Computation and application of tissue-specific gene set weights

Leveraging cell type-specificity for gene set analysis of single cell transcriptomics

Although single cell RNA-sequencing (scRNA-seq) provides unprecedented insights into the biology of complex tissues, analyzing such data on a gene-by-gene basis is challenging due to the large number of tested hypotheses and consequent low statistical power and difficult interpretation. These issues are magnified by the increased noise, significant sparsity and multi-modal distributions characteristic of single cell data. One promising approach for addressing these challenges is gene set testing, or pathway analysis. Unfortunately, statistical and biological differences between single cell and bulk transcriptomic data make it challenging to use existing gene set collections, which were developed for bulk tissue analysis, on scRNA-seq data. In this paper, we describe a procedure for customizing gene set collections originally created for bulk tissue analysis to reflect the structure of gene activity within specific cell types. Our approach leverages information about mean gene expression in the 81 human cell types profiled via scRNA-seq by the Human Protein Atlas (HPA) Single Cell Type Atlas. This HPA information is used to compute cell type-specific gene and gene set weights that can be used to filter or weight gene set collections. As demonstrated through the analysis of immune cell scRNA-seq data using gene sets from the Molecular Signatures Database (MSigDB), accounting for cell type-specificity can significantly improve gene set testing power and interpretability. An example vignette along with gene and gene set weights for the 81 HPA SCTA cell types and the MSigDB collections are available at https://hrfrost.host.dartmouth.edu/SCGeneSetOpt/.

Sex-biased gene expression and gene-regulatory networks of sex-biased adverse event drug targets and drug metabolism genes

BACKGROUND

Previous pharmacovigilance studies and a retroactive review of cancer clinical trial studies identified that women were more likely to experience drug adverse events (i.e., any unintended effects of medication), and men were more likely to experience adverse events that resulted in hospitalization or death. These sex-biased adverse events (SBAEs) are due to many factors not entirely understood, including differences in body mass, hormones, pharmacokinetics, and liver drug metabolism enzymes and transporters.

METHODS

We first identified drugs associated with SBAEs from the FDA Adverse Event Reporting System (FAERS) database. Next, we evaluated sex-specific gene expression of the known drug targets and metabolism enzymes for those SBAE-associated drugs. We also constructed sex-specific tissue gene-regulatory networks to determine if these known drug targets and metabolism enzymes from the SBAE-associated drugs had sex-specific gene-regulatory network properties and predicted regulatory relationships.

RESULTS

We identified liver-specific gene-regulatory differences for drug metabolism genes between males and females, which could explain observed sex differences in pharmacokinetics and pharmacodynamics. In addition, we found that ~ 85% of SBAE-associated drug targets had sex-biased gene expression or were core genes of sex- and tissue-specific network communities, significantly higher than randomly selected drug targets. Lastly, we provide the sex-biased drug-adverse event pairs, drug targets, and drug metabolism enzymes as a resource for the research community.

CONCLUSIONS

Overall, we provide evidence that many SBAEs are associated with drug targets and drug metabolism genes that are differentially expressed and regulated between males and females. These SBAE-associated drug metabolism enzymes and drug targets may be useful for future studies seeking to explain or predict SBAEs.

Optimized Transcriptional Signature for Evaluation of MEK/ERK Pathway Baseline Activity and Long-Term Modulations in Ovarian Cancer

Ovarian cancer is the most aggressive and lethal of all gynecologic malignancies. The high activity of the MEK/ERK signaling pathway is tightly associated with tumor growth, high recurrence rate, and treatment resistance. Several transcriptional signatures were proposed recently for evaluation of MEK/ERK activity in tumor tissue. In the present study, we validated the performance of a robust multi-cancer MPAS 10-gene signature in various experimental models and publicly available sets of ovarian cancer samples. Expression of four MPAS genes (PHLDA1, DUSP4, EPHA2, and SPRY4) displayed reproducible responses to MEK/ERK activity modulations across several experimental models in vitro and in vivo. Levels of PHLDA1, DUSP4, and EPHA2 expression were also significantly associated with baseline levels of MEK/ERK pathway activity in multiple human ovarian cancer cell lines and ovarian cancer patient samples available from the TCGA database. Initial platinum therapy resistance and advanced age at diagnosis were independently associated with poor overall patient survival. Taken together, our results demonstrate that the performance of transcriptional signatures is significantly affected by tissue specificity and aspects of particular experimental models. We therefore propose that gene expression signatures derived from comprehensive multi-cancer studies should be always validated for each cancer type.

