Publications by authors named "Lee A D Cooper"

46 Publications

Explainable nucleus classification using Decision Tree Approximation of Learned Embeddings.

Bioinformatics 2021 Sep 29. Epub 2021 Sep 29.

Department of Pathology, Northwestern University, Chicago, IL, USA.

Motivation: Nucleus detection, segmentation, and classification is fundamental to high-resolution mapping of the tumor microenvironment using whole-slide histopathology images. The growing interest in leveraging the power of deep learning to achieve state-of-the-art performance often comes at the cost of explainability, yet there is general consensus that explainability is critical for trustworthiness and widespread clinical adoption. Unfortunately, current explainability paradigms that rely on pixel saliency heatmaps or superpixel importance scores are not well-suited for nucleus classification. Techniques like Grad-CAM or LIME provide explanations that are indirect, qualitative, and/or non-intuitive to pathologists.

Results: In this paper, we present techniques to enable scalable nuclear detection, segmentation and explainable classification. First, we show how modifications to the widely used Mask R-CNN architecture, including decoupling the detection and classification tasks, improves accuracy and enables learning from hybrid annotation datasets like NuCLS, which contain mixtures of bounding boxes and segmentation boundaries. Second, we introduce an explainability method called Decision Tree Approximation of Learned Embeddings (DTALE), which provides explanations for classification model behavior globally, as well as for individual nuclear predictions. DTALE explanations are simple, quantitative, and can flexibly use any measurable morphological features that make sense to practicing pathologists, without sacrificing model accuracy. Together, these techniques present a step towards realizing the promise of computational pathology in computer-aided diagnosis and discovery of morphologic biomarkers.

Availability: Relevant code can be found at github.com/CancerDataScience/NuCLS.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btab670DOI Listing
September 2021

Learning from crowds in digital pathology using scalable variational Gaussian processes.

Sci Rep 2021 06 2;11(1):11612. Epub 2021 Jun 2.

Department of Electrical and Computer Engineering at Nothwestern University, Evanston, IL, 60208, USA.

The volume of labeled data is often the primary determinant of success in developing machine learning algorithms. This has increased interest in methods for leveraging crowds to scale data labeling efforts, and methods to learn from noisy crowd-sourced labels. The need to scale labeling is acute but particularly challenging in medical applications like pathology, due to the expertise required to generate quality labels and the limited availability of qualified experts. In this paper we investigate the application of Scalable Variational Gaussian Processes for Crowdsourcing (SVGPCR) in digital pathology. We compare SVGPCR with other crowdsourcing methods using a large multi-rater dataset where pathologists, pathology residents, and medical students annotated tissue regions breast cancer. Our study shows that SVGPCR is competitive with equivalent methods trained using gold-standard pathologist generated labels, and that SVGPCR meets or exceeds the performance of other crowdsourcing methods based on deep learning. We also show how SVGPCR can effectively learn the class-conditional reliabilities of individual annotators and demonstrate that Gaussian-process classifiers have comparable performance to similar deep learning methods. These results suggest that SVGPCR can meaningfully engage non-experts in pathology labeling tasks, and that the class-conditional reliabilities estimated by SVGPCR may assist in matching annotators to tasks where they perform well.
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http://dx.doi.org/10.1038/s41598-021-90821-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8172863PMC
June 2021

GestAltNet: aggregation and attention to improve deep learning of gestational age from placental whole-slide images.

Lab Invest 2021 07 5;101(7):942-951. Epub 2021 Mar 5.

Department of Pathology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.

The placenta is the first organ to form and performs the functions of the lung, gut, kidney, and endocrine systems. Abnormalities in the placenta cause or reflect most abnormalities in gestation and can have life-long consequences for the mother and infant. Placental villi undergo a complex but reproducible sequence of maturation across the third-trimester. Abnormalities of villous maturation are a feature of gestational diabetes and preeclampsia, among others, but there is significant interobserver variability in their diagnosis. Machine learning has emerged as a powerful tool for research in pathology. To capture the volume of data and manage heterogeneity within the placenta, we developed GestaltNet, which emulates human attention to high-yield areas and aggregation across regions. We used this network to estimate the gestational age (GA) of scanned placental slides and compared it to a baseline model lacking the attention and aggregation functions. In the test set, GestaltNet showed a higher r (0.9444 vs. 0.9220) than the baseline model. The mean absolute error (MAE) between the estimated and actual GA was also better in the GestaltNet (1.0847 weeks vs. 1.4505 weeks). On whole-slide images, we found the attention sub-network discriminates areas of terminal villi from other placental structures. Using this behavior, we estimated GA for 36 whole slides not previously seen by the model. In this task, similar to that faced by human pathologists, the model showed an r of 0.8859 with an MAE of 1.3671 weeks. We show that villous maturation is machine-recognizable. Machine-estimated GA could be useful when GA is unknown or to study abnormalities of villous maturation, including those in gestational diabetes or preeclampsia. GestaltNet points toward a future of genuinely whole-slide digital pathology by incorporating human-like behaviors of attention and aggregation.
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http://dx.doi.org/10.1038/s41374-021-00579-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7933605PMC
July 2021

Interactive Classification of Whole-Slide Imaging Data for Cancer Researchers.

