[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126031-en":3,"doc-seo-126031-105":31,"detail-sidebar-cat-0-en-105":93},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126031,2336474466412,"Ezra","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",7,"Healthcare","Machine learning to detect the SINEs of cancer - A-PLUS 方案与验证结果","RealSeqS previously evaluated aneuploidy in plasma cell-free DNA by amplifying ~350,000 repeated elements with a single primer. Building on the hypothesis that sequencing generated by RealSeqS may contain additional cancer-related differences between plasma samples, the work introduces A-PLUS, a machine-learning method that profiles Alu element representation in cfDNA. Models were trained and validated using prespecified cohorts from 5108 individuals (2037 with cancer), achieving 40.5% sensitivity across 11 cancer types at 98.5% specificity.","Commented [CD1]: Not a good idea to use median specificity when prostate, kidney and head and neck are all new to the validation set and all very low. Median sensitivity is not very impressive unless you restrict to the original cancer types  \nMachine learning to detect the SINEs of cancer  \nAbstract  \nWe previously described an approach called RealSeqS to evaluate aneuploidy in plasma cell-free DNA (cfDNA) through the amplification of ~350,000 repeated elements with a single primer. We hypothesized that an unbiased evaluation of the large amount of sequencing data obtained with RealSeqS might reveal other differences between plasma samples from patients with and without cancer. This hypothesis was tested through the development of a novel machine-learning approach called Alu Profile Learning Using Sequencing (A-PLUS) and its application to samples from 5108 individuals, 2037 with cancer and the remainder without cancer. Samples from cancer patients and controls were pre-specified into four cohorts used for: 1) model training, 2) analyte integration and threshold determination, 3) validation, and 4) reproducibility. A-PLUS alone provided a sensitivity of  \n40.5% across 11 different cancer types in the Validation Cohort, at a specificity of 98.5% . Combining APLUS with aneuploidy and 8 common protein biomarkers detected 51% of 1167 cancers at 98.9% specificity. We found that part of the power of A-PLUS could be ascribed to a single feature – the global reduction of AluS sub-family elements in the circulating DNA of cancer patients. We confirmed this reduction through the analysis of another independent dataset obtained with a very different approach (whole genome sequencing) . The evaluation of Alu elements therefore has the potential to enhance the performance of several methods designed for the earlier detection of cancer.  \nIntroduction  \nAlu’s are short interspersed nuclear elements (SINEs) of ~ 300 bp, with more than 1 million copies spread throughout the genome 1. Their role in biology and evolution is an ongoing area of research, but some elements have already been shown to be involved in the regulation of tissue-specific genes. In cancer cells, they participate in structural changes, probably through homologous recombination given their widespread distribution throughout the genome and highly similar sequences 2 3. Moreover, Alu’s are hypomethylated early during tumor progression 4 5 6 7 8 9 10, and this feature has been incorporated into methods for the earlier detection of cancer through plasma cell-free DNA (cfDNA ) analysis 11 . Alu’s also reflect the altered fragmentation patterns found in cfDNA in cancer patients: one of the first plasma multi-cancer biomarkers used qPCR to calculate the ratio of short and long Alu segments 12 13 14.  \nWhole genome sequencing (WGS) has been widely employed in recent blood-based multi-cancer earlier detection assays. WGS should in theory allow evaluation of Alu elements, but predictive algorithms often discard them as a result of bioinformatic challenges stemming from their resemblance to eachother and difficulties in mapping them unambiguously 15. Even with the inclusion of mappable Alu elements, shallow WGS is inefficient to optimally evaluate Alu elements because they represent only a small fraction of the genome ~11% 1.  \nWe have previously developed an approach, called RealSeqS, to specifically amplify Alu sequences 16. RealSeqS offers advantages over WGS, including a simpler workflow that does not require library construction, a reduced requirement for input DNA, faster computational analysis, and higher sequencing coverage at individual Alu loci. Specifically, the RealSeqS workflow uses a single-primer pair to concomitantly amplify ~350,000 Alu elements. For an equivalent sequencing depth, RealSeqS achieves ~28-fold greater coverage of the Alu elements it amplifies than achievable with WGS at an equivalent sequencing depth, enabling improved predictive modeling.  \nAs noted a","cbCaifNUdblZSzq3","https://ap.wps.com/l/cbCaifNUdblZSzq3","pdf",308880,5,1,14,"English","en",105,"# Introduction\n## Background on Alu SINEs and cfDNA\n## RealSeqS and motivation for machine learning\n# Rationale and background of the assay\n## Feature selection challenges and assay goals","[{\"question\":\"RealSeqS在研究中解决了什么问题？\",\"answer\":\"RealSeqS通过单引物扩增~350,000个重复元件来评估血浆游离DNA中的非整倍体（aneuploidy），并为后续从测序数据中挖掘其他差异提供了基础。\"},{\"question\":\"A-PLUS相对于RealSeqS新增了什么能力？\",\"answer\":\"A-PLUS是在RealSeqS测序数据上建立的机器学习方案，用于基于cfDNA中Alu元件的表征差异区分癌症患者与非癌症对照，并为癌症分型提供额外指标。\"},{\"question\":\"该方法在验证队列中的关键性能指标是什么？\",\"answer\":\"仅使用A-PLUS时，在验证队列的11种癌症类型中实现40.5%的敏感度、98.5%的特异度；将A-PLUS与非整倍体及8种常见蛋白生物标志物结合后，可在98.9%特异度下检出51%的1167例癌症。\"}]","Machine learning to detect the SINEs of cancer - A-PLUS 方案与验证结果 | PDF",1785902642,35,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"machine-learning-to-detect-the-sines-of-cancer-a-plus-approach-and-validation-results","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/healthcare/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/machine-learning-to-detect-the-sines-of-cancer-a-plus-approach-and-validation-results/126031/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"RealSeqS在研究中解决了什么问题？","Question",{"text":77,"@type":78},"RealSeqS通过单引物扩增~350,000个重复元件来评估血浆游离DNA中的非整倍体（aneuploidy），并为后续从测序数据中挖掘其他差异提供了基础。","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"A-PLUS相对于RealSeqS新增了什么能力？",{"text":82,"@type":78},"A-PLUS是在RealSeqS测序数据上建立的机器学习方案，用于基于cfDNA中Alu元件的表征差异区分癌症患者与非癌症对照，并为癌症分型提供额外指标。",{"name":84,"@type":75,"acceptedAnswer":85},"该方法在验证队列中的关键性能指标是什么？",{"text":86,"@type":78},"仅使用A-PLUS时，在验证队列的11种癌症类型中实现40.5%的敏感度、98.5%的特异度；将A-PLUS与非整倍体及8种常见蛋白生物标志物结合后，可在98.9%特异度下检出51%的1167例癌症。","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,111,116,119,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":117,"slug":118},40,"healthcare",{"id":120,"doc_module":4,"doc_module_name":47,"category_name":121,"show_sort_weight":122,"slug":123},8,"Research & Report",30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":20,"slug":139},19,"General","general"]