Faculty Bibliography

Background: Treatment effectiveness and overall prognosis for glioma patients depend heavily on the genetic and epigenetic factors in each individual tumor. However, intra-tumoral genetic heterogeneity is known to exist and needs to be managed. Currently, evidence for genetic changes varying spatially within the tumor is qualitative, and quantitative data is lacking. We hypothesized that a greater genetic diversity or "genetic distance" would be observed for distinct tumor samples taken with larger physical distances between them. Methods: Stereotactic biopsies were obtained from untreated primary glioma patients as part of a clinical trial between 2011 and 2016, with at least one biopsy pair collected in each case. The physical (Euclidean) distance between biopsy sites was determined using coordinates from imaging studies. The tissue samples underwent whole exome DNA sequencing and epigenetic methylation profiling and genomic distances were defined in three separate ways derived from differences in number of genes, copy number variations (CNV), and methylation profiles. Results: Of the 31 patients recruited to the trial, 23 were included in DNA methylation analysis, for a total of 71 tissue samples (14 female, 9 male patients, age range 21-80). Samples from an 8 patient subset of the 23 evaluated patients were further included in whole exome and copy number variation analysis. Physical and genomic distances were found to be independently and positively correlated for each of the three genomic distance measures. The correlation coefficients were 0.63, 0.65, and 0.35, respectively for (a) gene level mutations, (b) copy number variation, and (c) methylation status. We also derived quantitative linear relationships between physical and genomic distances. Conclusion: Primary brain tumors are genetically heterogeneous, and the physical distance within a given glioma correlates to genomic distance using multiple orthogonal genomic assessments. These data should be helpful in the clinical diagnostic and therapeutic management of glioma, for example by: managing sampling error, and estimating genetic heterogeneity using simple imaging inputs.

BACKGROUND:Glioblastoma (GBM) represents an aggressive cancer type with a median survival of only 14 months. With fewer than 5% of patients surviving five years, comprehensive profiling of these rare patients could elucidate prognostic biomarkers that may confer better patient outcomes. We utilized multiple molecular approaches to characterize the largest patient cohort of long-term IDH-wildtype GBM survivors (LTS) to date. METHODS:Retrospective analysis was performed on 49 archived formalin-fixed paraffin embedded tumor specimens from patients diagnosed with GBM at the Mayo Clinic between December 1995 and September 2013. These patient samples were subdivided into two groups based on survival (12 LTS, 37 short-term survivors (STS)) and subsequently examined by mutation sequencing, copy number analysis, methylation profiling, and gene expression. RESULTS:Of the 49 patients analyzed in this study, LTS were younger at diagnosis (p=0.016), more likely to be female (p=0.048), and MGMT promoter methylated (UniD, p=0.01). IDH-wildtype STS and LTS demonstrated classic GBM mutations and copy number changes. Pathway analysis of differentially expressed genes showed LTS enrichment for sphingomyelin metabolism, which has been linked to decreased GBM growth, invasion, and angiogenesis. STS enriched for DNA repair and cell cycle control networks. CONCLUSIONS:While our findings largely report remarkable similarity between these LTS and more typical STS, unique attributes were observed in regard to altered gene expression and pathway enrichment. These attributes may be valuable prognostic markers and are worth further examination. Importantly, this study also underscores the limitations of existing biomarkers and classification methods in predicting patient prognosis.

Ionizing radiation (IR) and chemotherapy are standard-of-care treatments for glioblastoma (GBM) patients and both result in DNA damage, however, the clinical efficacy is limited due to therapeutic resistance. We identified a mechanism of such resistance mediated by phosphorylation of PTEN on tyrosine 240 (pY240-PTEN) by FGFR2. pY240-PTEN is rapidly elevated and bound to chromatin through interaction with Ki-67 in response to IR treatment and facilitates the recruitment of RAD51 to promote DNA repair. Blocking Y240 phosphorylation confers radiation sensitivity to tumors and extends survival in GBM preclinical models. Y240F-Pten knockin mice showed radiation sensitivity. These results suggest that FGFR-mediated pY240-PTEN is a key mechanism of radiation resistance and is an actionable target for improving radiotherapy efficacy.

