Up regulation of the steroid hormone synthesis regulator HSD3B2 is linked to early PSA recurrence in prostate cancer

A Knowledge Graph-Enhanced Tensor Factorisation Model for Discovering Drug Targets

The drug discovery and development process is a long and expensive one, costing over 1 billion USD on average per drug and taking 10-15 years. To reduce the high levels of attrition throughout the process, there has been a growing interest in applying machine learning methodologies to various stages of drug discovery and development in the recent decade, especially at the earliest stage - identification of druggable disease genes. In this paper, we have developed a new tensor factorisation model to predict potential drug targets (genes or proteins) for treating diseases. We created a three-dimensional data tensor consisting of 1,048 gene targets, 860 diseases and 230,011 evidence attributes and clinical outcomes connecting them, using data extracted from the Open Targets and PharmaProjects databases. We enriched the data with gene target representations learned from a drug discovery-oriented knowledge graph and applied our proposed method to predict the clinical outcomes for unseen gene target and disease pairs. We designed three evaluation strategies to measure the prediction performance and benchmarked several commonly used machine learning classifiers together with Bayesian matrix and tensor factorisation methods. The result shows that incorporating knowledge graph embeddings significantly improves the prediction accuracy and that training tensor factorisation alongside a dense neural network outperforms all other baselines. In summary, our framework combines two actively studied machine learning approaches to disease target identification, namely tensor factorisation and knowledge graph representation learning, which could be a promising avenue for further exploration in data-driven drug discovery.

Chromatin conformation changes in peripheral blood can detect prostate cancer and stratify disease risk groups

BACKGROUND

Current diagnostic blood tests for prostate cancer (PCa) are unreliable for the early stage disease, resulting in numerous unnecessary prostate biopsies in men with benign disease and false reassurance of negative biopsies in men with PCa. Predicting the risk of PCa is pivotal for making an informed decision on treatment options as the 5-year survival rate in the low-risk group is more than 95% and most men would benefit from surveillance rather than active treatment. Three-dimensional genome architecture and chromosome structures undergo early changes during tumourigenesis both in tumour and in circulating cells and can serve as a disease biomarker.

METHODS

In this prospective study we screened whole blood of newly diagnosed, treatment naïve PCa patients (n = 140) and cancer-free controls (n = 96) for the presence of 14,241 chromosomal loops in the loci of 425 genes.

RESULTS

We have detected specific chromosome conformation changes in the loci of ETS1, MAP3K14, SLC22A3 and CASP2 genes in peripheral blood from PCa patients yielding PCa detection with 80% sensitivity and 80% specificity. Further analysis between PCa risk groups yielded prognostic validation sets consisting of HSD3B2, VEGFC, APAF1, BMP6, ERG, MSR1, MUC1, ACAT1 and DAPK1 genes that achieved 80% sensitivity and 93% specificity stratifying high-risk category 3 vs low risk category 1 and 84% sensitivity and 89% specificity stratifying high risk category 3 vs intermediate risk category 2 disease.

CONCLUSIONS

Our results demonstrate specific chromosome conformations in the blood of PCa patients that allow PCa diagnosis and risk stratification with high sensitivity and specificity.

Reduced anoctamin 7 (ANO7) expression is a strong and independent predictor of poor prognosis in prostate cancer

Objective

Anoctamin 7 (ANO7) is a calcium²⁺-dependent chloride ion channel protein. Its expression is restricted to prostate epithelial cells. The exact function is unknown. This study aimed to analyze ANO7 expression and its clinical significance in prostate cancer (PCa).

Methods

ANO7 expression was assessed by immunohistochemistry in 17,747 clinical PCa specimens.

Results

ANO7 was strongly expressed in normal prostate glandular cells but often less abundant in cancer cells. ANO7 staining was interpretable in 13,594 cancer tissues and considered strong in 34.4%, moderate in 48.7%, weak in 9.3%, and negative in 7.6%. Reduced staining was tightly linked to adverse tumor features [high classical and quantitative Gleason grade, lymph node metastasis, advanced tumor stage, high Ki67 labeling index, positive surgical margin, and early biochemical recurrence (P < 0.0001 each)]. The univariate Cox hazard ratio for prostate-specific antigen (PSA) recurrence after prostatectomy in patients with negative vs. strong ANO7 expression was 2.98 (95% confidence interval 2.61-3.38). The prognostic impact was independent of established pre- or postoperatively available parameters (P < 0.0001). Analysis of annotated molecular data showed that low ANO7 expression was linked to TMPRSS2:ERG fusions (P < 0.0001), elevated androgen receptor expression (P < 0.0001), as well as presence of 9 of 11 chromosomal deletions (P < 0.05 each). A particularly strong association of low ANO7 expression with phosphatase and tensin homolog (PTEN) deletion may indicate a functional relationship with the PTEN/AKT pathway.

Conclusions

These data identify reduced ANO7 protein expression as a strong and independent predictor of poor prognosis in PCa. ANO7 measurement, either alone or in combination, might provide clinically useful prognostic information in PCa.

A Targeted Bioinformatics Assessment of Adrenocortical Carcinoma Reveals Prognostic Implications of GABA System Gene Expression

Adrenocortical carcinoma (ACC) is a rare but deadly cancer for which few treatments exist. Here, we have undertaken a targeted bioinformatics study of The Cancer Genome Atlas (TCGA) ACC dataset focusing on the 30 genes encoding the γ-aminobutyric acid (GABA) system—an under-studied, evolutionarily-conserved system that is an emerging potential player in cancer progression. Our analysis identified a subset of ACC patients whose tumors expressed a distinct GABA system transcriptome. Transcript levels of ABAT (encoding a key GABA shunt enzyme), were upregulated in over 40% of tumors, and this correlated with several favorable clinical outcomes including patient survival; while enrichment and ontology analysis implicated two cancer-related biological pathways involved in metastasis and immune response. The phenotype associated with ABAT upregulation revealed a potential metabolic heterogeneity among ACC tumors associated with enhanced mitochondrial metabolism. Furthermore, many GABA_A receptor subunit-encoding transcripts were expressed, including two (GABRB2 and GABRD) prognostic for patient survival. Transcripts encoding GABA_B receptor subunits and GABA transporters were also ubiquitously expressed. The GABA system transcriptome of ACC tumors is largely mirrored in the ACC NCI-H295R cell line, suggesting that this cell line may be appropriate for future functional studies investigating the role of the GABA system in ACC cell growth phenotypes and metabolism.

Canonical and Noncanonical Androgen Metabolism and Activity

Androgens are critical drivers of prostate cancer. In this chapter we first discuss the canonical pathways of androgen metabolism and their alterations in prostate cancer progression, including the classical, backdoor and 5α-dione pathways, the role of pre-receptor DHT metabolism, and recent findings on oncogenic splicing of steroidogenic enzymes. Next, we discuss the activity and metabolism of non-canonical 11-oxygenated androgens that can activate wild-type AR and are less susceptible to glucuronidation and inactivation than the canonical androgens, thereby serving as an under-recognized reservoir of active ligands. We then discuss an emerging literature on the potential non-canonical role of androgen metabolizing enzymes in driving prostate cancer. We conclude by discussing the potential implications of these findings for prostate cancer progression, particularly in context of new agents such as abiraterone and enzalutamide, which target the AR-axis for prostate cancer therapy, including mechanisms of response and resistance and implications of these findings for future therapy.