Sequence and expression of complement factor H gene cluster variants and their roles in age-related macular degeneration risk

Purpose: To investigate how potentially functional genetic variants are coinherited on each of four common complement factor H (CFH) and CFH-related gene haplotypes and to measure expression of these genes in eye and liver tissues.

Methods: We sequenced the CFH region in four individuals (one homozygote for each of four common CFH region haplotypes) to identify all genetic variants. We studied associations between the haplotypes and AMD phenotypes in 2157 cases and 1150 controls. We examined RNA-seq profiles in macular and peripheral retina and retinal pigment epithelium/choroid/sclera (RCS) from eight eye donors and three liver samples.

Results: The haplotypic coinheritance of potentially functional variants (including missense variants, novel splice sites, and the CFHR3–CFHR1 deletion) was described for the four common haplotypes. Expression of the short and long CFH transcripts differed markedly between the retina and liver. We found no expression of any of the five CFH-related genes in the retina or RCS, in contrast to the liver, which is the main source of the circulating proteins.

Conclusions: We identified all genetic variants on common CFH region haplotypes and described their coinheritance. Understanding their functional effects will be key to developing and stratifying AMD therapies. The small scale of our expression study prevented us from investigating the relationships between CFH region haplotypes and their expression, and it will take time and collaboration to develop epidemiologic-scale studies. However, the striking difference between systemic and ocular expression of complement regulators shown in this study suggests important implications for the development of intraocular and systemic treatments.

QUADrATiC: scalable gene expression connectivity mapping for repurposing FDA-approved therapeutics

Background: Gene expression connectivity mapping has proven to be a powerful and flexible tool for research. Its application has been shown in a broad range of research topics, most commonly as a means of identifying potential small molecule compounds, which may be further investigated as candidates for repurposing to treat diseases. The public release of voluminous data from the Library of Integrated Cellular Signatures (LINCS) programme further enhanced the utilities and potentials of gene expression connectivity mapping in biomedicine. Results: We describe QUADrATiC (http://go.qub.ac.uk/QUADrATiC), a user-friendly tool for the exploration of gene expression connectivity on the subset of the LINCS data set corresponding to FDA-approved small molecule compounds. It enables the identification of compounds for repurposing therapeutic potentials. The software is designed to cope with the increased volume of data over existing tools, by taking advantage of multicore computing architectures to provide a scalable solution, which may be installed and operated on a range of computers, from laptops to servers. This scalability is provided by the use of the modern concurrent programming paradigm provided by the Akka framework. The QUADrATiC Graphical User Interface (GUI) has been developed using advanced Javascript frameworks, providing novel visualization capabilities for further analysis of connections. There is also a web services interface, allowing integration with other programs or scripts.Conclusions: QUADrATiC has been shown to provide an improvement over existing connectivity map software, in terms of scope (based on the LINCS data set), applicability (using FDA-approved compounds), usability and speed. It offers potential to biological researchers to analyze transcriptional data and generate potential therapeutics for focussed study in the lab. QUADrATiC represents a step change in the process of investigating gene expression connectivity and provides more biologically-relevant results than previous alternative solutions.

Immune-derived PD-L1 gene expression defines a subgroup of stage II/III colorectal cancer patients with favorable prognosis that may be harmed by adjuvant chemotherapy

A recent phase 2 study of metastatic colorectal carcinoma (CRC) patients showed that mismatch repair gene status was predictive of clinical response to PD-1-targeting immune checkpoint blockade. Further examination revealed strong correlation between PD-L1 protein expression and microsatellite instability (MSI) in stage IV CRC, suggesting that the amount of PD-L1 protein expression could identify late stage patients who may benefit from immunotherapy. To assess whether the clinical associations between PD-L1 gene expression and MSI identified in metastatic CRC are also present in stage II/III CRC, we used in silico analysis to elucidate the cell types expressing the PD-L1 gene. We found a significant association of PD-L1 gene expression with MSI in early stage CRC (P < 0.001) and show that unlike in non-CRC tumors, PD-L1 is derived predominantly from the immune infiltrate. We demonstrate that PD-L1 gene expression has positive prognostic value in the adjuvant disease setting (PD-L1low v PD-L1high HR = 9.09; CI, 2.11-39.10). PD-L1 gene expression had predictive value, as patients with high PD-L1 expression appear to be harmed by standard-of-care treatment (HR = 4.95; CI,1.10-22.35). Building on the promising results from the metastatic CRC PD-1-targeting trial, we provide compelling evidence that PD-L1high/MSI/immunehigh stage II/III CRC patients should not receive standard chemotherapy. This conclusion supports the rationale to clinically evaluate this patient subgroup for PD-1 blockade treatment.

Network signatures based on gene pair expression ratios improve classification and the analysis of muscle-invasive urothelial cancer

Urothelial cancer (UC) is highly recurrent and can progress from non-invasive (NMIUC) to a more aggressive muscle-invasive (MIUC) subtype that invades the muscle tissue layer of the bladder. We present a proof of principle study that network-based features of gene pairs can be used to improve classifier performance and the functional analysis of urothelial cancer gene expression data. In the first step of our procedure each individual sample of a UC gene expression dataset is inflated by gene pair expression ratios that are defined based on a given network structure. In the second step an elastic net feature selection procedure for network-based signatures is applied to discriminate between NMIUC and MIUC samples. We performed a repeated random subsampling cross validation in three independent datasets. The network signatures were characterized by a functional enrichment analysis and studied for the enrichment of known cancer genes. We observed that the network-based gene signatures from meta collections of proteinprotein interaction (PPI) databases such as CPDB and the PPI databases HPRD and BioGrid improved the classification performance compared to single gene based signatures. The network based signatures that were derived from PPI databases showed a prominent enrichment of cancer genes (e.g., TP53, TRIM27 and HNRNPA2Bl). We provide a novel integrative approach for large-scale gene expression analysis for the identification and development of novel diagnostical targets in bladder cancer. Further, our method allowed to link cancer gene associations to network-based expression signatures that are not observed in gene-based expression signatures.