Imaging Genomics: Correlation of Invasive Genomic Composition and Patient Survival Using Qualitative...

Imaging Genomics: Correlation of Invasive Genomic Composition and Patient Survival

Using Qualitative and Quantitative MR Imaging Parameters

RR Colen1, B Mahajan1, A Flanders2, E Huang3, R Jain4, D Gutman5, S Hwang5, J Kirby6, J Freyman6, TCGA Glioma Phenotype Research Group, , F Jolesz1, PO Zinn2

1 Brigham and Women's Hospital, Boston, MA, USA.2 Thomas Jefferson University Hospital, Philadelphia, PA, USA.3 National Cancer Institute, Bethesda, MD, USA. 4 Henry Ford, Detroit, MI, USA. 5 Emory University, Atlanta, GA, USA. 6 SAIC-Frederick, Bethesda, MD, USA.7 M.D. Anderson Cancer Center, Houston, TX, USA.

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© NlH National Center for Image Guided Therapy, 2012

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No Disclosures.

R25 CA089017(RRC) P41 RR019703 (FAJ)

Disclosure


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Microarray technology is a novel method that allows for the simultaneous analysis of whole genome gene- and microRNA expression events.

However, despite the discovery of many new molecular targets and pathways that has resulted from these discoveries, the search for an effective therapy continues.

In order for personalized medicine to transpire, a cost-effective biomarker that accurately reflects underlying molecular cancer compositions is urgentlyneeded.

Introduction


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• Large scale gene- and microRNA based cancer characterization is commonly not performed due to high cost, time and manpower required for data analysis and interpretation.

• Imaging, specifically MRI, is a promising biomarker that can reflect underlying tumor pathology and biological function.

• It can evaluate the entire tumor, including its peritumoral regions which harbor microscopic invasion of cancer cells, the major cause for tumor recurrence.

• It follows that if imaging phenotypes can serve as non-invasive surrogates for cancer genomic events, MRI can provide important information as to the diagnosis, prognosis, and optimal treatment based on personalized genomic based medicine.

Introduction


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• Imaging genomics has emerged as a new field which links the specific imaging traits (radiophenotypes) with gene-expression profiles.

• Advantages of Imaging genomics:• It is non-invasive.• Specific imaging traits(radiophenotypes) can be correlated with gene-

expression profiles• MRI biomarkers can be developed from the conventional and advanced

imaging sequences/techniques.• MRI biomarker signatures can be created based on tumor biology

- Invasion (Flair + Peritumoral perfusion)- Tumor growth (CE + necrosis)- Tumor aggressiveness (Invasion + growth)

Imaging genomics


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• In this presentation, we wish to describe and identify the invasive MRI characteristics in GBM and the implicated genes and microRNAs associated with these invasive features.

• To document the pre-operative qualitative imaging data reflective of invasive tumor growth patterns.

Purpose


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• Retrospective study of 78 treatment naïve GBM patients, whom had both gene- and microRNA expression profiles and pretreatment MR-neuroimaging.

• Image data were obtained from The Cancer Imaging Archive(TCGA) {http://cancerimagingarchive.net/} sponsored by the Cancer Imaging Program, DCTD/NCI/NIH.

Methods and Materials

http://cancerimagingarchive.net/


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Discovery and Validation sets

•To increase the robustness and validity of the analysis, the 78 patients were randomly separated into a discovery and validation set each consisting of 39 patients.

•By using the FLAIR signal volume as criteria for further subgrouping the patients, each set was sub-stratified into high, medium, and low FLAIR volumes (each consisting of 13 patients) corresponding to volumes of high, medium, and low peritumoral edema/invasion, respectively.



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Discovery (N = 26) and validation (N = 26) sets.


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• Image Analysis - Qualitative assessment

VASARI feature set criteria was used for visual assessment of key features of invasion:

- presence of either T1 contrast enhancement of increase T2/FLAIR hyperintensity involving the basal ganglia, corpus callosum (unilateral, bilateral, or contralateral) or brainstem

- presence of subependymal enhancement

- presence of pial enhancement

- presence of peritumoral nonenhancing FLAIR hyperintensity.