Prognostic value of Glypican family genes in early-stage pancreatic ductal adenocarcinoma after pancreaticoduodenectomy and possible mechanisms

Background

This study explored the prognostic significance of Glypican (GPC) family genes in patients with pancreatic ductal adenocarcinoma (PDAC) after pancreaticoduodenectomy using data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO).

Methods

A total of 112 PDAC patients from TCGA and 48 patients from GEO were included in the analysis. The relationship between overall survival and the expression of GPC family genes as well as basic clinical characteristics was analyzed using the Kaplan-Meier method with the log-rank test. Joint effects survival analysis was performed to further examine the relationship between GPC genes and prognosis. A prognosis nomogram was established based on clinical characteristics and prognosis-related genes. Prognosis-related genes were investigated by genome-wide co-expression analysis and gene set enrichment analysis (GSEA) was carried out to identify potential mechanisms of these genes affecting prognosis.

Results

In TCGA database, high expression of GPC2, GPC3, and GPC5 was significantly associated with favorable survival (log-rank P = 0.031, 0.021, and 0.028, respectively; adjusted P value = 0.005, 0.022, and 0.020, respectively), and joint effects analysis of these genes was effective for prognosis prediction. The prognosis nomogram was applied to predict the survival probability using the total scores calculated. Genome-wide co-expression and GSEA analysis suggested that the GPC2 may affect prognosis through sequence-specific DNA binding, protein transport, cell differentiation and oncogenic signatures (KRAS, RAF, STK33, and VEGFA). GPC3 may be related to cell adhesion, angiogenesis, inflammatory response, signaling pathways like Ras, Rap1, PI3K-Akt, chemokine, GPCR, and signatures like cyclin D1, p53, PTEN. GPC5 may be involved in transcription factor complex, TFRC1, oncogenic signatures (HOXA9 and BMI1), gene methylation, phospholipid metabolic process, glycerophospholipid metabolism, cell cycle, and EGFR pathway.

Conclusion

GPC2, GPC3, and GPC5 expression may serve as prognostic indicators in PDAC, and combination of these genes showed a higher efficiency for prognosis prediction.

Transcriptomic Differences between Primary Colorectal Adenocarcinomas and Distant Metastases Reveal Metastatic Colorectal Cancer Subtypes

Approximately 20% of colorectal cancer patients with colorectal adenocarcinomas present with metastases at the time of diagnosis, and therapies that specially target these metastases are lacking. We present a novel approach for investigating transcriptomic differences between primary colorectal adenocarcinoma and distant metastases, which may help to identify primary tumors with high risk for future dissemination and to inform the development of metastasis-targeted therapies. To effectively compare the transcriptomes of primary colorectal adenocarcinoma and metastatic lesions at both the gene and pathway levels, we eliminated tissue specificity of the "host" organs where tumors are located and adjusted for confounders such as exposure to chemotherapy and radiation, and identified that metastases were characterized by reduced epithelial-mesenchymal transition (EMT) but increased MYC target and DNA-repair pathway activities. FBN2 and MMP3 were the most differentially expressed genes between primary tumors and metastases. The two subtypes of colorectal adenocarcinoma metastases that were identified, EMT inflammatory and proliferative, were distinct from the consensus molecular subtype (CMS) 3, suggesting subtype exclusivity. In summary, this study highlights transcriptomic differences between primary tumors and colorectal adenocarcinoma metastases and delineates pathways that are activated in metastases that could be targeted in colorectal adenocarcinoma patients with metastatic disease. SIGNIFICANCE: These findings identify a colorectal adenocarcinoma metastasis-specific gene-expression signature that is free from potentially confounding background signals coming from treatment exposure and the normal host tissue that the metastasis is now situated within.