Analyses of germline variants associated with ovarian cancer survival identify functional candidates at the 1q22 and 19p12 outcome loci

Genome-wide association analyses of ovarian cancer patients undergoing primary debulking surgery identify candidate genes for residual disease

Survival from ovarian cancer depends on the resection status after primary surgery. We performed genome-wide association analyses for resection status of 7705 ovarian cancer patients, including 4954 with high-grade serous carcinoma (HGSOC), to identify variants associated with residual disease. The most significant association with resection status was observed for rs72845444, upstream of MGMT, in HGSOC (p = 3.9 × 10-8). In gene-based analyses, PPP2R5C was the most strongly associated gene in HGSOC after stage adjustment. In an independent set of 378 ovarian tumours from the AGO-OVAR 11 study, variants near MGMT and PPP2R5C correlated with methylation and transcript levels, and PPP2R5C mRNA levels predicted progression-free survival in patients with residual disease. MGMT encodes a DNA repair enzyme, and PPP2R5C encodes the B56γ subunit of the PP2A tumour suppressor. Our results link heritable variation at these two loci with resection status in HGSOC.

Artificial Intelligence in Epigenetic Studies: Shedding Light on Rare Diseases

More than 7,000 rare diseases (RDs) exist worldwide, affecting approximately 350 million people, out of which only 5% have treatment. The development of novel genome sequencing techniques has accelerated the discovery and diagnosis in RDs. However, most patients remain undiagnosed. Epigenetics has emerged as a promise for diagnosis and therapies in common disorders (e.g., cancer) with several epimarkers and epidrugs already approved and used in clinical practice. Hence, it may also become an opportunity to uncover new disease mechanisms and therapeutic targets in RDs. In this “big data” age, the amount of information generated, collected, and managed in (bio)medicine is increasing, leading to the need for its rapid and efficient collection, analysis, and characterization. Artificial intelligence (AI), particularly deep learning, is already being successfully applied to analyze genomic information in basic research, diagnosis, and drug discovery and is gaining momentum in the epigenetic field. The application of deep learning to epigenomic studies in RDs could significantly boost discovery and therapy development. This review aims to collect and summarize the application of AI tools in the epigenomic field of RDs. The lower number of studies found, specific for RDs, indicate that this is a field open to expansion, following the results obtained for other more common disorders.

SCAMP3 is a mutant EGFR phosphorylation target and a tumor suppressor in lung adenocarcinoma

Mutations in the epidermal growth factor receptor (EGFR) tyrosine kinase domain constitutively activate EGFR resulting in lung tumorigenesis. Activated EGFR modulates downstream signaling by altering phosphorylation-driven interactions that promote growth and survival. Secretory carrier membrane proteins (SCAMPs) are a family of transmembrane proteins that regulate recycling of receptor proteins, including EGFR. The potential role of SCAMPs in mutant EGFR function and tumorigenesis has not been elucidated. Using quantitative mass-spectrometry-based phosphoproteomics, we identified SCAMP3 as a target of mutant EGFRs in lung adenocarcinoma and sought to further investigate the role of SCAMP3 in the regulation of lung tumorigenesis. Here we show that activated EGFR, either directly or indirectly phosphorylates SCAMP3 at Y86 and this phosphorylation increases the interaction of SCAMP3 with both wild-type and mutant EGFRs. SCAMP3 knockdown increases lung adenocarcinoma cell survival and increases xenograft tumor growth in vivo, demonstrating a tumor suppressor role of SCAMP3 in lung tumorigenesis. The tumor suppressor function is a result of SCAMP3 promoting EGFR degradation and attenuating MAP kinase signaling pathways. SCAMP3 knockdown also increases multinucleated cells in culture, suggesting that SCAMP3 is required for efficient cytokinesis. The enhanced growth, increased colony formation, reduced EGFR degradation and multinucleation phenotype of SCAMP3-depleted cells were reversed by re-expression of wild-type SCAMP3, but not SCAMP3 Y86F, suggesting that Y86 phosphorylation is critical for SCAMP3 function. Taken together, the results of this study demonstrate that SCAMP3 functions as a novel tumor suppressor in lung cancer by modulating EGFR signaling and cytokinesis that is partly Y86 phosphorylation-dependent.

