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Genomic Imaging: Gliomas and Perfusion Imaging
A TCGA Glioma Phenotype Research Group Project
Rajan Jain, MD
Division of Neuroradiology, Department of Radiology
Department of Neurosurgery
Henry Ford Health System
Assistant Professor, WSU School of Medicine
Detroit, MI, US
ASNR 50th Annual Meeting
eEdE-17
Disclosures
 Radio-genomics work funded by NCI Contract No.
HHSN261200800001E.
 Research support: Lantheus Medical Imaging.
TCGA Glioma Phenotype
Research Group Project
Rajan Jain,1,2 Laila Poisson,3 Jayant Narang,1 David Gutman,4
Adam Flanders,5 Lisa Scarpace,2 Scott N Hwang,4 Chad
Holder,4 Max Wintermark,6 Rivka R Colen,7 Justin Kirby,8 John
Freymann,8 Brat Daniel,4 Carl Jaffe,9 Tom Mikkelsen 2
1Division
of Neuroradiology, Department of Radiology, 2Department of
Neurosurgery and 3Department of Public Health Sciences, Henry Ford Health
System, Detroit, MI
4Emory University, Atlanta, GA; 5Thomas Jefferson University Hospital,
Philadelphia, PA; 6University of Virginia, Charlottesville, VA; 7Brigham &
Womens Hospital, Boston, MA; 8SAIC-Frederick, Inc.; 9Boston University,
Boston, MA
https://wiki.cancerimagingarchive.net/display/Public/TCGA+Glio
ma+Phenotype+Research+Group
Learning Objectives
 Genomic mapping of High-grade Gliomas: Sub-classifications based on
molecular markers
– TCGA (The Cancer Genome Atlas)
– TCIA (The Cancer Imaging Archive)
 Radio-genomics: Correlation of imaging markers with gene expression
pathways
 Brain Tumor Perfusion Imaging: Vascular Parameters
 Tumor Blood Volume
 Tumor Permeability
 Correlation of Perfusion Parameters with Genomic Markers of
Angiogenesis
 Correlation of Perfusion Parameters with Genomic Mapping and Patient
Survival in GBM
Genomic Mapping of Gliomas
High-Grade Gliomas: Genomic Mapping
 Recently, there has been progress in understanding the
molecular basis of the tumor aggressiveness and
heterogeneity.
 Various molecular sub-classifications have been proposed
based on the genetic makeup of high-grade gliomas with the
hope that a better understanding of origin of tumor cells and
molecular pathogenesis may predict response to targeted
therapies.
 Phillips HS, et al. Molecular subclasses of high-grade glioma predict
prognosis, delineate a pattern of disease progression, and resemble
stages in neurogenesis. Cancer Cell 2006;9(3):157-173.
 Verhaak RG, et al. Integrated genomic analysis identifies clinically relevant
subtypes of glioblastoma characterized by abnormalities in PDGFRA,
IDH1, VEGFR, and NF1. Cancer Cell 2010;17(1):98-110.
.
High-Grade Gliomas: Molecular Sub-classes
Phillips HS, et al. Molecular subclasses of high-grade glioma predict
prognosis, delineate a pattern of disease progression, and resemble stages
in neurogenesis. Cancer Cell 2006;9(3):157-173.
GBM: Molecular Sub-classes
 Verhaak RG, et al. Integrated genomic analysis identifies clinically relevant
subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1,
VEGFR, and NF1. Cancer Cell 2010;17(1):98-110.
TCGA and TCIA
 The Cancer Genome Atlas (TCGA) researchers have
recently cataloged recurrent genomic abnormalities in GBM
providing a platform for better understanding of the
molecular basis of these aggressive but heterogeneous
tumors.
 In parallel the Cancer Imaging Program is retrospectively
obtaining radiological imaging data for TCGA patients and
making it available via The Cancer Imaging Archive (TCIA).
