Citations

Abstract Glioblastomas (GBMs) are known to be complex tumors comprising multiple subpopulations of genetically-distinct cancer cells; it is thought that this genetic variation is a major factor in the lack of observed survival benefit of treatment regimes that target one of these subpopulations. The field of radiogenomics seeks to study correlations between MRI patterns and genetic features of GBM tumors. Spatial radiogenomic maps produced using machine-learning (ML) methods that are trained against information from image-localized patient biopsies identify regions where particular cancer sub-populations are predicted to occur within a GBM, thus non-invasively characterizing the regional genetic variability of these tumors. These tumor subpopulations may also interact with one another, in ways which may be of a competitive or cooperative nature to varying degrees. It is important to ascertain the nature of these interactions, as they may have implications for treatment response to targeted therapies, and characterization of the spatio-temporal dynamics of these co-evolving sub-populations will shed light on why some therapies fail. Here we combine mathematical modeling techniques and spatially-resolved radiogenomic maps to study the nature of these interactions between molecularly-distinct GBM subpopulations. We model the interactions between cell populations using a partial differential equation based formalism. The model is parameterized using radiogenomic ML maps from which we infer the nature of interactions between subpopulations. Furthermore, using maps as inputs, the model turns static maps into dynamic information, thus providing insight into how these subpopulations composing the tumor change over time and the effect this has on observed treatment response for individual patients.

Abstract Therapeutic resistance stemming from inter and intra-tumoral heterogeneity is a significant impediment towards development of effective therapeutics for glioblastoma. We hypothesized that microRNAs can potentially counteract resistance emanating from such heterogeneity as they simultaneously modulate the expression of multiple proteins. We identified microRNA-34a as a unique microRNA which modulates multiple oncoproteins in GBM using two different in silico approaches. We investigated the therapeutic effects of microRNA-34a in three primary patient-derived xenografts (PDX) representing classical (GBM6), proneural (GBM118) and mesenchymal (GBM118) subtypes; four established cell lines (T98G, U251, A172, LN229) and two cell lines with acquired resistance to temozolomide (A172-TR, LN229-TR) in vitro. Glioblastoma cell cultures showed variable responses to temozolomide but microRNA-34a inhibited proliferation in all cell cultures. Furthermore, microRNA-34a also sensitized all tested cell lines to temozolomide (combination index < 0.8, p=.03) and radiation treatment (dose enhancement factor 1.7–2.2, p=0.02). Mechanistically, microRNA 34a down-regulates at least six distinct therapeutic resistance proteins. Importantly, these resistance proteins are expressed in distinct spatial niches and are prognostic for patient survival based on our analysis of the cancer genome atlas (TCGA) data. For in vivo delivery of microRNA-34a, we utilized nanocells which are derived from genetically modified bacteria, loaded with microRNA-34a and tagged with a bispecific antibody targeting EGFR. Nanocells were injected intravenously while temozolomide was administered by oral gavage in an orthotopic PDX model. We confirmed delivery of microRNA-34a to tumor by observing down-regulation of cMet and phosphorylated Akt in treated mice. Importantly, microRNA-34a nanocells resulted in significant reduction in tumor growth (p=0.021), increased survival (p<0.001) with microRNA-34a monotherapy and synergy in combination with temozolomide in vivo. Taken together, our results suggest that delivery of miR-34a may be a powerful new adjuvant for the treatment of glioblastoma in combination with temozolomide that can mitigate both inter- and intra-tumor heterogeneity.

Abstract Fibroblast growth factor-inducible 14 (Fn14) is the cell surface receptor for the tumor necrosis factor (TNF) family member TNF-like weak inducer of apoptosis (TWEAK). The Fn14 gene is not normally expressed in healthy tissues, but expression is significantly increased in many primary solid tumor types, including glioblastoma (GBM). High Fn14 expression in tumors is often associated with the stimulation of cancer cell invasion leading to diffuse, aggressive tumors and metastasis. To investigate the effects of increased Fn14 expression in brain tumors, two different tumor types were developed using the RCAS/tv-a transgenic rat model: a low-Fn14, “proneural-like” (PDGFA/shP53 derived) tumor and a high-Fn14 (PDGFA/shP53/Fn14-derived) tumor. Interestingly, rats with high-Fn14 brain tumors had a significantly shorter median survival compared to those with low-Fn14 with 42 days and 70 days median survival, respectively. Using MR proton spectroscopy, both tumor types exhibited the markers of a malignant glioma such as increases in choline to creatinine ratio (Cho/Cr) and decreases in neuronal activity (NAA); however, high-Fn14 tumors also displayed an increase in lactate during tumor progression which is often a sign of high grade, necrotic tumors and poor prognosis. Morphologically, the two tumor subtypes appear quite different. H&E staining of the high-Fn14 tumors showed a highly malignant tumor with several areas of pseudopalisading necrosis within the tumor core. This is different than the somewhat more homogeneous morphology of the low-Fn14 proneural-like tumor. The pattern of tumor cell invasion also differs between the two brain cancer subtypes. High-Fn14 tumors show a more human GBM-like pattern by invading along the white matter tracts whereas low-Fn14 tumors appear to invade more locally with minimal white matter invasion. High-Fn14 tumors were positive for Fn14 throughout the tumor including the invasive rim of the tumor, which is also seen in human GBM, whereas the low-Fn14 tumor revealed very little staining even at the invasive rim. These findings indicate that increased Fn14 expression creates a more malignant brain tumors that develops and behaves more aggressively then low-Fn14 tumors. Citation Format: Nina Connolly, Heather Ames, Nhan L. Tran, Anthony Kim, Jeff Winkles, Graeme Woodworth. Fn14 overexpression in RCAS/tv-a rat gliomas promotes tumor progression, increases invasion and decreases survival [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3701.

Abstract Summary We present MetaMarker, a pipeline for discovering metagenomic biomarkers from whole-metagenome sequencing samples. Different from existing methods, MetaMarker is based on a de novo approach that does not require mapping raw reads to a reference database. We applied MetaMarker on whole-metagenome sequencing of colorectal cancer (CRC) stool samples from France to discover CRC specific metagenomic biomarkers. We showed robustness of the discovered biomarkers by validating in independent samples from Hong Kong, Austria, Germany and Denmark. We further demonstrated these biomarkers could be used to build a machine learning classifier for CRC prediction. Availability and implementation MetaMarker is freely available at https://bitbucket.org/mkoohim/metamarker under GPLv3 license. Supplementary information Supplementary data are available at Bioinformatics online.