Faculty Bibliography

Purpose The American Society for Radiation Oncology (ASTRO) produced an evidence-based guideline on radiation therapy for glioblastoma. Because of its relevance to the ASCO membership, ASCO reviewed the guideline and applied a set of procedures and policies used to critically examine guidelines developed by other organizations. Methods The ASTRO guideline on radiation therapy for glioblastoma was reviewed for developmental rigor by methodologists. An ASCO endorsement panel updated the literature search and reviewed the content and recommendations. Results The ASCO endorsement panel determined that the recommendations from the ASTRO guideline, published in 2016, are clear, thorough, and based on current scientific evidence. ASCO endorsed the ASTRO guideline on radiation therapy for glioblastoma and added qualifying statements. Recommendations Partial-brain fractionated radiotherapy with concurrent and adjuvant temozolomide is the standard of care after biopsy or resection of newly diagnosed glioblastoma in patients up to 70 years of age. Hypofractionated radiotherapy for elderly patients with fair to good performance status is appropriate. The addition of concurrent and adjuvant temozolomide to hypofractionated radiotherapy seems to be safe and efficacious without impairing quality of life for elderly patients with good performance status. Reasonable options for patients with poor performance status include hypofractionated radiotherapy alone, temozolomide alone, or best supportive care. Focal reirradiation represents an option for select patients with recurrent glioblastoma, although this is not supported by prospective randomized evidence. Additional information is available at www.asco.org/glioblastoma-radiotherapy-endorsement and www.asco.org/guidelineswiki .

BACKGROUND:Dexamethasone is reported to induce both tumor-suppressive and tumor-promoting effects. The purpose of this study was to identify the genomic impact of dexamethasone in glioblastoma stem cell (GSC) lines and its prognostic value; furthermore, to identify drugs that can counter these side effects of dexamethasone exposure. METHODS:We utilized 3 independent GSC lines with tumorigenic potential for this study. Whole-genome expression profiling and pathway analyses were done with dexamethasone-exposed and control cells. GSCs were also co-exposed to dexamethasone and temozolomide. Risk scores were calculated for most affected genes, and their associations with survival in The Cancer Genome Atlas and Repository of Molecular Brain Neoplasia Data databases. In silico Connectivity Map analysis identified camptothecin as antagonist to dexamethasone-induced negative effects. RESULTS:Pathway analyses predicted an activation of dexamethasone network (z-score: 2.908). Top activated canonical pathways included "role of breast cancer 1 in DNA damage response" (P=1.07E-04). GSCs were protected against temozolomide-induced apoptosis when coincubated with dexamethasone. Altered cellular functions included cell movement, cell survival, and apoptosis with z-scores of 2.815, 5.137, and -3.122, respectively. CCAAT/enhancer binding protein beta (CEBPB) was activated in a dose dependent manner specifically in slow-dividing "stem-like" cells. CEBPB was activated in dexamethasone-treated orthotopic tumors. Patients with high risk scores had significantly shorter survival. Camptothecin was validated as potential partial neutralizer of dexamethasone-induced oncogenic effects. CONCLUSIONS:Dexamethasone exposure induces a genetic program and CEBPB expression in GSCs that adversely affects key cellular functions and response to therapeutics. High risk scores associated with these genes have negative prognostic value in patients. Our findings further suggest camptothecin as a potential neutralizer of adverse dexamethasone-mediated effects.

BACKGROUND:Glioma, the most common primary brain neoplasm, describes a heterogeneous tumor of multiple histologic subtypes and cellular origins. At clinical presentation, gliomas are graded according to the World Health Organization guidelines (WHO), which reflect the malignant characteristics of the tumor based on histopathological and molecular features. Lower grade diffuse gliomas (LGGs) (WHO Grade II-III) have fewer malignant characteristics than high-grade gliomas (WHO Grade IV), and a better clinical prognosis, however, accurate discrimination of overall survival (OS) remains a challenge. In this study, we aimed to identify tissue-derived image features using a machine learning approach to predict OS in a mixed histology and grade cohort of lower grade glioma patients. To achieve this aim, we used H and E stained slides from the public LGG cohort of The Cancer Genome Atlas (TCGA) to create a machine learned dictionary of "image-derived visual words" associated with OS. We then evaluated the combined efficacy of using these visual words in predicting short versus long OS by training a generalized machine learning model. Finally, we mapped these predictive visual words back to molecular signaling cascades to infer potential drivers of the machine learned survival-associated phenotypes. METHODS:We analyzed digitized histological sections downloaded from the LGG cohort of TCGA using a bag-of-words approach. This method identified a diverse set of histological patterns that were further correlated with OS, histology, and molecular signaling activity using Cox regression, analysis of variance, and Spearman correlation, respectively. A support vector machine (SVM) model was constructed to discriminate patients into short and long OS groups dichotomized at 24-month. RESULTS:This method identified disease-relevant phenotypes associated with OS, some of which are correlated with disease-associated molecular pathways. From these image-derived phenotypes, a generalized SVM model which could discriminate 24-month OS (area under the curve, 0.76) was obtained. CONCLUSION/CONCLUSIONS:Here, we demonstrated one potential strategy to incorporate image features derived from H and E stained slides into predictive models of OS. In addition, we showed how these image-derived phenotypic characteristics correlate with molecular signaling activity underlying the etiology or behavior of LGG.

Glioblastoma stem cells (GSCs) are implicated in tumor neovascularization, invasiveness, and therapeutic resistance. To illuminate mechanisms governing these hallmark features, we developed a de novo glioblastoma multiforme (GBM) model derived from immortalized human neural stem/progenitor cells (hNSCs) to enable precise system-level comparisons of pre-malignant and oncogene-induced malignant states of NSCs. Integrated transcriptomic and epigenomic analyses uncovered a PAX6/DLX5 transcriptional program driving WNT5A-mediated GSC differentiation into endothelial-like cells (GdECs). GdECs recruit existing endothelial cells to promote peritumoral satellite lesions, which serve as a niche supporting the growth of invasive glioma cells away from the primary tumor. Clinical data reveal higher WNT5A and GdECs expression in peritumoral and recurrent GBMs relative to matched intratumoral and primary GBMs, respectively, supporting WNT5A-mediated GSC differentiation and invasive growth in disease recurrence. Thus, the PAX6/DLX5-WNT5A axis governs the diffuse spread of glioma cells throughout the brain parenchyma, contributing to the lethality of GBM.

Molecularly defined subclassification is associated with phenotypic malignancy of glioblastoma (GBM). However, current understanding of the molecular basis of subclass conversion that is often involved in GBM recurrence remain rudimentary at best. Here we report that canonical Wnt signalling that is active in proneural (PN) but inactive in mesenchymal (MES) GBM, along with miR-125b and miR-20b that are expressed at high levels in PN compared with MES GBM, comprise a regulatory circuit involving TCF4-miR-125b/miR-20b-FZD6. FZD6 acts as a negative regulator of this circuit by activating CaMKII-TAK1-NLK signalling, which, in turn, attenuates Wnt pathway activity while promoting STAT3 and NF-κB signalling that are important regulators of the MES-associated phenotype. These findings are confirmed by targeting differentially enriched pathways in PN versus MES GBM that results in inhibition of distinct GBM subtypes. Correlative expressions of the components of this circuit are prognostic relevant for clinical GBM. Our findings provide insights for understanding GBM pathogenesis and for improving treatment of GBM.

INTRODUCTION/BACKGROUND: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/CONCLUSIONS: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.