 Integration of this vast genomic information with imaging
data (radio-genomics) may not only strengthen this
understanding but also provide an opportunity to use some
of the non-invasive imaging features or parameters as
biomarkers.
http://cancerimagingarchive.net
 TCIA is a large and growing archive service providing
DICOM images for use in research
 TCGA-GBM collection currently includes over 170 deidentified glioblastoma subjects with accrual still ongoing
 TCGA-LGG collection to be formed for collection of lower
grade gliomas in coming months
 Additional brain related image sets include REMBRANDT
(130 subjects) and RIDER Neuro MRI (19 subjects)
 Registration is free and new data is being added regularly.
Sign up at: http://cancerimagingarchive.net
Clinical Features
Radio-genomics
Imaging
Features
Genomic
Features
Pathologic
Features
Radio-genomics: Morphologic Features
 Integration of this vast genomic information with imaging
data (radio-genomics) may not only strengthen this
understanding but also provide an opportunity to use some
of the non-invasive imaging features or parameters as
biomarkers.
 A limited number of publications on this topic have
correlated morphologic imaging features (presence or
absence of contrast enhancement) with various gene
expression pathways affecting tumor cell mitosis, migration,
angiogenesis, hypoxia, edema and apoptosis.
Proc Natl Acad Sci U S A 2008;105(13):5213-5218.
Diagn Mol Pathol 2006;15(4):195-205.
Radiology 2008;249(1):268-277.
Radio-genomics: Functional Imaging Markers
 Barajas et al correlated histologic features with ADC and rCBV
estimates, but the authors did
not directly correlate physiologic
measures with gene expression.
Radiology 2010;254(2):564-576.
 Pope et al correlated ADC histogram analysis with differential
gene expression. AJNR published online
 Jain et al correlated perfusion parameters (tumor blood
volume and permeability) with
angiogenesis related gene
expression in GBM. AJNR published online
Perfusion Surrogate Markers of Angiogenesis
 Tumor Blood Volume
 Tumor Vascular Leakiness or Permeability
Perfusion Surrogate Markers of Angiogenesis:
Tumor Blood Volume
 CBV is the total volume of blood traversing a given
region of brain, ml/100g
– CBV estimated from integral of signal intensity versus
time curve
 Deconvolution, allows calculation of MTT
 Central volume principle
CBF = CBV/MTT
Measuring Tumor Blood Volume:
Why is it important?
 It represents total tumor vascularity.
– Thus, in vivo measurement of tumor blood volume could
be important for several reasons:
 Grading of tumors
 Assessing response of tumors to various therapies
 Treatment effects – radiation injury or necrosis
 Prognosis
Perfusion Surrogate Markers of Angiogenesis:
Tumor Vascular Leakiness/Permeability
 Physiological terms – leakage of contrast agent from
intravascular to extravascular compartment through a
deficient BBB
 Various imaging techniques and various parameters
 Permeability surface area-product (PS), ml/100g/min
 Forward Transfer Constant (Ktrans), min-1
 No standardization of technical parameters of acquisition
and post-processing
Measuring Tumor Vascular Permeability:
Why is it important?
 In vivo measurement of vessel permeability could be important for
several reasons:
 Grading of tumors - Increased permeability is associated with
immature blood vessels, which is seen with angiogenesis and
higher grade tumors.
 Studying response of tumors to various therapies, especially
anti-angiogenic therapy.
 Understanding the mechanism of entry of therapeutic agents into
the central nervous system.
 Development of methods to selectively alter the blood-brainbarrier to enhance drug delivery.
Understanding Angiogenesis
Hypoxia leads to
expression of hypoxiainducible factor (HIF-1α)
and formation of
Tumor cell growth
outstrips its blood
supply leading to
hypoxia
angiogenic mediators
such as VEGF and
stromal derived factor-1
(SDF-1)
Tumor angiogenesis involves a multitude of controlled
signaling cascades and structural changes.
