The circular RNome of primary breast cancer

Abstract

Circular RNAs (circRNAs) are a class of RNAs that is under increasing scrutiny, although their functional roles are debated. We analyzed RNA-seq data of 348 primary breast cancers and developed a method to identify circRNAs that does not rely on unmapped reads or known splice junctions. We identified 95,843 circRNAs, of which 20,441 were found recurrently. Of the circRNAs that match exon boundaries of the same gene, 668 showed a poor or even negative (R < 0.2) correlation with the expression level of the linear gene. In silico analysis showed only a minority (8.5%) of circRNAs could be explained by known splicing events. Both these observations suggest that specific regulatory processes for circRNAs exist. We confirmed the presence of circRNAs of CNOT2, CREBBP, and RERE in an independent pool of primary breast cancers. We identified circRNA profiles associated with subgroups of breast cancers and with biological and clinical features, such as amount of tumor lymphocytic infiltrate and proliferation index. siRNA-mediated knockdown of circCNOT2 was shown to significantly reduce viability of the breast cancer cell lines MCF-7 and BT-474, further underlining the biological relevance of circRNAs. Furthermore, we found that circular, and not linear, CNOT2 levels are predictive for progression-free survival time to aromatase inhibitor (AI) therapy in advanced breast cancer patients, and found that circCNOT2 is detectable in cell-free RNA from plasma. We showed that circRNAs are abundantly present, show characteristics of being specifically regulated, are associated with clinical and biological properties, and thus are relevant in breast cancer.

Figure 1.

Schematic overview of identifying circular RNA (circRNA) regions. Assuming a circRNA molecule is present, a sequence read crossing the junction (green arrow) and its read-mate (gold arrow) would map to a linear reference in the manner depicted. The junction read would get multiple alignments, and the read-mate would be located in between the position of the junction read. Multiple read-pairs at the same junction strengthen the support for the circRNA. Subsequent additional filtering (details are in the Methods section) and annotation produced the list of circRNA regions.

Figure 2.

General characteristics of circRNAs in primary breast cancer. (A) Numbers of unique and recurrent circRNAs. Purple and gold indicate the number of circRNAs that, respectively, did or did not have a start and end position of a circRNA region exactly matching the start and end position of an exon of the same gene. (B) The number of circRNAs per sample, grouped by ER status. In black, the total number of circRNAs; in peach, the number of recurrent (identified in at least two samples) circRNAs. (C) Violin plots of the intron size (in log base pair) of noncircular regions and those located directly upstream of or downstream from a circRNA region.

Figure 3.

circRNAs are not just residues of splicing. (A) Sashimi plot of the number of reads that are aligned to WDR1, showing only the reads that span exons. In red are the normal exon–exon reads; in purple, the reads that span the circular junction. The line and boxes indicate the exons of the gene (the whole gene is not shown). (B) Isoform of ESR1. The arcs indicate the number of samples that have a particular circRNA. (C) Two isoforms of CREBBP that are known in the first five exons (other isoforms are described, but these start downstream from exon 5). Exon 2 (purple box) is an identified circRNA that is not a remainder of a splicing event.

Figure 4.

PCR products of circRNAs. PCR product sizes of circRNAs visualized using the MultiNA Microchip Electrophoresis System. (M) DNA size marker (25-bp fragment ladder); (−) the negative control (genomic DNA).

Figure 5.

siRNA-mediated knockdown of circCNOT2 affects viability in breast cancer cells. The effect of reduced circCNOT2 expression on viability is shown in MCF-7 and BT-474 cells. Both cell lines show a significant decrease in viability (P < 0.01) following circCNOT2 knockdown relative to cells transfected with nontargeting control (NTC). Error bars, SD of five wells.

Figure 6.

Analysis of sample groups according to circRNA presence. Multiple correspondence analysis (MCA) was used to find naturally occurring groups in the circRNA data. In an MCA plot, samples and circRNAs are projected onto the same plane, in which the relative distance to either the samples or the circRNAs is meaningful. The 0,0 point corresponds to a sample or circRNA with an average profile. (A, left) Samples are colored according to ER status: red, ER-positive; black, ER-negative. (Right) Purple and green indicate genes with or without circRNA expression, respectively. (B) Clustering identified samples with similar circRNA profiles; samples in the MCA plot are colored according to the cluster to which the sample belongs. (C) ER status (purple, ER-positive; peach, ER-negative) and (D) TIL status of the six sample groups: Red and orange are high-TIL cases and blue and green are low-TIL cases for ER-negative and ER-positive, respectively. (E) Number of circRNAs per sample group. (F) Relapse-free survival plot by sample group. (N) number of patients; (F) number of patients who relapse; (x-axis) months; (y-axis) the cumulative probability of relapse-free survival.

Figure 7.

Clinical evaluation and presence in plasma samples. (A) Kaplan–Meier survival curve of progression-free survival for AI therapy in which patients were grouped in three equally sized groups based on their circCNOT2 expression: red, blue, and green indicate the samples with high, intermediate, and low expression. The x-axis is in months; y-axis depicts the cumulative probability of progression-free survival on AI therapy. The P-value is the log-rank test for trend. (B) Expression levels of circCNOT2 in plasma samples. Four metastatic breast cancer patients were evaluated. The y-axis depicts delta-C_t values of circCNOT2 relative to GUSB. Error bars, SD of two measurements.