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ASNR 2012
An Introduction into Imaging Genomic Mapping in
Brain Tumors
Rivka R. Colen, M.D.1, Bhanu Mahajan, M.B.B.S.1, Ferenc A. Jolesz, M.D.1 , Pascal O.
Zinn, M.D.2
1
Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA.
2 M.D. Anderson Cancer Center, Houston, TX, USA.
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Disclosures
No Disclosures.
R25 CA089017(RRC)
P41 RR019703 (FAJ)
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ASNR 2012
Introduction
• Imaging genomics (also termed radiogenomics) has emerged as a new field which
links the specific imaging traits (radiophenotypes) with gene-expression profiles.
•
The different MRI characteristics (also termed radiophenotypes, phenotypes or
biomarkers) can be correlated with the underlying genomic composition in tumors,
in this case Glioblastoma (GBM).
•
The different MRI radiophenotypes/ MRI biomarkers are obtained from
conventional( T1 and T2- weighted images) or advanced (MRI- diffusion, MRIperfusion, MRI spectroscopy) techniques.
•
For stand alone genomic analysis, large scale gene- and microRNA based cancer
microarray analysis is commonly not performed due to high cost, time and
manpower required for large dataset analysis and interpretation.
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Introduction
• In order to leverage genomic data for therapy so that
personalized medicine transpire, a cost-effective
biomarker that accurately reflects underlying
molecular cancer compositions is urgently needed.
• Imaging, specifically MRI is a promising biomarker to
reflect underlying tumor pathology and biological
function.
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Introduction
• It follows that if imaging phenotypes of tumors
obtained from routine clinical MRI studies can be
associated with specific gene and microRNA
expression signatures, imaging phenotypes can
serve as non-invasive surrogates for tumor gene
expression information and routinely provide
information at a large-scale genomic level regarding
the diagnosis, prognosis, and optimal treatment
indicated.
• Thus, the new field of imaging genomics can be
anticipated to be instrumental in identifying genomic
tumor compositions.
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Purpose
•
In this presentation, we seek to introduce, define and detail the concept of
imaging genomics and how to perform imaging genomic mapping in brain
tumors, specifically Glioblastoma, using large scale- genomic and imaging
analysis.
•
Here, we present the first quantitative imaging genomic mapping largescale study in GBM
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Purpose
•
Imaging genomic analysis can be bidirectional:
•
Correlating imaging to genomic data ( phenome to genome)
• Correlating genomic to imaging data (genome to phenome)
Phenome
(Imaging)
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Genome
(Genomics)
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Purpose
We present the first quantitative imaging genomic mapping large- scale study
in GBM
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Methods and Materials
•
Retrospective study
•
Using 78 treatment naïve GBM patients with imaging data obtained from The Cancer
Imaging Archive (http://cancerimagingarchive.net/) sponsored by the Cancer Imaging
Program, DCTD/NCI/NIH, segmentation were performed.
•
The volumes of these were then correlated with Gene- and micoRNA- expression
profiles obtained from The Cancer Genome Atlas (TCGA).
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Methods and Materials
•
Image Analysis was done in Slicer 3.6 (slicer.org) using the Segmentation
module
- T2/FLAIR was registered to the post- contrast T1WI.
- Volumetric segmentation was performed in a simple hierarchical model of
anatomy, proceeding from peripheral to central.
- 3 distinct structures were segmented:
• edema/invasion
• enhancing tumor
• necrosis
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Methods and Materials
Tumor Segmentation. 65 year old male patient with a right temporal GBM. Segmentation of tumor edema (blue),
enhancement (yellow) and necrosis (red) was performed and edema volume was obtained.
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Methods and Materials
•
Image Genomic- Biostatistics analysis
- 12,764 genes and 555 microRNAs were analyzed (Affymetrix/Agilent
chip technology) in each patient and correlated to selected volumes
-
Comparative Marker Selection (Broad Institute) identified
preferentially upregulated genomic events in one vs. another
predefined patient group (high_low volumes groups)
Low (left) and High (right) FLAIR phenotype (colored in blue) reflecting tumor infiltration/edema
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Results
•
Top upregulated and downregulated genes and microRNAs not previously
described in the literature were identified which had underlying concordant
biological processes of edema/invasion, necrosis, and enhancing tumor
MRI phenotypes.
In this example, patients with a high
volume of peritumoral non-enhancing
FLAIR hyperintensity (known
biologically to reflect a mixture of
edema and tumor infiltration) had
increases in genes and microRNAs
associated with tumor invasion.
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Results
•
Kaplan Meier Analysis survival curves demonstrated that these associated
genes and microRNAs were associated with patient overall- and
progression-free survival.
POSTN and miR-219
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Results
•
Furthermore, this gene expression was a stronger prognostic
variable than either molecular subtype (as defined by Veerkak and
colleagues) in the Cox proportional hazards ratio.
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Conclusion
•
It allows for bidirectional imaging phenotype-genotype correlations and
discoveries.
•
Imaging genomics links imaging phenotypes to underlying genotypes in
GBM patients and vice versa.
•
Imaging genomic mapping can be used to discover biologically
meaningful genes and microRNA that can be used for
•
•
•
the development of therapeutic targets
identify candidates which have the target gene
help predict response and nonresponse to specific target therapies
based on genomic targets and tumor genetic composition.
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Conclusion
Thank you for your interest!
Acknowledgements: This work was supported by NIH grant R25 CA089017-06A2 (RRC).
Any questions please email: [email protected].
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