Analysis of Promoter-Associated Chromatin Interactions Reveals Biologically Relevant Candidate Target Genes at Endometrial Cancer Risk Loci

The identification of target genes at genome-wide association study (GWAS) loci is a major obstacle for GWAS follow-up. To identify candidate target genes at the 16 known endometrial cancer GWAS risk loci, we performed HiChIP chromatin looping analysis of endometrial cell lines. To enrich for enhancer-promoter interactions, a mechanism through which GWAS variation may target genes, we captured chromatin loops associated with H3K27Ac histone, characteristic of promoters and enhancers. Analysis of HiChIP loops contacting promoters revealed enrichment for endometrial cancer GWAS heritability and intersection with endometrial cancer risk variation identified 103 HiChIP target genes at 13 risk loci. Expression of four HiChIP target genes (SNX11, SRP14, HOXB2 and BCL11A) was associated with risk variation, providing further evidence for their targeting. Network analysis functionally prioritized a set of proteins that interact with those encoded by HiChIP target genes, and this set was enriched for pan-cancer and endometrial cancer drivers. Lastly, HiChIP target genes and prioritized interacting proteins were over-represented in pathways related to endometrial cancer development. In summary, we have generated the first global chromatin looping data from normal and tumoral endometrial cells, enabling analysis of all known endometrial cancer risk loci and identifying biologically relevant candidate target genes.

Overexpression of MEF2D contributes to oncogenic malignancy and chemotherapeutic resistance in ovarian carcinoma

The transcription factor MEF2 promotes survival in various cell types and a number of studies indicate that abnormal regulation of MEF2 is linked to oncogenicity in several carcinomas. We have found that MEF2D, a member of the MEF2 family, is upregulated in Ovarian Cancer (OC). Immunohistochemistry analysis of tumor sections of 402 OC patients revealed that MEF2D is significantly elevated at the protein level. We have also found that the expression level of MEF2D is associated with cisplatin-resistance and poor prognosis by a retrospective analysis. Furthermore, Downregulation of MEF2D by siRNA reduces proliferation and invasiveness of OC cells SKOV3 and OVCAR3, induces apoptosis in vitro, and abolishes OVCAR3 tumorigenicity in xenograft model. Mechanistic study via ChIP analysis identified two of MEF2D-targeted genes, HPSE and IKBKE, which are associated with tumor invasion and chemotherapy-resistance, in accord with MEF2D expression in OC. Remarkably, knock-down of MEF2D invariably lead to the downregulation of IKBKE and reversed cisplatin (DDP)-resistance in cisplatin-resistant cells SKOV3-DDP. Our results suggest that MEF2D promotes malignant biological behaviors and cisplatin-resistance in OC and establish MEF2D as a new therapeutic target in OC treatment.

Enhancing the Promise of Drug Repositioning through Genetics

The development of new drugs has become challenging as the necessary investments in time and money have increased while drug approval rates have decreased. A potential solution to this problem is drug repositioning which aims to use existing drugs to treat conditions for which they were not originally intended. One approach that may enhance the likelihood of success is to reposition drugs against a target that has a genetic basis. The multitude of genome-wide association studies (GWASs) conducted in recent years represents a large potential pool of novel targets for drug repositioning. Although trait-associated variants identified from GWAS still need to be causally linked to a target gene, recently developed functional genomic techniques, databases, and workflows are helping to remove this bottleneck. The pre-clinical validation of repositioning against these targets also needs to be carefully performed to ensure that findings are not confounded by off-target effects or limitations of the techniques used. Nevertheless, the approaches described in this review have the potential to provide a faster, cheaper and more certain route to clinical approval.