FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4200119028> ?p ?o ?g. }

• W4200119028 endingPage "S1383" @default.
• W4200119028 startingPage "S1383" @default.
• W4200119028 abstract "BackgroundImmune checkpoint inhibitors (ICIs) are one of the most advanced cancer treatments with good responses in metastatic melanoma (MM). However, the response does not exceed 40%, while 7% to 15% of patients may develop hyperprogressive disease (HPD or early death) upon ICIs administration. Currently, oncologists lack effective and reliable markers to predict patient response or identify patients that will eventually develop HPD (early death).MethodsTo develop and validate biomarker panels for HPD (early death) and response/non-response (measured by RECIST 1.1) to immunotherapy in melanoma patients, a total of 273 metastatic melanoma patients treated with PD1/PD-L1 inhibitor or its combinations with CTLA4 inhibitor were selected for analysis. These included 210 patients from 4 past interventional clinical trials and 63 real-world evidence data from an ongoing observational retrospective study in 3 hospitals in Belgium and Austria (NCT04860076). The patients enrolled in the study were treated with Nivolumab or Pembrolizumab, or their combinations with Ipilimumab. We obtained molecular, immunohistochemistry, and clinical data and applied advanced machine learning methods to investigate predictive factors that are indicative of therapy response or/and development of HPD. We use ensemble machine learning to discriminate clinical and molecular characteristics and build a predictive model. The model was developed on an initial discovery cohort and then confirmed with high accuracy on a validation cohort.ResultsWe identified the signature of 10 genes which was able to predict clinical outcomes (RECIST 1.1 status and/or early death) for metastatic melanoma patients treated by ICIs with high accuracy. Depending on the classification, the model accuracy reached 73% to 78% measured as ROC AUC on an independent validation cohort.ConclusionsIdentified biomarkers can have the potential to aid patient stratification for immune therapy and personalized treatment options for metastatic melanoma patients for whom there is currently no complementary diagnostics available on the market.Clinical trial identificationNCT04860076.Legal entity responsible for the studyAsylia Diagnostics BV.FundingAsylia Diagnostics BV.DisclosureV. Siozopoulou: Financial Interests, Institutional, Advisory Role: MSD. A. Khmelevskiy: Financial Interests, Personal and Institutional, Full or part-time Employment: Asylia Diagnostics. E. Richtig: Financial Interests, Personal and Institutional, Advisory Role: MSD; Financial Interests, Personal and Institutional, Advisory Role: Amgen; Financial Interests, Personal and Institutional, Advisory Role: Bristol Myers Squibb; Financial Interests, Personal and Institutional, Advisory Role: Merck; Financial Interests, Personal and Institutional, Advisory Role: Novartis; Financial Interests, Personal and Institutional, Advisory Role: Sanofi; Financial Interests, Personal and Institutional, Advisory Role: Pierre Fabre. All other authors have declared no conflicts of interest. BackgroundImmune checkpoint inhibitors (ICIs) are one of the most advanced cancer treatments with good responses in metastatic melanoma (MM). However, the response does not exceed 40%, while 7% to 15% of patients may develop hyperprogressive disease (HPD or early death) upon ICIs administration. Currently, oncologists lack effective and reliable markers to predict patient response or identify patients that will eventually develop HPD (early death). Immune checkpoint inhibitors (ICIs) are one of the most advanced cancer treatments with good responses in metastatic melanoma (MM). However, the response does not exceed 40%, while 7% to 15% of patients may develop hyperprogressive disease (HPD or early death) upon ICIs administration. Currently, oncologists lack effective and reliable markers to predict patient response or identify patients that will eventually develop HPD (early death). MethodsTo develop and validate biomarker panels for HPD (early death) and response/non-response (measured by RECIST 1.1) to immunotherapy in melanoma patients, a total of 273 metastatic melanoma patients treated with PD1/PD-L1 inhibitor or its combinations with CTLA4 inhibitor were selected for