Cancer Res 2021 02 21;81(4):1171-1177. Epub 2020 Dec 21.

Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, Illinois.

Whole-slide histology images contain information that is valuable for clinical and basic science investigations of cancer but extracting quantitative measurements from these images is challenging for researchers who are not image analysis specialists. In this article, we describe HistomicsML2, a software tool for learn-by-example training of machine learning classifiers for histologic patterns in whole-slide images. This tool improves training efficiency and classifier performance by guiding users to the most informative training examples for labeling and can be used to develop classifiers for prospective application or as a rapid annotation tool that is adaptable to different cancer types. HistomicsML2 runs as a containerized server application that provides web-based user interfaces for classifier training, validation, exporting inference results, and collaborative review, and that can be deployed on GPU servers or cloud platforms. We demonstrate the utility of this tool by using it to classify tumor-infiltrating lymphocytes in breast carcinoma and cutaneous melanoma. SIGNIFICANCE: An interactive machine learning tool for analyzing digital pathology images enables cancer researchers to apply this tool to measure histologic patterns for clinical and basic science studies.
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http://dx.doi.org/10.1158/0008-5472.CAN-20-0668DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8026494PMC
February 2021

Artificial intelligence and algorithmic computational pathology: an introduction with renal allograft examples.

Histopathology 2021 May 8;78(6):791-804. Epub 2021 Mar 8.

Department of Surgery, Emory University, Atlanta, GA, USA.

Whole slide imaging, which is an important technique in the field of digital pathology, has recently been the subject of increased interest and avenues for utilisation, and with more widespread whole slide image (WSI) utilisation, there will also be increased interest in and implementation of image analysis (IA) techniques. IA includes artificial intelligence (AI) and targeted or hypothesis-driven algorithms. In the overall pathology field, the number of citations related to these topics has increased in recent years. Renal pathology is one anatomical pathology subspecialty that has utilised WSIs and IA algorithms; it can be argued that renal transplant pathology could be particularly suited for whole slide imaging and IA, as renal transplant pathology is frequently classified by use of the semiquantitative Banff classification of renal allograft pathology. Hypothesis-driven/targeted algorithms have been used in the past for the assessment of a variety of features in the kidney (e.g. interstitial fibrosis, tubular atrophy, inflammation); in recent years, the amount of research has particularly increased in the area of AI/machine learning for the identification of glomeruli, for histological segmentation, and for other applications. Deep learning is the form of machine learning that is most often used for such AI approaches to the 'big data' of pathology WSIs, and deep learning methods such as artificial neural networks (ANNs)/convolutional neural networks (CNNs) are utilised. Unsupervised and supervised AI algorithms can be employed to accomplish image or semantic classification. In this review, AI and other IA algorithms applied to WSIs are discussed, and examples from renal pathology are covered, with an emphasis on renal transplant pathology.
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http://dx.doi.org/10.1111/his.14304DOI Listing
May 2021

High expression of MKK3 is associated with worse clinical outcomes in African American breast cancer patients.

J Transl Med 2020 09 1;18(1):334. Epub 2020 Sep 1.

Department of Pharmacology and Chemical Biology, Emory University School of Medicine, Emory University, 1510 Clifton Road, Atlanta, GA, 30322, USA.

Background: African American women experience a twofold higher incidence of triple-negative breast cancer (TNBC) and are 40% more likely to die from breast cancer than women of other ethnicities. However, the molecular bases for the survival disparity in breast cancer remain unclear, and no race-specific therapeutic targets have been proposed. To address this knowledge gap, we performed a systematic analysis of the relationship between gene mRNA expression and clinical outcomes determined for The Cancer Genome Atlas (TCGA) breast cancer patient cohort.

Methods: The systematic differential analysis of mRNA expression integrated with the analysis of clinical outcomes was performed for 1055 samples from the breast invasive carcinoma TCGA PanCancer cohorts. A deep learning fully-convolutional model was used to determine the association between gene expression and tumor features based on breast cancer patient histopathological images.

Results: We found that more than 30% of all protein-coding genes are differentially expressed in White and African American breast cancer patients. We have determined a set of 32 genes whose overexpression in African American patients strongly correlates with decreased survival of African American but not White breast cancer patients. Among those genes, the overexpression of mitogen-activated protein kinase kinase 3 (MKK3) has one of the most dramatic and race-specific negative impacts on the survival of African American patients, specifically with triple-negative breast cancer. We found that MKK3 can promote the TNBC tumorigenesis in African American patients in part by activating of the epithelial-to-mesenchymal transition induced by master regulator MYC.

Conclusions: The poor clinical outcomes in African American women with breast cancer can be associated with the abnormal elevation of individual gene expression. Such genes, including those identified and prioritized in this study, could represent new targets for therapeutic intervention. A strong correlation between MKK3 overexpression, activation of its binding partner and major oncogene MYC, and worsened clinical outcomes suggests the MKK3-MYC protein-protein interaction as a new promising target to reduce racial disparity in breast cancer survival.
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http://dx.doi.org/10.1186/s12967-020-02502-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7465409PMC
September 2020

Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group.

NPJ Breast Cancer 2020 12;6:16. Epub 2020 May 12.

30Nuffield Department of Population Health, University of Oxford, Oxford, UK.

