Homo sapiens
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ABSTRACT:
PROVIDER: PRJNA404047 | ENA |
REPOSITORIES: ENA
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Project description:Background: To analyze the potential of radiomics for disease stratification beyond key molecular, clinical and standard imaging features in patients with glioblastoma. Methods: Quantitative imaging features (n=1043) were extracted from the multiparametric MRI of 181 patients with newly-diagnosed glioblastoma prior to standard-of-care treatment (allocated to a discovery and validation set, 2:1 ratio). A subset of 386/1043 features were identified as reproducible (in an independent MRI-test-retest cohort) and selected for analysis. A penalized Cox-model with 10-fold cross-validation (Coxnet) was fitted on the discovery set to construct a radiomic signature for predicting progression-free and overall survival (PFS, OS). The incremental value of a radiomic signature beyond molecular (MGMT-promoter methylation, DNA-methylation subgroups), clinical (patients age, KPS, extent-of-resection, adjuvant treatment) and standard imaging parameters (tumor volumes) for stratifying PFS and OS was assessed with multivariate Cox-models (performance quantified with prediction error curves). Results: The radiomic signature (constructed from 8/386 features identified through Coxnet) increased the prediction accuracy for PFS and OS (in both discovery and validation set) beyond the assessed molecular, clinical and standard imaging parameters (p≤0.01). Prediction errors decreased by 36% for PFS and 37% for OS when adding the radiomic signature (as compared to 29% and 27% with molecular + clinical features alone). The radiomic signature was - along with MGMT-status - the only parameter with independent significance on multivariate analysis (p≤0.01). Conclusions: Our study stresses the role of integrating radiomics into a multi-layer decision framework with key molecular and clinical features to improve disease stratification and to potentially advance personalized treatment of patients with glioblastoma.
2018-01-05 | GSE103659 | GEO
Project description:To exploit molecular subtyping for risk stratification in breast cancer patients
2024-01-01 | E-MTAB-13550 | biostudies-arrayexpress
Project description:To determine whether adding Decipher to standard risk stratification tools (CAPRA-S and Stephenson nomogram) improves accuracy in prediction of metastatic disease within 5 years after surgery in men with adverse pathologic features after RP.
2014-11-21 | GSE62667 | GEO
Project description:Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous malignancy with limited predictive markers to guide personalized treatment, particularly in human papillomavirus (HPV)-negative cases, which exhibit poor outcomes. Identifying reliable biomarkers for prognosis and therapeutic response remains a critical challenge. In a retrospective cohort of 51 patients with primary HPV-negative HNSCC, we investigated the prognostic significance of the Hedgehog (HH) signaling pathway and its association with imaging biomarkers. Genomic and transcriptomic analysis revealed that HH pathway activation correlated with distinct [18F]FDG PET/CT radiomic features, notably the PET-derived “histogram:ih.max” - a surrogate for peak [18F]FDG uptake and was associated with inferior survival outcomes. Functionally, pharmacologic inhibition of HH signaling demonstrated anticancer efficacy across multiple models, including HNSCC cell lines, patient-derived tumoroids, and in vivo xenograft models. Importantly, HH inhibition altered imaging characteristics in HNSCC xenografts, leading to a measurable reduction in [18F]FDG uptake. This imaging phenotype closely mirrored our clinical findings, suggesting that [18F]FDG PET/CT radiomics may serve as a non-invasive biomarker to identify and monitor HH-driven HNSCC tumors. The integration of multi-level molecular profiling and functional imaging supports a potential precision oncology strategy, in which HH inhibition may offer a viable therapeutic approach for HPV-negative HNSCC. Our study underscores the value of [18F]FDG PET/CT multiomics in linking tumor biology with imaging features, paving the way for improved patient stratification and treatment monitoring. These findings provide a compelling rationale for further investigation into HH-targeted therapies in this aggressive subset of HNSCC and their clinical implications.
2026-02-09 | GSE316548 | GEO
Project description:The key radiomic features were found to vary after neoadjuvant chemotherapy. Moreover, lncRNAs were discovered to be significantly correlated with radiomics and recurrence-free survival (RFS). The findings indicate that the radiomic signature can be conveniently used for individualized prediction of RFS and that radiomics is associated with lncRNAs in breast cancer.
