The identification of genes with specific patterns of change (e.g. down-regulated and methylated) as phenotype drivers or samples with similar profiles for a given gene set as drivers of clinical outcome, requires the integration of several genomic data types for which an 'integrate by intersection' (IBI) approach is often applied. In this approach, results from separate analyses of each data type are intersected, which has the limitation of a smaller intersection with more data types. We introduce a new method, GISPA (Gene Integrated Set Profile Analysis) for integrated genomic analysis and its variation, SISPA (Sample Integrated Set Profile Analysis) for defining respective genes and samples with the context of similar, a priori specified molecular profiles. With GISPA, the user defines a molecular profile that is compared among several classes and obtains ranked gene sets that satisfy the profile as drivers of each class. With SISPA, the user defines a gene set that satisfies a profile and obtains sample groups of profile activity. Our results from applying GISPA to human multiple myeloma (MM) cell lines contained genes of known profiles and importance, along with several novel targets, and their further SISPA application to MM coMMpass trial data showed clinical relevance.
Introduction: MR imaging can noninvasively visualize tumor phenotype characteristics at the macroscopic level. Here, we investigated whether somatic mutations are associated with and can be predicted by MRI-derived tumor imaging features of glioblastoma (GBM). Methods: Seventy-six GBM patients were identified from The Cancer Imaging Archive for whom preoperative T1-contrast (T1C) and T2-FLAIR MR images were available. For each tumor, a set of volumetric imaging features and their ratios were measured, including necrosis, contrast enhancing, and edema volumes. Imaging genomics analysis assessed the association of these features with mutation status of nine genes frequently altered in adult GBM. Finally, area under the curve (AUC) analysis was conducted to evaluate the predictive performance of imaging features for mutational status. Results: Our results demonstrate that MR imaging features are strongly associated with mutation status. For example, TP53-mutated tumors had significantly smaller contrast enhancing and necrosis volumes (p = 0.012 and 0.017, respectively) and RB1-mutated tumors had significantly smaller edema volumes (p = 0.015) compared to wild-type tumors. MRI volumetric features were also found to significantly predict mutational status. For example, AUC analysis results indicated that TP53, RB1, NF1, EGFR, and PDGFRA mutations could each be significantly predicted by at least one imaging feature. Conclusion: MRI-derived volumetric features are significantly associated with and predictive of several cancer-relevant, drug-targetable DNA mutations in glioblastoma. These results may shed insight into unique growth characteristics of individual tumors at the macroscopic level resulting from molecular events as well as increase the use of noninvasive imaging in personalized medicine.
Alzheimer's disease (AD) is a progressive neurological disorder that affects more than 30 million people worldwide. While various dementia-related losses in cognitive functioning are its hallmark clinical symptoms, ultimate diagnosis is based on manual neuropathological assessments using various schemas, including Braak staging, CERAD (Consortium to Establish a Registry for Alzheimer's Disease) and Thal phase scoring. Since these scoring systems are based on subjective assessment, there is inevitably some degree of variation between readers, which could affect ultimate neuropathology diagnosis. Here, we report a pilot study investigating the applicability of computer-driven image analysis for characterizing neuropathological features, as well as its potential to supplement or even replace manually derived ratings commonly performed in medical settings. In this work, we quantitatively measured amyloid beta (Aβ) plaque in various brain regions from 34 patients using a robust digital quantification algorithm. We next verified these digitally derived measures to the manually derived pathology ratings using correlation and ordinal logistic regression methods, while also investigating the association with other AD-related neuropathology scoring schema commonly used at autopsy, such as Braak and CERAD. In addition to successfully verifying our digital measurements of Aβ plaques with respective categorical measurements, we found significant correlations with most AD-related scoring schemas. Our results demonstrate the potential for digital analysis to be adapted to more complex staining procedures commonly used in neuropathological diagnosis. As the efficiency of scanning and digital analysis of histology images increases, we believe that the basis of our semi-automatic approach may better standardize quantification of neuropathological changes and AD diagnosis, ultimately leading to a more comprehensive understanding of neurological disorders and more efficient patient care.
by
Rivka R Colen;
Mark Vangel;
Jixin Wang;
David Andrew Gutman;
Scott N Hwang;
Max Wintermark;
Rajan Jain;
Manal Jilwan-Nicolas;
James Y Chen;
Prashant Raghavan;
Chad A Holder;
Daniel Rubin;
Eric Huang;
Justin Kirby;
John Freymann;
Carl C Jaffe;
Adam Flanders;
Pascal O Zinn
Background
Invasion of tumor cells into adjacent brain parenchyma is a major cause of treatment failure in glioblastoma. Furthermore, invasive tumors are shown to have a different genomic composition and metabolic abnormalities that allow for a more aggressive GBM phenotype and resistance to therapy. We thus seek to identify those genomic abnormalities associated with a highly aggressive and invasive GBM imaging-phenotype.
