A genome-wide siRNA screen identifies proteasome addiction as a vulnerability of basal-like triple-negative breast cancer cells

Abstract

Basal-like triple-negative breast cancers (TNBCs) have poor prognosis. To identify basal-like TNBC dependencies, a genome-wide siRNA lethality screen compared two human breast epithelial cell lines transformed with the same genes: basal-like BPLER and myoepithelial HMLER. Expression of the screen's 154 BPLER dependency genes correlated with poor prognosis in breast, but not lung or colon, cancer. Proteasome genes were overrepresented hits. Basal-like TNBC lines were selectively sensitive to proteasome inhibitor drugs relative to normal epithelial, luminal, and mesenchymal TNBC lines. Proteasome inhibition reduced growth of established basal-like TNBC tumors in mice and blocked tumor-initiating cell function and macrometastasis. Proteasome addiction in basal-like TNBCs was mediated by NOXA and linked to MCL-1 dependence.

Figure 1. BPLER has a basal-like phenotype and is enriched for tumor-initiating cells compared to HMLER

(A) Schematic depicting the method used to generate BPLER and HMLER from normal breast epithelial cells. Breast organoids maintained in chemically defined media (WIT and MEGM) were sequentially transformed with retroviral vectors encoding TERT, SV40 early region and HRAS^V12. (B) CellTiter-Glo assay showing proliferation of BPLER, HMLER and BPE cells in WIT medium. (C) Tumor incidence in Nu/J mice 8 weeks after subcutaneous injection of the indicated numbers of BPLER, HMLER or MCF7 cells. (D, E) qRT-PCR quantification of E-cadherin (D) and vimentin (E) mRNA in BPLER and HMLER cells. (F) Immunofluorescence staining of cytokeratins CK14 and CK18 in BPLER and HMLER cells. Images are representative of three independent experiments. Luminal MCF7 and mesenchymal MB231 cells in (D–F) were used as control. (G) Principal component analysis of mRNA expression profiles of 6 BPLER tumors, generated in NOD/scid mice, and 337 human primary breast tumors classified as luminal A, luminal B, normal-like, HER2⁺, basal-like and claudin-low in the UNC337 set of primary breast cancers. The two first components are plotted with the proportion of variance explained by each component contained in the axis labels. Data in (B), (D) and (E) indicate mean +/⁻ SD. See also Figure S1.

Figure 2. Identification of BPLER selective dependencies by high throughput siRNA screening

(A) Cell viability in BPLER and HMLER transfected in high throughput screening conditions with a custom-made siRNA library containing non-targeting siRNAs (192 wells/plate) or si-PLK1 (192 wells/plate). Each dot represents the relative CellTiter-Glo signal from individual wells at a given coordinate in three separate microplates. The Z′ factor, a measure of screening reproducibility, was calculated (Zhang et al., 1999). For both cell lines, Z′ was >0.7 in each of 6 microplates transfected in two separate experiments. No significant edge effect was detected in any experiment. (B) Distribution of R (ratio of viability of BPLER vs HMLER) and BPLER median absolute deviation (MAD)-based Z score for all 17,378 genes in the siRNA primary screen library. The Z score measures the deviation of BPLER viability from the plate median. A Z score outside the range of −1 to +1 is significant. Genes were considered hits if R<0.75 and the BPLER Z score was <-1.5. Colors indicate the relative selectivity of BPLER vs HMLER lethality (green, modestly selective; blue, moderately selective; red, highly selective). (C) Numbers of hits in the primary screen and the confirmed hits in the secondary screen for which at least one individual siRNA from the library pool scored positive. (D) Confirmed highly selective BPLER dependency genes. Hits involved in specific functions are shown in different colors. See also Figure S2 and Tables S1–2.

Figure 3. BPLER dependency genes cluster within defined functional categories

Functional interaction network of BPLER dependency genes. Genes were grouped according to their participation in the indicated processes. The network was constructed using Cytoscape. The most highly selective hits are colored red. See also Figure S3 and Tables S3–4.

Figure 4. BPLER dependency genes are associated with a basal-like TNBC phenotype and are up regulated in poor prognosis breast tumors