2023-11-21 | GSE189371 | GEO
Project description:To determine whether adding Decipher to standard risk stratification tools (CAPRA-S and Stephenson nomogram) improves accuracy in prediction of metastatic disease within 5 years after surgery in men with adverse pathologic features after RP. The study population consisted of 182 patients selected from 2,641 men who underwent RP at the Cleveland Clinic between 1987-2008 who met the following criteria: 1) preoperative PSA>20 ng/mL, stage pT3 or margin positive, or Gleason score >/8; 2) pathologic node negative; 3) undetectable post-RP PSA; 4) no neoadjuvant or adjuvant therapy; and 5) minimum of 5 years follow-up for the controls.
2014-11-21 | E-GEOD-62667 | biostudies-arrayexpress
Project description:Introduction: The aim of this pilot study is to establish a radiogenomic characterisation of a clear-cell renal cell carcinoma (ccRCC) subpopulation, focusing on the transcriptomic underpinnings of radiomic features. Materials & Methods: To establish the viability of conducting a combined analysis of both radiomic and genomic data, a pilot cohort of 6 patients with <5cm G2 unilateral non-metastatic T1a-b ccRCC, who underwent surgery, was evaluated. Transcriptomic analysis was conducted through RNA-seq on tumor samples, while radiomic data was extracted from pre-operative 4 phase contrast-enhanced multidetector CT scans. Genomic heterogeneity was assessed with principal component analysis run on unrestricted data, on a clear-cell renal cell carcinoma associated gene list with zero-centered Reads Per Kilobase of transcript, per million mapped reads values. The underlying pathways and gene ontologies were established with enrichment analysis. In addition, Pearson’s correlation between radiomic data and the transcription of significant genes was fitted, and dendrogram and heatmap plots were drawn. Results: Even in a clinically homogeneous population, the employed analyses have demonstrated that RCC should be regarded as an intrinsically heterogeneous disease. The analysis of the radiomic features and gene expression correlation using heatmap and dendrogram showed four distinct radiogenomic correlation patterns: with one including 5 radiomic features, and the other three including 2 features each. Conclusion: The current pilot study is the first investigation demonstrating an innovative radiogenomic characterisation of clear-cell RCC. Based on such observations, further investigation into the radiomic and genomic approaches for the enhanced diagnosis of RCC is warranted.
2019-06-28 | GSE133460 | GEO
Project description:Comparison the mRNA expression profiles of 101 CRC tissues to those from matched 35 non-neoplastic colon mucosal tissues from patients with stage III CRCs treated with FOLFOX adjuvant chemotherapy in each molecular subtype. Gene expression–based subtyping is widely accepted as a relevant source of disease stratification. Results provide important information of molecular marker genes for molecular classification.
2017-05-31 | GSE83889 | GEO
Project description:We report the application of RNA-seq for molecular profiling of cultured, U87MG cells that stably express TrkB. Our data set is based on about 40 million unique reads per sample, in four independent mRNA preparations, for nine different testing conditions. U87MG is a standard human glioblastoma patient derived cell line. It serves as a cellular model for studying molecular characteristics of glioblastoma. We describe in our study the intracellular self-activation of TrkB via Y705 and its role in reducing actin dynamics and cell migraton. With this transcriptome dataset, we wished to highlight key players that are activated after TrkB self-activation. The dataset suggest a transcriptional level reprogramming and switching on of several genes that play a role in modulating immune responses and defense mechanisms, in the modified U87MG cells.
2024-06-20 | GSE186207 | GEO
Project description:Revised risk estimation and treatment stratification of low- and intermediate-risk neuroblastoma patients by integrating clinical and molecular prognostic markers. To optimize neuroblastoma treatment stratification, we aimed at developing a novel risk estimation system by integrating gene expression-based classification and established prognostic markers. Gene expression profiles were generated from 709 neuroblastoma specimens using customized 4x44K microarrays. Classification models were built using 75 tumors with contrasting courses of disease. Validation was performed in an independent test set (n=634) by Kaplan-Meier estimates and Cox regression analyses. Combination of gene expression-based classification and established prognostic markers improves risk estimation of LR/IR neuroblastoma patients. We propose to implement our revised treatment stratification system in a prospective clinical trial.
2014-10-09 | E-MTAB-1781 | biostudies-arrayexpress