FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4387491480> ?p ?o ?g. }

• W4387491480 abstract "Abstract The classification of B cell lymphomas - mainly based on light microscopy evaluation by a pathologist - requires many years of training. Since the B cell receptor (BCR) of the lymphoma clonotype and the microenvironmental immune architecture are important features discriminating different lymphoma subsets, we asked whether BCR repertoire next-generation sequencing (NGS) of lymphoma-infiltrated tissues in conjunction with machine learning algorithms could have diagnostic utility in the subclassification of these cancers. We trained a random forest and a linear classifier via logistic regression based on patterns of clonal distribution, VDJ gene usage and physico-chemical properties of the top-n most frequently represented clonotypes in the BCR repertoires of 620 paradigmatic lymphomas - nodular lymphocyte predominant B cell lymphoma (NLPBL), diffuse large B cell lymphoma (DLBCL) and chronic lymphocytic leukemia (CLL) - as well as 291 control tissues. With regard to DLBCL and CLL, the models demonstrated optimal performance when utilizing only the most prevalent clonotype for classification, while in NLPBL - that has a dominant background of non-malignant bystander cells - a broader array of clonotypes enhanced model accuracy. Surprisingly, the straightforward logistic regression model performed best in this seemingly complex classification problem, suggesting linear separability in our chosen dimensions. It achieved a weighted F1-score of 0.84 on a test cohort including 125 cases from all three lymphoma entities and 58 healthy individuals. Together, we provide proof-of-concept that at least the 3 studied lymphoma entities can be differentiated from each other using BCR repertoire NGS on lymphoma-infiltrated tissues by a trained machine learning model. Author Summary Lymphoma, a complex group of malignant blood cancers, poses a significant diagnostic challenge due to its diverse subtypes. Yet, precise classification is crucial for tailored treatment. In our research, we developed a machine learning algorithm and conducted comprehensive validation to discern distinct B cell lymphoma subtypes. We therefore leveraged B cell repertoires of lymphoma-infiltrated tissue, as ascertained through next-generation sequencing. Our data offers three key insights: We detail the creation and training of our machine learning algorithm, explaining how we selected features and designed the model. We demonstrate the algorithm’s diagnostic precision using sequencing data from a test-set of patients. Moreover, through a deep dive into the most distinguishing aspects of our algorithm, we unveil distinctive disease-related patterns present within the malignant B cell and its surrounding environment. This analysis showed that both the malignant lymphoma cell, but also healthy bystander immune cells contribute to the distinctive architecture that characterizes a specific lymphoma subtype. We hope our work will contribute towards creating tools to diagnose lymphoma more easily and accurately ultimately leading to better outcomes for patients with this type of cancer." @default.
• W4387491480 created "2023-10-11" @default.
• W4387491480 creator A5009603075 @default.
• W4387491480 creator A5014071836 @default.
• W4387491480 creator A5015712828 @default.
• W4387491480 creator A5019299921 @default.
• W4387491480 creator A5033350585 @default.
• W4387491480 creator A5041171016 @default.
• W4387491480 creator A5053146707 @default.
• W4387491480 creator A5076345010 @default.
• W4387491480 creator A5077205301 @default.
• W4387491480 creator A5090578154 @default.
• W4387491480 creator A5091441095 @default.
• W4387491480 date "2023-10-10" @default.
• W4387491480 modified "2023-10-15" @default.
• W4387491480 title "Detection of disease-specific signatures in B cell repertoires of lymphomas using machine learning" @default.
• W4387491480 cites W1883768105 @default.
• W4387491480 cites W1984284869 @default.
• W4387491480 cites W1995875735 @default.
• W4387491480 cites W2021945571 @default.
• W4387491480 cites W2026496528 @default.
• W4387491480 cites W2050600340 @default.
• W4387491480 cites W2051540789 @default.
• W4387491480 cites W2089041106 @default.
• W4387491480 cites W2129509656 @default.
• W4387491480 cites W2158819525 @default.
