An integrative approach for in silico glioma research

Lee A D Cooper; Jun Kong; David A. Gutman; Fusheng Wang; Sharath R. Cholleti; Tony C. Pan; Patrick M. Widener; Ashish Sharma; Tom Mikkelsen; Adam E. Flanders; Daniel L. Rubin; Erwin G. Van Meir; Tahsin M. Kurc; Carlos S. Moreno; Daniel J. Brat; Joel H. Saltz

doi:10.1109/TBME.2010.2060338

An integrative approach for in silico glioma research

Lee A D Cooper, Jun Kong, David A. Gutman, Fusheng Wang, Sharath R. Cholleti, Tony C. Pan, Patrick M. Widener, Ashish Sharma, Tom Mikkelsen, Adam E. Flanders, Daniel L. Rubin, Erwin G. Van Meir, Tahsin M. Kurc, Carlos S. Moreno, Daniel J. Brat, Joel H. Saltz

Pathology

Research output: Contribution to journal › Article › peer-review

44 Scopus citations

Abstract

The integration of imaging and genomic data is critical to forming a better understanding of disease. Large public datasets, such as The Cancer Genome Atlas, present a unique opportunity to integrate these complementary data types for in silico scientific research. In this letter, we focus on the aspect of pathology image analysis and illustrate the challenges associated with analyzing and integrating large-scale image datasets with molecular characterizations. We present an example study of diffuse glioma brain tumors,where themorphometric analysis of 81 million nuclei is integrated with clinically relevant transcriptomic and genomic characterizations of glioblastoma tumors. The preliminary results demonstrate the potential of combining morphometric and molecular characterizations for in silico research.

Original language	English (US)
Pages (from-to)	2617-2621
Number of pages	5
Journal	IEEE Transactions on Biomedical Engineering
Volume	57
Issue number	10 PART 2
DOIs	https://doi.org/10.1109/TBME.2010.2060338
State	Published - Oct 2010

Keywords

Biology
Brain tumor
Image analysis
In silico
Microscopy

ASJC Scopus subject areas

Biomedical Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/TBME.2010.2060338

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Cooper, L. A. D., Kong, J., Gutman, D. A., Wang, F., Cholleti, S. R., Pan, T. C., Widener, P. M., Sharma, A., Mikkelsen, T., Flanders, A. E., Rubin, D. L., Van Meir, E. G., Kurc, T. M., Moreno, C. S., Brat, D. J., & Saltz, J. H. (2010). An integrative approach for in silico glioma research. IEEE Transactions on Biomedical Engineering, 57(10 PART 2), 2617-2621. https://doi.org/10.1109/TBME.2010.2060338

@article{ffaaca28f4c347e8b81788f303db5862,

title = "An integrative approach for in silico glioma research",

abstract = "The integration of imaging and genomic data is critical to forming a better understanding of disease. Large public datasets, such as The Cancer Genome Atlas, present a unique opportunity to integrate these complementary data types for in silico scientific research. In this letter, we focus on the aspect of pathology image analysis and illustrate the challenges associated with analyzing and integrating large-scale image datasets with molecular characterizations. We present an example study of diffuse glioma brain tumors,where themorphometric analysis of 81 million nuclei is integrated with clinically relevant transcriptomic and genomic characterizations of glioblastoma tumors. The preliminary results demonstrate the potential of combining morphometric and molecular characterizations for in silico research.",

keywords = "Biology, Brain tumor, Image analysis, In silico, Microscopy",

author = "Cooper, {Lee A D} and Jun Kong and Gutman, {David A.} and Fusheng Wang and Cholleti, {Sharath R.} and Pan, {Tony C.} and Widener, {Patrick M.} and Ashish Sharma and Tom Mikkelsen and Flanders, {Adam E.} and Rubin, {Daniel L.} and {Van Meir}, {Erwin G.} and Kurc, {Tahsin M.} and Moreno, {Carlos S.} and Brat, {Daniel J.} and Saltz, {Joel H.}",

note = "Funding Information: Manuscript received April 15, 2010; revised June 21, 2010; accepted July 8, 2010. Date of publication July 23, 2010; date of current version September 15, 2010. This work was supported by Federal funds from the National Cancer Institute, National Institutes of Health (NIH) under Contract HHSN261200800001E, Contract 94995NBS23, Contract N01-CO-12400, and Contract 85983CBS43; by TCGA Contract 29X55193; by National Heart, Lung, and Blood Institute under Grant R24HL085343; by NIH under Grant U54 CA113001, Grant R01 CA86335, and Grant R01 CA116804; and NIH Public Health Service under Grant UL1 RR025008, Grant KL2 RR025009, or Grant TL1 RR025010 from the Clinical and Translational Science Awards program of National Center for Research Resources; by National Library of Medicine under Grant R01LM009239; and by Biomedical Information Science and Technology Initiative under Grant P20 EB000591. Asterisk indicates corresponding author. *L. A. D. Cooper is with the Center for Comprehensive Informatics, Atlanta, GA 30322 USA, and also with Emory University, Atlanta, GA 30322 USA (e-mail: lee.cooper@emory.edu).",

