{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Okereke LC"],"funding":["Commonwealth Scholarship Commission"],"pagination":["3604"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9688486"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["11(22)"],"pubmed_abstract":["Tumor-infiltrating immune cells (TIICs) form a critical part of the ecosystem surrounding a cancerous tumor. Recent advances in radiobiology have shown that, in addition to damaging cancerous cells, radiotherapy drives the upregulation of immunosuppressive and immunostimulatory TIICs, which in turn impacts treatment response. Quantifying TIICs in tumor samples could form an important predictive biomarker guiding patient stratification and the design of radiotherapy regimens and combined immune-radiation treatments. As a result of several limitations associated with experimental methods for quantifying TIICs and the availability of extensive gene sequencing data, deconvolution-based computational methods have appeared as a suitable alternative for quantifying TIICs. Accordingly, we introduc"],"journal":["Cells"],"pubmed_title":["Toward Precision Radiotherapy: A Nonlinear Optimization Framework and an Accelerated Machine Learning Algorithm for the Deconvolution of Tumor-Infiltrating Immune Cells."],"pmcid":["PMC9688486"],"funding_grant_id":["NGCN-2020-263"],"pubmed_authors":["Onwukwe EA","Bello AU","Okereke LC"],"additional_accession":[]},"is_claimable":false,"name":"Toward Precision Radiotherapy: A Nonlinear Optimization Framework and an Accelerated Machine Learning Algorithm for the Deconvolution of Tumor-Infiltrating Immune Cells.","description":"Tumor-infiltrating immune cells (TIICs) form a critical part of the ecosystem surrounding a cancerous tumor. Recent advances in radiobiology have shown that, in addition to damaging cancerous cells, radiotherapy drives the upregulation of immunosuppressive and immunostimulatory TIICs, which in turn impacts treatment response. Quantifying TIICs in tumor samples could form an important predictive biomarker guiding patient stratification and the design of radiotherapy regimens and combined immune-radiation treatments. As a result of several limitations associated with experimental methods for quantifying TIICs and the availability of extensive gene sequencing data, deconvolution-based computational methods have appeared as a suitable alternative for quantifying TIICs. Accordingly, we introduc","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Nov","modification":"2025-04-18T20:32:08.191Z","creation":"2024-11-15T04:25:38.247Z"},"accession":"S-EPMC9688486","cross_references":{"pubmed":["36429031"],"doi":["10.3390/cells11223604"]}}