Human Genetics

The pivotal role of long non-coding RNAs as potential biomarkers and modulators of chemoresistance in ovarian cancer (OC)

Ovarian cancer (OC) is a fatal gynecological disease that is often diagnosed at later stages due to its asymptomatic nature and the absence of efficient early-stage biomarkers. Previous studies have identified genes with abnormal expression in OC that couldn't be explained by methylation or mutation, indicating alternative mechanisms of gene regulation. Recent advances in human transcriptome studies have led to research on non-coding RNAs (ncRNAs) as regulators of cancer gene expression. Long non-coding RNAs (lncRNAs), a class of ncRNAs with a length greater than 200 nucleotides, have been identified as crucial regulators of physiological processes and human diseases, including cancer. Dysregulated lncRNA expression has also been found to play a crucial role in ovarian carcinogenesis, indicating their potential as novel and non-invasive biomarkers for improving OC management. However, despite the discovery of several thousand lncRNAs, only one has been approved for clinical use as a biomarker in cancer, highlighting the importance of further research in this field. In addition to their potential as biomarkers, lncRNAs have been implicated in modulating chemoresistance, a major problem in OC. Several studies have identified altered lncRNA expression upon drug treatment, further emphasizing their potential to modulate chemoresistance. In this review, we highlight the characteristics of lncRNAs, their function, and their potential to serve as tumor markers in OC. We also discuss a few databases providing detailed information on lncRNAs in various cancer types. Despite the promising potential of lncRNAs, further research is necessary to fully understand their role in cancer and develop effective strategies to combat this devastating disease.

Genetic analyses of gynecological disease identify genetic relationships between uterine fibroids and endometrial cancer, and a novel endometrial cancer genetic risk region at the WNT4 1p36.12 locus

Endometriosis, polycystic ovary syndrome (PCOS) and uterine fibroids have been proposed as endometrial cancer risk factors; however, disentangling their relationships with endometrial cancer is complicated due to shared risk factors and comorbidities. Using genome-wide association study (GWAS) data, we explored the relationships between these non-cancerous gynecological diseases and endometrial cancer risk by assessing genetic correlation, causal relationships and shared risk loci. We found significant genetic correlation between endometrial cancer and PCOS, and uterine fibroids. Adjustment for genetically predicted body mass index (a risk factor for PCOS, uterine fibroids and endometrial cancer) substantially attenuated the genetic correlation between endometrial cancer and PCOS but did not affect the correlation with uterine fibroids. Mendelian randomization analyses suggested a causal relationship between only uterine fibroids and endometrial cancer. Gene-based analyses revealed risk regions shared between endometrial cancer and endometriosis, and uterine fibroids. Multi-trait GWAS analysis of endometrial cancer and the genetically correlated gynecological diseases identified a novel genome-wide significant endometrial cancer risk locus at 1p36.12, which replicated in an independent endometrial cancer dataset. Interrogation of functional genomic data at 1p36.12 revealed biologically relevant genes, including WNT4 which is necessary for the development of the female reproductive system. In summary, our study provides genetic evidence for a causal relationship between uterine fibroids and endometrial cancer. It further provides evidence that the comorbidity of endometrial cancer, PCOS and uterine fibroids may partly be due to shared genetic architecture. Notably, this shared architecture has revealed a novel genome-wide risk locus for endometrial cancer.

Regulating cancer risk prediction: legal considerations and stakeholder perspectives on the Canadian context

Abstract Risk prediction models hold great promise to reduce the impact of cancer in society through advanced warning of risk and improved preventative modalities. These models are evolving and becoming more complex, increasingly integrating genetic screening data and polygenic risk scores as well as calculating risk for multiple types of a disease. However, unclear regulatory compliance requirements applicable to these models raise significant legal uncertainty and new questions about the regulation of medical devices. This paper aims to address these novel regulatory questions by presenting an initial assessment of the legal status likely applicable to risk prediction models in Canada, using the CanRisk tool for breast and ovarian cancer as an exemplar. Legal analysis is supplemented with qualitative perspectives from expert stakeholders regarding the accessibility and compliance challenges of the Canadian regulatory framework. While the paper focuses on the Canadian context, it also refers to European and U.S. regulations in this domain to contrast them. Legal analysis and stakeholder perspectives highlight the need to clarify and update the Canadian regulatory framework for Software as a Medical Device as it applies to risk prediction models. Findings demonstrate how normative guidance perceived as convoluted, contradictory or overly burdensome can discourage innovation, compliance, and ultimately, implementation. This contribution aims to initiate discussion about a more optimal legal framework for risk prediction models as they continue to evolve and are increasingly integrated into landscape for public health.

Germline copy number variants and endometrial cancer risk

AbstractKnown risk loci for endometrial cancer explain approximately one third of familial endometrial cancer. However, the association of germline copy number variants (CNVs) with endometrial cancer risk remains relatively unknown. We conducted a genome-wide analysis of rare CNVs overlapping gene regions in 4115 endometrial cancer cases and 17,818 controls to identify functionally relevant variants associated with disease. We identified a 1.22-fold greater number of CNVs in DNA samples from cases compared to DNA samples from controls (p = 4.4 × 10–63). Under three models of putative CNV impact (deletion, duplication, and loss of function), genome-wide association studies identified 141 candidate gene loci associated (p < 0.01) with endometrial cancer risk. Pathway analysis of the candidate loci revealed an enrichment of genes involved in the 16p11.2 proximal deletion syndrome, driven by a large recurrent deletion (chr16:29,595,483-30,159,693) identified in 0.15% of endometrial cancer cases and 0.02% of control participants. Together, these data provide evidence that rare copy number variants have a role in endometrial cancer susceptibility and that the proximal 16p11.2 BP4-BP5 region contains 25 candidate risk gene(s) that warrant further analysis to better understand their role in human disease.