Differential Functions of Splicing Factors in Mammary Transformation and Breast Cancer Metastasis

Abstract

Misregulation of alternative splicing is a hallmark of human tumors, yet to what extent and how it contributes to malignancy are only beginning to be unraveled. Here, we define which members of the splicing factor SR and SR-like families contribute to breast cancer and uncover differences and redundancies in their targets and biological functions. We identify splicing factors frequently altered in human breast tumors and assay their oncogenic functions using breast organoid models. We demonstrate that not all splicing factors affect mammary tumorigenesis in MCF-10A cells. Specifically, the upregulation of SRSF4, SRSF6, or TRA2β disrupts acinar morphogenesis and promotes cell proliferation and invasion in MCF-10A cells. By characterizing the targets of these oncogenic splicing factors, we identify shared spliced isoforms associated with well-established cancer hallmarks. Finally, we demonstrate that TRA2β is regulated by the MYC oncogene, plays a role in metastasis maintenance in vivo, and its levels correlate with breast cancer patient survival.

Keywords: MYC; SR protein; TRA2-beta; alternative RNA splicing; breast cancer; metastasis; splicing factor; triple negative breast cancer.

Figure 1.. SF Alterations Are Detected Frequently in Human Breast Tumors

Graphical representation of SF alterations in TCGA human breast tumors (n = 960) sorted by frequency. CNVs and expression changes are assessed by DNA-and RNA-seq. Individual genes are represented as rows and patients as columns. Alterations in breast cancer genes BRCA1, BRCA2, TP53, MYC, and ERBB2 are in the bottom panel. See also Figure S1.

Figure 2.. Specificity of SF-Mediated Transformation

(A) Expression of T7-tagged SFs in MCF-10A cells, detected by immunofluorescence using T7-tag antibody and DAPI nuclei co-stain (scale bar: 50 μm). (B) Representative bright-field images of acinar size and morphology for control and SF-OE 3D MCF-10A cells on days 8 and 16 (scale bar: 100 μm). (C) Quantification of acinar sizes in control and SF-OE 3D MCF-10A cells on day 16. The dot plot shows the size distribution of all of the structures and the median (horizontal line) for each condition (n = 4, >50 acini per experiment; t test, **p < 0.001, ***p < 0.0001; n.s., not significant). See also Figure S3.

Figure 3.. SF Overexpression Is Associated with Differentially Spliced Events in MCF-10A

(A) DSEs detected by RNA-seq in SF-OE versus control MCF-10A day 8 acini (n = 3; ∣ΔPSI∣ ≥ 10%, false discovery rate [FDR] < 5%, p < 0.01), sorted by AS event types. CA, cassette exon; MXE, mutually exclusive exon; RI, retained intron; A5′SS, alternative 5′SS; A3′SS, alternative 3′SS. (B) Skipped (ΔPSI ≤ −10%) and included (ΔPSI R 10%) DSEs in SF-OE versus control are plotted by ΔPSI values for each SF for all AS event types. (C) Overlap in DSEs for each SF pair. Bubble size is proportional to the number of shared DSEs and color indicates p values. (D) RT-PCR validations of DSEs. A representative gel is shown, along with isoform structures. PSI for all samples and ΔPSI for significant SFs are calculated from RT-PCR (n = 3; mean ± SD; t test, *p < 0.05, n.s., not significant) and RNA-seq. See also Figure S4 and Table S3.

Figure 4.. Cooperation of TRA2β with the MYC Oncogene in Breast Cancer

(A) Representative bright-field images of control or TRA2β-OE 3D MCF-10A acini expressing estrogen receptor ligand-binding domain (ER)-inducible MYC, activated by 4-hydroxytamoxifen (4-OHT) (scale bar: 100 μm). (B) Acinar size distribution and median (horizontal line) of SRSF4-, SRSF6-, TRA2β-OE MYC.ER MCF-10A ± 4-OHT (n ≥ 3, >100 acini per condition; t test, *p < 0.01, n.s., not significant). (C) Association plot showing the correlation between MYC and TRA2β expression in human tumors. Dataset identification numbers (IDs) and numbers of samples are indicated. MYC and TRA2β expression are grouped into four categories: (1) both high, (2) TRA2β high and MYC low, (3) TRA2β low and MYC high, and (4) both low. (D) The TRA2β RNA and protein levels in MYC.ER MCF-10A +4-OHT at indicated time points are measured by qRT-PCR and western blotting (n = 3; mean ± SD; t test, *p < 0.05). RNA levels are normalized to GAPDH and HPRT; protein levels are normalized to tubulin. (E) MYC binding to TRA2β genomic region as detected by MYC ChIP-seq experiments in MCF-7 and MCF-10A cells. Fold changes over control are calculated from pooled replicates; significant peaks are shown by rectangles. (F) MYC amplification (AMP) or OE status inTRA2β-high versus TRA2β-low breast tumors.