The effects of
VEGF and SDF-1
lead to formation of
immature and leaky
blood vessels
Leaky blood vessels allow
extravasation of plasma, plasma
proteins, and deposition of proangiogenic matrix proteins and
microvascular cellular proliferation
(MVCP)
Griffith, B. Jain, R., et al. Blood-Brain-Barrier Imaging in Brain Tumors: Concepts
and Methods. Presented at ASNR 2011, Neurographics (in press)
Understanding Angiogenesis
↑ MVD
↑ TVA
↑
Permeability
As pericyte-poor new blood vessels (mother vessels) enlarge and give
rise to daughter vessels through a complex series of endothelial
rearrangements --- microvascular density (MVD) and total vascular area
(TVA) increase, which in turn leads to increased microvascular cellular
proliferation (MVCP) and increase in permeability
Griffith, B. Jain, R., et al. Blood-Brain-Barrier Imaging in Brain Tumors: Concepts
and Methods. Presented at ASNR 2011, Neurographics (in press)
Understanding Angiogenesis:
Correlation with Perfusion Parameters
 In the initial phase, vessel leakiness (measured by permeability
PS or Ktrans) increases more than the total number and area
of blood vessels (measured by blood volume).
 Finally, as vessels mature, the total number and area of
blood vessels increases more than vessel leakiness,
evolving into a very heterogeneous tumor with regions
showing different mixtures of vessel characteristics and
angiogenesis.
Immuno-histochemical Basis for Tumor
Vascular Surrogate Markers
Jain, R. et al. Am J Neuroradiology 2011; 32: 388 - 394.
CBV
2.41
PS 3.43
Jain, R. et al. Am J Neuroradiology 2011; 32: 388 - 394.
Multiple Regression Analysis
PCT
Parameter
CBV
PS
p-value*
0.777
0.001
MVD
CBV
PS
<0.001
0.364
Tumor Cellularity
CBV
PS
0.104
0.115
WHO grade
CBV
PS
0.098
0.060
Histopathology
MVCP
Jain, R. et al. Am J Neuroradiology 2011; 32: 388 - 394.
Grade IV Glioblastoma
Cellularity=3, MVCP score=2, MVD score=3 (263 vessels/20x),VEGFR-2+
CBV 6.98
CBV 1.22
PS 10.04
PS 0.50
Cellularity=2, MVCP score=0, MVD score=1 (55 vessels/20x),VEGFR-2-
Grade II Astrocytoma
Grade IV Glioblastoma
Cellularity=3, MVCP score=2, MVD score=3 (263 vessels/20x),VEGFR-2+
H & E 20x
CD 34 20x
VEGFR-2 20x
Cellularity=2, MVCP score=0, MVD score=1 (55 vessels/20x),VEGFR-2-
Grade II Astrocytoma
Radio-genomics: Perfusion Surrogate
Markers
Jain, R. et al. Am J Neuroradiology Published March 15, 2012
as 10.3174/ajnr.A2956.
Correlation of Perfusion Parameters with Genes
Related to Angiogenesis Regulation in GBM
 Given our interest in CBV and
PS estimates we considered the
Gene Ontology biological process
pathways for angiogenesis
regulation only.
 We observed expression levels for
92 genes (332 probes) in the
regulation of angiogenesis
(pathways GO:0045766,
GO:0005923)
Jain, R. et al. Am J Neuroradiology Published March 15, 2012
as 10.3174/ajnr.A2956.
Correlation of Perfusion Parameters with Genes
Related to Angiogenesis Regulation in GBM
 19 genes had
significant
correlation with PS
(p<0.05)
 9 genes had
significant
correlation with CBV
(p<0.05)
 5 genes showed
significant
correlation with both
CBV and PS.
Jain, R. et al. Am J Neuroradiology Published March 15, 2012
as 10.3174/ajnr.A2956.
Pro-angiogenic Genes
• KDR VEGFR-2
(CBV correlation co-efficient 0.60, p=0.0084; PS 0.59,
p=0.0097)
• HIF 1a (Hypoxia inducible factor 1alpha)
(CBV 0.29, p=0.29; PS 0.66, p=0.008)
• TNFRSF-1A (Tumor necrosis factor
receptor superfamily, member 1A)
(CBV -0.23, p=0.3673; PS 0.53, p=0.0239)
• TIE1
(CBV 0.54, P = 0.0217; PS 0.49, P = 0.0389)
• TIE2/TEK
(CBV 0.58, P = 0.0119; PS 0.46, P = 0.0550 )
Significant Correlation with CBV
Significant Correlation with PS
Significant correlation with both CBV and PS
Jain, R. et al. Am J Neuroradiology Published March 15, 2012
as 10.3174/ajnr.A2956.
