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A Coclinical Radiogenomic Validation Study

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A Coclinical Radiogenomic Validation Study Published OnlineFirst July 27, 2018; DOI: 10.1158/1078-0432.CCR-17-3420 Precision Medicine and Imaging Clinical Cancer Research A Coclinical Radiogenomic Validation Study: Conserved Magnetic Resonance Radiomic Appearance of Periostin-Expressing Glioblastoma in Patients and Xenograft Models Pascal O. Zinn1,2,3,4, Sanjay K. Singh2,5, Aikaterini Kotrotsou5, Islam Hassan5, Ginu Thomas5, Markus M. Luedi2,6, Ahmed Elakkad5, Nabil Elshafeey5, Tagwa Idris5, Jennifer Mosley2, Joy Gumin3, Gregory N. Fuller7, John F. de Groot8, Veera Baladandayuthapani9, Erik P. Sulman10, Ashok J. Kumar5, Raymond Sawaya3, Frederick F. Lang3, David Piwnica-Worms2, and Rivka R. Colen2,5 Abstract Purpose: Radiomics is the extraction of multidimensional Results: Our robust pipeline consists of segmentation, imaging features, which when correlated with genomics, is radiomic-feature extraction, feature normalization/selection, termed radiogenomics. However, radiogenomic biological and predictive modeling. The combination of skull stripping, validation is not sufficiently described in the literature. brain-tissue focused normalization, and patient-specific nor- We seek to establish causality between differential gene malization are unique to this study, providing comparable expression status and MRI-extracted radiomic-features in cross-platform, cross-institution radiomic features. POSTN glioblastoma. expression status was not associated with qualitative or vol- Experimental Design: Radiogenomic predictions and val- umetric MRI parameters. Radiomic features significantly pre- idation were done using the Cancer Genome Atlas and Repos- dicted POSTN expression status in patients (AUC: 76.56%; itory of Molecular Brain Neoplasia Data glioblastoma patients sensitivity/specificity: 73.91/78.26%) and OX (AUC: 92.26%; (n ¼ 93) and orthotopic xenografts (OX; n ¼ 40). Tumor sensitivity/specificity: 92.86%/91.67%). Furthermore, radio- phenotypes were segmented, and radiomic-features extracted mic features in OX were significantly associated with patients using the developed radiome-sequencing pipeline. Patients with similar POSTN expression levels (AUC: 93.36%; sensi- and animals were dichotomized on the basis of Periostin tivity/specificity: 82.61%/95.74%; P ¼ 02.021EÀ15). (POSTN) expression levels. RNA and protein levels confirm- Conclusions: We determined causality between radiomic ed RNAi-mediated POSTN knockdown in OX. Total RNA of texture features and POSTN expression levels in a preclinical tumor cells isolated from mouse brains (knockdown and model with clinical validation. Our biologically validated control) was used for microarray-based expression profiling. radiomic pipeline also showed the potential application Radiomic-features were utilized to predict POSTN expression for human–mouse matched coclinical trials. Clin Cancer Res; status in patient, mouse, and interspecies. 24(24); 6288–99. Ó2018 AACR. Introduction post-care follow-up. However, despite recent exponential refine- Imaging has transformed the medical field by providing a ments in imaging technologies in terms of acquisition time and noninvasive method to interrogate the human body and under- resolution, we have barely begun to tap the potential of imaging to lying biological processes. Particularly in patients with cancer, characterize tissues or tumors beyond qualitative description or imaging plays a key role throughout the entire treatment para- gross tumor size on routine imaging sequences. In fact, all current digm ranging from diagnosis and assessing treatment response to imaging assessment criteria [such as RECIST, response assessment 1Department of Neurosurgery, Baylor College of Medicine, Houston Texas. Houston, Texas. 10Department of Radiation Oncology, The University of Texas 2Department of Cancer Systems Imaging, The University of Texas MD Anderson MD Anderson Cancer Center, Houston, Texas. Cancer Center, Houston, Texas. 3Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, Texas. 4Department of Cancer Note: Supplementary data for this article are available at Clinical Cancer Biology, Division of Basic Science Research, The University of Texas MD Research Online (http://clincancerres.aacrjournals.org/). Anderson Cancer Center, Houston, Texas. 5Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston Texas. 6Depart- Corresponding Author: Rivka R. Colen, The University of Texas MD Anderson ment of Anesthesiology, Bern University Hospital Inselspital, University of Bern, Cancer Center, 3SCRB4.3606, Unit 1907, 1881 East Road, Houston, TX 77054. Bern, Switzerland. 7Department of Pathology, Section Neuropathology, The Phone: 713-745-8552; Fax: 713-794-5456; E-mail: [email protected] University of Texas MD Anderson Cancer Center, Houston, Texas. 