Background and Purpose Tumor location has been shown to be a significant prognostic factor in patients with glioblastoma (GBM). The purpose of this study is to characterize GBM lesions by identifying MRI voxel-based tumor location features that are associated with tumor molecular profiles, patient characteristics and clinical outcomes. Materials and Methods Preoperative T1 anatomic MR images of 384 GBM patients were obtained from two independent cohorts (N=253 from our local (name withheld to preserve anonymity) Medical Center for training and N=131 from the Cancer Genome Atlas (TCGA) for validation). An automated computational image analysis pipeline was developed to determine the anatomic locations of tumor in each patient. Voxel-based differences in tumor location between good (overall survival (OS) > 17 months) and poor (OS < 11 months) survival groups identified in the training cohort were used to classify patients in the TCGA cohort into two brain location groups, for which clinical features, mRNA expression, and copy number changes were compared to elucidate the biological basis of tumors located in different brain regions. Results Tumors in the right occipito-temporal periventricular white matter were significantly associated with poor survival in both training and test cohorts (both log-rank P < 0.05) and had larger tumor volume compared to tumors in other locations. Tumors in the right peri-atrial location were associated with hypoxia pathway enrichment and PDGFRA amplification, making them potential targets for subgroup-specific therapies. Conclusion Voxel-based location in GBM is associated with patient outcome and may have a potential role for guiding personalized treatment.

Corrective actions for image scaling are suggested for manufacturers and quantitative imaging community.

Our findings suggest that an angiogenic subtype of GBM patients may benefit from antiangiogenic therapy with improved overall survival.

BackgroundWe developed a classifier using RNA sequencing data that identifies the usual interstitial pneumonia (UIP) pattern for the diagnosis of idiopathic pulmonary fibrosis. We addressed significant challenges, including limited sample size, biological and technical sample heterogeneity, and reagent and assay batch effects.ResultsWe identified inter- and intra-patient heterogeneity, particularly within the non-UIP group. The models classified UIP on transbronchial biopsy samples with a receiver-operating characteristic area under the curve of ~ 0.9 in cross-validation. Using in silico mixed samples in training, we prospectively defined a decision boundary to optimize specificity at ≥85%. The penalized logistic regression model showed greater reproducibility across technical replicates and was chosen as the final model. The final model showed sensitivity of 70% and specificity of 88% in the test set.ConclusionsWe demonstrated that the suggested methodologies appropriately addressed challenges of the sample size, disease heterogeneity and technical batch effects and developed a highly accurate and robust classifier leveraging RNA sequencing for the classification of UIP.Electronic supplementary materialThe online version of this article (10.1186/s12864-018-4467-6) contains supplementary material, which is available to authorized users.

Brain tumor-initiating cells (BTICs) are self-renewing multipotent cells critical for tumor maintenance and growth. Using single-cell microfluidic profiling, we identified multiple subpopulations of BTICs coexisting in human glioblastoma, characterized by distinct surface marker expression and single-cell molecular profiles relating to divergent bulk tissue molecular subtypes. These data suggest BTIC subpopulation heterogeneity as an underlying source of intra-tumoral bulk tissue molecular heterogeneity, and will support future studies into BTIC subpopulation-specific therapies. STEM CELLS 2016;34:1702-1707 SIGNIFICANCE STATEMENTIdentification of different brain tumor initiating cell (BTIC) subsets within individual tumors suggests BTIC heterogeneity as an underlying cause of glioblastoma resistance and potentially guides development of subpopulation-specific therapy.

A widening array of novel imaging biomarkers is being developed using ever more powerful clinical and preclinical imaging modalities. These biomarkers have demonstrated effectiveness in quantifying biological processes as they occur in vivo and in the early prediction of therapeutic outcomes. However, quantitative imaging biomarker data and knowledge are not standardized, representing a critical barrier to accumulating medical knowledge based on quantitative imaging data. We use an ontology to represent, integrate, and harmonize heterogeneous knowledge across the domain of imaging biomarkers. This advances the goal of developing applications to (1) improve precision and recall of storage and retrieval of quantitative imaging-related data using standardized terminology; (2) streamline the discovery and development of novel imaging biomarkers by normalizing knowledge across heterogeneous resources; (3) effectively annotate imaging experiments thus aiding comprehension, re-use, and reproducibility; and (4) provide validation frameworks through rigorous specification as a basis for testable hypotheses and compliance tests. We have developed the Quantitative Imaging Biomarker Ontology (QIBO), which currently consists of 488 terms spanning the following upper classes: experimental subject, biological intervention, imaging agent, imaging instrument, image postprocessing algorithm, biological target, indicated biology, and biomarker application. We have demonstrated that QIBO can be used to annotate imaging experiments with standardized terms in the ontology and to generate hypotheses for novel imaging biomarker-disease associations. Our results established the utility of QIBO in enabling integrated analysis of quantitative imaging data.

No abstract

BackgroundPatient stratification to identify subtypes with different disease manifestations, severity, and expected survival time is a critical task in cancer diagnosis and treatment. While stratification approaches using various biomarkers (including high-throughput gene expression measurements) for patient-to-patient comparisons have been successful in elucidating previously unseen subtypes, there remains an untapped potential of incorporating various genotypic and phenotypic data to discover novel or improved groupings.MethodsHere, we present HOCUS, a unified analytical framework for patient stratification that uses a community detection technique to extract subtypes out of sparse patient measurements. HOCUS constructs a patient-to-patient network from similarities in the data and iteratively groups and reconstructs the network into higher order clusters. We investigate the merits of using higher-order correlations to cluster samples of cancer patients in terms of their associations with survival outcomes.ResultsIn an initial test of the method, the approach identifies cancer subtypes in mutation data of glioblastoma, ovarian, breast, prostate, and bladder cancers. In several cases, HOCUS provides an improvement over using the molecular features directly to compare samples. Application of HOCUS to glioblastoma images reveals a size and location classification of tumors that improves over human expert-based stratification.ConclusionsSubtypes based on higher order features can reveal comparable or distinct groupings. The distinct solutions can provide biologically- and treatment-relevant solutions that are just as significant as solutions based on the original data.Electronic supplementary materialThe online version of this article (doi:10.1186/s12920-017-0256-3) contains supplementary material, which is available to authorized users.