Assessment of tumor-infiltrating lymphocytes (TILs) is increasingly recognized as an integral part of the prognostic workflow in triple-negative (TNBC) and HER2-positive breast cancer, as well as many other solid tumors. This recognition has come about thanks to standardized visual reporting guidelines, which helped to reduce inter-reader variability. Now, there are ripe opportunities to employ computational methods that extract spatio-morphologic predictive features, enabling computer-aided diagnostics. We detail the benefits of computational TILs assessment, the readiness of TILs scoring for computational assessment, and outline considerations for overcoming key barriers to clinical translation in this arena. Specifically, we discuss: 1. ensuring computational workflows closely capture visual guidelines and standards; 2. challenges and thoughts standards for assessment of algorithms including training, preanalytical, analytical, and clinical validation; 3. perspectives on how to realize the potential of machine learning models and to overcome the perceptual and practical limits of visual scoring.
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http://dx.doi.org/10.1038/s41523-020-0154-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7217824PMC
May 2020

Machine-based detection and classification for bone marrow aspirate differential counts: initial development focusing on nonneoplastic cells.

Lab Invest 2020 01 30;100(1):98-109. Epub 2019 Sep 30.

Department of Pathology and Laboratory Medicine, Emory University, Atlanta, GA, USA.

Bone marrow aspirate (BMA) differential cell counts (DCCs) are critical for the classification of hematologic disorders. While manual counts are considered the gold standard, they are labor intensive, time consuming, and subject to bias. A reliable automated counter has yet to be developed, largely due to the inherent complexity of bone marrow specimens. Digital pathology imaging coupled with machine learning algorithms represents a highly promising emerging technology for this purpose. Yet, training datasets for BMA cellular constituents, critical for building and validating machine learning algorithms, are lacking. Herein, we report our experience creating and employing such datasets to develop a machine learning algorithm to detect and classify BMA cells. Utilizing a web-based system that we developed for annotating and managing digital pathology images, over 10,000 cells from scanned whole slide images of BMA smears were manually annotated, including all classes that comprise the standard clinical DCC. We implemented a two-stage, detection and classification approach that allows design flexibility and improved classification accuracy. In a sixfold cross-validation, our algorithms achieved high overall accuracy in detection (0.959 ± 0.008 precision-recall AUC) and classification (0.982 ± 0.03 ROC AUC) using nonneoplastic samples. Testing on a small set of acute myeloid leukemia and multiple myeloma samples demonstrated similar detection and classification performance. In summary, our algorithms showed promising early results and represent an important initial step in the effort to devise a reliable, objective method to automate DCCs. With further development to include formal clinical validation, such a system has the potential to assist in disease diagnosis and prognosis, and significantly impact clinical practice.
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http://dx.doi.org/10.1038/s41374-019-0325-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6920560PMC
January 2020

Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization.

NPJ Digit Med 2019 4;2:21. Epub 2019 Apr 4.

1Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine of Cornell University, New York, NY USA.

Visual morphology assessment is routinely used for evaluating of embryo quality and selecting human blastocysts for transfer after in vitro fertilization (IVF). However, the assessment produces different results between embryologists and as a result, the success rate of IVF remains low. To overcome uncertainties in embryo quality, multiple embryos are often implanted resulting in undesired multiple pregnancies and complications. Unlike in other imaging fields, human embryology and IVF have not yet leveraged artificial intelligence (AI) for unbiased, automated embryo assessment. We postulated that an AI approach trained on thousands of embryos can reliably predict embryo quality without human intervention. We implemented an AI approach based on deep neural networks (DNNs) to select highest quality embryos using a large collection of human embryo time-lapse images (about 50,000 images) from a high-volume fertility center in the United States. We developed a framework (STORK) based on Google's Inception model. STORK predicts blastocyst quality with an AUC of >0.98 and generalizes well to images from other clinics outside the US and outperforms individual embryologists. Using clinical data for 2182 embryos, we created a decision tree to integrate embryo quality and patient age to identify scenarios associated with pregnancy likelihood. Our analysis shows that the chance of pregnancy based on individual embryos varies from 13.8% (age ≥41 and poor-quality) to 66.3% (age <37 and good-quality) depending on automated blastocyst quality assessment and patient age. In conclusion, our AI-driven approach provides a reproducible way to assess embryo quality and uncovers new, potentially personalized strategies to select embryos.
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http://dx.doi.org/10.1038/s41746-019-0096-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6550169PMC
April 2019

Structured crowdsourcing enables convolutional segmentation of histology images.

Bioinformatics 2019 09;35(18):3461-3467

Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA.

Motivation: While deep-learning algorithms have demonstrated outstanding performance in semantic image segmentation tasks, large annotation datasets are needed to create accurate models. Annotation of histology images is challenging due to the effort and experience required to carefully delineate tissue structures, and difficulties related to sharing and markup of whole-slide images.

Results: We recruited 25 participants, ranging in experience from senior pathologists to medical students, to delineate tissue regions in 151 breast cancer slides using the Digital Slide Archive. Inter-participant discordance was systematically evaluated, revealing low discordance for tumor and stroma, and higher discordance for more subjectively defined or rare tissue classes. Feedback provided by senior participants enabled the generation and curation of 20 000+ annotated tissue regions. Fully convolutional networks trained using these annotations were highly accurate (mean AUC=0.945), and the scale of annotation data provided notable improvements in image classification accuracy.

