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Glioblastoma is the most aggressive primary brain tumor with an unmet need for more effective therapies. Here we report that the combination of L19TNF, an antibody-cytokine fusion protein based on tumor necrosis factor that selectively localizes on the tumor neo-vasculature, with the alkylating agen...
ORGANISM(S): Mus musculus (Mouse) 
2026-02-23 | PXD041234 | Pride
The array of peptides presented to CD8+ T cells by major histocompatibility complex (MHC) class I molecules is referred to as the MHC class I immunopeptidome. Although the MHC class I immunopeptidome is ubiquitous in mammals and represents a critical component of the adaptive immune system, very lit...
ORGANISM(S): Mus Musculus (ncbitaxon:10090) 
2020-02-19 | MSV000084980 | MassIVE
Background: Aggressiveness guides treatment in IDH-mutant gliomas. Objective grading of oligodendrogliomas is therefore urgently needed. Material and Methods: 211 primary and recurrent resections from 111 oligodendroglioma patients were collected, complemented with 91 samples for validation. Samples...
ORGANISM(S): Homo sapiens (Human) 
2026-01-19 | PXD070222 | Pride
We employed a gene complementation strategy combined with microarray screening to identify miRNAs involved in the formation of erythroid (red blood) cells. To search for GATA-1-regulated erythroid miRNAs, we used the Gata-1– erythroblast line G1E. These cells proliferate in culture as immature ery...
ORGANISM(S): Mus musculus 
The FFPE samples of the primary vs. recurrent glioblastoma validation cohort
ORGANISM(S): Homo Sapiens 
2022-08-08 | PXD035867 |
Glioblastoma, the most aggressive primary brain cancer, has dismal prognosis, yet systemic treatment is limited to DNA-alkylating chemotherapies. New therapeutic strategies may emerge from exploring neurodevelopmental and neurophysiological vulnerabilities of glioblastoma. To this end, we here syste...
ORGANISM(S): Homo Sapiens (ncbitaxon:9606) 
In this study, the authors had developed a machine learning model to predict immune checkpoint blockade (ICB) response by integrating genomic, molecular, demographic and clinical data from a curated cohort (MSK-IMPACT) with 1479 patients treated with ICB across 16 different types of cancer. This mod...
2024-07-23 | MODEL2407210002 | BioModels
This is a Random Forest algorithm-based machine learning model called RF16, which incorporates a total of 16 genomic, molecular, demographic, and clinical features to predict the immunotherapy response for a patient. The model assigns a value of 0 for NonResponder and 1 for Responder. Please be awar...
2023-05-09 | BIOMD0000001066 | BioModels
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