Radiogenomics correlation between MR imaging features and mRNA-based subtypes in lower-grade glioma

Implementation of Oxygen Enhanced Magnetic Resonance Imaging (OE-MRI) and a Pilot Genomic Study of Hypoxia in Bladder Cancer Xenografts

BACKGROUND/AIM

Patients with hypoxic bladder cancer benefit from hypoxia modification added to radiotherapy, but no biomarkers exist to identify patients with hypoxic tumours. We, herein, aimed to implement oxygen-enhanced MRI (OE-MRI) in xenografts derived from muscle-invasive bladder cancer (MIBC) for future hypoxia biomarker discovery work; and generate gene expression data for future biomarker discovery.

MATERIALS AND METHODS

The flanks of female CD-1 nude mice inoculated with HT1376 MIBC cells. Mice with small (300 mm3) or large (700 mm3) tumours were imaged, breathing air then 100% O2, 1 h post injection with pimonidazole in an Agilant 7T 16cm bore magnet interfaced to a Bruker Avance III console with a T2-TurboRARE sequence using a dynamic MPRAGE acquisition. Dynamic Spoiled Gradient Recalled Echo images were acquired for 5 min, with 0.1mmol/kg Gd-DOTA (Dotarem, Guerbet, UK) injected after 60 s (1 ml/min). Voxel size and field of view of dynamic contrast enhanced (DCE)-MRI and OE-MRI scans were matched. The voxels considered as perfused with significant post-contrast enhancement (p<0.05) in DCE-MRI scans and tissue were further split into pOxyE (normoxic) and pOxyR (hypoxic) regions. Tumours harvested in liquid N2, sectioned, RNA was extracted and transcriptomes analysed using Clariom S microarrays.

RESULTS

Imaged hypoxic regions were greater in the larger versus smaller tumour. Expression of known hypoxia-inducible genes and a 24 gene bladder cancer hypoxia score were higher in pimonidazole-high versus -low regions: CA9 (p=0.012) and SLC2A1 (p=0.012) demonstrating expected transcriptomic behaviour.

CONCLUSION

OE-MRI was successfully implemented in MIBC-derived xenografts. Transcriptomic data derived from hypoxic and non-hypoxic xenograft regions will be useful for future studies.

Semantic Computed Tomography Features for Predicting BRCA1-associated Protein 1 and/or Tumor Protein p53 Gene Mutation Status in Clear Cell Renal Cell Carcinoma

PURPOSE

The purpose of this study was to explore the semantic computed tomography (CT) features associated with BRCA1-associated protein 1 (BAP1) and/or tumor protein p53 (TP53) mutation in clear cell renal cell carcinoma (ccRCC).

METHODS AND MATERIALS

Clinical characteristics and gene mutation information of 336 ccRCC patients were retrieved from The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma database (TCGA-KIRC). Kaplan-Meier analysis was performed to examine prognosis by gene mutation. The CT imaging data and gene mutation information of 156 ccRCC patients treated between January 2019 and January 2021 (the training cohort) were retrospectively analyzed. The CT imaging information and gene mutation data of 123 patients with ccRCC were downloaded from The Cancer Imaging Archive and The Cancer Genome Atlas database (the external validation cohort). Univariate Chi-square test and multivariate binary logistic regression analysis were performed to determine predictors of gene mutation; a nomogram was developed using these predictors. Receiver operating characteristic curve analysis and the Hosmer-Lemeshow test were performed to evaluate the performance of the nomogram.

RESULTS

Kaplan-Meier analysis showed that BAP1 and/or TP53 mutation was significantly correlated with worse survival outcome. Multivariate binary logistic regression analysis indicated ill-defined margin (P = .001), spiculated margin (P = .018), renal vein invasion (P = .002), and renal pelvis invasion (P = .001) were independent predictors of BAP1 and/or TP53 mutation. A nomogram containing these 4 semantic CT features was constructed; the area under the receiver operating characteristic curves was 0.872 (95% CI, 0.809-0.920). The Hosmer-Lemeshow test showed acceptable goodness-of-fit for the nomogram (X² = 1.194, P = .742). The nomogram was validated in the validation cohort; it showed good accuracy (area under the receiving operating characteristic curve = 0.819, 95% CI, 0.740-0.883) and was well calibrated (X² = 3.934, P = .559).

CONCLUSIONS

Semantic CT features are a potential and promising method for predicting BAP1 and/or TP53 mutation status in ccRCC patients.