Anti-angiogenic Genes
 VASH 2 Vasohibin 2
(CBV correlation co-efficient -0.35, P = 0.1568,
PS -0.71, P = 0.0011)
 CX3CR1
(CBV -0.66, P = 0.0028; PS -0.49, P = 0.0375)
• WNT5A
(CBV -0.10, P = 0.6833; PS -0.52, P = 0.0284)
• C3
(CBV -0.63, P = 0.0051; PS -0.41, P = 0.0953)
Jain, R. et al. Am J Neuroradiology Published March 15, 2012
as 10.3174/ajnr.A2956.
Correlation of Perfusion Parameters with Genes
Related to Angiogenesis Regulation in GBM
 CBV and PS estimates in GBMs can correlate positively
with pro-angiogenic genes
 and inversely with anti-angiogenic genes.
 The results of this preliminary analysis can help
establish a genomic/molecular basis for these commonly
used imaging biomarkers and potentially add to our
knowledge of their immuno-histological bases.
Jain, R. et al. Am J Neuroradiology Published March 15, 2012
as 10.3174/ajnr.A2956.
Genomic mapping and survival prediction in GBM:
Molecular sub-classification as an adjunct to
hemodynamic imaging markers - A TCGA Glioma Phenotype
Research Group Project
Purpose
The purpose of this study was to correlate tumor blood
volume, measured using DSC T2* magnetic resonance
(MR) perfusion with patient survival and also correlate it
with molecular sub-classes of GBM.
Session: 34a - Adult Brain Neoplasms III , abstract 876
Presentation #: Paper O-285, Date/Start Time: 4/24/2012 3:00 PM
Material and Methods: Patient Population
•57 patients with treatment naïve GBM underwent DSC T2* MR
perfusion studies at 2 different institutions and were selected from
TCIA’s TCGA-GBM collection.
•50 patients had gene expression data available from TCGA.
•35 patients at Institute 1 HFH
•3.0 T scanner, n=14
•1.5 T scanner, n=21
•15 patients at Institute 2 UCSF
• 1.5 T scanner, n=15
•According to those TCGA requirements, the pathology was confirmed
as GBM using adequate frozen tissue ≥ 0.5 g consisting of ≥70% tumor
nuclei and < 50% necrosis.
https://wiki.cancerimagingarchive.net/display/Public/DSC+T2*+M
R+Perfusion+Analysis
Material and Methods: MRI Image acquisition
Institute 1
Institute 2
0.1 mmol/kg Gd-DTPA, 5ml/sec
0.1 mmol/kg Gd-DTPA, 5ml/sec
95 phases of GE T2*
60 phases of GE T2*
TR = 1900 msec, TE =40 msec,
and flip angle =90°
TR = 2000 msec, TE =54 msec,
and flip angle =30°
Temporal resolution 2.0 sec
Temporal resolution 2.0 sec
Matrix size128 x 128, 26-cm
FOV
Matrix size128 x 128, 26-cm
FOV
Slice thickness 5 mm
Slice thickness 3-6 mm
Material and Methods: MRP Post-processing
•Studies from both institutions were processed using
NordicICE software (NordicImagingLab AS) using the FDA
approved DSCT2* perfusion module.
•The module corrects for contrast agent leakage from the
intravascular to extracellular space using the method
published by Boxerman et al AJNR Am J Neuroradiol
2006;27(4):859-867.
•Normalized relative cerebral blood volume (rCBV) maps
with leakage correction were produced by the software,
which normalizes the CBV relative to a globally determined
mean value.
Material and Methods: Image Analysis
•ROIs were drawn on the rCBV
maps fused with post-contrast
T1W images and FLAIR images.