Availability And Implementation: Dataset is freely available at: https://goo.gl/cNM4EL.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btz083DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6748796PMC
September 2019

Incorporation of a spectral model in a convolutional neural network for accelerated spectral fitting.

Magn Reson Med 2019 05 21;81(5):3346-3357. Epub 2019 Jan 21.

Wallace H. Coulter Department of Biomedical Engineering, Emory University School of Medicine and Georgia Institute of Technology, Atlanta, Georgia.

Purpose: MRSI has shown great promise in the detection and monitoring of neurologic pathologies such as tumor. A necessary component of data processing includes the quantitation of each metabolite, typically done through fitting a model of the spectrum to the data. For high-resolution volumetric MRSI of the brain, which may have ~10,000 spectra, significant processing time is required for spectral analysis and generation of metabolite maps.

Methods: A novel unsupervised deep learning architecture that combines a convolutional neural network with a priori models of the spectrum is presented. This architecture, a convolutional encoder-model decoder (CEMD), combines the strengths of adaptive and unbiased convolutional networks with models of magnetic resonance and is readily interpretable.

Results: The CEMD architecture performs accurate spectral fitting for volumetric MRSI in patients with glioblastoma, provides whole-brain fitting in 1 min on a standard computer, and handles a variety of spectral artifacts.

Conclusion: A new architecture combining physics domain knowledge with convolutional neural networks has been developed and is able to perform rapid spectral fitting of whole-brain data. Rapid processing is a critical step toward routine clinical practice.
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http://dx.doi.org/10.1002/mrm.27641DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6414236PMC
May 2019

Informatics Approaches to Address New Challenges in the Classification of Lymphoid Malignancies.

JCO Clin Cancer Inform 2018 9;2. Epub 2018 Feb 9.

Emory University School of Medicine, Atlanta, GA; Metin Gurcan, Ohio State University, Columbus, OH.

Purpose: Lymphoid malignancies are remarkably heterogeneous, with variations in outcomes and clinical, biologic, and histologic presentation complicating classification according to the World Health Organization guidelines. Incorrect classification of lymphoid neoplasms can result in suboptimal therapeutic strategies for individual patients and confound the interpretation of clinical trials involving personalized, class-based treatments. This review discusses the potential role of pathology informatics in improving the classification accuracy and objectivity for lymphoid malignancies.

Design: We identified peer-reviewed publications examining pathology informatics approaches for the classification of lymphoid malignancies, reviewed developments in the lymphoma classification systems, and summarized computational methods for pathologic assessment that can impact practice.

Results: Computer-assisted pathology image analysis algorithms in lymphoma most commonly have been applied to follicular lymphoma to address biologic heterogeneity and subjectivity in the process of classification.

Conclusion: Objective methods are available to assist pathologists in lymphoma classification and grading, and have been demonstrated to provide measurable benefits in specific contexts. Future validation and extension of these approaches will require datasets that link high resolution pathology images available for image analysis algorithms with clinical variables and follow up outcomes.
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http://dx.doi.org/10.1200/CCI.17.00039DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6324571PMC
September 2019

Multi-faceted computational assessment of risk and progression in oligodendroglioma implicates NOTCH and PI3K pathways.

NPJ Precis Oncol 2018 6;2:24. Epub 2018 Nov 6.

14Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL USA.

Oligodendrogliomas are diffusely infiltrative gliomas defined by -mutation and co-deletion of 1p/19q. They have highly variable clinical courses, with survivals ranging from 6 months to over 20 years, but little is known regarding the pathways involved with their progression or optimal markers for stratifying risk. We utilized machine-learning approaches with genomic data from The Cancer Genome Atlas to objectively identify molecular factors associated with clinical outcomes of oligodendroglioma and extended these findings to study signaling pathways implicated in oncogenesis and clinical endpoints associated with glioma progression. Our multi-faceted computational approach uncovered key genetic alterations associated with disease progression and shorter survival in oligodendroglioma and specifically identified Notch pathway inactivation and PI3K pathway activation as the most strongly associated with MRI and pathology findings of advanced disease and poor clinical outcome. Our findings that Notch pathway inactivation and PI3K pathway activation are associated with advanced disease and survival risk will pave the way for clinically relevant markers of disease progression and therapeutic targets to improve clinical outcomes. Furthermore, our approach demonstrates the strength of machine learning and computational methods for identifying genetic events critical to disease progression in the era of big data and precision medicine.
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http://dx.doi.org/10.1038/s41698-018-0067-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6219505PMC
November 2018

TASI: A software tool for spatial-temporal quantification of tumor spheroid dynamics.

Sci Rep 2018 05 8;8(1):7248. Epub 2018 May 8.

Department of Biomedical Informatics, Emory University, Atlanta, GA, USA.