• W4387491480 cites W2400090719 @default.
• W4387491480 cites W2773009681 @default.
• W4387491480 cites W2778020060 @default.
• W4387491480 cites W2913283761 @default.
• W4387491480 cites W2915999573 @default.
• W4387491480 cites W2938678047 @default.
• W4387491480 cites W2948816402 @default.
• W4387491480 cites W2966282780 @default.
• W4387491480 cites W2977512532 @default.
• W4387491480 cites W2984351101 @default.
• W4387491480 cites W2987990236 @default.
• W4387491480 cites W3004586992 @default.
• W4387491480 cites W3018609885 @default.
• W4387491480 cites W3018796293 @default.
• W4387491480 cites W3020657373 @default.
• W4387491480 cites W3021413349 @default.
• W4387491480 cites W3025106781 @default.
• W4387491480 cites W3026078932 @default.
• W4387491480 cites W3032044812 @default.
• W4387491480 cites W3038110350 @default.
• W4387491480 cites W3043902401 @default.
• W4387491480 cites W3045611814 @default.
• W4387491480 cites W3045714119 @default.
• W4387491480 cites W3080575430 @default.
• W4387491480 cites W3092983525 @default.
• W4387491480 cites W3103855458 @default.
• W4387491480 cites W3109772564 @default.
• W4387491480 cites W3152578950 @default.
• W4387491480 cites W3162236650 @default.
• W4387491480 cites W3165067450 @default.
• W4387491480 cites W3187735702 @default.
• W4387491480 cites W3208716322 @default.
• W4387491480 cites W4210984018 @default.
• W4387491480 cites W4220847978 @default.
• W4387491480 cites W4229363592 @default.
• W4387491480 cites W4245248161 @default.
• W4387491480 cites W4292849294 @default.
• W4387491480 cites W4296709263 @default.
• W4387491480 cites W4307178597 @default.
• W4387491480 cites W4380323986 @default.
• W4387491480 doi "https://doi.org/10.1101/2023.10.05.561150" @default.
• W4387491480 hasPublicationYear "2023" @default.
• W4387491480 type Work @default.
• W4387491480 citedByCount "0" @default.
• W4387491480 crossrefType "posted-content" @default.
• W4387491480 hasAuthorship W4387491480A5009603075 @default.
• W4387491480 hasAuthorship W4387491480A5014071836 @default.
• W4387491480 hasAuthorship W4387491480A5015712828 @default.
• W4387491480 hasAuthorship W4387491480A5019299921 @default.
• W4387491480 hasAuthorship W4387491480A5033350585 @default.
• W4387491480 hasAuthorship W4387491480A5041171016 @default.
• W4387491480 hasAuthorship W4387491480A5053146707 @default.
• W4387491480 hasAuthorship W4387491480A5076345010 @default.
• W4387491480 hasAuthorship W4387491480A5077205301 @default.
• W4387491480 hasAuthorship W4387491480A5090578154 @default.
• W4387491480 hasAuthorship W4387491480A5091441095 @default.
• W4387491480 hasBestOaLocation W43874914801 @default.
• W4387491480 hasConcept C104317684 @default.
• W4387491480 hasConcept C126322002 @default.
• W4387491480 hasConcept C143998085 @default.
• W4387491480 hasConcept C151956035 @default.
• W4387491480 hasConcept C154945302 @default.
• W4387491480 hasConcept C157695867 @default.
• W4387491480 hasConcept C159654299 @default.
• W4387491480 hasConcept C169258074 @default.
• W4387491480 hasConcept C203014093 @default.
• W4387491480 hasConcept C2777938653 @default.
• W4387491480 hasConcept C2778453870 @default.
• W4387491480 hasConcept C2778461978 @default.
• W4387491480 hasConcept C2779338263 @default.
• W4387491480 hasConcept C2780653079 @default.
• W4387491480 hasConcept C2781214270 @default.
• W4387491480 hasConcept C41008148 @default.
• W4387491480 hasConcept C43907098 @default.

• next