Objectives To develop and validate a genetic tool to predict age of onset of aggressive prostate cancer (PCa) and to guide decisions of who to screen and at what age. Design Analysis of genotype, PCa status, and age to select single nucleotide polymorphisms (SNPs) associated with diagnosis. These polymorphisms were incorporated into a survival analysis to estimate their effects on age at diagnosis of aggressive PCa (that is, not eligible for surveillance according to National Comprehensive Cancer Network guidelines; any of Gleason score ≥7, stage T3-T4, PSA (prostate specific antigen) concentration ≥10 ng/L, nodal metastasis, distant metastasis). The resulting polygenic hazard score is an assessment of individual genetic risk. The final model was applied to an independent dataset containing genotype and PSA screening data. The hazard score was calculated for these men to test prediction of survival free from PCa. Setting Multiple institutions that were members of international PRACTICAL consortium. Participants All consortium participants of European ancestry with known age, PCa status, and quality assured custom (iCOGS) array genotype data. The development dataset comprised 31 747 men; the validation dataset comprised 6411 men. Main Outcome Measures Prediction with hazard score of age of onset of aggressive cancer in validation set. Results In the independent validation set, the hazard score calculated from 54 single nucleotide polymorphisms was a highly significant predictor of age at diagnosis of aggressive cancer (z=11.2, P<10−16). When men in the validation set with high scores (>98th centile) were compared with those with average scores (30th-70th centile), the hazard ratio for aggressive cancer was 2.9 (95% confidence interval 2.4 to 3.4). Inclusion of family history in a combined model did not improve prediction of onset of aggressive PCa (P=0.59), and polygenic hazard score performance remained high when family history was accounted for. Additionally, the positive predictive value of PSA screening for aggressive PCa was increased with increasing polygenic hazard score. Conclusions Polygenic hazard scores can be used for personalised genetic risk estimates that can predict for age at onset of aggressive PCa.

Attention-dependent modulation of neural activity in visual association cortex (VAC) is thought to depend on top-down modulatory control signals emanating from the prefrontal cortex (PFC). In a previous functional magnetic resonance imaging study utilizing a working memory task, we demonstrated that activity levels in scene-selective VAC (ssVAC) regions can be enhanced above or suppressed below a passive viewing baseline level depending on whether scene stimuli were attended or ignored (Gazzaley, Cooney, McEvoy, et al. 2005). Here, we use functional connectivity analysis to identify possible sources of these modulatory influences by examining how network interactions with VAC are influenced by attentional goals at the time of encoding. Our findings reveal a network of regions that exhibit strong positive correlations with a ssVAC seed during all task conditions, including foci in the left middle frontal gyrus (MFG). This PFC region is more correlated with the VAC seed when scenes were remembered and less correlated when scenes were ignored, relative to passive viewing. Moreover, the strength of MFG-VAC coupling correlates with the magnitude of attentional enhancement and suppression of VAC activity. Although our correlation analyses do not permit assessment of directionality, these findings suggest that PFC biases activity levels in VAC by adjusting the strength of functional coupling in accordance with stimulus relevance.

Background and Purpose Brain radiotherapy is limited in part by damage to white matter, contributing to neurocognitive decline. We utilized diffusion tensor imaging (DTI) with multiple b-values (diffusion weightings) to model the dose-dependency and time course of radiation effects on white matter. Materials and Methods Fifteen patients with high-grade gliomas treated with radiotherapy and chemotherapy underwent MRI with DTI prior to radiotherapy, and after months 1, 4-6, and 9-11. Diffusion tensors were calculated using three weightings (high, standard, and low b-values) and maps of fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (λ‖), and radial diffusivity (λ⊥) were generated. The region of interest was all white matter. Results MD, λ‖, and λ⊥increased significantly with time and dose, with corresponding decrease in FA. Greater changes were seen at lower b-values, except for FA. Time-dose interactions were highly significant at 4-6 months and beyond (p < .001), and the difference in dose response between high and low b-values reached statistical significance at 9-11 months for MD, λ‖, and λ⊥ (p < .001, p < .001, p = .005 respectively) as well as at 4-6 months for λ‖ (p = .04). Conclusions We detected dose-dependent changes across all doses, even <10 Gy. Greater changes were observed at low b-values, suggesting prominent extracellular changes possibly due to vascular permeability and neuroinflammation.