Spheroid cultures derived from explanted cancer specimens are an increasingly utilized resource for studying complex biological processes like tumor cell invasion and metastasis, representing an important bridge between the simplicity and practicality of 2-dimensional monolayer cultures and the complexity and realism of in vivo animal models. Temporal imaging of spheroids can capture the dynamics of cell behaviors and microenvironments, and when combined with quantitative image analysis methods, enables deep interrogation of biological mechanisms. This paper presents a comprehensive open-source software framework for Temporal Analysis of Spheroid Imaging (TASI) that allows investigators to objectively characterize spheroid growth and invasion dynamics. TASI performs spatiotemporal segmentation of spheroid cultures, extraction of features describing spheroid morpho-phenotypes, mathematical modeling of spheroid dynamics, and statistical comparisons of experimental conditions. We demonstrate the utility of this tool in an analysis of non-small cell lung cancer spheroids that exhibit variability in metastatic and proliferative behaviors.
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http://dx.doi.org/10.1038/s41598-018-25337-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5940855PMC
May 2018

Predicting cancer outcomes from histology and genomics using convolutional networks.

Proc Natl Acad Sci U S A 2018 03 12;115(13):E2970-E2979. Epub 2018 Mar 12.

Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA 30322;

Cancer histology reflects underlying molecular processes and disease progression and contains rich phenotypic information that is predictive of patient outcomes. In this study, we show a computational approach for learning patient outcomes from digital pathology images using deep learning to combine the power of adaptive machine learning algorithms with traditional survival models. We illustrate how these survival convolutional neural networks (SCNNs) can integrate information from both histology images and genomic biomarkers into a single unified framework to predict time-to-event outcomes and show prediction accuracy that surpasses the current clinical paradigm for predicting the overall survival of patients diagnosed with glioma. We use statistical sampling techniques to address challenges in learning survival from histology images, including tumor heterogeneity and the need for large training cohorts. We also provide insights into the prediction mechanisms of SCNNs, using heat map visualization to show that SCNNs recognize important structures, like microvascular proliferation, that are related to prognosis and that are used by pathologists in grading. These results highlight the emerging role of deep learning in precision medicine and suggest an expanding utility for computational analysis of histology in the future practice of pathology.
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http://dx.doi.org/10.1073/pnas.1717139115DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5879673PMC
March 2018

A convolutional neural network to filter artifacts in spectroscopic MRI.

Magn Reson Med 2018 11 9;80(5):1765-1775. Epub 2018 Mar 9.

Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia.

Purpose: Proton MRSI is a noninvasive modality capable of generating volumetric maps of in vivo tissue metabolism without the need for ionizing radiation or injected contrast agent. Magnetic resonance spectroscopic imaging has been shown to be a viable imaging modality for studying several neuropathologies. However, a key hurdle in the routine clinical adoption of MRSI is the presence of spectral artifacts that can arise from a number of sources, possibly leading to false information.

Methods: A deep learning model was developed that was capable of identifying and filtering out poor quality spectra. The core of the model used a tiled convolutional neural network that analyzed frequency-domain spectra to detect artifacts.

Results: When compared with a panel of MRS experts, our convolutional neural network achieved high sensitivity and specificity with an area under the curve of 0.95. A visualization scheme was implemented to better understand how the convolutional neural network made its judgement on single-voxel or multivoxel MRSI, and the convolutional neural network was embedded into a pipeline capable of producing whole-brain spectroscopic MRI volumes in real time.

Conclusion: The fully automated method for assessment of spectral quality provides a valuable tool to support clinical MRSI or spectroscopic MRI studies for use in fields such as adaptive radiation therapy planning.
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http://dx.doi.org/10.1002/mrm.27166DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6107370PMC
November 2018

The OncoPPi Portal: an integrative resource to explore and prioritize protein-protein interactions for cancer target discovery.

Bioinformatics 2018 04;34(7):1183-1191

Department of Pharmacology and Emory Chemical Biology Discovery Center, Emory University School of Medicine.

Motivation: As cancer genomics initiatives move toward comprehensive identification of genetic alterations in cancer, attention is now turning to understanding how interactions among these genes lead to the acquisition of tumor hallmarks. Emerging pharmacological and clinical data suggest a highly promising role of cancer-specific protein-protein interactions (PPIs) as druggable cancer targets. However, large-scale experimental identification of cancer-related PPIs remains challenging, and currently available resources to explore oncogenic PPI networks are limited.

Results: Recently, we have developed a PPI high-throughput screening platform to detect PPIs between cancer-associated proteins in the context of cancer cells. Here, we present the OncoPPi Portal, an interactive web resource that allows investigators to access, manipulate and interpret a high-quality cancer-focused network of PPIs experimentally detected in cancer cell lines. To facilitate prioritization of PPIs for further biological studies, this resource combines network connectivity analysis, mutual exclusivity analysis of genomic alterations, cellular co-localization of interacting proteins and domain-domain interactions. Estimates of PPI essentiality allow users to evaluate the functional impact of PPI disruption on cancer cell proliferation. Furthermore, connecting the OncoPPi network with the approved drugs and compounds in clinical trials enables discovery of new tumor dependencies to inform strategies to interrogate undruggable targets like tumor suppressors. The OncoPPi Portal serves as a resource for the cancer research community to facilitate discovery of cancer targets and therapeutic development.

Availability And Implementation: The OncoPPi Portal is available at http://oncoppi.emory.edu.

Contact: [email protected] or [email protected]
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http://dx.doi.org/10.1093/bioinformatics/btx743DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6030952PMC
April 2018

5-Aminolevulinic Acid Guided Sampling of Glioblastoma Microenvironments Identifies Pro-Survival Signaling at Infiltrative Margins.