Methods
We retrospectively identified 104 treatment-naïve glioblastoma patients from The Cancer Genome Atlas (TCGA) whom had gene expression profiles and corresponding MR imaging available in The Cancer Imaging Archive (TCIA). The standardized VASARI feature-set criteria were used for the qualitative visual assessments of invasion. Patients were assigned to classes based on the presence (Class A) or absence (Class B) of statistically significant invasion parameters to create an invasive imaging signature; imaging genomic analysis was subsequently performed using GenePattern Comparative Marker Selection module (Broad Institute).
Results
Our results show that patients with a combination of deep white matter tracts and ependymal invasion (Class A) on imaging had a significant decrease in overall survival as compared to patients with absence of such invasive imaging features (Class B) (8.7 versus 18.6 months, p < 0.001). Mitochondrial dysfunction was the top canonical pathway associated with Class A gene expression signature. The MYC oncogene was predicted to be the top activation regulator in Class A.
Conclusion
We demonstrate that MRI biomarker signatures can identify distinct GBM phenotypes associated with highly significant survival differences and specific molecular pathways. This study identifies mitochondrial dysfunction as the top canonical pathway in a very aggressive GBM phenotype. Thus, imaging-genomic analyses may prove invaluable in detecting novel targetable genomic pathways.
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Hugo J.W.L. Aerts;
Emmanuel Rios Velazquez;
Raphael Meier;
William D. Dunn Jr;
Brian Alexander;
Roland Wiest;
Stefan Bauer;
David Gutman;
Mauricio Reyes
Reproducible definition and quantification of imaging biomarkers is essential. We evaluated a fully automatic MR-based segmentation method by comparing it to manually defined sub-volumes by experienced radiologists in the TCGA-GBM dataset, in terms of sub-volume prognosis and association with VASARI features. MRI sets of 109 GBM patients were downloaded from the Cancer Imaging archive. GBM sub-compartments were defined manually and automatically using the Brain Tumor Image Analysis (BraTumIA). Spearman's correlation was used to evaluate the agreement with VASARI features. Prognostic significance was assessed using the C-index. Auto-segmented sub-volumes showed moderate to high agreement with manually delineated volumes (range (r): 0.4 - 0.86). Also, the auto and manual volumes showed similar correlation with VASARI features (auto r=0.35, 0.43 and 0.36; manual r=0.17, 0.67, 0.41, for contrast-enhancing, necrosis and edema, respectively). The auto-segmented contrast-enhancing volume and post-contrast abnormal volume showed the highest AUC (0.66, CI: 0.55-0.77 and 0.65, CI: 0.54-0.76), comparable to manually defined volumes (0.64, CI: 0.53-0.75 and 0.63, CI: 0.52-0.74, respectively). BraTumIA and manual tumor sub-compartments showed comparable performance in terms of prognosis and correlation with VASARI features. This method can enable more reproducible definition and quantification of imaging based biomarkers and has potential in high-throughput medical imaging research.
Glioblastoma (GBM) contains diverse microenvironments with uneven distributions of oncogenic alterations and signaling networks. The diffusely infiltrative properties of GBM result in residual tumor at neurosurgical resection margins, representing the source of relapse in nearly all cases and suggesting that therapeutic efforts should be focused there. To identify signaling networks and potential druggable targets across tumor microenvironments (TMEs), we utilized 5-ALA fluorescence-guided neurosurgical resection and sampling, followed by proteomic analysis of specific TMEs. Reverse phase protein array (RPPA) was performed on 205 proteins isolated from the tumor margin, tumor bulk, and perinecrotic regions of 13 previously untreated, clinically-annotated and genetically-defined high grade gliomas. Differential protein and pathway signatures were established and then validated using western blotting, immunohistochemistry, and comparable TCGA RPPA datasets. We identified 37 proteins differentially expressed across high-grade glioma TMEs. We demonstrate that tumor margins were characterized by pro-survival and anti-apoptotic proteins, whereas perinecrotic regions were enriched for pro-coagulant and DNA damage response proteins. In both our patient cohort and TCGA cases, the data suggest that TMEs possess distinct protein expression profiles that are biologically and therapeutically relevant.
Recent progress in diffusion MRI and tractography algorithms as well as the launch of the Human Connectome Project (HCP)1 have provided brain research with an abundance of structural connectivity data. In this work, we describe and evaluate a method that can infer the structural brain network that interconnects a given set of Regions of Interest (ROIs) from probabilistic tractography data. The proposed method, referred to as Minimum Asymmetry Network Inference Algorithm (MANIA), does not determine the connectivity between two ROIs based on an arbitrary connectivity threshold. Instead, we exploit a basic limitation of the tractography process: the observed streamlines from a source to a target do not provide any information about the polarity of the underlying white matter, and so if there are some fibers connecting two voxels (or two ROIs) X and Y, tractography should be able in principle to follow this connection in both directions, from X to Y and from Y to X. We leverage this limitation to formulate the network inference process as an optimization problem that minimizes the (appropriately normalized) asymmetry of the observed network. We evaluate the proposed method using both the FiberCup dataset and based on a noise model that randomly corrupts the observed connectivity of synthetic networks. As a case-study, we apply MANIA on diffusion MRI data from 28 healthy subjects to infer the structural network between 18 corticolimbic ROIs that are associated with various neuropsychiatric conditions including depression, anxiety and addiction.
