caGrid, Fog and Clouds

Joel Saltz MD, PhDDirector Center for

Comprehensive Informatics

Overview

• Introduction• Tools• Use Case• Fog and Clouds

Fog ComputingGrid interoperable enterprise virtualization

caGrid, Enterprise and Clouds

Biomedical Middleware: caGrid, TRIAD, i2b2

caGrid Components– Security (GAARDS)– Language (metadata,

ontologies)– Semantic/Federated

query– Workflow – Grid Service Graphical

Development Toolkit (Introduce)

– DICOM, IHE compatibility– Advertisement and

Discovery

Preferred Name

Synonyms

Definition

Relationships

Concept Code

Vocabulary/Ontology

Interoperability– Registered metadata– Ontology concept

codes used to annotate models

– XML schemas that define data structures also registered

– Thus both data semantics AND data structures are registered. That is how we achieve (relative) interoperability.

Use Case: Will Treatment work and if not, why not?

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/query

Active Data workflows

Avastin and GBMs in RTOG-0825Analysis 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 biomarkersAnalyzing 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: Support Human/Algorithm analyses, annotation, markup

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.

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: massMass ID: 1

Margins: spiculatedLength: 2.3cmWidth: 1.2cmCavitary: YCalcified: N

Spatial relationships: Abuts pleural surface; invades aorta

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.

Annotation and Markup of Pathology Data needs Human/Algorithm Cooperation

Astrocytoma vs Oligodendroglima• TCGA finds genetic, gene expression

overlap• Pathologists have also long seen

overlap• Relationship between Pathology,

Molecular, Radiology• Relationship to Outcome, treatment

response

Example: Compute Intensive Workflow

Fog Computing

Attributes of Fog Computing

• For legal and organizational reasons, data location is constrained

• Virtual machines used to create software stacks that use caGrid + other middleware to expose data services (we regularly ship VMs with caGrid based software stacks)

• Enterprises such as Emory increasingly rely on virtualization architectures

• Mid-range active storage platforms (eg Emory 1PB archival, 100TB fast disk, 2K cores, infiniband interconnect) will rely heavily on virtualization

Relationship between Fog and Cloud Computing

• Workflows have enterprise, caGrid and compute intensive components

• Interoperability between enterprise and caGrid software stacks major current NCI/ONR issue

• Given virtualization, HPC and large scale data requirements can be tackled with cloud approach

Issues• Organizational, legal requirements create

constraints on placement of datasets, where VMs can be run

• Mapping of VMs, datasets:– Huge variation in communication, I/O bandwidth,

compute capabilities in Fog/Cloud continua– Multiple software stacks, heterogeneous hardware

architectures• Security: Fog to Cloud: multiple distinct but

overlapping security related requirements and constraints

Thank you