•rCBV mean the contrast enhancing
portion of the tumor (excluding any
areas of necrosis and vessels)
•rCBV max 10 x 10 voxel ROI was
placed on the hottest appearing
part of the tumor
•rCBVNEL 3, 10 x 10 voxel ROIs on
non-enhancing FLAIR abnormality
within 1 cm of the edge of the
enhancing lesion
Material and Methods: Statistical Analysis
 Comparison of average rCBV measures between groups was done
using two-sample t-tests or one-way ANOVA.
 Kaplan-Meier estimation was used to calculate median survival and for
some univariate testing.
 For the Kaplan-Meier curves, the log-rank statistic assesses group
differences equally across the full observation time whereas the
Wilcoxon statistic weights the early events more heavily thus identifying
early separation in the curves.
 Survival analysis with Cox proportional hazards models was employed
primarily to estimate hazard ratios and for testing multivariable models.
Material and Methods: Statistical Analysis
 Potential covariates in the multivariable models
– Patient age at diagnosis (years, continuous),
– MR scanner type (1.5T, 3T)
– molecular classification (Verhaak or Phillips) Huse J et al. Glia
2011;59(8):1190-1199.
– Karnofsky performance score (KPS, continuous)
– level of resection (gross-total, sub-total)
 Age and scanner were not significant predictors and did not enhance
the models so they were excluded from the presented models for the
sake of parsimony given the sample size.
 KPS and resection data were only available for samples from
institution 1.
Results: rCBV analysis using
molecular sub-classification
rCBVmean
rCBVmax
rCBVNEL
p=0.66
p=0.95
p=0.43
2.66 (0.78)
4.55 (0.76)
0.66 (0.24)
2.61 (1.26)
4.80 (1.49)
0.88 (0.45)
Neural (n=11)
2.30 (0.84)
4.68 (0.95)
0.81 (0.27)
Proneural (n=12)
2.27 (0.68)
5.06 (3.61)
0.84 (0.26)
p=0.32
p=0.57
p=0.70
Mesenchymal (n=24)
2.68 (1.16)
4.76 (1.33)
0.83 (0.40)
Proneural (n=20)
2.32 (0.72)
5.03 (2.79)
0.83 (0.25)
Proliferative (n=6)
2.15 (0.59)
4.04 (0.65)
0.70 (0.30)
Verhaak
Classical (n=10)
Mesenchymal (n=17)
Phillips
Results: Survival analysis using
Verhaak sub-classification
Present study
•
•
Verhaak et al
Cancer Cell 2010;17(1):98-110
Median overall survival (OS) was 1.14 years (IQR: 0.49, 2.11).
When the Verhaak classification scheme was applied to these samples, the classical
sub-class had the best survival, with median of 2.13 years (IQR: 1.53, 2.59) and the
proneural sub-class had the worst survival with median 0.41 years (IQR: 0.65, 1.19).
Results: Survival analysis using
Verhaak sub-classification
Present study


Verhaak et al
Cancer Cell 2010;17(1):98-110
The difference in survival by Verhaak sub-classification was significant between groups
with the difference being more prominent earlier during follow-up (Wilcoxon P=0.0445,
log-rank P=0.0696).
However, the proneural subclass also had the worst median survival (0.94 years, IQR:
0.78, 1.23) in the publication by Verhaak et al.
Results: Survival analysis using
Phillips sub-classification
Present study
•
•
Phillips et al
Cancer Cell 2006;9(3):157-173
There was no evidence that the Phillips classification was associated with survival in our
sample (log-rank P=0.6432, Wilcoxon P=0.4548).
The best median survival is attributed to the mesenchymal sub-class with 1.28 years
(IQR: 0.61, 2.22), followed closely by the proneural subclass with 1.12 years (IQR: 0.33,
1.86).