8Department of Neuro-Oncology, Division of Cancer Medicine, The University of Texas MD doi: 10.1158/1078-0432.CCR-17-3420 Anderson Cancer Center, Houston, Texas. 9Department of Biostatistics, Division of Quantitative Sciences, The University of Texas MD Anderson Cancer Center, Ó2018 American Association for Cancer Research. 6288 Clin Cancer Res; 24(24) December 15, 2018 Downloaded from clincancerres.aacrjournals.org on October 1, 2021. © 2018 American Association for Cancer Research. Published OnlineFirst July 27, 2018; DOI: 10.1158/1078-0432.CCR-17-3420 Validation of Radiomics and Radiogenomics (8, 9). Recent studies have demonstrated an association between Translational Relevance imaging features (also termed phenotypes) and cancer histology, Radiomics, the automated high-throughput extraction of tumor grades, and genomics (5, 10–13). The linkage of imaging multidimensional imaging features, captures microscale infor- phenotypes with genomic data is termed radiogenomics (also mation hidden within conventional imaging beyond what is termed imaging genomics; ref. 10). Fundamental to radioge- visible to the naked human eye. The linkage of imaging nomics is the hypothesis that expression of specific sets of genes phenotypes with genomic data is termed radiogenomics. or driver mutations impact the extractable imaging features, but Fundamental to radiogenomics is the hypothesis that expres- this has not been directly demonstrated in a highly robust model. sion of specific sets of genes or driver mutations impacts the Imaging-molecular connections must be established in a extractable imaging features, but this has not been directly robust manner, specifically by establishing causality (as opposed demonstrated in a highly robust model; to date, the linkages to correlations), for its acceptance as a validated tool. To establish between imaging and genomics remain at a correlative stage causality, a closed system is required for in vivo manipulation of and lack established causality. In this study, we seek to address genomic expression patterns and linkage to radiomic features this gap in knowledge; we bring forward an approach for using a preclinical model system. A few studies have tried to integrated end-to-end methodology for extraction of imaging elucidate the biological significance of imaging characteristics radiomic features and validation in both a preclinical closed (14, 15). Joo and colleagues demonstrated that xenograft tumors model system and patient cohort. Further, we demonstrate produced from patient-derived glioblastoma (GBM) cell lines the potential of radiomics in coclinical trials. To our knowl- were similar to their paired human parental tumors (14). In the edge, this is the first study functionally validating imaging- latter study, they found the invasiveness of the parental tumor on molecular features. MRI correlated with invasive measures based on paraffin histo- pathology sections of the xenograft tumor. In a different study using a heterotopic colorectal cancer mouse model, Panth and colleagues found changes in CT imaging features after induced alterations in gene expression and radiation treatment (15). in neuro-oncology (RANO), immune related RECIST (irRECIST), However, to date, the linkages between imaging and genomics and immune related response criteria (irRC)] used to evaluate largely remain at a correlative stage and lack established causality tumor response in the clinical setting and in clinical trials are in a model system relevant for human disease. dependent on changes in tumor size and do not accurately capture In this study, we seek to address this gap in knowledge, which responses to therapy (1–4). With the development of immune can be expected to change patient management. To our knowl- and molecularly targeted therapies, alternative imaging assess- edge, this is the first study to establish causality between imaging- ment criteria and markers are needed that go beyond mere molecular (radiogenomic) features. In this study, we bring for- changes in tumor size. In fact, with some therapies such as ward an approach for an integrated end-to-end methodology immunotherapy, positive response to treatment is associated with for extraction of imaging radiomic features and its validation in initial decrease in tumor size in only 10% of patients, whereas both a preclinical closed model system and in a clinical patient other positive responders fail to show such a measurable initial cohort. We use the most common primary malignant brain decline in tumor size (4). This uncoupling of tumor metrics and tumor characterized by extreme heterogeneity and poor patient response to therapy is a clinical dilemma and challenge for survival (16–18), GBM, as the solid cancer prototype
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