analysis. These included 210 patients from 4 past interventional clinical trials and 63 real-world evidence data from an ongoing observational retrospective study in 3 hospitals in Belgium and Austria (NCT04860076). The patients enrolled in the study were treated with Nivolumab or Pembrolizumab, or their combinations with Ipilimumab. We obtained molecular, immunohistochemistry, and clinical data and applied advanced machine learning methods to investigate predictive factors that are indicative of therapy response or/and development of HPD. We use ensemble machine learning to discriminate clinical and molecular characteristics and build a predictive model. The model was developed on an initial discovery cohort and then confirmed with high accuracy on a validation cohort. To develop and validate biomarker panels for HPD (early death) and response/non-response (measured by RECIST 1.1) to immunotherapy in melanoma patients, a total of 273 metastatic melanoma patients treated with PD1/PD-L1 inhibitor or its combinations with CTLA4 inhibitor were selected for analysis. These included 210 patients from 4 past interventional clinical trials and 63 real-world evidence data from an ongoing observational retrospective study in 3 hospitals in Belgium and Austria (NCT04860076). The patients enrolled in the study were treated with Nivolumab or Pembrolizumab, or their combinations with Ipilimumab. We obtained molecular, immunohistochemistry, and clinical data and applied advanced machine learning methods to investigate predictive factors that are indicative of therapy response or/and development of HPD. We use ensemble machine learning to discriminate clinical and molecular characteristics and build a predictive model. The model was developed on an initial discovery cohort and then confirmed with high accuracy on a validation cohort. ResultsWe identified the signature of 10 genes which was able to predict clinical outcomes (RECIST 1.1 status and/or early death) for metastatic melanoma patients treated by ICIs with high accuracy. Depending on the classification, the model accuracy reached 73% to 78% measured as ROC AUC on an independent validation cohort. We identified the signature of 10 genes which was able to predict clinical outcomes (RECIST 1.1 status and/or early death) for metastatic melanoma patients treated by ICIs with high accuracy. Depending on the classification, the model accuracy reached 73% to 78% measured as ROC AUC on an independent validation cohort. ConclusionsIdentified biomarkers can have the potential to aid patient stratification for immune therapy and personalized treatment options for metastatic melanoma patients for whom there is currently no complementary diagnostics available on the market. Identified biomarkers can have the potential to aid patient stratification for immune therapy and personalized treatment options for metastatic melanoma patients for whom there is currently no complementary diagnostics available on the market." @default.
• W4200119028 created "2021-12-31" @default.
• W4200119028 creator A5001373520 @default.
• W4200119028 creator A5006392150 @default.
• W4200119028 creator A5007525817 @default.
• W4200119028 creator A5009748809 @default.
• W4200119028 creator A5018782895 @default.
• W4200119028 creator A5033010789 @default.
• W4200119028 creator A5041435375 @default.
• W4200119028 creator A5051995068 @default.
• W4200119028 creator A5056727791 @default.
• W4200119028 creator A5076023713 @default.
• W4200119028 creator A5076626731 @default.
• W4200119028 creator A5083370627 @default.
• W4200119028 creator A5084126858 @default.
• W4200119028 date "2021-12-01" @default.
• W4200119028 modified "2023-09-27" @default.
• W4200119028 title "24P Using real-world evidence data and machine learning to identify molecular biomarkers for patient response to immune checkpoint inhibitors in metastatic melanoma" @default.
• W4200119028 doi "https://doi.org/10.1016/j.annonc.2021.10.040" @default.
• W4200119028 hasPublicationYear "2021" @default.
• W4200119028 type Work @default.
• W4200119028 citedByCount "0" @default.
• W4200119028 crossrefType "journal-article" @default.
• W4200119028 hasAuthorship W4200119028A5001373520 @default.
• W4200119028 hasAuthorship W4200119028A5006392150 @default.
• W4200119028 hasAuthorship W4200119028A5007525817 @default.
• W4200119028 hasAuthorship W4200119028A5009748809 @default.
