[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-342981-105":59,"doc-detail-342981-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","patient-specific-computational-models-predict-prognosis-in-b-cell-lymphoma-by-quantifying-pro-proliferative-and-antiapoptotic-signatures-from-genetic-sequencing-data","Patient-specific computational models predict prognosis in B cell lymphoma by quantifying pro-proliferative and antiapoptotic signatures from genetic sequencing data","","Mathematical, patient-specific computational models are used to predict clinical prognosis in B cell lymphomas by translating co-occurring genetic mutations into effects on cellular signalling and cell fate. Simulations in diffuse large B cell lymphoma and multiple myeloma show that mutation combinations that simultaneously drive anti-apoptotic and pro-proliferative signalling yield adverse prognostic impact. Patient mutational profiles are integrated into personalised lymphoma models, enabling risk stratification across genomic and cell-of-origin classifications and producing distinct survival outcomes. The approach identifies novel patient subgroups for future risk-adapted clinical trials.",{"@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/patient-specific-computational-models-predict-prognosis-in-b-cell-lymphoma-by-quantifying-pro-proliferative-and-antiapoptotic-signatures-from-genetic-sequencing-data/342981/",{"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/patient-specific-computational-models-predict-prognosis-in-b-cell-lymphoma-by-quantifying-pro-proliferative-and-antiapoptotic-signatures-from-genetic-sequencing-data/342981.png","ImageObject",300,407,{"name":92,"@type":93},"Noah","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-23","2026-09-22",true,{"@type":102,"interactionType":103,"userInteractionCount":8},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What signaling signature combination is most predictive of poor prognosis in the study?","Question",{"text":112,"@type":113},"Adverse prognosis is predicted when mutation combinations induce both anti-apoptotic (AA) and pro-proliferative (PP) signalling, described as AAPP signalling states.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were patient-specific mutational profiles used in the computational models?",{"text":117,"@type":113},"The study integrated each patient's mutational profile into personalised lymphoma models to simulate how mutations affect cellular signalling and cell fate decisions.",{"name":119,"@type":110,"acceptedAnswer":120},"What clinical outcomes were observed for different AAPP signalling groups?",{"text":121,"@type":113},"Patients with neither AA nor PP upregulation showed good prognosis, those with only one signalling type showed intermediate prognosis, and those with both (AAPP) showed poor prognosis, including substantially lower median overall survival in poor genetic clusters.","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},342981,1790195262,{"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":8,"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":145},8796095462418,"https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780","Blood Cancer [Journal](Journal www.nature.com/bcj)[ www.nature.com/bcj](Journal www.nature.com/bcj)  \nARTICLE OPEN   \nPatient-specific computational models predict prognosis in B cell lymphoma by quantifying pro-proliferative and antiapoptotic signatures from genetic sequencing data  \nRichard Norris1, John Jones 1, Erika Mancini2, Timothy Chevassut1, Fabio A. Simoes1, Chris Pepper 1, Andrea Pepper 1 and Simon Mitchell 1 ✉  \n© The Author(s) 2024  \n|  |  |  |\n| --- | --- | --- |\n|  | Genetic heterogeneity and co-occurring driver mutations impact clinical outcomes in blood cancers, but predicting the emergent effect of co-occurring mutations that impact multiple complex and interacting signalling networks is challenging. Here, we used mathematical models to predict the impact of co-occurring mutations on cellular signalling and cell fates in diffuse large B cell lymphoma and multiple myeloma. Simulations predicted adverse impact on clinical prognosis when combinations of mutations induced both anti-apoptotic (AA) and pro-proliferative (PP) signalling. We integrated patient-specific mutational profiles into personalised lymphoma models, and identified patients characterised by simultaneous upregulation of anti-apoptotic and proproliferative (AAPP) signalling in all genomic and cell-of-origin classifications (8-25% of patients) . In a discovery cohort and two validation cohorts, patients with upregulation of neither, one (AA or PP), or both (AAPP) signalling states had good, intermediate and poor prognosis respectively. Combining AAPP signalling with genetic or clinical prognostic predictors reliably stratified patients into striking prognostic categories. AAPP patients in poor prognosis genetic clusters had 7.8 months median overall survival, while patients lacking both features had 90% overall survival at 120 months in a validation cohort. Personalised computational models |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n| enable identification of novel risk-stratified patient subgroups, providing a valuable tool for future risk-adapted clinical trials. |  |  |\n|  | Blood Cancer Journal (2024)14:105; [https://doi.org/10.1038/s41408-024-01090-y](https://doi.org/10.1038/s41408-024-01090-y) |  |\n|  |  |  |\n\nINTRODUCTION  \nMutational heterogeneity in haematological malignancies represents a major barrier to reliable prognostication and the development of rationally targeted novel treatment strategies. With the advent of whole exome sequencing (WES) many malignancies, including B cell malignancies such as Diffuse Large B cell Lymphoma (DLBCL) and Multiple Myeloma (MM), have become characterised by interpatient mutational heterogeneity [1, 2] . This heterogeneity contributes to variation in response to current treatments.  \nThe most aggressive haematological malignancies frequently contain genetic aberrations affecting multiple genes, either through co-occurring mutations, e.g. double hit (DH) DLBCL or changes in the copy number of chromosomal regions containing multiple genes, e.g. gain1q MM. DH DLBCL, featuring overexpression of MYC and BCL2 (or BCL6), is among the most aggressive lymphoid malignancies, with very poor patient outcomes [3, 4] . However, DH DLBCL represents fewer than 10% of all cases, while many more (30–40%) DLBCL patients relapse following frontline treatment [5] . So, new approaches are clearly needed to prospectively identify poor prognosis patients with the aim of developing more effective treatment strategies for these patients.  \nGene expression profiling can split DLBCL into subgroups based on their putative cell of origin (germinal centre-GC, or activated B cell-ABC) [6] . Subsequent studies leveraged genomic sequencing to identify 5 or more prognostically informative patient clusters [7–9] . These genetic groups have been arbitrarily named clusters 1–5 (C1–C5) or with a nomenclature referencing the most mutated signalling pathways (MCD = MYD","cbCaic2r6araYkkd","https://ap.wps.com/l/cbCaic2r6araYkkd","pdf",3787758,11,"English","# Introduction\n## Mutational heterogeneity and prognostic challenges\n## Co-occurring mutations and aggressive subtypes\n## Limitations of gene-expression and clustering approaches\n## Prior signaling models and remaining clinical uncertainty\n## Study hypothesis and objective","[{\"question\":\"What signaling signature combination is most predictive of poor prognosis in the study?\",\"answer\":\"Adverse prognosis is predicted when mutation combinations induce both anti-apoptotic (AA) and pro-proliferative (PP) signalling, described as AAPP signalling states.\"},{\"question\":\"How were patient-specific mutational profiles used in the computational models?\",\"answer\":\"The study integrated each patient's mutational profile into personalised lymphoma models to simulate how mutations affect cellular signalling and cell fate decisions.\"},{\"question\":\"What clinical outcomes were observed for different AAPP signalling groups?\",\"answer\":\"Patients with neither AA nor PP upregulation showed good prognosis, those with only one signalling type showed intermediate prognosis, and those with both (AAPP) showed poor prognosis, including substantially lower median overall survival in poor genetic clusters.\"}]","Patient-specific computational models predict prognosis in B cell lymphoma by quantifying pro-proliferative and antiapoptotic signatures from genetic sequencing data | PDF",1790049230,28]