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Integrated Morphologic Analysis for Identification and Characterization of Disease Subtypes

Lee Cooper

Center for Comprehensive Informatics, Emory University

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Agenda

• Background • Pipeline for integrated morphologic analysis • Results and validation • Software Infrastructure • Future Work and Conclusions • Acknowledgements

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Background

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NCI caBIG® In Silico Brain Tumor Research Center

Jefferson Hospital Philadelphia, PA

Henry Ford Hospital Detroit, MI

Emory University Atlanta, GA

Joel Saltz, MD PhD Director

Daniel Brat, MD PhD Science PI

Stanford University Stanford, CA

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Application domain: glioblastoma

• Most common primary brain tumor in adults • Median survival 50 weeks

• ISBTRC Goals:

• To leverage rich datasets to understand the mechanisms of glioma progression through In Silico analysis

• To manage, explore and share semantically complex data among researchers

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Glioblastoma Histology

Necrosis Angiogenesis

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The Cancer Genome Atlas (TCGA) • Characterize 500 tumors for each of a variety of cancers • Clinical records • Genomics: gene, miRNA expression, copy number, sequence,

DNA methylation • Imaging: pathology and radiology

histology radiology

clincal\pathology

Integrated Analysis

genomic

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Slide scanning and image analysis

• High throughput slide scanning systems • Digitize entire slides at 200X / 400X magnification • 250 slides / day • Algorithms to segment and describe cells and structures

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Glioblastoma morphology

• Themes: morphology, subtypes, rich datasets

Are there natural clusters of GBM morphology? Are there links to patient outcome and molecular

characteristics?

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Methodology

Cooper LA, Kong J, Gutman DA, Wang F, Gao J, Appin C, Cholleti S, Pan T, Sharma A, Scarpace L, Mikkelsen T, Kurc T, Moreno CS, Brat DJ, Saltz JH, “Integrated morphologic analysis for the identification and characterization of disease subtypes,” Journal of the American Medical Informatics Association, 2012 19:317-323

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Computational Pathology and Correlative Analysis

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Morphology engine

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Clustering engine

Patient Morphology Profiles

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Correlative engine

Patient Cluster Labels

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Genome wide analysis

GISTIC

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Results

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Clustering identifies three morphological groups

• Analyzed 200 million nuclei from 162 TCGA GBMs (462 slides) • Named for functions of associated genes: Cell Cycle (CC), Chromatin Modification (CM), Protein Biosynthesis (PB) • Prognostically-significant (logrank p=4.5e-4)

Feat

ure

Indi

ces

CC CM PB

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20

30

40

50

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Gene Expression Class Associations

• Cox proportional hazards • Verhaak expression class1 not significant p=0.58 • Morphology clustering p=5.0e-3

CC CM PB0

20

40

60

80

100

Cluster

Sub

type

Per

cent

age

(%)

ClassicalMesenchymalNeuralProneural

1Verhaak RG, Hoadley KA, Purdom E, et al; Cancer Genome Atlas Research Network. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 2010;17:98e110.

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Clustering Validation

• Separate set of 84 GBMs from Henry Ford Hospital • ClusterRepro: CC p=7.2e-3, CM p=1.3e-2

Feat

ure

Indi

ces

CC Mixed CM

10

20

30

40

500 20 40 60 80 100

0

0.2

0.4

0.6

0.8

1

Months

Sur

viva

l

CCMixedCM

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Representative nuclei

Large, hyperchromatic

nuclei

Small light nuclei, Eosinophilic cyoplasm

Intermediate

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Associations

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From Gene Lists to Biology

• Nuclear lumen localization most highly enriched in cluster associated genes

(CC p=2.8e-36, CM p=2.17e-19, PB p=1.08e-15)

• Other enriched GO terms: DNA repair, m-phase , cell cycle, protein biosynthesis, chromatin modification

• Differences in activation of cancer-related pathways including ATM and TP53 DNA damage checkpoints, NFκB pathway, Wnt signaling and PTEN/AKT pathways

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Software Infrastructure

Wang F, Kong J, Cooper L, Pan T, Kurc T, Chen W, Sharma A, Niedermayr C, Oh T-W, Brat D, Farris A, Foran D, Saltz J, “A Data Model and Database for High-resolution Pathology Analytical Image Informatics,” Journal of Pathology Informatics, Vol. 2, Issue 1, pp. 32-40, 2011. Teodoro G, Kurc T, Pan T, Cooper L, Kong J, Widener P, Saltz J, “Accelerating Large Scale Image Analyses on Parallel CPU-GPU Equipped Systems”, Accepted for presentation at the International Parallel and Distributed Processing Symposium, China, 2012. Also available as Emory University, Center for Comprehensive Informatics, Technical Report: CCI-TR-2011-4, 2011.

