SUMMARY Sarcomas are a broad family of mesenchymal malignancies exhibiting remarkable histologic diversity. We describe the multi-platform molecular landscape of 206 adult soft tissue sarcomas representing 6 major types. Along with novel insights into the biology of individual sarcoma types, we report three overarching findings: 1) unlike most epithelial malignancies, these sarcomas (excepting synovial sarcoma) are characterized predominantly by copy number changes, with low mutational loads and only a few genes (TP53, ATRX, RB1) highly recurrently mutated across sarcoma types, 2) within sarcoma types, genomic and regulomic diversity of driver pathways defines molecular subtypes associated with patient outcome, and 3) the immune microenvironment, inferred from DNA methylation and mRNA profiles, associates with outcome and may inform clinical trials of immune checkpoint inhibitors. Overall, this large-scale analysis reveals previously unappreciated sarcoma type-specific changes in copy number, methylation, RNA, and protein, providing insights into refining sarcoma therapy and relationships to other cancer types.

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 (WSI) 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 paper 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.

Technological advances in computing, imaging and genomics have created new opportunities for exploring relationships between histology, molecular events and clinical outcomes using quantitative methods. Slide scanning devices are now capable of rapidly producing massive digital image archives that capture histological details in high-resolution. Commensurate advances in computing and image analysis algorithms enable mining of archives to extract descriptions of histology, ranging from basic human annotations to automatic and precisely quantitative morphometric characterization of hundreds of millions of cells. These imaging capabilities represent a new dimension in tissue-based studies, and when combined with genomic and clinical endpoints, can be used to explore biologic characteristics of the tumor microenvironment and to discover new morphologic biomarkers of genetic alterations and patient outcomes. In this paper we review developments in quantitative imaging technology and illustrate how image features can be integrated with clinical and genomic data to investigate fundamental problems in cancer. Using motivating examples from the study of glioblastomas (GBMs), we demonstrate how public data from The Cancer Genome Atlas (TCGA) can serve as an open platform to conduct in silico tissue based studies that integrate existing data resources. We show how these approaches can be used to explore the relation of the tumor microenvironment to genomic alterations and gene expression patterns and to define nuclear morphometric features that are predictive of genetic alterations and clinical outcomes. Challenges, limitations and emerging opportunities in the area of quantitative imaging and integrative analyses are also discussed.

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 histologic elements, yielding measurements for hundreds of millions of 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. Here we present HistomicsML, an interactive machinelearning framework for large whole-slide imaging data. HistomicsML uses active learning direct user feedback, making classifier training efficient and scalable in datasets containing 10 8 + histologic objects. We demonstrate how HistomicsML 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. INTRODUCTIONSlide scanning microscopes can digitize entire histologic sections at 200X-400X magnification, generating expansive high-resolution images containing 10 9 + pixels. For cancer tissues, these images contain important biologic and prognostic information, capturing the diverse cytologic elements involved in angiogenesis, immune response, and tumor/stroma interactions. Image analysis algorithms can mine wholeslide images to delineate objects like cell nuclei, and to extract 10s-100s of quantitative features that describe the shape, color, and texture of each object. These histology-omic or "histomic" features can be used to train machine-learning algorithms to classify important elements like tumor-infiltrating lymphocytes, vascular endothelial cells, or fibroblasts. Identifying these elements in tissues requires considerable expertise, and imparting this knowledge to algorithms enables precise characterization of large imaging datasets in ways not possible by subjective visual assessment. Quantitative measures of the abundance, morphologies and spatial patterns of these elements can help investigators understand relationships between histologic phenotypes and survival, treatment response, and underlying molecular mechanisms. Studies that generate whole slide images can yield histomic annotations of 10 8 + objects, and a central challenge in utilizing this data is in enabling domain experts to train classification rules and to evaluate their accuracy. With each image containing up to 10 6 + discrete objects, facilitating interaction with domain experts requires fluid navigation of gigapixel images, visualization of derived image segmentation boundaries, mechanisms to intelligently acquire training data from experts, and to visualize classifications for millions of objects.Histopathology image analysis has received significant attention with algorithms having been developed to predict metastasis (1), survival (...

BackgroundWe describe a suite of tools and methods that form a core set of capabilities for researchers and clinical investigators to evaluate multiple analytical pipelines and quantify sensitivity and variability of the results while conducting large-scale studies in investigative pathology and oncology. The overarching objective of the current investigation is to address the challenges of large data sizes and high computational demands.ResultsThe proposed tools and methods take advantage of state-of-the-art parallel machines and efficient content-based image searching strategies. The content based image retrieval (CBIR) algorithms can quickly detect and retrieve image patches similar to a query patch using a hierarchical analysis approach. The analysis component based on high performance computing can carry out consensus clustering on 500,000 data points using a large shared memory system.ConclusionsOur work demonstrates efficient CBIR algorithms and high performance computing can be leveraged for efficient analysis of large microscopy images to meet the challenges of clinically salient applications in pathology. These technologies enable researchers and clinical investigators to make more effective use of the rich informational content contained within digitized microscopy specimens.

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 108+ 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.

Recent advances in microscopy imaging and genomics have created an explosion of patient data in the pathology domain. Whole-slide images (WSIs) of tissues can now capture disease processes as they unfold in high resolution, recording the visual cues that have been the basis of pathologic diagnosis for over a century. Each WSI contains billions of pixels and up to a million or more microanatomic objects whose appearances hold important prognostic information. Computational image analysis enables the mining of massive WSI datasets to extract quantitative morphologic features describing the visual qualities of patient tissues. When combined with genomic and clinical variables, this quantitative information provides scientists and clinicians with insights into disease biology and patient outcomes. To facilitate interaction with this rich resource, we have developed a web-based machine-learning framework that enables users to rapidly build classifiers using an intuitive active learning process that minimizes data labeling effort. In this paper we describe the architecture and design of this system, and demonstrate its effectiveness through quantification of glioma brain tumors.

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