Mutational inactivation of ATRX (α-thalassemia mental retardation X-linked) represents a defining molecular alteration in large subsets of malignant glioma. Yet the pathogenic consequences of ATRX deficiency remain unclear, as do tractable mechanisms for its therapeutic targeting. Here we report that ATRX loss in isogenic glioma model systems induces replication stress and DNA damage by way of G-quadruplex (G4) DNA secondary structure. Moreover, these effects are associated with the acquisition of disease-relevant copy number alterations over time. We then demonstrate, both in vitro and in vivo, that ATRX deficiency selectively enhances DNA damage and cell death following chemical G4 stabilization. Finally, we show that G4 stabilization synergizes with other DNA-damaging therapies, including ionizing radiation, in the ATRX-deficient context. Our findings reveal novel pathogenic mechanisms driven by ATRX deficiency in glioma, while also pointing to tangible strategies for drug development.

Cancer stem cells (CSCs) are a heterogeneous and dynamic self-renewing population that stands at the top of tumor cellular hierarchy and contribute to tumor recurrence and therapeutic resistance. As methods of CSC isolation and functional interrogation advance, there is a need for a reliable and accessible quantitative approach to assess heterogeneity and state transition dynamics in CSCs. We developed a high-throughput automated single cell imaging analysis (HASCIA) approach for the quantitative assessment of protein expression with single-cell resolution and applied the method to investigate spatiotemporal factors that influence CSC state transition using glioblastoma (GBM) CSCs (GSCs) as a model system. We were able to validate the quantitative nature of this approach through comparison of the protein expression levels determined by HASCIA to those determined by immunoblotting. A virtue of HASCIA was exemplified by detection of a subpopulation of SOX2-low cells, which expanded in fraction size during state transition. HASCIA also revealed that GSCs were committed to loose stem cell state at an earlier time point than the average SOX2 level decreased. Functional assessment of stem cell frequency in combination with the quantification of SOX2 expression by HASCIA defined a stable cutoff of SOX2 expression level for stem cell state. We also developed an approach to assess local cell density and found that denser monolayer areas possess higher average levels of SOX2, higher cell diversity, and a presence of a sub-population of slowly proliferating SOX2-low GSCs. HASCIA is an open source software that facilitates understanding the dynamics of heterogeneous cell population such as that of GSCs and their progeny. It is a powerful and easy-to-use image analysis and statistical analysis tool available at https://hascia.lerner.ccf.org. © 2019 International Society for Advancement of Cytometry.

Unresectable glioblastoma (GBM) cells in the invading tumor edge can act as seeds for recurrence. The molecular and phenotypic properties of these cells remain elusive. Here, we report that the invading edge and tumor core have two distinct types of glioma stem-like cells (GSCs) that resemble proneural (PN) and mesenchymal (MES) subtypes, respectively. Upon exposure to ionizing radiation (IR), GSCs, initially enriched for a CD133⁺ PN signature, transition to a CD109⁺ MES subtype in a C/EBP-β-dependent manner. Our gene expression analysis of paired cohorts of patients with primary and recurrent GBMs identified a CD133-to-CD109 shift in tumors with an MES recurrence. Patient-derived CD133^-/CD109⁺ cells are highly enriched with clonogenic, tumor-initiating, and radiation-resistant properties, and silencing CD109 significantly inhibits these phenotypes. We also report a conserved regulation of YAP/TAZ pathways by CD109 that could be a therapeutic target in GBM.

First-order radiomic features, such as metabolic tumor volume (MTV) and total lesion glycolysis (TLG), are associated with disease progression in early-stage classical Hodgkin lymphoma (HL). We hypothesized that a model incorporating first- and second-order radiomic features would more accurately predict outcome than MTV or TLG alone. We assessed whether radiomic features extracted from baseline PET scans predicted relapsed or refractory disease status in a cohort of 251 patients with stage I-II HL who were managed at a tertiary cancer center. Models were developed and tested using a machine-learning algorithm. Features extracted from mediastinal sites were highly predictive of primary refractory disease. A model incorporating 5 of the most predictive features had an area under the curve (AUC) of 95.2% and total error rate of 1.8%. By comparison, the AUC was 78% for both MTV and TLG and was 65% for maximum standardize uptake value (SUV_max). Furthermore, among the patients with refractory mediastinal disease, our model distinguished those who were successfully salvaged from those who ultimately died of HL. We conclude that our PET radiomic model may improve upfront stratification of early-stage HL patients with mediastinal disease and thus contribute to risk-adapted, individualized management.