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How to scale to 14,000 images?

• TCGA contains 20 cancer types • 14K images – 4 Terabytes

• How to analyze larger datasets? HPC Pipeline • How to organize results? PAIS Database • How to interact with the data? CDSA Portal

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HPC Segmentation and Feature Extraction Pipeline

Tony Pan and George Teodoro

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PAIS (Pathology Analytical Imaging Standards) PAIS Logical Model: • 62 UML classes • markups, annotations,

imageReferences, provenance

• Semantic enabled PAIS Data Representation: • XML (compressed) or HDF5 PAIS Databases: • loading, managing and

querying and sharing data • RDBMS + SDBMS + parallel

DBMS

Fusheng Wang

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Microscopy Image Database

Segmentation

Feature extraction

Image analysis

Modeling and management of markup and annotation for querying and sharing through parallel RDBMS + spatial DBMS

PAIS model PAIS data management

On the fly data processing for algorithm validation/algorithm sensitivity studies, or discovery of preliminary results

HDFS data staging MapReduce based queries

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Cancer Digital Slide Archive

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cancer.digitalslidearchive.net

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cancer.digitalslidearchive.net

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cancer.digitalslidearchive.net

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Future Work and Conclusions

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Radiology Imaging Correlative Study

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Studying Protein Expression Patterns Using Quantum Dot Immunohistochemistry

Nucleus

Cytoplasm

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Conclusions

• Pathology imagery contains important cues • Pipeline for analyzing whole slide imagery • Tooling to handle large datasets

• Other TCGA diseases (14000 Images!) • Developing richer descriptions of image content

• Resources:

• Emory Websites: bmi.emory.edu cci.emory.edu • Cancer Digital Slide Archive: cancer.digitalslidearchive.net • TCGA Symposium Talk:

http://cancergenome.nih.gov/newsevents/multimedialibrary/videos/morphologicalcooper

• JAMIA Paper: http://jamia.bmj.com/content/19/2/317.abstract

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In Silico Brain Tumor Research Center Team • Emory University

• Joel Saltz (Director) • Daniel Brat (Science PI) • Carlos Moreno (Bioinformatics

Lead) • Lee Cooper • David Gutman • Jun Kong • Fusheng Wang • Chad Holder • Christina Appin • Candace Chisolm • Erwin van Meir • Tahsin Kurc • Sharath Cholleti • Tony Pan • Ashish Sharma

• Henry Ford Hospital • Tom Mikkelsen • Lisa Scarpace

• Thomas Jefferson University

• Adam Flanders (Radiology Lead)

• Stanford University

• Daniel Rubin

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Related Papers and Acknowledgements

• Cooper LA, Kong J, Gutman DA, Wang F, Gao J, Appin C, Cholleti S, Pan T, Sharma A, Scarpace L, Mikkelsen T, Kurc T, Moreno CS, Brat DJ, Saltz JH, “Integrated morphologic analysis for the identification and characterization of disease subtypes”, Journal of the American Medical Informatics Association, in press, 2012. Pre-print Available: http://jamia.bmj.com/content/19/2/317.long

• Wang F, Kong J, Cooper L, Pan T, Kurc T, Chen W, Sharma A, Niedermayr C, Oh T-W, Brat D, Farris A, Foran D, Saltz J, “A Data Model and Database for High-resolution Pathology Analytical Image Informatics,” Journal of Pathology Informatics, Vol. 2, Issue 1, pp. 32-40, 2011.

• Teodoro G, Kurc T, Pan T, Cooper L, Kong J, Widener P, Saltz J, “Accelerating Large Scale Image Analyses on Parallel CPU-GPU Equipped Systems”, Accepted for presentation at the International Parallel and Distributed Processing Symposium, China, 2012. Also available as Emory University, Center for Comprehensive Informatics, Technical Report: CCI-TR-2011-4, 2011.

This work is supported in part by NCI HHSN261200800001E, NHLBI R24HL085343, NLM R01LM011119-01 and R01LM009239, NIH RC4MD005964, NIH NIBIB BISTI P20EB000591, and CTSA PHS Grant UL1RR025008.