Clinical Trials, TCGA: Deep Integrative Research RT, Imaging, Pathology, “ omics ”

1. Clinical Trials, TCGA: Deep Integrative ResearchRT, Imaging, Pathology, “omics” Joel Saltz MD, PhD Director Center for Comprehensive Informatics

2. Will Treatment work and if not, why not? Example: Avastin and Glioblastoma in RTOG-0825 Treatment: Radiation therapy and Avastin (anti angiogenesis) Predict and Explain: Genetic, gene expression, microRNA, Pathology, Imaging RT, imaging, Pathology markup/annotations

3. Avastin and GBMs in RTOG-0825 Analysis on pre-treatment tissue to extract imaging and molecular biomarkers that are indicative of Outcome/Avastin response. whole genome mRNA and microRNA expression profiling of GBM tumor specimens to identify outcome/Avastin response biomarkers Analyzing the Pathology imaging and diagnostic imaging registered with the therapy plan to extract any biomarkers that can indicate Avastin response. Does advanced imaging (eg: diffusion weighted imaging) provide markers that can predict patient response? RT, Diagnostic Imaging and Pathology: Tools to support Human/Algorithm analyses, annotation, markup (extensions of AIM) Data management and display framework that integrates the pathology with the radiology, therapy treatment information and the clinical data. This involves integrating platforms that manage imaging data at ACRIN, pathology at UCSF and molecular data from Emory.

4. For the sake of quality control, reproducibility and data sharing, results of RT, imaging, Pathology observations and analyses need to be described in a well defined manner Finding: mass Mass ID: 1 Margins: spiculated Length: 2.3cm Width: 1.2cm Cavitary: Y Calcified: N Spatial relationships: Abuts pleural surface; invades aorta

5. Distinguish (and maybe redefine) astrocytic, oligodendroglial and oligoastrocytic tumors using TCGA and Rembrandt Important since treatment and Outcome differ • Link nuclear shape, texture to biological and clinical behavior • How is nuclear shape, texture related to gene expression category defined by clustering analysis of Rembrandt data sets? • Relate nuclear morphometry and gene expression to neuroimaging features (Vasari feature set) • Genetic and gene expression correlates of high resolution nuclear morphometry and relation to MR features using Rembrandt and TCGA datasets.

6. Annotation and Markup of Pathology Data needs Human/Algorithm Cooperation • Astrocytoma vsOligodendroglima • TCGA finds genetic, gene expression overlap • Pathologists have also long seen overlap • Relationship between Pathology, Molecular, Radiology • Relationship to Outcome, treatment response

7. What you find depends on where you look • GBM gene expression patterns will be influenced by necrosis, degree and type of angiogenesis • Degree and pattern of necrosis/angiogenesis varies within a given tumor so molecular analyses need to be interpreted in the context of what was sampled

8. Use of randomly selected sample to determine whether the earth is wet or dry …

9. Prediction using Rembrandt/TCGA • Matched sets of cases where low grade gliomas progressed to GlioblastomaMultiforme • Examine the gene expression profile of low grade gliomas that progress to GBM for predictive clustering, prognostic significance and correlates with pathologic and radiologic features. • investigate the gene expression profiles, pathologic properties and MR imaging characteristics of low grade astrocytomas and the GBMs that have progressed from them within the Rembrandt data set. Determine if gene expression profiles of the low grade gliomas cluster closely with any one of the gene cluster families represented in the TCGA analysis or are more evenly distributed

10. Link Radiology to Pathology, Molecular “Ground Truth” • Identify correlates of MRI enhancement patterns in astrocyticneoplasms with underlying vascular changes and gene expression profiles. • Better understanding of the relation of enhancement patterns to histologic features and grade would facilitate the ability to prognosticate and devise treatment strategies non-invasively based on neuroimaging findings. • Define the precise relationship between imaging characteristics related to contrast-enhancement and those seen by advanced imaging sequences with the underlying histopathologic features, especially those associated with vascular changes • Correlate specific MR characteristics defined by the Vasari Feature Set with pathologic grade, vascular morphology and underlying gene expression profiles

11. TCGA. Rembrandt, Vasari

12. Annotation Markup, Tools, Metadata Management, Query • Annotation/Markup for RT, standard MR and advanced imaging, Pathology • Importance of many features will be determined via research so in some cases we need ad-hoc shared annotation/markup schemes • Human/algorithm interaction plays key role in Pathology, advanced imaging and likely RT • Multiple platforms will be needed to support human/algorithm annotation and markup – leverage XIP, caMicroscope, GridImageetc

13. Summary • Semantic query to pose questions involving annotation/markup (e.g. IQ project) • Grid based metadata management – AIM server, NBIA and more • Consistent scheme for defining and enforcing data level security across grid-distributed data sources • Grid based auditing • Workflow (taverna, wings etc)