Sci Rep 2017 Nov 15;7(1):15593. Epub 2017 Nov 15.

Departments of Pathology and Laboratory Medicine, Emory University, Atlanta, GA 30322, Georgia.

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.
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http://dx.doi.org/10.1038/s41598-017-15849-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5688093PMC
November 2017

Interactive phenotyping of large-scale histology imaging data with HistomicsML.

Sci Rep 2017 11 6;7(1):14588. Epub 2017 Nov 6.

Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, USA.

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+ 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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http://dx.doi.org/10.1038/s41598-017-15092-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5674015PMC
November 2017

The Digital Slide Archive: A Software Platform for Management, Integration, and Analysis of Histology for Cancer Research.

Cancer Res 2017 11;77(21):e75-e78

Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia.

Tissue-based cancer studies can generate large amounts of histology data in the form of glass slides. These slides contain important diagnostic, prognostic, and biological information and can be digitized into expansive and high-resolution whole-slide images using slide-scanning devices. Effectively utilizing digital pathology data in cancer research requires the ability to manage, visualize, share, and perform quantitative analysis on these large amounts of image data, tasks that are often complex and difficult for investigators with the current state of commercial digital pathology software. In this article, we describe the Digital Slide Archive (DSA), an open-source web-based platform for digital pathology. DSA allows investigators to manage large collections of histologic images and integrate them with clinical and genomic metadata. The open-source model enables DSA to be extended to provide additional capabilities. .
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http://dx.doi.org/10.1158/0008-5472.CAN-17-0629DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5898232PMC
November 2017

Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models.

Sci Rep 2017 09 15;7(1):11707. Epub 2017 Sep 15.

Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, 30322, USA.

Translating the vast data generated by genomic platforms into accurate predictions of clinical outcomes is a fundamental challenge in genomic medicine. Many prediction methods face limitations in learning from the high-dimensional profiles generated by these platforms, and rely on experts to hand-select a small number of features for training prediction models. In this paper, we demonstrate how deep learning and Bayesian optimization methods that have been remarkably successful in general high-dimensional prediction tasks can be adapted to the problem of predicting cancer outcomes. We perform an extensive comparison of Bayesian optimized deep survival models and other state of the art machine learning methods for survival analysis, and describe a framework for interpreting deep survival models using a risk backpropagation technique. Finally, we illustrate that deep survival models can successfully transfer information across diseases to improve prognostic accuracy. We provide an open-source software implementation of this framework called SurvivalNet that enables automatic training, evaluation and interpretation of deep survival models.
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http://dx.doi.org/10.1038/s41598-017-11817-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5601479PMC
September 2017

The OncoPPi network of cancer-focused protein-protein interactions to inform biological insights and therapeutic strategies.

Nat Commun 2017 02 16;8:14356. Epub 2017 Feb 16.

Department of Pharmacology and Emory Chemical Biology Discovery Center, Emory University, Atlanta, Georgia 30322, USA.

As genomics advances reveal the cancer gene landscape, a daunting task is to understand how these genes contribute to dysregulated oncogenic pathways. Integration of cancer genes into networks offers opportunities to reveal protein-protein interactions (PPIs) with functional and therapeutic significance. Here, we report the generation of a cancer-focused PPI network, termed OncoPPi, and identification of >260 cancer-associated PPIs not in other large-scale interactomes. PPI hubs reveal new regulatory mechanisms for cancer genes like MYC, STK11, RASSF1 and CDK4. As example, the NSD3 (WHSC1L1)-MYC interaction suggests a new mechanism for NSD3/BRD4 chromatin complex regulation of MYC-driven tumours. Association of undruggable tumour suppressors with drug targets informs therapeutic options. Based on OncoPPi-derived STK11-CDK4 connectivity, we observe enhanced sensitivity of STK11-silenced lung cancer cells to the FDA-approved CDK4 inhibitor palbociclib. OncoPPi is a focused PPI resource that links cancer genes into a signalling network for discovery of PPI targets and network-implicated tumour vulnerabilities for therapeutic interrogation.
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http://dx.doi.org/10.1038/ncomms14356DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5316855PMC
February 2017

MEDICI: Mining Essentiality Data to Identify Critical Interactions for Cancer Drug Target Discovery and Development.

PLoS One 2017 24;12(1):e0170339. Epub 2017 Jan 24.

Department of Biomedical Informatics, Emory University, Atlanta, Georgia, United States of America.

Protein-protein interactions (PPIs) mediate the transmission and regulation of oncogenic signals that are essential to cellular proliferation and survival, and thus represent potential targets for anti-cancer therapeutic discovery. Despite their significance, there is no method to experimentally disrupt and interrogate the essentiality of individual endogenous PPIs. The ability to computationally predict or infer PPI essentiality would help prioritize PPIs for drug discovery and help advance understanding of cancer biology. Here we introduce a computational method (MEDICI) to predict PPI essentiality by combining gene knockdown studies with network models of protein interaction pathways in an analytic framework. Our method uses network topology to model how gene silencing can disrupt PPIs, relating the unknown essentialities of individual PPIs to experimentally observed protein essentialities. This model is then deconvolved to recover the unknown essentialities of individual PPIs. We demonstrate the validity of our approach via prediction of sensitivities to compounds based on PPI essentiality and differences in essentiality based on genetic mutations. We further show that lung cancer patients have improved overall survival when specific PPIs are no longer present, suggesting that these PPIs may be potentially new targets for therapeutic development. Software is freely available at https://github.com/cooperlab/MEDICI. Datasets are available at https://ctd2.nci.nih.gov/dataPortal.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0170339PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5261804PMC
August 2017

AKT1, LKB1, and YAP1 Revealed as MYC Interactors with NanoLuc-Based Protein-Fragment Complementation Assay.

