Citations

Aims We sought to investigate the association of obesity and metabolic health status with the incidence of clinical hypertrophic cardiomyopathy (HCM) diagnosis in the general population. Our goal was to identify modifiable risk factors to attenuate clinical expression of HCM, enabling management evolution from a mostly passive strategy of risk stratification to a proactive strategy of modifying disease expression. Methods Using nationwide population-based data from the Korean National Health Insurance Service, 28,679,891 people who were free of prevalent HCM and who underwent health examinations between 2009 and 2015 were followed until 31 December 2016. The primary outcome was clinical HCM that was defined as incident diagnosis of HCM during the follow-up, after a blanking period of 12 months. Results Over a median follow-up of 5.2 years, 0.027% (n = 7851) of the study participants were diagnosed as incident HCM. The incidence rate per 1000 person-years was 0.059. A significant association was found between body mass index (BMI) and the incidence of clinical HCM after multivariate adjustment, with a hazard ratio per 1 kg/m2 increase in BMI of 1.063 (95% confidence interval 1.051–1.075). Metabolically unhealthy participants had a greater incidence of HCM than metabolically healthy participants, regardless of obesity status. The effect of BMI was more pronounced in several subgroups, including participants with no hypertension, those aged less than 65 years and men. Conclusion We found that individuals with obesity and/or metabolic abnormalities had a significantly higher incidence of clinical HCM diagnosis than their counterparts. Efforts to manage obesity and metabolic abnormalities may be important in modifying clinical expression of HCM.

Abstract Glioblastoma (GBM) is increasingly recognized as a disease involving dysfunctional cellular metabolism. GBMs are known to be complex heterogeneous systems containing multiple distinct cell populations and are supported by an aberrant network of blood vessels. A better understanding of GBM metabolism, its variation with respect to the tumor microenvironment, and resulting regional changes in chemical composition is required. This may shed light on the observed heterogeneous drug distribution, which cannot be fully described by limited or uneven disruption of the blood–brain barrier. In this work, we used mass spectrometry imaging (MSI) to map metabolites and lipids in patient-derived xenograft models of GBM. A data analysis workflow revealed that distinctive spectral signatures were detected from different regions of the intracranial tumor model. A series of long-chain acylcarnitines were identified and detected with increased intensity at the tumor edge. A 3D MSI dataset demonstrated that these molecules were observed throughout the entire tumor/normal interface and were not confined to a single plane. mRNA sequencing demonstrated that hallmark genes related to fatty acid metabolism were highly expressed in samples with higher acylcarnitine content. These data suggest that cells in the core and the edge of the tumor undergo different fatty acid metabolism, resulting in different chemical environments within the tumor. This may influence drug distribution through changes in tissue drug affinity or transport and constitute an important consideration for therapeutic strategies in the treatment of GBM. Significance: GBM tumors exhibit a metabolic gradient that should be taken into consideration when designing therapeutic strategies for treatment. See related commentary by Tan and Weljie, p. 1231

Goals: The study aimed to compare the level and characteristics of disease-related knowledge of patients with inflammatory bowel disease (IBD) between the West and the East using an international version of a questionnaire regarding knowledge of inflammatory bowel disease (IBD-KNOW). Background: The authors recently developed a new questionnaire regarding IBD-KNOW, which showed excellent test characteristics in Korea. Study: The IBD-KNOW questionnaire was administered to 100 patients with IBD from tertiary referral hospitals in the United States and Korea. Scores were calculated and compared between US and Korean patients, and factors associated with high scores were analyzed. Results: A total of 196 (100 US and 96 Korean) patients with IBD completed the questionnaires. Analysis of the baseline characteristics revealed that male sex, smoking status, disease duration, history of IBD-related operations, family history of IBD, and use of corticosteroids or biologics were significantly different between US and Korean patients. The mean IBD-KNOW score was higher in US patients than in Korean patients (14.8 vs. 11.3; P<0.001). Multivariate analyses showed that a high IBD-KNOW score (top 25%) was associated with a positive family history of IBD (odds ratio, 2.90; P=0.025) in US patients with IBD and with the use of biologics (odds ratio, 3.65; P=0.008) in Korean patients with IBD. Conclusions: The IBD-KNOW questionnaire, an updated assessment tool of IBD-related knowledge, is simple, reliable, and available in various patient populations. IBD-KNOW can be used to identify the factors affecting the level of IBD-related knowledge to improve the quality of care in patients with IBD through a personalized approach.

