{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Uhlik M"],"funding":["NCI NIH HHS"],"pagination":["1158345"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10213262"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["13"],"pubmed_abstract":["<h4>Introduction</h4>Most predictive biomarkers approved for clinical use measure single analytes such as genetic alteration or protein overexpression. We developed and validated a novel biomarker with the aim of achieving broad clinical utility. The Xerna™ TME Panel is a pan-tumor, RNA expression-based classifier, designed to predict response to multiple tumor microenvironment (TME)-targeted therapies, including immunotherapies and anti-angiogenic agents.<h4>Methods</h4>The Panel algorithm is an artificial neural network (ANN) trained with an input signature of 124 genes that was optimized across various solid tumors. From the 298-patient training data, the model learned to discriminate four TME subtypes: Angiogenic (A), Immune Active (IA), Immune Desert (ID), and Immune Suppressed (IS). "],"journal":["Frontiers in oncology"],"pubmed_title":["Xerna™ TME Panel is a machine learning-based transcriptomic biomarker designed to predict therapeutic response in multiple cancers."],"pmcid":["PMC10213262"],"funding_grant_id":["R01 CA263575"],"pubmed_authors":["Liu H","Benjamin L","Krieg AM","Pointing D","Cvitkovic R","Uhlik M","Culm K","Santos VC","Lee J","Stajdohar M","Zganec M","Ausec L","Malafa M","Iyer S","Rosengarten R","Pytowski B"],"additional_accession":[]},"is_claimable":false,"name":"Xerna™ TME Panel is a machine learning-based transcriptomic biomarker designed to predict therapeutic response in multiple cancers.","description":"<h4>Introduction</h4>Most predictive biomarkers approved for clinical use measure single analytes such as genetic alteration or protein overexpression. We developed and validated a novel biomarker with the aim of achieving broad clinical utility. The Xerna™ TME Panel is a pan-tumor, RNA expression-based classifier, designed to predict response to multiple tumor microenvironment (TME)-targeted therapies, including immunotherapies and anti-angiogenic agents.<h4>Methods</h4>The Panel algorithm is an artificial neural network (ANN) trained with an input signature of 124 genes that was optimized across various solid tumors. From the 298-patient training data, the model learned to discriminate four TME subtypes: Angiogenic (A), Immune Active (IA), Immune Desert (ID), and Immune Suppressed (IS). ","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023","modification":"2026-05-29T02:21:33.669Z","creation":"2025-02-19T02:23:55.316Z"},"accession":"S-EPMC10213262","cross_references":{"pubmed":["37251949"],"doi":["10.3389/fonc.2023.1158345"]}}