Whole-slide imaging of histologic sections captures tissue microenvironments and cytologic details in expansive high-resolution images. These images can be mined to extract quantitative features that describe tissues, yielding measurements for hundreds of millions of histologic objects. A central challenge in utilizing this data is enabling investigators to train and evaluate classification rules for identifying objects related to processes like angiogenesis or immune response. In this paper we describe HistomicsML, an interactive machine-learning system for digital pathology imaging datasets. This framework uses active learning to direct user feedback, making classifier training efficient and scalable in datasets containing 10 8 + histologic objects. We demonstrate how this system can be used to phenotype microvascular structures in gliomas to predict survival, and to explore the molecular pathways associated with these phenotypes. Our approach enables researchers to unlock phenotypic information from digital pathology datasets to investigate prognostic image biomarkers and genotype-phenotype associations.
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Roeland Verhaak;
Gary Whitman;
Rivka Colen;
Ian Foster;
Robert Gatenby;
Mary Ellen Giger;
Robert Gillies;
David Andrew Gutman;
Matthew Heller;
Rajan Jain;
Anant Madabhushi;
Subha Madhavan;
Sandy Napel;
Arvind Rao;
Joel Saltz;
James Tatum;
Roeland Verhaak;
Gary Whitman
The National Cancer Institute (NCI) Cancer Imaging Program organized two related workshops on June 26-27, 2013, entitled "Correlating Imaging Phenotypes with Genomics Signatures Research" and "Scalable Computational Resources as Required for Imaging-Genomics Decision Support Systems." The first workshop focused on clinical and scientific requirements, exploring our knowledge of phenotypic characteristics of cancer biological properties to determine whether the field is sufficiently advanced to correlate with imaging phenotypes that underpin genomics and clinical outcomes, and exploring new scientific methods to extract phenotypic features from medical images and relate them to genomics analyses. The second workshop focused on computational methods that explore informatics and computational requirements to extract phenotypic features from medical images and relate them to genomics analyses and improve the accessibility and speed of dissemination of existing NIH resources. These workshops linked clinical and scientific requirements of currently known phenotypic and genotypic cancer biology characteristics with imaging phenotypes that underpin genomics and clinical outcomes. The group generated a set of recommendations to NCI leadership and the research community that encourage and support development of the emerging radiogenomics research field to address short-and longer-term goals in cancer research.
The G4C2 hexanucleotide repeat expansion mutation in the C9orf72 gene is the most common genetic cause underlying both amyotrophic lateral sclerosis and frontotemporal dementia. Pathologically, these two neurodegenerative disorders are linked by the common presence of abnormal phosphorylated TDP-43 neuronal cytoplasmic inclusions. We compared the number and size of phosphorylated TDP-43 inclusions and their morphology in hippocampi from patients dying with sporadic versus C9orf72-related amyotrophic lateral sclerosis with pathologically defined frontotemporal lobar degeneration with phosphorylated TDP-43 inclusions, the pathological substrate of clinical frontotemporal dementia in patients with amyotrophic lateral sclerosis. In sporadic cases, there were numerous consolidated phosphorylated TDP-43 inclusions that were variable in size, whereas inclusions in C9orf72 amyotrophic lateral sclerosis/frontotemporal lobar degeneration were quantitatively smaller than those in sporadic cases. Also, C9orf72 amyotrophic lateral sclerosis/frontotemporal lobar degeneration homogenized brain contained soluble cytoplasmic TDP-43 that was largely absent in sporadic cases. To better understand these pathological differences, we modelled TDP-43 inclusion formation in fibroblasts derived from sporadic or C9orf72-related amyotrophic lateral sclerosis/frontotemporal dementia patients. We found that both sporadic and C9orf72 amyotrophic lateral sclerosis/frontotemporal dementia patient fibroblasts showed impairment in TDP-43 degradation by the proteasome, which may explain increased TDP-43 protein levels found in both sporadic and C9orf72 amyotrophic lateral sclerosis/frontotemporal lobar degeneration frontal cortex and hippocampus. Fibroblasts derived from sporadic patients, but not C9orf72 patients, demonstrated the ability to sequester cytoplasmic TDP-43 into aggresomes via microtubule-dependent mechanisms. TDP-43 aggresomes in vitro and TDP-43 neuronal inclusions in vivo were both tightly localized with autophagy markers and, therefore, were likely to function similarly as sites for autophagic degradation. The inability for C9orf72 fibroblasts to form TDP-43 aggresomes, together with the observations that TDP-43 protein was soluble in the cytoplasm and formed smaller inclusions in the C9orf72 brain compared with sporadic disease, suggests a loss of protein quality control response to sequester and degrade TDP-43 in C9orf72-related diseases.