Abstract Pathogenic sequence variants (PSV) in BRCA1 or BRCA2 (BRCA1/2) are associated with increased risk and severity of prostate cancer. We evaluated whether PSVs in BRCA1/2 were associated with risk of overall prostate cancer or high grade (Gleason 8+) prostate cancer using an international sample of 65 BRCA1 and 171 BRCA2 male PSV carriers with prostate cancer, and 3,388 BRCA1 and 2,880 BRCA2 male PSV carriers without prostate cancer. PSVs in the 3′ region of BRCA2 (c.7914+) were significantly associated with elevated risk of prostate cancer compared with reference bin c.1001-c.7913 [HR = 1.78; 95% confidence interval (CI), 1.25–2.52; P = 0.001], as well as elevated risk of Gleason 8+ prostate cancer (HR = 3.11; 95% CI, 1.63–5.95; P = 0.001). c.756-c.1000 was also associated with elevated prostate cancer risk (HR = 2.83; 95% CI, 1.71–4.68; P = 0.00004) and elevated risk of Gleason 8+ prostate cancer (HR = 4.95; 95% CI, 2.12–11.54; P = 0.0002). No genotype–phenotype associations were detected for PSVs in BRCA1. These results demonstrate that specific BRCA2 PSVs may be associated with elevated risk of developing aggressive prostate cancer. Significance: Aggressive prostate cancer risk in BRCA2 mutation carriers may vary according to the specific BRCA2 mutation inherited by the at-risk individual.

Abstract BACKGROUND Conventional magnetic resonance imaging (MR) guides patient care in GBM. However, there is mounting awareness that MR enhancement is non-specific reflecting either tumor progression or non-tumoral inflammatory changes. Histological evaluation of GBM is held as the gold standard for disease assessment. However, the invasiveness of this methodology and the sample sparsity limit its usefulness. Methods to infer histological underpinnings of MRI are needed to improve clinical care. METHODS A transfer learning mixed effects model based on T1Gd and FLAIR MR voxel based image features trained and cross-validated to predict FPKM values of CD68, CASP3, CD8A and IL13RA2. Training data included RNAseq from 38 image-localized biopsies from 15 newly-diagnosed GBM patients. These models were then applied to two independent patients, chosen based on therapy and the quality of image registration, at three different time points, just prior to receiving IL13RA2 targeted CAR-T therapy for rGBM, after 2 cycles and after 4 cycles, resulting in a voxel-based prediction of the relative expression levels of these genes. RESULTS Cross-validation of the proposed machine learning models demonstrated a Pearson Correlation coefficient between predicted and observed FPKM values of 0.89 (CD68), 0.92 (CASP3), 0.93 (CD8A), and 0.88 (IL13RA2). When the models were applied to the two CAR-T patients, CD68-modeled expression levels were seen to increase throughout therapy for one patient, while remaining stable for the other. Survival from first CAR-T infusion was, respectively, 412 and 83 days suggesting that the increased CD68 was indicative of therapeutic effectivity. Further, the spatial activity predictions were consistent with expected therapeutic action as the correlation coefficient between CASP3 and the product of IL13R2 and CD8A expression was 0.81, suggesting the T-cells targeting IL13RA2 were inducing cell death. CONCLUSIONS Preliminary results with our model highlights the potential of spatially resolved radiomic maps to provide insight into regional therapeutic effectivity.

