Ahmed Elakkad

Glioblastoma-infiltrated innate immune cells resemble M0 macrophage phenotype

Glioblastomas are highly infiltrated by diverse immune cells, including microglia, macrophages, and myeloid-derived suppressor cells (MDSCs). Understanding the mechanisms by which glioblastoma-associated myeloid cells (GAMs) undergo metamorphosis into tumor-supportive cells, characterizing the heterogeneity of immune cell phenotypes within glioblastoma subtypes, and discovering new targets can help the design of new efficient immunotherapies. In this study, we performed a comprehensive battery of immune phenotyping, whole-genome microarray analysis, and microRNA expression profiling of GAMs with matched blood monocytes, healthy donor monocytes, normal brain microglia, nonpolarized M0 macrophages, and polarized M1, M2a, M2c macrophages. Glioblastoma patients had an elevated number of monocytes relative to healthy donors. Among CD11b+ cells, microglia and MDSCs constituted a higher percentage of GAMs than did macrophages. GAM profiling using flow cytometry studies revealed a continuum between the M1- and M2-like phenotype. Contrary to current dogma, GAMs exhibited distinct immunological functions, with the former aligned close to nonpolarized M0 macrophages.

139 Clinically Applicable and Biologically Validated MRI Radiomic Test Method Predicts Glioblastoma Genomic Landscape and Survival.

INTRODUCTION Imaging is the modality of choice for noninvasive characterization of biological tissue and organ systems; imaging serves as early diagnostic tool for most disease processes and is rapidly evolving, thus transforming the way we diagnose and follow patients over time. A vast number of cancer imaging characteristics have been correlated to underlying genomics; however, none have established causality. Therefore, our objectives were to test if there is a causal relationship between imaging and genomic information; and to develop a clinically relevant radiomic pipeline for glioblastoma molecular characterization. METHODS Functional validation was performed using a prototypic in vivo RNA-interference-based orthotopic xenograft mouse model. The automated pipeline collects 4800 MRI-derived texture features per tumor. Using univariate feature selection and boosted tree predictive modeling, a patient-specific genomic probability map was derived and patient survival predicted (The Cancer Genome Atlas/MD Anderson data sets). RESULTS Data demonstrated a significant xenograft to human association (area under the curve [AUC] 84%, P < .001). Further, epidermal growth factor receptor amplification (AUC 86%, P < .0001), O-methylguanine-DNA-methyltransferase methylation/expression (AUC 92%, P = .001), glioblastoma molecular subgroups (AUC 88%, P = .001), and survival in 2 independent data sets (AUC 90%, P < .001) was predicted. CONCLUSION Our results for the first time illustrate a causal relationship between imaging features and genomic tumor composition. We present a directly clinically applicable analytical imaging method termed Radiome Sequencing to allow for automated image analysis, prediction of key genomic events, and survival. This method is scalable and applicable to any type of medical imaging. Further, it allows for human-mouse matched coclinical trials, in-depth end point analysis, and upfront noninvasive high-resolution radiomics-based diagnostic, prognostic, and predictive biomarker development.

A co-clinical radiogenomic validation study - Conserved magnetic resonance radiomic appearance of Periostin expressing Glioblastoma in patients and xenograft models

Purpose: Radiomics is the extraction of multidimensional imaging features, which when correlated with genomics, is termed radiogenomics. However, radiogenomic biological validation is not sufficiently described in the literature. We seek to establish causality between differential gene expression status and MRI-extracted radiomic-features in glioblastoma. Experimental Design: Radiogenomic predictions and validation were done using the Cancer Genome Atlas and Repository of Molecular Brain Neoplasia Data glioblastoma patients (n = 93) and orthotopic xenografts (OX; n = 40). Tumor phenotypes were segmented, and radiomic-features extracted using the developed radiome-sequencing pipeline. Patients and animals were dichotomized on the basis of Periostin (POSTN) expression levels. RNA and protein levels confirmed RNAi-mediated POSTN knockdown in OX. Total RNA of tumor cells isolated from mouse brains (knockdown and control) was used for microarray-based expression profiling. Radiomic-features were utilized to predict POSTN expression status in patient, mouse, and interspecies. Results: Our robust pipeline consists of segmentation, radiomic-feature extraction, feature normalization/selection, and predictive modeling. The combination of skull stripping, brain-tissue focused normalization, and patient-specific normalization are unique to this study, providing comparable cross-platform, cross-institution radiomic features. POSTN expression status was not associated with qualitative or volumetric MRI parameters. Radiomic features significantly predicted POSTN expression status in patients (AUC: 76.56%; sensitivity/specificity: 73.91/78.26%) and OX (AUC: 92.26%; sensitivity/specificity: 92.86%/91.67%). Furthermore, radiomic features in OX were significantly associated with patients with similar POSTN expression levels (AUC: 93.36%; sensitivity/specificity: 82.61%/95.74%; P = 02.021E−15). Conclusions: We determined causality between radiomic texture features and POSTN expression levels in a preclinical model with clinical validation. Our biologically validated radiomic pipeline also showed the potential application for human–mouse matched coclinical trials.

Abstract 4217: First pre-clinical validation of radiogenomics in glioblastoma

A plethora of Magnetic Resonance Imaging (MRI) features have been correlated to cancer genomics to date, however, none have established causality. Here, we present an in vivo xenograft RNA interference validated, potentially clinically applicable test method termed “Magnetic Resonance Radiomic Sequencing” (MRRS) for the noninvasive detection of cancer genomics in Glioblastoma. MRRS comprehensively assesses the entire tumor mass using imaging texture-based algorithms that generate thousands of variables (features) inherent to the tumor. Two independent glioblastoma stem cells (GSC1 and GSC3) harboring doxycycline inducible short hairpin RNA against Periostin (POSTN), a gene previously identified in our radiogenomic screen, were implanted at orthotopic location in nude mouse brain. In vivo knockdown of >90% and ∼40% POSTN gene was achieved in GSC3 and GSC1 respectively. The T2 and T1 post MRI texture features, in edema and contrast enhancement phenotype features were compared between doxycycline (POSTN knockdown) and sucrose (control) group of mice using T test statistics. The significant features were included in a Stepwise Forward Logistic Regression analysis to build the final predictive model. The accuracy of the model was tested using ROC cure analysis. Among 3600 features in GSC3 mice cohort, 117 features were significantly (p value Citation Format: Pascal Zinn, Sanjay Singh, Markus M. Luedi, Faramak Zandi, Aikaterini Kotrotsou, Masumeh Hatami, Ginu Thomas, Ahmed Elakkad, Joy Gumin, Erik P. Sulman, Frederick Lang, David Piwnica-Worms, Rivka R. Colen. First pre-clinical validation of radiogenomics in glioblastoma. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4217.