Figure 5.. Differential Roles of SFs in Cell Migration and Invasion

(A) Representative bright-field images of acinar morphology for control or SF-OE 3D MCF-10A cells grown in Matrigel-collagen invasion assay at day 8. Multicellular protrusions and inter-acinar bridges are indicated by arrowheads (scale bar: 50 μm). (B) Percentage of invasive versus non-invasive acini at day 8 (n = 3, >50 acini per condition). (C) SF levels in MDA-MB231 expressing scrambled CTLsh or SF-targeting shRNAs are quantified 72 h after DOX treatment by western blotting using SF-specific antibodies and normalized to tubulin. Percentage of SF in +DOX is normalized to the corresponding −DOX (n = 3; mean ± SD). (D and E) Migration of CTLsh or SFsh MDA-MB231 ± DOX in 2D wound-healing (D) or transwell assay (E). Distribution and median (horizontal line) for each condition +DOX normalized to −DOX (n = 3; t test, *p < 0.05, **p < 0.005). (F and G) Representative bright-field images of CTLsh or TRA2βsh MDA-MB231 cells, grown in 3D ± DOX (scale bar: 100 μm). DOX is added from either day 0 (F) or day 8 (G). See also Figures S5 and S6.

Figure 6.. TRA2β-KD Promotes Changes in Spliced Isoforms in 3D MDA-MB231 Cells

(A) DSEs detected by RNA-seq in TRA2βsh2 or TRA2βsh1 MDA-MB231 cells, grown in 3D ± DOX, at day 8 (n = 3; ∣ΔPSI∣ ≥ 10%, FDR < 5%, p < 0.01). (B) Skipped (ΔPSI ≤−10%) and included (ΔPSI ≥ 10%) DSEs in TRA2βsh2 or TRA2βsh1 +DOX versus −DOX plotted by ΔPSI values. (C) Gene set enrichment analysis for DSEs in TRA2βsh2 or TRA2βsh1 MDA-MB231 cells showing the top 10 hallmark gene sets. (D) RT-PCR validations of selected DSEs. A representative gel is shown, along with isoform structures. PSI for all samples and ΔPSI for TRA2βsh2 are calculated from RT-PCR (n = 3; mean ± SD; t test, *p < 0.05, **p < 0.005, ***p < 0.0005, n.s., not significant) and RNA-seq. (E) PSI for DSEs detected both in TRA2β-high versus TRA2β-low breast tumors (n = 146 and 449) and MDA-MB231 cells. See also Table S5.

Figure 7.. TRA2β Plays a Role in Metastasis In Vivo in a TNBC Mouse Model and in Breast Cancer Patients

(A) CTLsh or TRA2βsh2MDA-MB231 cells are injected into the mammary fat pad of NSG mice; primary tumors and metastasis are monitored by bioluminescence imaging. (B) Bioluminescence detection of primary tumors and metastasis in mice injected with CTLsh or TRA2βsh2 MDA-MB231 cells ± DOX at 8 weeks post-injection. (C) Representative H&E lung and liver sections of mice injected with TRA2βsh2 MDA-MB231 cells ± DOX (scale bar: 1 mm). Metastatic areas are circled in blue. (D) Quantification of metastasis burden in mice injected with CTLsh or TRA2βsh2 MDA-MB231 cells ± DOX (n ≥ 4; t test, *p < 0.01, **p < 0.001, n.s., not significant). (E and F) Correlation between TRA2β expression and overall survival (E) or distant metastasis-free survival (F) in cohorts of breast cancer patients stratified by TRA2β levels. Cohort size, dataset, and probe ID, and corrected p value (log-rank test) are indicated. See also Figure S7.