Purpose Radiation-induced cognitive deficits may be mediated by tissue damage to cortical regions. Volumetric changes in cortex can be reliably measured using high-resolution magnetic resonance imaging (MRI). We used these methods to study the association between radiation therapy (RT) dose and change in cortical thickness in high-grade glioma (HGG) patients. Methods and Materials We performed a voxel-wise analysis of MR imaging from 15 HGG patients who underwent fractionated partial brain RT. Three-dimensional MRI was acquired pre- and 1-year post-RT. Cortex was parcellated with well-validated segmentation software (Freesufer). Surgical cavities were censored. Each cortical voxel was assigned a change in cortical thickness between time points, RT dose value, and neuroanatomic label by lobe. Effect of dose, neuroanatomic location, age, and chemotherapy on cortical thickness was tested using linear mixed effects (LME) modeling. Results Cortical atrophy was seen after 1-year post RT, with greater effects at higher doses. Estimates from LME modeling showed that cortical thickness decreased by −0.0033 mm (p<0.001) for every 1 Gy increase in RT dose. Temporal and limbic cortex exhibited the largest change in cortical thickness per Gy, compared to other regions (p<0.001). Age and chemotherapy were not significantly associated with cortical thickness change. Conclusions We found dose-dependent thinning of the cerebral cortex, with varying neuroanatomical regional sensitivity, one year after fractionated partial brain RT. The magnitude of thinning parallels one-year atrophy rates seen in neurodegenerative diseases, and may contribute to cognitive decline following high-dose RT.

Background and Purpose Regional differences in sensitivity to white matter damage after brain radiotherapy (RT) are not well-described. We characterized the spatial heterogeneity of dose-response across white matter tracts using diffusion tensor imaging (DTI). Materials and Methods Forty-nine patients with primary brain tumors underwent MRI with DTI before and 9–12 months after partial-brain RT. Maps of fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were generated. Atlas-based white matter tracts were identified. A secondary analysis using skeletonized tracts was also performed. Linear mixed-model analysis of the relationship between mean and max dose and percent change in DTI metrics was performed. Results Tracts with the strongest correlation of FA change with mean dose were the fornix (−0.46 percent/Gy), cingulum bundle (−0.44 percent/Gy), and body of corpus callosum (−0.23 percent/Gy), p<.001. These tracts also showed dose-sensitive changes in MD and RD. In the skeletonized analysis, the fornix and cingulum bundle remained highly dose-sensitive. Maximum and mean dose were similarly predictive of DTI change. Conclusions The corpus callosum, cingulum bundle, and fornix show the most prominent dose-dependent changes following RT. Future studies examining correlation with cognitive functioning and potential avoidance of critical white matter regions are warranted.

Purpose/Objectives Following brain radiation therapy (RT), patients often experience memory impairment, which may be partially mediated by damage to the hippocampus. Hippocampal sparing in RT planning is the subject of recent and ongoing clinical trials. Calculating appropriate hippocampal dose constraints would be improved by efficient in vivo measurements of hippocampal damage. In this study we sought to determine whether brain RT was associated with dose-dependent hippocampal atrophy. Materials/Methods Hippocampal volume was measured with MRI in 52 patients who underwent fractionated, partial brain RT for primary brain tumors. Study patients had high-resolution, 3D volumetric MRI prior to and one year post-RT. Images were processed using software with FDA clearance and CE (Conformité Européene) marking for automated measurement of hippocampal volume. Automated results were inspected visually for accuracy. Tumor and surgical changes were censored. Mean hippocampal dose was tested for correlation with hippocampal atrophy one year post-RT. Average hippocampal volume change was also calculated for hippocampi receiving high (>40 Gy) or low (<10 Gy) mean RT dose. A multivariate analysis was conducted with linear mixed-effects modeling to evaluate other potential predictors of hippocampal volume change, including patient (random effect), age, hemisphere, sex, seizure history, and baseline volume. Statistical significance was evaluated at α=0.05. Results Mean hippocampal dose was significantly correlated with hippocampal volume loss (r=−0.24, p=0.03). Mean hippocampal volume was significantly reduced one year after high-dose RT (mean −6%, p=0.009), but not after low-dose RT. In multivariate analysis, both RT dose and patient age were significant predictors of hippocampal atrophy (p<0.01). Conclusions The hippocampus demonstrates radiation dose-dependent atrophy following treatment for brain tumors. Quantitative MRI is a non-invasive imaging technique capable of measuring radiation effects on intracranial structures. This technique could be investigated as a potential biomarker for development of reliable dose constraints for improved cognitive outcomes.

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Genetic models for cancer have been evaluated using almost exclusively European data, which could exacerbate health disparities. A polygenic hazard score (PHS1) is associated with age at prostate cancer diagnosis and improves screening accuracy in Europeans. Here, we evaluate performance of PHS2 (PHS1, adapted for OncoArray) in a multi-ethnic dataset of 80,491 men (49,916 cases, 30,575 controls). PHS2 is associated with age at diagnosis of any and aggressive (Gleason score ≥ 7, stage T3-T4, PSA ≥ 10 ng/mL, or nodal/distant metastasis) cancer and prostate-cancer-specific death. Associations with cancer are significant within European (n = 71,856), Asian (n = 2,382), and African (n = 6,253) genetic ancestries (p < 10−180). Comparing the 80th/20th PHS2 percentiles, hazard ratios for prostate cancer, aggressive cancer, and prostate-cancer-specific death are 5.32, 5.88, and 5.68, respectively. Within European, Asian, and African ancestries, hazard ratios for prostate cancer are: 5.54, 4.49, and 2.54, respectively. PHS2 risk-stratifies men for any, aggressive, and fatal prostate cancer in a multi-ethnic dataset.