paper. e-mail: tero.aittokallio@helsinki.fi  \nendothelial cells, stromal fibroblasts, anda variety ofimmune cells that control tumor growth and invasion. Notable studies in melanoma, ovarian and colorectal cancers have demonstrated that specific characteristics of the tumor immune microenvironment (TME) can, to some extent, predict a patient’s clinical outcome10. For instance, clonal analysis and longitudinal sampling of patients with high-grade serous ovarian carcinoma (HGSC) revealed evolutionary trajectories, with distinct genomic and morphological features across patients that associate with therapy responses11. Moreover, the interaction between immune and non-immune cells within the TME can influence the effectiveness of immune responses, leading to varied treatment outcomes among patients.  \nSingle-cell analyzes in cancer research utilize a wide ar","cbCaih3tFPYRKoKo","https://ap.wps.com/l/cbCaih3tFPYRKoKo","pdf",4757521,16,"English","# Introduction\n## Intratumoral heterogeneity and therapy resistance\n## Single-cell analysis in cancer research\n## Challenges in precision medicine translation\n## Need for selective multi-targeting combinations","[{\"question\":\"What problem does scTherapy address in cancer treatment selection?\",\"answer\":\"It tackles the difficulty of systematically identifying patient-specific therapies that selectively co-inhibit cancer cell populations despite the combinatorial explosion of drug-dose possibilities.\"},{\"question\":\"How does scTherapy use single-cell data?\",\"answer\":\"It leverages single-cell transcriptomic profiles to prioritize multi-targeting treatment options tailored to individual patients.\"},{\"question\":\"What validation results are reported for the predicted multi-targeting treatments?\",\"answer\":\"Experimental validation indicates 96% of multi-targeting treatments show selective efficacy or synergy, and 83% demonstrate low toxicity to normal cells.\"}]","Single-cell transcriptomes identify patient-tailored therapies for selective co-inhibition of cancer clones | PDF",1789982397]