ABSTRACT
Effective treatments that extend survival of malignant brain tumor glioblastoma (GBM) have not changed in more than a decade; however, there exists a minority patient group (<5%) whose survival is longer than 3 yr. We herein present a case report of a long-term surviving 51-yr-old female diagnosed with a MGMT unmethylated GBM. The patient was progression-free for 23 mo. Fresh primary and recurrent tumor samples were collected and processed for patient-derived model development. Whole-genome sequencing (WGS) was performed concurrently with additional standard of care diagnostics. WGS revealed a hypermutated genotype in the germline tissue and in both the primary and recurrent tumor samples. Specific to the matched tumors, an average of 30 cancer driver genes were mutated. Noteworthy was the identification of a nonsynonymous mutation in the POLE gene. As a possible instigator of the hypermutational genotype observed in the tumors, we identified nonsynonymous germline mutations within the mismatch repair genes, MLH1 and PMS2 Mutations within these genes are often indicative of the pan-cancer phenotype known as Lynch syndrome; however, their pathogenicity remains unreported. We performed a drug screen of 165 compounds, which identified one compound, YM155, an experimental survivin inhibitor, that showed effectivity to the patient-derived cell lines of both tumors. Treatment selection based on a patient's genome to individualize treatment for GBM patients could potentially be useful in the clinic. This is a promising avenue for further translational research, with larger databases and integrated platforms to increase the efficiency of analyzing and interpreting the individual genomic data of GBM.
Subject(s)
Brain Neoplasms/genetics , Glioblastoma/genetics , Imidazoles/pharmacology , Naphthoquinones/pharmacology , Brain Neoplasms/diagnostic imaging , Brain Neoplasms/drug therapy , DNA Mismatch Repair/genetics , Drug Screening Assays, Antitumor , Female , Gene Regulatory Networks , Genotype , Germ-Line Mutation , Glioblastoma/diagnostic imaging , Glioblastoma/drug therapy , Humans , Middle Aged , Mutation , Neoplasm Recurrence, Local , Phenotype , Whole Genome SequencingABSTRACT
INTRODUCTION: There are many potential biomarkers in glioblastoma (GBM), and meta-analyses represent the highest level of evidence when inferring their prognostic significance. It is possible however, that inherent design properties of the studies included in these meta-analyses may affect the pooled hazard ratio (HR) of the meta-analyses. This meta-epidemiological study aims to investigate the potential bias of three study-level properties in meta-analyses of GBM biomarkers currently published in the literature. METHODS: Seven electronic databases from inception to December 2017 were searched for meta-analyses evaluating different GBM biomarkers, which were screened against specific criteria. Study-level data were extracted from each meta-analysis, and analyzed using logistic regression to yield the ratio of HR (RHR) summary statistic. RESULTS: Nine meta-analyses investigating different GBM biomarkers were included. Of all the meta-analyses, the HRs of two studies were significantly underestimated by older studies; they investigated biomarkers IDH1 (RHR = 1.145; p = 0.017) and CD133 (RHR = 0.850; p = 0.013). Study-level size and design showed non-significant trends towards affecting the overall HR in all included meta-analyses. CONCLUSIONS: This meta-epidemiological study demonstrated that study-level year can already significantly affect the pooled HR of GBM biomarkers reported by meta-analyses. It is possible that in the future, more study-level properties will exert significant effect. In terms of future GBM biomarker meta-analyses, special consideration of bias should be given to these study-level properties as potential sources of effect on the prognostic pooled HR.
Subject(s)
Biomarkers, Tumor/metabolism , Brain Neoplasms/pathology , Glioblastoma/pathology , Brain Neoplasms/epidemiology , Brain Neoplasms/metabolism , Epidemiologic Studies , Glioblastoma/epidemiology , Glioblastoma/metabolism , Humans , PrognosisABSTRACT
Quantification of cellular antigens and their interactions via antibody-based detection methods are widely used in scientific research. Accurate high-throughput quantitation of these assays using general image analysis software can be time consuming and challenging, particularly when attempted by users with limited image processing and analysis knowledge. To overcome this, we have designed Andy's Algorithms, a series of automated image analysis pipelines for FIJI, that permits rapid, accurate and reproducible batch-processing of 3,3'-diaminobenzidine (DAB) immunohistochemistry, proximity ligation assays (PLAs) and other common assays. Andy's Algorithms incorporates a step-by-step tutorial and optimization pipeline to make batch image analysis simple for the untrained user and adaptable across laboratories. Andy's algorithms provide a simpler, faster, standardized work flow compared to existing programs, while offering equivalent performance and additional features, in a free to use open-source application of FIJI. Andy's Algorithms are available at GitHub, publicly accessed at https://github.com/andlaw1841/Andy-s-Algorithm .
