Androgen receptor variant shows heterogeneous expression in prostate cancer according to differentiation stage

Quantitation of androgen receptor variant (AR-V) expression in circulating tumor cells (CTCs) from patients with metastatic castration-resistant prostate cancer (mCRPC) has great potential for treatment customization. However, the absence of a uniform CTC isolation platform and consensus on an analytical assay has prevented the incorporation of these measurements in routine clinical practice. Here, we present a single-CTC sensitive digital droplet PCR (ddPCR) assay for the quantitation of the two most common AR-Vs, AR-V7, and AR-v567es, using antigen agnostic CTC enrichment. In a cohort of 29 mCRPC patients, we identify AR-V7 in 66% and AR-v567es in 52% of patients. These results are corroborated using another gene expression platform (NanoStringTM) and by analysis of RNA-Seq data from patients with mCRPC (SU2C- PCF Dream Team). We next quantify AR-V expression in matching EpCAM-positive vs EpCAM-negative CTCs, as EpCAM-based CTC enrichment is commonly used. We identify lower AR-V prevalence in the EpCAM-positive fraction, suggesting that EpCAM-based CTC enrichment likely underestimates AR-V prevalence. Lastly, using single CTC analysis we identify enrichment for AR-v567es in patients with neuroendocrine prostate cancer (NEPC) indicating that AR-v567es may be involved in lineage plasticity, which warrants further mechanistic interrogation.

CRISPR/Cas9 Ribonucleoprotein-Based Genome Editing Methodology in the Marine Protozoan Parasite Perkinsus marinus

Perkinsus marinus (Perkinsozoa), a close relative of apicomplexans, is an osmotrophic facultative intracellular marine protozoan parasite responsible for “Dermo” disease in oysters and clams. Although there is no clinical evidence of this parasite infecting humans, HLA-DR40 transgenic mice studies strongly suggest the parasite as a natural adjuvant in oral vaccines. P. marinus is being developed as a heterologous gene expression platform for pathogens of medical and veterinary relevance and a novel platform for delivering vaccines. We previously reported the transient expression of two rodent malaria genes Plasmodium berghei HAP2 and MSP8. In this study, we optimized the original electroporation-based protocol to establish a stable heterologous expression method. Using 20 μg of pPmMOE[MOE1]:GFP and 25.0 × 106P. marinus cells resulted in 98% GFP-positive cells. Furthermore, using the optimized protocol, we report for the first time the successful knock-in of GFP at the C-terminus of the PmMOE1 using ribonucleoprotein (RNP)-based CRISPR/Cas9 gene editing methodology. The GFP was expressed 18 h post-transfection, and expression was observed for 8 months post-transfection, making it a robust and stable knock-in system.

Development of a gene expression–based prognostic signature for IDH wild-type glioblastoma

Background We aimed to develop a gene expression–based prognostic signature for isocitrate dehydrogenase (IDH) wild-type glioblastoma using clinical trial datasets representative of glioblastoma clinical trial populations. Methods Samples were collected from newly diagnosed patients with IDH wild-type glioblastoma in the ARTE, TAMIGA, EORTC 26101 (referred to as “ATE”), AVAglio, and GLARIUS trials, or treated at UCLA. Transcriptional profiling was achieved with the NanoString gene expression platform. To identify genes prognostic for overall survival (OS), we built an elastic net penalized Cox proportional hazards regression model using the discovery ATE dataset. For validation in independent datasets (AVAglio, GLARIUS, UCLA), we combined elastic net–selected genes into a robust z-score signature (ATE score) to overcome gene expression platform differences between discovery and validation cohorts. Results NanoString data were available from 512 patients in the ATE dataset. Elastic net identified a prognostic signature of 9 genes (CHEK1, GPR17, IGF2BP3, MGMT, MTHFD1L, PTRH2, SOX11, S100A9, and TFRC). Translating weighted elastic net scores to the ATE score conserved the prognostic value of the genes. The ATE score was prognostic for OS in the ATE dataset (P < 0.0001), as expected, and in the validation cohorts (AVAglio, P < 0.0001; GLARIUS, P = 0.02; UCLA, P = 0.004). The ATE score remained prognostic following adjustment for O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status and corticosteroid use at baseline. A positive correlation between ATE score and proneural/proliferative subtypes was observed in patients with MGMT non-methylated promoter status. Conclusions The ATE score showed prognostic value and may enable clinical trial stratification for IDH wild-type glioblastoma.

High-Throughput Functional Annotation of Natural Products by Integrated Activity Profiling

Determining mechanism of action (MOA) is one of the biggest challenges in natural products discovery. Here, we report a comprehensive platform that uses Similarity Network Fusion (SNF) to improve MOA predictions by integrating data from the cytological profiling high-content imaging platform and the gene expression platform FUSION. The predictive value of the integrative approach was assessed using a library of target-annotated small molecules as benchmarks. Using KS-tests to compare in-class to out-of-class similarity, we found that SNF resulted in improved power to correctly assign MOA over either dataset alone. Furthermore, we integrated untargeted metabolomics of complex natural product fractions to map biological signatures to specific metabolites. Three examples are presented where SNF coupled with metabolomics was used to directly functionally characterize natural products and accelerate identification of bioactive metabolites. Our results support SNF integration of multiple phenotypic screening approaches along with untargeted metabolomics as powerful approach for advancing natural products drug discovery.

bcGST - an interactive bias-correction method to identify over-represented gene-sets in boutique arrays

Motivation: Gene annotation and pathway databases such as Gene Ontology and Kyoto Encyclopedia of Genes and Genomes are important tools in Gene Set Test (GST) that describe gene biological functions and associated pathways. GST aims to establish an association relationship between a gene set of interest and an annotation. Importantly, GST tests for over-representation of genes in an annotation term. One implicit assumption of GST is that the gene expression platform captures the complete or a very large proportion of the genome. However, this assumption is neither satisfied for the increasingly popular boutique array nor the custom designed gene expression profiling platform. Specifically, conventional GST is no longer appropriate due to the gene set selection bias induced during the construction of these platforms.Results: We propose bcGST, a bias-corrected Gene Set Test by introducing bias correction terms in the contingency table needed for calculating the Fisher’s Exact Test (FET). The adjustment method works by estimating the proportion of genes captured on the array with respect to the genome in order to assist filtration of annotation terms that would otherwise be falsely included or excluded. We illustrate the practicality of bcGST and its stability through multiple differential gene expression analyses in melanoma and TCGA cancer studies.Availability: The bcGST method is made available as a Shiny web application at http://shiny.maths.usyd.edu.au/bcGST/Contact:[email protected]