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• Image Analysis - Quantitative assessment- Slicer 3.6 (slicer.org) - Segmentation module

The 3D Slicer software 3.6 (http://www.slicer.org) was used for all purposes of image analysis, manipulation and segmentation.

3D Slicer is an open-source software platform developed at our institution (BWH/Harvard Medical School) for medical image processing and 3D visualization of image data.

- Slicer platform provides functionality for segmentation, registration and 3D visualization of imaging data and advanced MRI analysis algorithms.


http://www.slicer.org/


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• Image Analysis - Quantitative assessment

- 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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A 55 year old male patient with a right temporal GBM.

(a) Axial FLAIR image demonstrates segmentation (in blue) of the region of FLAIR hyperintensity corresponding to the area of edema/tumor infiltration.

(b) The segmented edema/tumor infiltration (blue), enhancement (yellow) and necrosis (orange) are seen overlaid on a base post- contrast T1WI.

(c) Axial post-contrast enhanced T1WI demonstrates the segmentation of the enhancement (yellow) and necrosis (orange).


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Models of edema, tumor and necrosis were generated from the previously performed segmentations, and the volumes of the same were automatically calculated.

Volumes of each radiophenotype ( edema/tumor infiltration, enhancing tumor, and necrosis) were then correlated with the genomic findings.


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• Biostatistical Image-Genomic Analysis: A total of 12,764 genes and 555 microRNAs were analyzed (Affymetrix/Agilent chip technology) in each patient.

• Comparative Marker Selection (Broad Institute) identified preferentially up-regulated genomic events in one vs. another predefined patient group (high_low).

• Ingenuity pathway analysis (IPA) Analysis provided insight into molecular-cellular-disease


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Results

High FLAIR RadiophenotypeHigh FLAIR Radiophenotype


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Results

Low FLAIR Low FLAIR RadiophenotypeRadiophenotype


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Results

• Our MRI screen on an invasive MRI radiophenotype identified genes and microRNAs associated with tumor invasion.

• Bioinformatically predicted gene-microRNA regulatory networks in high FLAIR signal GBMs were seen.


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• Kaplan Meier Analysis demonstrated the top upregulated gene, POSTN (known to be associated with invasion and mesenchymal change), to stratify patients into good (low POSTN) and poor (high POSTN) survival groups.

Results

Kaplan Meier curves for Periostin. (a)Days to death(b)Progression free survival.


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Results

(c) Periostin expression levels across the two main GBM subtypes, Mesenchymal and Proneural showed an increase in POSTN expression in the mesenchymal group and (d) shows the concordant inverse expression levels of miR-219 across the Mesenchymal and Proneural subtypes. (e) Note the inverse correlation (Rsq=0.204) with Periostin.

d)

e)


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Results

Kaplan Meier Survival Curve

Qualitative analysis using the Vasari featuresetTwo qualitative invasive features were statistically significant in this patient population: 1) Ependymal enhancement and 2) enhancement across the midline.

Ependymal Enhancement. Those patients with ependymal enhancement had a worse prognosis than those without ( p= 0.0044). This was further a better predictor of survival than age (p=0.4606), a currently used clinical demographic to stratify GBM patient prognosis.


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Results

Kaplan Meier Survival Curve

Qualitative analysis using the Vasari featuresetTwo qualitative invasive features were statistically significant in this patient population: 1) Ependymal enhancement and 2) enhancement across the midline.

Enhancement across the midline. Those patients with enhancement across the midline had a worse prognosis than those without ( p= 0.0087). This was further a better predictor of survival than age(p=0.2298), a currently used clinical demographic to stratify GBM patient prognosis.


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• Invasive features of Glioblastoma can be determined by both qualitative and quantitative assessment of MR imaging parameters.

• Imaging genomics reflect tumor compositions which have genes involved in:- tumor invasion- differences in patient survival

• Invasive MR imaging features and phenotypes can serve as biomarkers to help predict tumor genomic composition and patient survival

Conclusion


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Thank you for your interest!

Acknowledgements: This work was supported by NIH grant R25 CA089017-06A2 (RRC).

Any questions please email: [email protected].

Conclusion

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