Mol Pharmacol 2017 04 13;91(4):339-347. Epub 2017 Jan 13.

Department of Pharmacology and Emory Chemical Biology Discovery Center (X.M., Q.Q., A.A.I., Q.N., Y.L., J.H., R.G., S.B., M.A.J., Y.D., H.F.) and Department of Biomedical Informatics (L.A.D.C.), Emory University School of Medicine, Atlanta, Georgia; Departments of Hematology and Medical Oncology (F.R.K., H.F.) and Pathology and Laboratory Medicine (C.S.M.) and Winship Cancer Institute, Emory University, Atlanta, Georgia; State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China (Y.L.); and Department of Biomedical Engineering, Emory University School of Medicine/Georgia Institute of Technology, Atlanta, Georgia (L.A.D.C.)

The c-Myc (MYC) transcription factor is a major cancer driver and a well-validated therapeutic target. However, directly targeting MYC has been challenging. Thus, identifying proteins that interact with and regulate MYC may provide alternative strategies to inhibit its oncogenic activity. In this study, we report the development of a NanoLuc-based protein-fragment complementation assay (NanoPCA) and mapping of the MYC protein interaction hub in live mammalian cells. The NanoPCA system was configured to enable detection of protein-protein interactions (PPI) at the endogenous level, as shown with PRAS40 dimerization, and detection of weak interactions, such as PINCH1-NCK2. Importantly, NanoPCA allows the study of PPI dynamics with reversible interactions. To demonstrate its utility for large-scale PPI detection in mammalian intracellular environment, we have used NanoPCA to examine MYC interaction with 83 cancer-associated proteins in live cancer cell lines. Our new MYC PPI data confirmed known MYC-interacting proteins, such as MAX, GSK3A, and SMARCA4, and revealed a panel of novel MYC interaction partners, such as RAC-α serine/threonine-protein kinase (AKT)1, liver kinase B (LKB)1, and Yes-associated protein (YAP)1. The MYC interactions with AKT1, LKB1, and YAP1 were confirmed by coimmunoprecipitation of endogenous proteins. Importantly, AKT1, LKB1, and YAP1 were able to activate MYC in a transcriptional reporter assay. Thus, these vital growth control proteins may represent promising MYC regulators, suggesting new mechanisms that couple energetic and metabolic pathways and developmental signaling to MYC-regulated cellular programs.
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http://dx.doi.org/10.1124/mol.116.107623DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5363710PMC
April 2017

IMAGING GENOMICS.

Pac Symp Biocomput 2017 ;22:51-57

Center for Neuroimaging, Department of Radiology and Imaging Sciences, Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, 355 West 16th Street Suite 4100, Indianapolis, IN 46202, USA,

Imaging genomics is an emerging research field, where integrative analysis of imaging and omics data is performed to provide new insights into the phenotypic characteristics and genetic mechanisms of normal and/or disordered biological structures and functions, and to impact the development of new diagnostic, therapeutic and preventive approaches. The Imaging Genomics Session at PSB 2017 aims to encourage discussion on fundamental concepts, new methods and innovative applications in this young and rapidly evolving field.
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http://dx.doi.org/10.1142/9789813207813_0006DOI Listing
July 2017

A systematic pipeline for the objective comparison of whole-brain spectroscopic MRI with histology in biopsy specimens from grade III glioma.

Tomography 2016 Jun;2(2):106-116

Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA; Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA; Winship Cancer Institute of Emory University.

The diagnosis, prognosis, and management of patients with gliomas are largely dictated by the pathological analysis of tissue biopsied from a selected region within the lesion. However, due to the heterogeneous and infiltrative nature of gliomas, identifying the optimal region for biopsy with conventional magnetic resonance imaging (MRI) can be quite difficult. This is especially true for low grade gliomas, which often are non-enhancing tumors. To improve the management of patients with these tumors, the field of neuro-oncology requires an imaging modality that can specifically identify a tumor's most anaplastic/aggressive region(s) for biopsy targeting. The addition of metabolic mapping using spectroscopic MRI (sMRI) to supplement conventional MRI could improve biopsy targeting and, ultimately, diagnostic accuracy. Here, we describe a pipeline for the integration of state-of-the-art, high-resolution whole-brain 3D sMRI maps into a stereotactic neuronavigation system for guiding biopsies in gliomas with nonenhancing components. We also outline a machine-learning method for automated histology analysis that generates normalized, quantitative metrics describing tumor infiltration in immunohistochemically-stained tissue specimens. As a proof of concept, we describe the combination of these two techniques in a small cohort of grade III glioma patients. In this work, we aim to set forth a systematic pipeline to stimulate histopathology-image validation of advanced MRI techniques, such as sMRI.
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http://dx.doi.org/10.18383/j.tom.2016.00136DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4968944PMC
June 2016

Whole-brain spectroscopic MRI biomarkers identify infiltrating margins in glioblastoma patients.