Abstract Recurrent glioblastoma is challenging to distinguish from so called “treatment effect” on routine clinical imaging. Further, within tumor heterogeneity reveals that some regions can be histologically dominated by tumor progression whilst others can be dominated by secondary effects of treatment response. Apparent tumor progression on MRI can be very difficult to manage clinically as it is unclear the degree to which the imaging changes are actually tumor progression vs response to treatment (including inflammatory response and necrosis). In this analysis, we study a unique cohort of patients for whom image localized-biopsies reveal heterogeneity in response vs progression. Our dataset included 70 biopsy samples from 32 patients with GBM each histolopatholgically characterized for tumor abundance vs immune infiltrate. Six multiparametric MRI contrasts were available, including T1, T1gd, T2, FLAIR, SWI, and ADC. Images were co-registered. Radiomic (statistical + texture) features were extracted from the region of six image contrasts locally matched with each biopsy sample. Machine learning models were built to predict each biomarker using radiomic features. Leave-one-out cross validation was used to evaluate the prediction accuracy. Radiomic features were found to be informative to the prediction of biomarkers. ANOVA tests show significant improvement of using radiomic features compared with the null model. The prediction accuracy was higher when considering the biomarkers on a binary scale using the median as the cutoff than on a numerical scale. Spatially-informed radiomics models for tumor progression vs treatment effect are possible and can play an instrumental role in navigating confounding imaging changed common during treatment progression.

Abstract BACKGROUND An important challenge in radiomics research is reproducibility. Images are collected on different image scanners and protocols, which introduces significant variability even for the same type of image across institutions. In the present proof-of-concept study, we address the reproducibility issue by using domain adaptation – an algorithm that transforms the radiomic features of each new patient to align with the distribution of features formed by the patient samples in a training set. METHOD Our dataset included 18 patients in training with a total of 82 biopsy sample. The pathological tumor cell density was available for each sample. Radiomic (statistical + texture) features were extracted from the region of six image contrasts locally matched with each biopsy sample. A Gaussian Process (GP) classifier was built to predict tumor cell density using radiomic features. Another 6 patients were used to test the training model. These patients had a total of 31 biopsy samples. The images of each test patient were purposely normalized using a different approach, i.e., using the CSF instead of the whole brain as the reference. This was to mimic the practical scenario of image source discrepancy between different patients. Domain adaptation was applied to each test patient. RESULTS Among the 18 training patients, the leave-one-patient-out cross validation accuracy is 0.81 AUC, 0.78 sensitivity, and 0.83 specificity. When the trained model was applied to the 6 test patients (purposely normalized using a different approach than that of the training data), the accuracy dramatically reduced to 0.39 AUC, 0.08 sensitivity, and 0.61 specificity. After using domain adaption, the accuracy improved to 0.68 AUC, 0.62 sensitivity, and 0.72 specificity. CONCLUSION We provide candidate enabling tools to address reproducibility in radiomics models by using domain adaption algorithms to account for discrepancy of the images between different patients.

Abstract INTRODUCTION The quantification of intratumoral heterogeneity – through radiomics-based approaches - can help resolve the regionally distinct genetic drug targets that may co-exist within a single Glioblastoma (GBM) tumor. While this offers potential diagnostic value under the paradigm of individualized oncology, clinical decision-making must also consider the degree of uncertainty associated with each model. In this study, we evaluate the performance of a novel machine-learning (ML) algorithm, called Gaussian Process (GP) modeling, that can quantify the impact of multiple sources of uncertainty in ML model development and prediction accuracy, including variabilities in the copy number measurement, radiomics features, training sample characteristics, and training sample size. METHOD We collected 95 image-localized biopsies from 25 primary GBM patients. We coregistered stereotactic locations with preoperative multi-parametric MRI features (conventional MRI, DSC perfusion, Diffusion Tensor Imaging) to generate spatially matched pairs of MRI and copy number variants (CNV) for for each biopsy. We developed a Gaussian Process (GP) model to predict CNV for Epidermal Growth Factor Receptor (EGFR) based on MRI radiomic features in each patient. We used leave-one-patient-out cross validation to quantify prediction accuracy and model uncertainty. Spatial prediction and uncertainty (p-value) maps were overlaid to help visualize regional genetic variation of EGFR and uncertainty of the radiomic predictions. RESULT: The initial GP radiomics model for EGFR amplification (CNV > 3.5) produced a sensitivity of 0.8 and specificity of 0.8. Samples/regions associated with high uncertainty (p-value >0.05) correlated with either 1) extrapolation of radiomic features from the training set-defined feature space or 2) insufficient training samples in the feature space. CONCLUSION We present a ML-based model that quantifies spatial genetic heterogeneity in GBM, while also estimating model uncertainties that result from multi-source data variabilities. This approach lays the groundwork for prospective clinical integration of modeling-based diagnostic approaches in the paradigm of individualized medicine.

