[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-350517-105":3,"detail-sidebar-cat-0-en-105":79,"doc-detail-350517-en":129},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":72,"head_meta":74,"extra_data":76,"updated_unix":78},105,"en","machine-learning-algorithm-for-the-detection-of-tumor-microsatellite-instability-based-on-multiomics-biomarkers","Machine Learning Algorithm for the Detection of Tumor Microsatellite Instability Based on Multiomics Biomarkers","","Accurate classification of microsatellite instability (MSI) in advanced cancers is essential for identifying patients who may benefit from immune checkpoint inhibitors. Workflow variability can cause missed MSI-high cases, requiring complementary screening. Using colorectal tumor next-generation sequencing, a machine learning model predicts MSI status from immune-related gene expression plus pathogenic single-nucleotide and copy-number variant profiles, aiming to improve detection reliability and clinical decision-making.",{"@graph":14,"@context":71},[15,34,54],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/machine-learning-algorithm-for-the-detection-of-tumor-microsatellite-instability-based-on-multiomics-biomarkers/350517/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":48,"encodingFormat":47,"isAccessibleForFree":49,"interactionStatistic":50},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/machine-learning-algorithm-for-the-detection-of-tumor-microsatellite-instability-based-on-multiomics-biomarkers/350517.png","ImageObject",300,407,{"name":42,"@type":43},"Franzy","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-09-22",true,{"@type":51,"interactionType":52,"userInteractionCount":4},"InteractionCounter",{"@type":53},"ViewAction",{"@type":55,"mainEntity":56},"FAQPage",[57,63,67],{"name":58,"@type":59,"acceptedAnswer":60},"Why is accurate MSI classification important for advanced cancers?","Question",{"text":61,"@type":62},"Accurate MSI classification helps identify patients likely to benefit from immune checkpoint inhibitors, since MSI-high tumors are particularly responsive.","Answer",{"name":64,"@type":59,"acceptedAnswer":65},"How does the model predict MSI status in this study?",{"text":66,"@type":62},"It uses multiomics next-generation sequencing information, integrating immune-related gene expression profiles with pathogenic single-nucleotide variants and copy-number variants.",{"name":68,"@type":59,"acceptedAnswer":69},"What performance was reported across cancer cohorts and what does it mean for indeterminate cases?",{"text":70,"@type":62},"Feature selection identified 107 training features and the best CART model showed strong performance across colorectal, TCGA COAD/READ, uterine, and gastric cohorts; among indeterminate cases, a portion was classified as likely MSI-high, with many showing mismatch repair protein loss by immunohistochemistry.","https://schema.org",{"og:url":32,"og:type":73,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":75,"canonical":32},"index,follow",{"doc_id":77,"site_id":7},350517,1790089381,{"code":4,"msg":80,"data":81},"success",[82,86,90,94,99,104,109,113,118,121,125],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":83,"show_sort_weight":84,"slug":85},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":87,"show_sort_weight":88,"slug":89},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":91,"show_sort_weight":92,"slug":93},"Exam",70,"exam",{"id":95,"doc_module":4,"doc_module_name":25,"category_name":96,"show_sort_weight":97,"slug":98},5,"Comic",60,"comic",{"id":100,"doc_module":4,"doc_module_name":25,"category_name":101,"show_sort_weight":102,"slug":103},6,"Technology",50,"technology",{"id":105,"doc_module":4,"doc_module_name":25,"category_name":106,"show_sort_weight":107,"slug":108},7,"Healthcare",40,"healthcare",{"id":110,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":111,"slug":112},8,30,"research-report",{"id":114,"doc_module":4,"doc_module_name":25,"category_name":115,"show_sort_weight":116,"slug":117},9,"Religion & Spirituality",20,"religion-spirituality",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":119,"show_sort_weight":116,"slug":120},"World Cup","world-cup",{"id":122,"doc_module":4,"doc_module_name":25,"category_name":123,"show_sort_weight":122,"slug":124},10,"Lifestyle","lifestyle",{"id":126,"doc_module":4,"doc_module_name":25,"category_name":127,"show_sort_weight":95,"slug":128},19,"General","general",{"code":4,"msg":80,"data":130},{"doc_id":77,"user_id":131,"nickname":42,"user_avatar":132,"doc_module":4,"category_id":110,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":133,"file_id":134,"file_url":135,"file_type":136,"file_size":137,"view_count":4,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":122,"language":138,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":139,"faqs":140,"seo_title":141,"seo_description":12,"update_tm":78,"read_time":142},2336478945519,"https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45","Original Reports | Artificial Intelligence  \nMachine Learning Algorithm for the Detection of Tumor Microsatellite Instability Based on Multiomics Biomarkers  \nKyle C. Strickland, MD, PhD1,2  ; Zachary D. Wallen, PhD1  ; Sarabjot