Copy Number Variants Are Ovarian Cancer Risk Alleles at Known and Novel Risk Loci

BACKGROUND: Known risk alleles for epithelial ovarian cancer (EOC) account for approximately 40% of the heritability for EOC. Copy number variants (CNVs) have not been investigated as EOC risk alleles in a large population cohort. METHODS: Single nucleotide polymorphism array data from 13 071 EOC cases and 17 306 controls of White European ancestry were used to identify CNVs associated with EOC risk using a rare admixture maximum likelihood test for gene burden and a by-probe ratio test. We performed enrichment analysis of CNVs at known EOC risk loci and functional biofeatures in ovarian cancer-related cell types. RESULTS: We identified statistically significant risk associations with CNVs at known EOC risk genes; BRCA1 (PEOC = 1.60E-21; OREOC = 8.24), RAD51C (Phigh-grade serous ovarian cancer [HGSOC] = 5.5E-4; odds ratio [OR]HGSOC = 5.74 del), and BRCA2 (PHGSOC = 7.0E-4; ORHGSOC = 3.31 deletion). Four suggestive associations (P < .001) were identified for rare CNVs. Risk-associated CNVs were enriched (P < .05) at known EOC risk loci identified by genome-wide association study. Noncoding CNVs were enriched in active promoters and insulators in EOC-related cell types. CONCLUSIONS: CNVs in BRCA1 have been previously reported in smaller studies, but their observed frequency in this large population-based cohort, along with the CNVs observed at BRCA2 and RAD51C gene loci in EOC cases, suggests that these CNVs are potentially pathogenic and may contribute to the spectrum of disease-causing mutations in these genes. CNVs are likely to occur in a wider set of susceptibility regions, with potential implications for clinical genetic testing and disease prevention

Optimizing exosomal RNA isolation for RNA-Seq analyses of archival sera specimens.

Exosomes are endosome-derived membrane vesicles that contain proteins, lipids, and nucleic acids. The exosomal transcriptome mediates intercellular communication, and represents an understudied reservoir of novel biomarkers for human diseases. Next-generation sequencing enables complex quantitative characterization of exosomal RNAs from diverse sources. However, detailed protocols describing exosome purification for preparation of exosomal RNA-sequence (RNA-Seq) libraries are lacking. Here we compared methods for isolation of exosomes and extraction of exosomal RNA from human cell-free serum, as well as strategies for attaining equal representation of samples within pooled RNA-Seq libraries. We compared commercial precipitation with ultracentrifugation for exosome purification and confirmed the presence of exosomes via both transmission electron microscopy and immunoblotting. Exosomal RNA extraction was compared using four different RNA purification methods. We determined the minimal starting volume of serum required for exosome preparation and showed that high quality exosomal RNA can be isolated from sera stored for over a decade. Finally, RNA-Seq libraries were successfully prepared with exosomal RNAs extracted from human cell-free serum, cataloguing both coding and non-coding exosomal transcripts. This method provides researchers with strategic options to prepare RNA-Seq libraries and compare RNA-Seq data quantitatively from minimal volumes of fresh and archival human cell-free serum for disease biomarker discovery

Comparison of sera exosomal RNA using four different RNA extraction methods.

<p>(A) Total RNA yield from ultracentrifugation (UC) and ExoQuick (EQ) treated samples using the RNeasy Mini Kit combined with TRIzol LS, the RNeasy Mini Kit alone, conventional RNA precipitation, and AllPrep DNA/RNA Mini Kit. (B) Demonstration of RNA quality measured by OD₂₆₀/OD₂₈₀ in EQ and UC treated samples. Data are shown as the mean ± SD from six independent patient samples. ***<i>P</i><0.001, **<i>P</i><0.01, *<i>P</i><0.05, ns, not significant, student’s <i>t</i> test. Note: RNA could not be extracted from some samples using the AllPrep kit due to clogging.</p

Flow chart for evaluation of exosomal RNAs from cell-free sera as biomarkers for human diseases.

<p>Graphic summary of the workflow including time allotment for preparation for cell-free serum (steps ①-③), comparison of methods for exosome enrichment (step ④), validation by transmission electron microscopy (TEM) and immunoblotting for CD63 or other exosomal markers (step ⑤), RNA extraction (step ⑥), and preparation of RNA-Seq libraries (step ⑦). 10–100 nanograms RNA can be used for library preparation with the NEBNext Ultra Directional RNA Library Prep Kit. Step ② is an optional centrifugation step that can be included to ensure the most efficient removal of trace amounts of cell debris and shedding microvesicles. A validation step can be performed with RT-qPCR for specific candidate RNA following RNA-Seq analysis (step ⑧).</p

Validation of exosome enrichment from human cell-free sera.

<p>(A) TEM micrographs of exosomes in ultracentrifugation (UC) and ExoQuick (EQ) preparations. Data for 6 independent patient samples are shown (P1-6). Exosomes confirmed by size (30-100nm) and appearance. Scale bar in each image represents 100 nm. (B) Immunoblot of CD63 in unprocessed cell-free serum alone (-), UC, and EQ exosomal preparations.</p

Exosomal RNAs are stable over two decades.

<p>Dot plots of total RNA yield and OD₂₆₀/OD₂₈₀ from 105 EQ-treated archival patient samples using the RNeasy Mini Kit combined with TRIzol LS. Storage time for each sample is indicated on the x-axis. The Spearman’s rank correlations for RNA yield <i>versus</i> storing time and OD₂₆₀/OD₂₈₀ <i>versus</i> storage time are 0.185 (<i>P</i> = 0.06) and 0.04 (<i>P</i> = 0.70), respectively.</p

Minimal sera volume for exosome detection and RNA extraction.

<p>(A) Representative immunoblots for CD63 comparing exosome enrichment using ultracentrifugation (UC) and ExoQuick (EQ) among samples of varying volumes. (B) Scatter dot plots depict total exosomal RNA recovered per sample volume using EQ. (C) Scatter dot plots show RNA quality measured by OD₂₆₀/OD₂₈₀ per sample volume. Data are shown as the mean ± standard deviation (SD) from six independent patient samples. <i>P</i>-values were calculated using one-way ANOVA.</p

Super-Enhancer-Associated LncRNA UCA1 Interacts Directly with AMOT to Activate YAP Target Genes in Epithelial Ovarian Cancer.

Long noncoding RNAs (lncRNAs) have emerged as critical regulators of tumorigenesis, and yet their mechanistic roles remain challenging to characterize. Here, we integrate functional proteomics with lncRNA-interactome profiling to characterize Urothelial Cancer Associated 1 (UCA1), a candidate driver of ovarian cancer development. Reverse phase protein array (RPPA) analysis indicates that UCA1 activates transcription coactivator YAP and its target genes. In vivo RNA antisense purification (iRAP) of UCA1 interacting proteins identified angiomotin (AMOT), a known YAP regulator, as a direct binding partner. Loss-of-function experiments show that AMOT mediates YAP activation by UCA1, as UCA1 enhances the AMOT-YAP interaction to promote YAP dephosphorylation and nuclear translocation. Together, we characterize UCA1 as a lncRNA regulator of Hippo-YAP signaling and highlight the UCA1-AMOT-YAP signaling axis in ovarian cancer development