• W4200119028 hasAuthorship W4200119028A5018782895 @default.
• W4200119028 hasAuthorship W4200119028A5033010789 @default.
• W4200119028 hasAuthorship W4200119028A5041435375 @default.
• W4200119028 hasAuthorship W4200119028A5051995068 @default.
• W4200119028 hasAuthorship W4200119028A5056727791 @default.
• W4200119028 hasAuthorship W4200119028A5076023713 @default.
• W4200119028 hasAuthorship W4200119028A5076626731 @default.
• W4200119028 hasAuthorship W4200119028A5083370627 @default.
• W4200119028 hasAuthorship W4200119028A5084126858 @default.
• W4200119028 hasBestOaLocation W42001190281 @default.
• W4200119028 hasConcept C121608353 @default.
• W4200119028 hasConcept C126322002 @default.
• W4200119028 hasConcept C143998085 @default.
• W4200119028 hasConcept C167135981 @default.
• W4200119028 hasConcept C2777658100 @default.
• W4200119028 hasConcept C2777701055 @default.
• W4200119028 hasConcept C2778822529 @default.
• W4200119028 hasConcept C2779134260 @default.
• W4200119028 hasConcept C2779984678 @default.
• W4200119028 hasConcept C2780030458 @default.
• W4200119028 hasConcept C2780057760 @default.
• W4200119028 hasConcept C2780851360 @default.
• W4200119028 hasConcept C2781433595 @default.
• W4200119028 hasConcept C2994587330 @default.
• W4200119028 hasConcept C502942594 @default.
• W4200119028 hasConcept C535046627 @default.
• W4200119028 hasConcept C71924100 @default.
• W4200119028 hasConcept C72563966 @default.
• W4200119028 hasConceptScore W4200119028C121608353 @default.
• W4200119028 hasConceptScore W4200119028C126322002 @default.
• W4200119028 hasConceptScore W4200119028C143998085 @default.
• W4200119028 hasConceptScore W4200119028C167135981 @default.
• W4200119028 hasConceptScore W4200119028C2777658100 @default.
• W4200119028 hasConceptScore W4200119028C2777701055 @default.
• W4200119028 hasConceptScore W4200119028C2778822529 @default.
• W4200119028 hasConceptScore W4200119028C2779134260 @default.
• W4200119028 hasConceptScore W4200119028C2779984678 @default.
• W4200119028 hasConceptScore W4200119028C2780030458 @default.
• W4200119028 hasConceptScore W4200119028C2780057760 @default.
• W4200119028 hasConceptScore W4200119028C2780851360 @default.
• W4200119028 hasConceptScore W4200119028C2781433595 @default.
• W4200119028 hasConceptScore W4200119028C2994587330 @default.
• W4200119028 hasConceptScore W4200119028C502942594 @default.
• W4200119028 hasConceptScore W4200119028C535046627 @default.
• W4200119028 hasConceptScore W4200119028C71924100 @default.
• W4200119028 hasConceptScore W4200119028C72563966 @default.
• W4200119028 hasLocation W42001190281 @default.
• W4200119028 hasOpenAccess W4200119028 @default.
• W4200119028 hasPrimaryLocation W42001190281 @default.
• W4200119028 hasRelatedWork W2594695210 @default.
• W4200119028 hasRelatedWork W2607780130 @default.
• W4200119028 hasRelatedWork W2773756552 @default.
• W4200119028 hasRelatedWork W2781999345 @default.
• W4200119028 hasRelatedWork W2790643333 @default.
• W4200119028 hasRelatedWork W2903811712 @default.
• W4200119028 hasRelatedWork W2932851203 @default.
• W4200119028 hasRelatedWork W3205271171 @default.
• W4200119028 hasRelatedWork W4292373623 @default.
• W4200119028 hasRelatedWork W4306411150 @default.
• W4200119028 hasVolume "32" @default.
• W4200119028 isParatext "false" @default.
• W4200119028 isRetracted "false" @default.
• W4200119028 workType "article" @default.