Transcript Proneural - Cancer Imaging Archive Wiki

Genomic mapping and survival prediction in GBM:
Molecular sub-classification as an adjunct to
hemodynamic imaging biomarkers - 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
34a- 34a - Adult Brain Neoplasms III
Tuesday, April 24, 2012
12-O-876-ASNR
TCGA Glioma Phenotype
Research Group
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-GBM
Disclosures
 Funded by NCI Contract No. HHSN261200800001E.
Background : 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 GBM 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).
 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
http://cancerimagingarchive.net
Radio-genomics
•Integration of this vast
genomic information with
imaging data (radiogenomics)
•May provide an
opportunity to use some
of the non-invasive
imaging features or
parameters as
biomarkers.
Clinical Features
Imaging
Features
Genomic
Features
Pathologic
Features
Radio-genomics: Morphologic Features
 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
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.
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/TCGA-GBM
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.
 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
Conclusions
 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.
Conclusions
 Verhaak sub-classification schema remained significant
in the survival models providing additional survival
information about rCBVmean measurements.
 Molecular mapping of GBM can provide important
therapy targets by providing insight into the molecular
basis for tumor cell origin; however, in vivo imaging
markers (such as rCBV measures) can provide
important prognostic information that may be used as
an adjunct to genomic markers in future.
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