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Identified components are tested with Cox regression and random survival forest to find DFS-associated patterns, patient subgroups, and robust route-level biology underlying EMT and relapse.",{"@graph":14,"@context":72},[15,34,55],{"@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 & 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can bulk transcriptional profiling miss relapse-related biology in early colorectal cancer?","Question",{"text":62,"@type":63},"Because the transcriptional patterns linked to disease-free survival can be subtle and/or masked by other process-related patterns present in tumor bulk samples.","Answer",{"name":65,"@type":60,"acceptedAnswer":66},"What method is used to disentangle transcriptional signals in this study?",{"text":67,"@type":63},"The study uses consensus-independent component analysis (c-ICA) to decompose bulk transcriptomes into statistically independent transcriptional components (TCs).",{"name":69,"@type":60,"acceptedAnswer":70},"How are disease-free survival-associated components identified and used for patient stratification?",{"text":71,"@type":63},"Disease-free survival-associated TCs are identified using Cox regression, then random survival forest analyses treat activity of these TCs as classifiers to detect patient subgroups with distinct transcriptional 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patterns reveal biological processes associated with disease-free survival in early colorectal cancer  \n Check for updates  \n\n| Daan G. Knapen 1, Sara Hone Lopez 1, Derk Jan A. de Groot1, Jacco-Juri de Haan 1, Elisabeth G. E. de Vries 1, Rodrigo Dienstmann2, Steven de Jong 1, Arkajyoti Bhattacharya 1 & Rudolf S. N. Fehrmann 1  |  |  |\n| --- | --- | --- |\n| Abstract |  | Plain language summary |\n| Background Bulk transcriptional profiles of early colorectal cancer (CRC) can fail to detect biological processes associated with disease-free survival (DFS) if the transcriptional patterns are subtle and/or obscured by other processes ’ patterns. Consensus-independent component analysis (c-ICA) can dissect such transcriptomes into statistically independent transcriptional components (TCs), capturing both pronounced and subtle biological processes.\u003Cbr>Methods In this study we (1) integrated transcriptomes (n = 4228) from multiple early CRC studies,(2) performed c-ICA to define the TC landscape within this integrated data set, 3) determined the biological processes captured by these TCs,(4) performed Cox regression to identify DFS-associated TCs,(5) performed random survival forest (RSF) analyses with activity of DFS-associated TCs as classifiers to identify subgroups of patients, and 6) performed a sensitivity analysis to determine the robustness of our results\u003Cbr>Results We identify 191 TCs, 43 of which are associated with DFS, revealing transcriptional diversity among DFS-associated biological processes. A prominent example is the epithelial-mesenchymal transition (EMT), for which we identify an association with nine independent DFS-associated TCs, each with coordinated upregulation or downregulation of various sets of genes.\u003Cbr>Conclusions This finding indicates that early CRC may have nine distinct routes to achieve EMT, each requiring a specific peri-operative treatment strategy. Finally, we stratify patients into DFS patient subgroups with distinct transcriptional patterns associated with stage 2 and stage 3 CRC. |  | While treatments for patients with colorectal cancer have improved, many patients(around 30-50%) have cancers that will eventually relapse and these patients will die due to their disease. Researchers have been studying the genes involved in colorectal cancer to help us understand why some cancers might relapse. However, current methods to do this may miss subtle or hidden patterns in the gene activity related to cancer relapse. To deal with this, we used a special method called consensus-independent component analysis (c-ICA) to dig more deeply into the activity of genes. This helped us to uncover some potential biological processes underpinning colorectal cancer relapse, which ultimately could help researchers to identify better treatments for patients with colorectal cancer. |\n| Although colorectal cancer (CRC) is currently the second-leading cause of cancer-related death worldwide1, CRC-related mortality has decreased due to early detection followed by curative surgical resection2. The most recent advance in peri-operative systemic therapy was in 2004 when oxaliplatin was added to leucovorin-modulated 5-fluorouracil3. Despite improvements in the detection and treatment ofCRC, however, approximately 30-50% of | all patients treated for early CRC relapse and die due to the disease3. This alarming statistic underscores the need to gain new insights into the complex biology underlying early CRC and develop more effective treatment strategies.\u003Cbr>Previous studies involving the transcriptional profiling of CRC have greatly increased our understanding of the biological processes associated |  |\n\n1Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands. 2Oncology Data Science(ODysSey) Group, Vall d’Hebron Institute of Oncology, Universitat Autón","cbCaihtxT4bIWeZ7","https://ap.wps.com/l/cbCaihtxT4bIWeZ7","pdf",1643807,"English","# Abstract\n## Background\n## Methods\n## Results\n## Conclusions","[{\"question\":\"Why can bulk transcriptional profiling miss relapse-related biology in early colorectal cancer?\",\"answer\":\"Because the transcriptional patterns linked to disease-free survival can be subtle and/or masked by other process-related patterns present in tumor bulk samples.\"},{\"question\":\"What method is used to disentangle transcriptional signals in this study?\",\"answer\":\"The study uses consensus-independent component analysis (c-ICA) to decompose bulk transcriptomes into statistically independent transcriptional components (TCs).\"},{\"question\":\"How are disease-free survival-associated components identified and used for patient stratification?\",\"answer\":\"Disease-free survival-associated TCs are identified using Cox regression, then random survival forest analyses treat activity of these TCs as classifiers to detect patient subgroups with distinct transcriptional patterns.\"}]","Independent transcriptional patterns reveal biological processes associated with disease-free survival in early colorectal cancer | PDF",1790048086,25]