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Cooper, LAD, Kong, J, Gutman, DA, Wang, F, Cholleti, SR, Pan, TC, Widener, PM, Sharma, A, Mikkelsen, T, Flanders, AE, Rubin, DL, Van Meir, EG, Kurc, TM, Moreno, CS, Brat, DJ & Saltz, JH 2010, 'An integrative approach for in silico glioma research', IEEE Transactions on Biomedical Engineering, vol. 57, no. 10 PART 2, pp. 2617-2621. https://doi.org/10.1109/TBME.2010.2060338

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T1 - An integrative approach for in silico glioma research

AU - Cooper, Lee A D

AU - Kong, Jun

AU - Gutman, David A.

AU - Wang, Fusheng

AU - Cholleti, Sharath R.

AU - Pan, Tony C.

AU - Widener, Patrick M.

AU - Sharma, Ashish

AU - Mikkelsen, Tom

AU - Flanders, Adam E.

AU - Rubin, Daniel L.

AU - Van Meir, Erwin G.

AU - Kurc, Tahsin M.

AU - Moreno, Carlos S.

AU - Brat, Daniel J.

AU - Saltz, Joel H.

N1 - Funding Information: Manuscript received April 15, 2010; revised June 21, 2010; accepted July 8, 2010. Date of publication July 23, 2010; date of current version September 15, 2010. This work was supported by Federal funds from the National Cancer Institute, National Institutes of Health (NIH) under Contract HHSN261200800001E, Contract 94995NBS23, Contract N01-CO-12400, and Contract 85983CBS43; by TCGA Contract 29X55193; by National Heart, Lung, and Blood Institute under Grant R24HL085343; by NIH under Grant U54 CA113001, Grant R01 CA86335, and Grant R01 CA116804; and NIH Public Health Service under Grant UL1 RR025008, Grant KL2 RR025009, or Grant TL1 RR025010 from the Clinical and Translational Science Awards program of National Center for Research Resources; by National Library of Medicine under Grant R01LM009239; and by Biomedical Information Science and Technology Initiative under Grant P20 EB000591. Asterisk indicates corresponding author. *L. A. D. Cooper is with the Center for Comprehensive Informatics, Atlanta, GA 30322 USA, and also with Emory University, Atlanta, GA 30322 USA (e-mail: lee.cooper@emory.edu).

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N2 - The integration of imaging and genomic data is critical to forming a better understanding of disease. Large public datasets, such as The Cancer Genome Atlas, present a unique opportunity to integrate these complementary data types for in silico scientific research. In this letter, we focus on the aspect of pathology image analysis and illustrate the challenges associated with analyzing and integrating large-scale image datasets with molecular characterizations. We present an example study of diffuse glioma brain tumors,where themorphometric analysis of 81 million nuclei is integrated with clinically relevant transcriptomic and genomic characterizations of glioblastoma tumors. The preliminary results demonstrate the potential of combining morphometric and molecular characterizations for in silico research.

AB - The integration of imaging and genomic data is critical to forming a better understanding of disease. Large public datasets, such as The Cancer Genome Atlas, present a unique opportunity to integrate these complementary data types for in silico scientific research. In this letter, we focus on the aspect of pathology image analysis and illustrate the challenges associated with analyzing and integrating large-scale image datasets with molecular characterizations. We present an example study of diffuse glioma brain tumors,where themorphometric analysis of 81 million nuclei is integrated with clinically relevant transcriptomic and genomic characterizations of glioblastoma tumors. The preliminary results demonstrate the potential of combining morphometric and molecular characterizations for in silico research.

KW - Biology

KW - Brain tumor

KW - Image analysis

KW - In silico

KW - Microscopy

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JO - IRE transactions on medical electronics

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IS - 10 PART 2

ER -

An integrative approach for in silico glioma research

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

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