Results: Survival analysis using
Phillips sub-classification
Present study
•
Phillips et al
Cancer Cell 2006;9(3):157-173
The proliferative sub-class had the worst median survival at only 0.54 years (IQR: 0.34,
3.96) but this class was only represented by six patients (five deaths), one of whom was
still surviving at 3.96 years
Results: Survival analysis using
only rCBV measures
Parameters
Mean
Max
NEL
1.25 (0.1918)
1.24 (0.0131)
2.45 (0.0555)
1.46 (0.0212)
1.24 (0.0062)
2.56 (0.0704)
(0.0250)
(0.0476)
(0.0917)
Classical
0.21
0.26
0.30
Mesenchymal
0.43
0.48
0.48
Neural
0.44
0.41
0.55
Proneural
1.00
1.00
1.00
1.27 (0.1670)
1.24 (0.0152)
2.51 (0.0566)
(0.5892)
(0.6888)
(0.6533)
Mesenchymal
0.72
0.79
0.74
Proliferative
0.98
1.02
0.87
Proneural
1.00
1.00
1.00
Model 1: rCBV
Model 2: rCBV +
Verhaak
Model 3: rCBV +
Phillips
Results: Survival analysis using
rCBV and molecular sub-classification
Parameters
Mean
Max
NEL
1.25 (0.1918)
1.24 (0.0131)
2.45 (0.0555)
1.46 (0.0212)
1.24 (0.0062)
2.56 (0.0704)
(0.0250)
(0.0476)
(0.0917)
Classical
0.21
0.26
0.30
Mesenchymal
0.43
0.48
0.48
Neural
0.44
0.41
0.55
Proneural
1.00
1.00
1.00
1.27 (0.1670)
1.24 (0.0152)
2.51 (0.0566)
(0.5892)
(0.6888)
(0.6533)
Mesenchymal
0.72
0.79
0.74
Proliferative
0.98
1.02
0.87
Proneural
1.00
1.00
1.00
Model 1: rCBV
Model 2: rCBV +
Verhaak
Model 3: rCBV +
Phillips
Results: Survival analysis using
rCBV and molecular sub-classification
Parameters
Mean
Max
NEL
Model 1: rCBV
1.25 (0.1918)
1.24 (0.0131)
2.45 (0.0555)
Model 2: rCBV +
1.46 (0.0212)
1.24 (0.0062)
2.56 (0.0704)
(0.0250)
(0.0476)
(0.0917)
Classical
0.21
0.26
0.30
Mesenchymal
0.43
0.48
0.48
Neural
0.44
0.41
0.55
Proneural
1.00
1.00
1.00
1.27 (0.1670)
1.24 (0.0152)
2.51 (0.0566)
(0.5892)
(0.6888)
(0.6533)
Mesenchymal
0.72
0.79
0.74
Proliferative
0.98
1.02
0.87
Proneural
1.00
1.00
1.00
Verhaak
Model 3: rCBV +
Phillips
Genomic mapping and survival prediction in GBM:
Molecular sub-classification as an adjunct to
hemodynamic imaging markers
 Hemodynamic tumor measures (rCBV) using MR perfusion
did not have any significant correlation with the various subclasses using the two most commonly accepted molecular
sub-classification systems of GBM.
 But rCBV measures did provide important prognostic
information, as patients in whom the tumor had higher rCBV
showed worse prognosis and poor survival.
Genomic mapping and survival prediction in GBM:
Molecular sub-classification as an adjunct to
hemodynamic imaging markers
 Verhaak sub-classification schema remained significant in
the survival models providing additional survival information
about rCBVmean measurements.
 Even though this needs to be confirmed with a larger sample
size, it highlights the importance of non-invasive in vivo
imaging biomarkers for patient prognosis and survival and
their future role as an adjunct with molecular markers.
Conclusions
 Molecular mapping of GBM can provide important therapy targets by
providing insight into the molecular basis for tumor cell origin.
 Correlation of imaging (morphologic or functional) markers with gene
expression (Radio-genomics) will help in better understanding,
standardization and improved use of the Imaging Biomarkers.
 Integration of imaging markers with molecular or genomic data
(Radio-genomics) can provide important prognostic information that
may be used as an adjunct to genomic markers in future.
[email protected]