Pabla, PhD3  ; Heidi C. Ko, DO1  ; Rebecca A. Previs, MD, MS1,4  ; Michelle F. Green, PhD1  ; Stephanie Hastings, PhD1; Alicia Dillard, MD3; Pratheesh Sathyan, PhD5; Kamal S. Saini, MD6  ; Taylor J. Jensen, PhD1; Brian J. Caveney, MD, JD, MPH7  ; Marcia Eisenberg, PhD7; Shakti Ramkissoon, MD, PhD1,8; and Eric A. Severson, MD, PhD1  \nDOI [https://doi.org/10.1200/CCI-25-00367](https://doi.org/10.1200/CCI-25-00367)  \n\n| ABSTRACT |  |\n| --- | --- |\n| PURPOSE | Accurate classification of microsatellite instability (MSI) in advanced cancers is critical for identifying patients who may benefit from immune checkpoint inhibitors. However, variability in MSI detection workflows can lead to missed MSI-high cases, indicating need for complementary screening approaches. Using next-generation sequencing (NGS) data from colorectal tumors, we developed a machine learning (ML) model to predict MSI status using immunerelated gene expression profiles and pathogenic single-nucleotide variants (SNVs) and copy-number variants (CNVs) . |\n| MATERIALS\u003Cbr>AND METHODS | We analyzed NGS data from 2,756 patients with colorectal cancer (CRC), including DNA panel results for SNVs and CNVs, RNA sequencing of immunerelated genes, and tumor mutation burden (TMB) . ML algorithms were trained on 70% of the CRC cohort using TMB and selected features by Boruta algorithm. Trained models were tested on the remainder of the CRC cohort and The Cancer Genome Atlas (TCGA) colorectal (COAD) and rectal (READ) adenocarcinoma data sets. To assess the translatability to other cancer types, uterine and gastric cancer cases were tested. |\n| RESULTS | Feature selection identified 107 features for model training, including SNVs and CNVs. The CART model with the highest mean accuracy, precision, and recall showed strong performance across the CRC, TCGA COAD/READ, uterine, and gastric cancer cohorts, ranging from 78% sensitivity in uterine cancer to 99% - 100% specificity and negative predictive value in CRC. Of the 53 indeterminate CRC and uterine cases, 15% were classified as likely MSI-high. Of these, 75% had mismatch repair immunohistochemistry results available, with 83% showing MLH1 and PMS2 loss. |\n| CONCLUSION | Our ML approach accurately predicted MSI status in colorectal and uterine cancers using multiomics data derived from NGS, without relying on direct microsatellite sequencing. The ability to identify MSI-high tumors among indeterminate cases demonstrates potential to improve diagnostic precision and ensures timely access to immunotherapy for patients with MSI-high disease. |\n\nACCOMPANYING CONTENT  \n Data Sharing Statement  \n Data Supplement  \nAccepted May 5, 2026  \nPublished June 25, 2026  \nJCO Clin Cancer Inform 10:e2500367  \n© 2026 by American Society of Clinical Oncology  \nLicensed under the Creative Commons Attribution 4.0 License  \nINTRODUCTION  \nMicrosatellite instability (MSI) is a genomic hallmark of defective DNA mismatch repair (MMR), characterized by length alterations in short tandem repeats. MSI is most commonly observed in colorectal, endometrial, and gastric cancers, and is associated with a high neoantigen load that promotes immune recognition.1 ,2 Consequently, MSI-high (MSI-H) tumors are particularly responsive to immune checkpoint inhibitors (ICIs), often resulting in improved clinical outcomes.3-5  \nAccurate MSI detection is essential for identifying patients likely to benefit from ICI therapy. Conventional methods, including immunohistochemistry (IHC) and polymerase chain reaction (PCR)–based analysis of microsatellite loci, are widely used but can be limited by technical challenges. The repetitive nature of microsatellite sequences complicates amplification and alignment, leading to sequencing artifacts and interpretive difficulti","cbCaieNTXuoieHo2","https://ap.wps.com/l/cbCaieNTXuoieHo2","pdf",2248114,"English","# Abstract\n## Purpose\n## Materials and Methods\n## Results\n## Conclusion\n# Introduction\n## Background on MSI and clinical relevance\n## Limitations of conventional MSI detection\n# Context\n## Key objective\n## Knowledge generated\n## Relevance","[{\"question\":\"Why is accurate MSI classification important for advanced cancers?\",\"answer\":\"Accurate MSI classification helps identify patients likely to benefit from immune checkpoint inhibitors, since MSI-high tumors are particularly responsive.\"},{\"question\":\"How does the model predict MSI status in this study?\",\"answer\":\"It uses multiomics next-generation sequencing information, integrating immune-related gene expression profiles with pathogenic single-nucleotide variants and copy-number variants.\"},{\"question\":\"What performance was reported across cancer cohorts and what does it mean for indeterminate cases?\",\"answer\":\"Feature selection identified 107 training features and the best CART model showed strong performance across colorectal, TCGA COAD/READ, uterine, and gastric cohorts; among indeterminate cases, a portion was classified as likely MSI-high, with many showing mismatch repair protein loss by immunohistochemistry.\"}]","Machine Learning Algorithm for the Detection of Tumor Microsatellite Instability Based on Multiomics Biomarkers | PDF",25]