[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126843-en":3,"doc-seo-126843-105":30,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126843,1099523882367,"Hazel","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Predicting Response to Enzalutamide and Abiraterone in Metastatic Prostate Cancer Using Whole-Omics Machine Learning","Response to androgen receptor signaling inhibitors varies widely in metastatic castration-resistant prostate cancer, making reliable biomarkers essential for better treatment guidance. Using whole-genomics from biopsies matched with whole-transcriptomics in ARSI-treated patients, enriched genomic features and distinct expression profiles distinguish poor responders. Validated classification models identify the best-performing approaches by combining prior treatment information with either enriched genomic markers or global transcriptomic profiles. Results indicate that genomic, transcriptomic, and clinical integration can predict ARSI response.","Article [https://doi.org/10.1038/s41467-023-37647-x](https://doi.org/10.1038/s41467-023-37647-x)  \nPredicting response to enzalutamide andabiraterone in metastatic prostate cancer using whole-omics machine learning  \nReceived: 9 September 2022  \n\n| Accepted: 22 March 2023 |\n| --- |\n|  |\n| Check for updates |\n\nAnouk C. de Jong 1,4, Alexandra Danyi 2,4, Job van Riet 1, Ronald de Wit1, MartinSjöström 3, Felix Feng 3, Jeroen de Ridder 2 &MartijnP. Lolkema 1   \nResponse to androgen receptor signaling inhibitors (ARSI) varies widely in metastatic castration resistant prostate cancer (mCRPC). To improve treatment guidance, biomarkers are needed. We use whole-genomics (WGS; n = 155) with matching whole-transcriptomics (WTS; n = 113) from biopsies of ARSI-treated mCRPC patients for unbiased discovery of biomarkers and development of machine learning-based prediction models. Tumor mutational burden (q \u003C 0.001), structural variants (q \u003C 0.05), tandem duplications (q \u003C0.05) and deletions (q \u003C 0.05) are enriched in poor responders, coupled with distinct transcriptomic expression profiles. Validating various classification models predicting treatment duration with ARSI on our internal and external mCRPC cohort reveals two best-performing models, based on the combination of prior treatment information with either the four combined enriched genomic markers or with overall transcriptomic profiles. In conclusion, predictive models combining genomic, transcriptomic, and clinical data can predict response to ARSI in mCRPC patients and, with additional optimization and prospective validation, could improve treatment guidance.  \nWith approximately 350,000 men dying yearly of prostate cancer, prostate cancer is the fifth leading cause of cancer-related death worldwide1. Although early-phase prostate cancer is known for its favorable prognosis, the prognosis of metastatic prostate cancer is poor, especially when patients progress to the castration-resistant phase ofthe disease2,3. The treatment of metastatic castration-resistant prostate cancer (mCRPC) has significantly improved since the advent of second-generation androgen receptor signaling inhibitors (ARSI), like abiraterone acetate + prednisone (AAP) and enzalutamide3–5. However, response to these treatments varies widely between individual patients4,5 To improve therapy guidance and optimize patient outcome, biomarkers, which can predict response before or soon after the start of therapy, are needed.  \nThe existing biomarkers for treatment guidance in this setting are increasingly based on so-called liquid biopsies. It has been shown that  \nfive or more circulating tumor cells (CTCs) in 7.5ml of blood and high levels of cell-free DNA (cfDNA) before the start of treatment are associated with a poor prognosis6–8. In addition, more detailed molecular analyses can be performed to predict resistance toARSI. In CTCs, expression of androgen receptor variant 7 (AR-V7) is associated with resistance to ARSI, while this correlation is not found for chemotherapy8–14. To genotype cfDNA, gene panels targeting known driver and/or resistance-related genes are often used for sequencing or PCR. The most commonly identified alterations, that are associated with resistance to ARSI in patients, encompass AR mutations andamplifications8,15–19. Furthermore, RB1 loss, TP53 aberrations, ZFHX3 deletions and PI3K pathway defects were associated with worse survival8,15,19. However, liquid biopsy-based analyses are mostly targeted to a certain set of genes and rely on patients having a high tumor-derived cfDNA fraction in the blood. Therefore, liquid biopsies  \n1Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, the Netherlands. 2Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, the Netherlands. 3Department of Radiation Oncology, University of California, San Francisco, CA, USA. 4These authors contributed equally: Anouk C. de Jong, Alexandra Danyi. e-mail: [m.lolkema@erasmusmc.nl](m.lo","cbCairC7lTHAMx3X","https://ap.wps.com/l/cbCairC7lTHAMx3X","pdf",2781999,1,19,"English","en",105,"# Abstract and Background\n## Study Aim: Need for Predictive Biomarkers\n## Current Biomarkers and Limitations\n# Whole-Genome and Transcriptome Approach\n## Unbiased Discovery Using WGS/WTS\n## Prior Evidence from Genomic/Transcriptomic Signals\n# Machine Learning for Prediction\n## Challenges of High-Dimensional Features\n## Model Validation Strategy","[{\"question\":\"Why are biomarkers needed for ARSI treatment in metastatic castration-resistant prostate cancer?\",\"answer\":\"Responses to enzalutamide or abiraterone vary widely across patients. Biomarkers are required to predict response before or soon after therapy starts to improve guidance and outcomes.\"},{\"question\":\"What datasets and patient samples were used to build predictive models?\",\"answer\":\"Whole-genomics (WGS) was used with matching whole-transcriptomics (WTS) from biopsies of ARSI-treated mCRPC patients, enabling integrated discovery of predictive signals.\"},{\"question\":\"Which types of genomic and transcriptomic signals relate to poor ARSI response?\",\"answer\":\"Tumor mutational burden, specific structural variants and copy-number patterns were enriched in poor responders, together with distinct transcriptomic expression profiles.\"},{\"question\":\"How did the study determine the best-performing prediction models?\",\"answer\":\"Multiple classification models were validated on internal and external mCRPC cohorts, with the best models combining prior treatment information with either enriched genomic markers or overall transcriptomic profiles.\"}]","Predicting Response to Enzalutamide and Abiraterone in Metastatic Prostate Cancer Using Whole-Omics Machine Learning | PDF",1785935179,48,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":90,"head_meta":92,"extra_data":94,"updated_unix":28},"predicting-response-to-enzalutamide-and-abiraterone-in-metastatic-prostate-cancer-using-whole-omics-machine-learning","",{"@graph":36,"@context":89},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predicting-response-to-enzalutamide-and-abiraterone-in-metastatic-prostate-cancer-using-whole-omics-machine-learning/126843/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"Why are biomarkers needed for ARSI treatment in metastatic castration-resistant prostate cancer?","Question",{"text":75,"@type":76},"Responses to enzalutamide or abiraterone vary widely across patients. Biomarkers are required to predict response before or soon after therapy starts to improve guidance and outcomes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What datasets and patient samples were used to build predictive models?",{"text":80,"@type":76},"Whole-genomics (WGS) was used with matching whole-transcriptomics (WTS) from biopsies of ARSI-treated mCRPC patients, enabling integrated discovery of predictive signals.",{"name":82,"@type":73,"acceptedAnswer":83},"Which types of genomic and transcriptomic signals relate to poor ARSI response?",{"text":84,"@type":76},"Tumor mutational burden, specific structural variants and copy-number patterns were enriched in poor responders, together with distinct transcriptomic expression profiles.",{"name":86,"@type":73,"acceptedAnswer":87},"How did the study determine the best-performing prediction models?",{"text":88,"@type":76},"Multiple classification models were validated on internal and external mCRPC cohorts, with the best models combining prior treatment information with either enriched genomic markers or overall transcriptomic profiles.","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,124,127,132,135,139],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":125,"slug":126},30,"research-report",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":130,"slug":131},9,"Religion & Spirituality",20,"religion-spirituality",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":130,"slug":134},"World Cup","world-cup",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":136,"slug":138},10,"Lifestyle","lifestyle",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":140,"show_sort_weight":110,"slug":141},"General","general"]