Glioblastoma (GB) is the most common and aggressive malignant adult brain tumor, with grim prognosis and heterogeneous molecular and imaging profiles. Although the currently applicable treatment options (i.e., surgery, radiotherapy, chemotherapy) have expanded during the last 20 years, there is no substantial improvement in the OS rates. The major obstacle in treating GBM patients is the heterogeneity of their molecular landscape. Determination of molecular targets requires ex vivo postoperative tissue analyses, which are limited in assessing the tumor's spatial heterogeneity (sampling error due to single sample histopathological and molecular analysis) and temporal heterogeneity (not possible to continuously assess the molecular transformation of the tumor during treatment). Herein we propose to develop quantitative imaging phenomic (QIP) markers of a range of mutations of interest in GB. We will build on prior work on EGFR, IDH1 mutations and MGMT methylation QIP signatures, and develop an extensive panel of imaging signatures of 10 gene mutations, as well as MGMT promoter methylation, using machine learning methods applied to relatively routine clinical mpMRI (standard plus diffusion tensor and perfusion protocols). Availability of such biomarkers can contribute to non-invasive i) patient stratification into appropriate treatments, ii) measurement of individual molecular characteristics. In particular, we propose to carry out the following specific aims:
Specific Aim 1 (SA1): To develop the enabling methodologies for constructing Quantitative Imaging Phenomic signatures of GB mutations Specific Aim 2 (SA2): Establish QIP signatures of 10 mutations of interest in gliomas, plus MGMT promoter status, using next generation sequencing (NGS). We will use 709 datasets.
Specific Aim 3 (SA3): Characterize the molecular heterogeneity of GB using QIP signatures, leveraging the NGS samples of SA1, as well as a new sample that we will genotype, adding to a total of 600 tumor samples obtained from 4 different locations per patient from 150 patients. The first 150 tissue samples are already analyzed as part of ongoing work.
Specific Aim 4 (SA4): Integrate our methods into the Cancer Imaging Phenomics Toolkit (CaPTk), in order to allow easy access to them by users
This project will investigate the relationship between imaging characteristics of glioblastoma and its underlying genetic mutations/variants. As such, it aims to develop a number of imaging signatures of mutations that are important in GBM, using 1,159 (709 patients plus an additional 450 tissue samples of 150 of these patients obtained from different locations in the tumor) analyzed via next generation sequencing. The primary motivation of this work is to develop the imaging analytics methodologies and associated biomarkers that will allow us to evaluate the spatial, temporal and molecular heterogeneity of glioblastoma, thereby assisting in patient stratification and treatment.
|Rathore, Saima; Bakas, Spyridon; Pati, Sarthak et al. (2018) Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma. Brainlesion (2017) 10670:133-145|
|Fathi Kazerooni, Anahita; Nabil, Mahnaz; Zeinali Zadeh, Mehdi et al. (2018) Characterization of active and infiltrative tumorous subregions from normal tissue in brain gliomas using multiparametric MRI. J Magn Reson Imaging 48:938-950|
|Davatzikos, Christos; Rathore, Saima; Bakas, Spyridon et al. (2018) Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome. J Med Imaging (Bellingham) 5:011018|
|Rathore, Saima; Akbari, Hamed; Rozycki, Martin et al. (2018) Radiomic MRI signature reveals three distinct subtypes of glioblastoma with different clinical and molecular characteristics, offering prognostic value beyond IDH1. Sci Rep 8:5087|
|Akbari, Hamed; Bakas, Spyridon; Pisapia, Jared M et al. (2018) In vivo evaluation of EGFRvIII mutation in primary glioblastoma patients via complex multiparametric MRI signature. Neuro Oncol 20:1068-1079|
|Mang, Andreas; Biros, George (2017) A SEMI-LAGRANGIAN TWO-LEVEL PRECONDITIONED NEWTON-KRYLOV SOLVER FOR CONSTRAINED DIFFEOMORPHIC IMAGE REGISTRATION. SIAM J Sci Comput 39:B1064-B1101|
|Sotiras, Aristeidis; Toledo, Jon B; Gur, Raquel E et al. (2017) Patterns of coordinated cortical remodeling during adolescence and their associations with functional specialization and evolutionary expansion. Proc Natl Acad Sci U S A 114:3527-3532|
|Bakas, Spyridon; Akbari, Hamed; Pisapia, Jared et al. (2017) In Vivo Detection of EGFRvIII in Glioblastoma via Perfusion Magnetic Resonance Imaging Signature Consistent with Deep Peritumoral Infiltration: The ?-Index. Clin Cancer Res 23:4724-4734|
|Mang, Andreas; Ruthotto, Lars (2017) A LAGRANGIAN GAUSS-NEWTON-KRYLOV SOLVER FOR MASS- AND INTENSITY-PRESERVING DIFFEOMORPHIC IMAGE REGISTRATION. SIAM J Sci Comput 39:B860-B885|
|Mang, Andreas; Biros, George (2016) Constrained H1-regularization schemes for diffeomorphic image registration. SIAM J Imaging Sci 9:1154-1194|
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