Intravital imaging technology guides FAK-mediated priming in pancreatic cancer precision medicine according to Merlin status.

Pancreatic ductal adenocarcinoma (PDAC) is a highly metastatic, chemoresistant malignancy and is characterized by a dense, desmoplastic stroma that modulates PDAC progression. Here, we visualized transient manipulation of focal adhesion kinase (FAK), which integrates bidirectional cell-environment signaling, using intravital fluorescence lifetime imaging microscopy of the FAK-based Förster resonance energy transfer biosensor in mouse and patient-derived PDAC models. Parallel real-time quantification of the FUCCI cell cycle reporter guided us to improve PDAC response to standard-of-care chemotherapy at primary and secondary sites. Critically, micropatterned pillar plates and stiffness-tunable matrices were used to pinpoint the contribution of environmental cues to chemosensitization, while fluid flow­induced shear stress assessment, patient-derived matrices, and personalized in vivo models allowed us to deconstruct how FAK inhibition can reduce PDAC spread. Last, stratification of PDAC patient samples via Merlin status revealed a patient subset with poor prognosis that are likely to respond to FAK priming before chemotherapy.

A case study of a long-term glioblastoma survivor with unmethylated MGMT and hypermutated genotype.

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.

Integration of genomics, high throughput drug screening, and personalized xenograft models as a novel precision medicine paradigm for high risk pediatric cancer.

Pediatric high grade gliomas (HGG) are primary brain malignancies that result in significant morbidity and mortality. One of the challenges in their treatment is inter- and intra-tumoral heterogeneity. Precision medicine approaches have the potential to enhance diagnostic, prognostic and/or therapeutic information. In this case study we describe the molecular characterization of a pediatric HGG and the use of an integrated approach based on genomic, in vitro and in vivo testing to identify actionable targets and treatment options. Molecular analysis based on WGS performed on initial and recurrent tumor biopsies revealed mutations in TP53, TSC1 and CIC genes, focal amplification of MYCN, and copy number gains in SMO and c-MET. Transcriptomic analysis identified increased expression of MYCN, and genes involved in sonic hedgehog signaling proteins (SHH, SMO, GLI1, GLI2) and receptor tyrosine kinase pathways (PLK, AURKA, c-MET). HTS revealed no cytotoxic efficacy of SHH pathway inhibitors while sensitivity was observed to the mTOR inhibitor temsirolimus, the ALK inhibitor ceritinib, and the PLK1 inhibitor BI2536. Based on the integrated approach, temsirolimus, ceritinib, BI2536 and standard therapy temozolomide were selected for further in vivo evaluation. Using the PDX animal model (median survival 28 days) we showed significant in vivo activity for mTOR inhibition by temsirolimus and BI2536 (median survival 109 and 115.5 days respectively) while ceritinib and temozolomide had only a moderate effect (43 and 75.5 days median survival respectively). This case study demonstrates that an integrated approach based on genomic, in vitro and in vivo drug efficacy testing in a PDX model may be useful to guide the management of high risk pediatric brain tumor in a clinically meaningful timeframe.

Older studies can underestimate prognosis of glioblastoma biomarker in meta-analyses: a meta-epidemiological study for study-level effect in the current literature.

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.

Andy's Algorithms: new automated digital image analysis pipelines for FIJI.

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 .