Neuro Oncol 2016 08 15;18(8):1180-9. Epub 2016 Mar 15.

Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia (J.S.C., Z.L., S.S.G., C.A.H., H.S.); Department of Radiation Oncology, Emory University School of Medicine, Atlanta, Georgia(H.G.S., E.S.); Winship Cancer Institute of Emory University, Atlanta, Georgia(H.G.S., Z.L., J.J.O., C.G.H., H.S.); Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia(S.S.G., L.A.D.C., B.K., H.S.); Department of Biomedical informatics, Emory University School of Medicine, Atlanta, Georgia(L.A.D.C.); Department of Neurosurgery, Emory University School of Medicine, Atlanta, Georgia(J.J.O., C.G.H.); Department of Pathology, Emory University School of Medicine, Atlanta, Georgia(S.G.N.); Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York (C.G.H.).

Background: The standard of care for glioblastoma (GBM) is maximal safe resection followed by radiation therapy with chemotherapy. Currently, contrast-enhanced MRI is used to define primary treatment volumes for surgery and radiation therapy. However, enhancement does not identify the tumor entirely, resulting in limited local control. Proton spectroscopic MRI (sMRI), a method reporting endogenous metabolism, may better define the tumor margin. Here, we develop a whole-brain sMRI pipeline and validate sMRI metrics with quantitative measures of tumor infiltration.

Methods: Whole-brain sMRI metabolite maps were coregistered with surgical planning MRI and imported into a neuronavigation system to guide tissue sampling in GBM patients receiving 5-aminolevulinic acid fluorescence-guided surgery. Samples were collected from regions with metabolic abnormalities in a biopsy-like fashion before bulk resection. Tissue fluorescence was measured ex vivo using a hand-held spectrometer. Tissue samples were immunostained for Sox2 and analyzed to quantify the density of staining cells using a novel digital pathology image analysis tool. Correlations among sMRI markers, Sox2 density, and ex vivo fluorescence were evaluated.

Results: Spectroscopic MRI biomarkers exhibit significant correlations with Sox2-positive cell density and ex vivo fluorescence. The choline to N-acetylaspartate ratio showed significant associations with each quantitative marker (Pearson's ρ = 0.82, P < .001 and ρ = 0.36, P < .0001, respectively). Clinically, sMRI metabolic abnormalities predated contrast enhancement at sites of tumor recurrence and exhibited an inverse relationship with progression-free survival.

Conclusions: As it identifies tumor infiltration and regions at high risk for recurrence, sMRI could complement conventional MRI to improve local control in GBM patients.
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http://dx.doi.org/10.1093/neuonc/now036DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4933486PMC
August 2016

Somatic mutations associated with MRI-derived volumetric features in glioblastoma.

Neuroradiology 2015 Dec 4;57(12):1227-37. Epub 2015 Sep 4.

Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

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.
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http://dx.doi.org/10.1007/s00234-015-1576-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4648958PMC
December 2015

Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas.

N Engl J Med 2015 Jun 10;372(26):2481-98. Epub 2015 Jun 10.

Background: Diffuse low-grade and intermediate-grade gliomas (which together make up the lower-grade gliomas, World Health Organization grades II and III) have highly variable clinical behavior that is not adequately predicted on the basis of histologic class. Some are indolent; others quickly progress to glioblastoma. The uncertainty is compounded by interobserver variability in histologic diagnosis. Mutations in IDH, TP53, and ATRX and codeletion of chromosome arms 1p and 19q (1p/19q codeletion) have been implicated as clinically relevant markers of lower-grade gliomas.

Methods: We performed genomewide analyses of 293 lower-grade gliomas from adults, incorporating exome sequence, DNA copy number, DNA methylation, messenger RNA expression, microRNA expression, and targeted protein expression. These data were integrated and tested for correlation with clinical outcomes.

Results: Unsupervised clustering of mutations and data from RNA, DNA-copy-number, and DNA-methylation platforms uncovered concordant classification of three robust, nonoverlapping, prognostically significant subtypes of lower-grade glioma that were captured more accurately by IDH, 1p/19q, and TP53 status than by histologic class. Patients who had lower-grade gliomas with an IDH mutation and 1p/19q codeletion had the most favorable clinical outcomes. Their gliomas harbored mutations in CIC, FUBP1, NOTCH1, and the TERT promoter. Nearly all lower-grade gliomas with IDH mutations and no 1p/19q codeletion had mutations in TP53 (94%) and ATRX inactivation (86%). The large majority of lower-grade gliomas without an IDH mutation had genomic aberrations and clinical behavior strikingly similar to those found in primary glioblastoma.

Conclusions: The integration of genomewide data from multiple platforms delineated three molecular classes of lower-grade gliomas that were more concordant with IDH, 1p/19q, and TP53 status than with histologic class. Lower-grade gliomas with an IDH mutation either had 1p/19q codeletion or carried a TP53 mutation. Most lower-grade gliomas without an IDH mutation were molecularly and clinically similar to glioblastoma. (Funded by the National Institutes of Health.).
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http://dx.doi.org/10.1056/NEJMoa1402121DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4530011PMC
June 2015
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