Abstract High-grade gliomas (HGGs) nearly always recur after standard initial treatment, and the resulting mixture of recurrent tumor and treatment-induced reactive changes presents major diagnostic challenges. Anatomical imaging, such as MRI, cannot adequately distinguish progressive disease from treatment effect (pseudo-progression). Furthermore, there is marked intra-tumoral heterogeneity, such that some areas of a tumor may demonstrate necrotic treatment effect and others frank recurrence. Due to this difficulty reliably differentiating between these two clinical findings, analytic methods using multiple modalities are necessary to further our understanding of this disease process. To this end, we sought to correlate radiographic, histopathologic and molecular features of surgically sampled post-treatment suspected recurrence to identify markers distinguishing tumor growth from treatment effect. We performed Stimulated Raman Histology (SRH) imaging and highly multiplexed RNA-sequencing (PLATE-seq) on 84 MRI-localized biopsies from 39 patients with clinically suspected recurrent HGG. The SRH images were classified as recurrent tumor or gliotic/reactive tissue using a convolutional neural network trained on an independent cohort including a large set of recurrent HGG, and an automated cell-counting algorithm was used to quantify cellularity from the SRH image of each sample. Differential gene expression analysis of the PLATE-seq data was used to identify gene sets that distinguish recurrent tumor from treatment effect, and single sample gene set variation analysis (GSVA) was used to further assess the molecular and cellular composition of each MRI-localized sample. The histopathologic and molecular features of each sample were also correlated with the MRI features of the corresponding biopsy sites, and this data is currently being used to train machine learning models that predict the distribution of recurrent tumor and treatment-induced reactive changes within a patient’s radiographic lesion. These predictive radiomic models will help to guide neurosurgical sampling, and improve our ability to monitor glioma progression and response to therapy.

Abstract BACKGROUND NRG-CC001 sought to evaluate the neuro-protective effects of avoiding the peri-hippocampal stem cell niche using intensity-modulated radiotherapy during WBRT. METHODS Patients with brain metastasis were stratified by RPA class and prior radiosurgery/surgery and randomized to WBRT+M or HA-WBRT+ Memantine (M) (30Gy in 10 fractions). Standardized NCF tests and the M.D. Anderson Symptom Inventory Brain Tumor (MDASI-BT) were obtained at baseline, 2, 4, 6, and 12 months (mos). The primary endpoint was NCF failure defined using the reliable change index. Pre-specified secondary endpoints included patient-reported symptoms using the MDASI-BT. Time to NCF was reported as cumulative incidence (with death without NCF failure as a competing risk); between-arms differences were tested using Gray’s test. Deterioration at discrete time-points were tested using chi-square tests. MDASI-BT symptom burden, interference, and cognitive and neurologic burden were analyzed using mixed effects models and t-tests within the model using Hochberg’s multiplicity adjustment. RESULTS A total of 518 patients were randomized from 7/2016 to 3/2018. Median follow-up for alive patients was 12.1 mos, with no difference between arms in terms of toxicity, overall survival or intracranial progression-free survival. HA-WBRT+M was associated with lower risk of NCF failure (adjusted HR=0.739, 95% CI: 0.577–0.945, p=0.0.016), with differences first noted at 4 mos in Trail Making Test Part-B (23.3% vs. 40.4% deteriorated, p=0.012). Age did not dilute treatment effect. HA-WBRT+M was associated with reduced cognitive symptom burden in an imputed model (estimate=-0.29, p=0.0425), and also reduced overall symptom burden (p< 0.0001) and interference (p< 0.0016) at 6 mos. CONCLUSIONS The addition of HA to WBRT+M preserved NCF and reduced patient reported cognitive symptom burden, overall symptom burden and symptom interference and should be considered standard of care for any patient fit enough to have WBRT. Supported by grants UG1CA189867 (NCORP), U10CA180868 (NRG Oncology Operations), DCP from the National Cancer Institute.