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The study links tumour and normal-tissue transcriptional profiles of cell surface receptors (CSRs) to treatment variability by integrating multiple datasets. Results correlate breast cancer cell line drug responses with targeted CSR expression levels, reveal subtype-specific CSR expression differences between primary tumours and corresponding cell lines, and use clinical trial data to associate CSR gene expression in healthy tissues with adverse drug reactions. This integrative framework supports selecting optimal CSR therapeutic targets using dose-response data, normal-tissue CSR expression, and patient adverse-event profiles.",{"@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/machine-learning-and-bioinformatic-analyses-link-the-cell-surface-receptor-transcript-levels-to-the-drug-response-of-breast-cancer-cells-and-drug-off-target-effects-research-article/326857/",{"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/machine-learning-and-bioinformatic-analyses-link-the-cell-surface-receptor-transcript-levels-to-the-drug-response-of-breast-cancer-cells-and-drug-off-target-effects-research-article/326857.png","ImageObject",300,407,{"name":92,"@type":93},"Himbo","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-23","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What does the study propose as a source of variability in breast cancer treatment outcomes?","Question",{"text":112,"@type":113},"Treatment variability and drug-induced adverse events are linked to the transcriptional profiles of cell surface receptors (CSRs) in breast tumours and normal tissues.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were cell surface receptor (CSR) expression patterns compared in the study?",{"text":117,"@type":113},"The study analysed multiple datasets to compare CSR expression in breast tumours with that in non-cancerous human tissues, and related these to breast cancer cell line drug responses.",{"name":119,"@type":110,"acceptedAnswer":120},"How does the approach help predict or reduce off-target toxicity?",{"text":121,"@type":113},"By identifying CSR targets based on expression differences between tumour subtypes and corresponding cell lines, and by using clinical trial data to connect CSR gene expression in healthy tissues with adverse drug reactions, the framework supports selecting optimal CSR targets to improve therapy success.","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},326857,1790470050,{"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":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":46,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":31,"preview_page_count":4},687197100911,"https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697","PLOS ONE  \nOPEN ACCESS  \nCitation: Sinkala M, Naran K, Ramamurthy D, Mungra N, Dzobo K, Martin D, et al. (2024) Machine learning and bioinformatic analyses link the cell surface receptor transcript levels to the drug response of breast cancer cells and drug offtarget effects. PLoS ONE 19(2): e0296511 . [https://](https://)[ ](https://)[doi.org/10.1371/journal.pone.0296511](doi.org/10.1371/journal.pone.0296511)  \n[Editor:](Editor: Pan Li)[ Pan Li](Editor: Pan Li), Institute for Basic Science, REPUBLIC OF KOREA  \nReceived: October 1, 2023  \nAccepted: December 13, 2023  \nPublished: February 2, 2024  \nCopyright: © 2024 Sinkala et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nData Availability Statement: The data that support the findings of this study are available from the following repositories: The GTEx portal ([https://](https://)[ ](https://)[gtexportal.org/home/](gtexportal.org/home/)), ARCHS4 ([https://amp](https://amp). [pharm.mssm.edu/archs4/](pharm.mssm.edu/archs4/)), [clinicaltrial.gov](clinicaltrial.gov)[ ](clinicaltrial.gov)([https://clinicaltrials.gov/](https://clinicaltrials.gov/)), Genomics of Drug Sensitivity in Cancer ([https://www.cancerrxgene](https://www.cancerrxgene). org/) .  \nFunding: The author(s) received no specific funding for this work.  \nRESEARCH ARTICLE  \nMachine learning and bioinformatic analyses link the cell surface receptor transcript levels to the drug response of breast cancer cells and drug off-target effects  \nMusalula Sinkala1,2 *, Krupa Naran3, Dharanidharan Ramamurthy3, Neelakshi Mungra3, Kevin Dzobo4, Darren Martin2, Stefan Barth3,5 *  \n1 Department of Biomedical Sciences, School of Health Sciences, University of Zambia, Lusaka, Zambia, 2 Faculty of Health Sciences, Institute of Infectious Disease and Molecular Medicine & Department of Integrative Biomedical Sciences, Computational Biology Division, University of Cape Town, Cape Town, South Africa, 3 Faculty of Health Sciences, Institute of Infectious Disease and Molecular Medicine, Medical Biotechnology & Immunotherapy Research Unit, University of Cape Town, Cape Town, South Africa,  \n4 Faculty of Health Sciences, Department of Medicine, Division of Dermatology, Medical Research CouncilSA Wound Healing Unit, Hair and Skin Research Laboratory, Groote Schuur Hospital, University of Cape Town, Anzio Road, Observatory, Cape Town, South Africa, 5 Faculty of Health Sciences, Department of Integrative Biomedical Sciences, South African Research Chair in Cancer Biotechnology, University of Cape Town, CapeTown, South Africa  \n* [smsinks@icloud.com](smsinks@icloud.com) (MS); [stefan.barth@uct.ac.za](stefan.barth@uct.ac.za) (SB)  \nAbstract  \nBreast cancer responds variably to anticancer therapies, often leading to significant off-target effects. This study proposes that the variability in tumour responses and drug-induced adverse events is linked to the transcriptional profiles of cell surface receptors (CSRs) in breast tumours and normal tissues. We analysed multiple datasets to compare CSR expression in breast tumours with that in non-cancerous human tissues. Our findings correlate the drug responses of breast cancer cell lines with the expression levels of their targeted CSRs. Notably, we identified distinct differences in CSR expression between primary breast tumour subtypes and corresponding cell lines, which may influence drug response predictions. Additionally, we used clinical trial data to uncover associations between CSR gene expression in healthy tissues and the incidence of adverse drug reactions. This integrative approach facilitates the selection of optimal CSR targets for therapy, leveraging cell line dose-responses, CSR expression in normal tissues, and patient adverse event profiles.  \nIntroduction  \nThe aberrant overexpression of cell su","cbCaipyjvwpgsBxl","https://ap.wps.com/l/cbCaipyjvwpgsBxl","pdf",4525452,"English","# Abstract\n# Introduction","[{\"question\":\"What does the study propose as a source of variability in breast cancer treatment outcomes?\",\"answer\":\"Treatment variability and drug-induced adverse events are linked to the transcriptional profiles of cell surface receptors (CSRs) in breast tumours and normal tissues.\"},{\"question\":\"How were cell surface receptor (CSR) expression patterns compared in the study?\",\"answer\":\"The study analysed multiple datasets to compare CSR expression in breast tumours with that in non-cancerous human tissues, and related these to breast cancer cell line drug responses.\"},{\"question\":\"How does the approach help predict or reduce off-target toxicity?\",\"answer\":\"By identifying CSR targets based on expression differences between tumour subtypes and corresponding cell lines, and by using clinical trial data to connect CSR gene expression in healthy tissues with adverse drug reactions, the framework supports selecting optimal CSR targets to improve therapy success.\"}]","Machine learning and bioinformatic analyses link the cell surface receptor transcript levels to the drug response of breast cancer cells and drug off-target effects - research article | PDF",1789975569]