(A) Evaluation of a subset of BPLER screening hits by knockdown in 17 human breast cancer cell lines of different subtypes. Each cell line was transfected with a control siRNA or specific siRNA pools against the indicated genes. Cell viability was assessed after 72 h. Black boxes indicate viability ≤50% of control siRNA-transfected cells. Color codes for breast cancer cell lines: Luminal (blue), Basal-A (red), Basal-B (green); HCC1806 and HMLER are squamous (purple). Comparable transfection efficiency (>80%) for each cell line was verified using a fluorescent siRNA and showing <50% viability after transfection with PLK1 siRNA. (B) Network showing the degree of similarity in dependencies between BPLER and the other breast cancer cell lines, evaluated and color-coded as in (A). Cell lines with no or only 1 shared dependency are not included. Edge thickness increases with shared dependencies (max=10) and the most similar cell are closest. (C) Patients in the NKI database of 295 human breast primary cancers were analyzed by single sample gene set enrichment analysis (GSEA) for expression of BPLER dependency genes and the subset of highly selective genes. A Z-score for expression of the signature genes was calculated for each sample. The scores are shown as bean-plots to compare the distributions in the tumor subtypes (Basal, basal-like; Lum, luminal; NL, normal-like). Each bean consists of a green line for each sample with the overall distribution for the subtype represented as a gray density shape and a black line indicating the median Z score. (D–F) Breast tumors from the NKI dataset and lung and colon tumors from two independent datasets were divided into two groups based on their expression of the dependency genes (high, red; low, blue). Kaplan-Meier curves show survival in breast (D), lung (E) and colon (F), cancer patients with higher tumor expression of all dependency genes (top) or the highly selective subset (bottom). See also Figure S4 and Table S5.

Figure 5. Basal-like TNBC cells and their T-IC are selectively sensitive to proteasome inhibition

(A) Viability of 22 breast cancer cell lines 24 hr after treatment with bortezomib (12.5 nM) relative to vehicle control assessed by CellTiter-Glo. The top and the bottom of each box represent the 75^th and 25^th percentile of cell viability, respectively. Upper and lower whiskers represent maximum and minimum cell viability, respectively. The black horizontal band in each box corresponds to median viability. *, p < 0.005; **,n< 0.001. Data for individual cell lines are shown in Figure S5B. (B) Dose-response curve of breast cancer cell lines treated with bortezomib for 24 h. Colony (C) and sphere formation (D) assays of cell lines treated with bortezomib (12.5 nM) or paclitaxel (100 nM) for 18 hr and cultured for 2 weeks in drug-free medium. Colonies were allowed to overgrow to enhance detection of slow growing colonies. (E) Colony formation (left) and tumor-initiation (right) of viable mouse 4T1E cells after treatment with bortezomib (12.5 nM) for 18 hr ex vivo (at which point ~40% of cell are viable) that were then cultured for 2 weeks in drug-free medium (colony assay) or injected in the mammary fat-pad of BALB/c mice (tumor initiation). Data in (B, D, E) show mean +/− SD and are representative of at least 3 independent experiments. See also Figure S5.

Figure 6. Proteasome inhibition suppresses TNBC growth in vivo

(A) Proteasome activity in protein lysates from individual subcutaneous BPLER tumors (3 mice/group) 18 hr after intratumoral (i.t), intraperitoneal (i.p) or intravenous (i.v.) treatment with bortezomib at the indicated dose, as determined by Proteasome-Glo assay, normalized by tissue weight. Proteasome activity in BPLER treated with 12.5 nM bortezomib in vitro is shown as control (C). Shown are the mean +/−SD of 3 replicates. Red bars indicate significant proteasome inhibition (p < 0.05). (B, C) Tumor weight in BPLER tumor-bearing mice (5 mice/group) after treatment with bortezomib or DMSO. (D) Immunohistochemistry of BPLER tumors treated i.t. with 0.8 mg/kg bortezomib or DMSO every 3 days. (E) Immunoblot of protein lysates from BPLER tumors 18 hr after a single i.v. dose of bortezomib (1.6 mg/kg) or DMSO. Each lane represents a sample from an individual mouse tumor. (F–J) Tumor weight in HCC1187 (F), MB468 (G), MCF7-HRAS^V12 (H), AU565 (I) and 4T1E (J) tumor-bearing mice after treatment with weekly i.v. bortezomib or DMSO at the indicated dose, which was begun when tumors became palpable (50–100 mm³). HCC1187, MB468, MCF7-HRAS^V12 and AU565 cells were injected subcutaneously. 4T1E cells were injected in the mammary fatpad. (K, L) Representative India-ink-stained whole lungs from BALB/c mice (4 mice/group) after i.v. injection of 2×10⁵ 4T1E cells. Beginning 2 days after 4T1E injection, mice were treated with i.v. bortezomib (0.8 mg/kg q3d or 1.6 mg/kg q7d) or DMSO. The most prominent metastatic nodules are indicated by arrows (K) and the mean +/− SD number of metastatic nodules determined by counting is shown in (L). (M) Tumor weight after weekly i.v. bortezomib (1.6 mg/kg) or DMSO in mice implanted in the mammary fat pad with tumor fragments from the same spontaneously arising tumor (5 mice/group). Treatment was started 2 days after implantation. Box-and-whisker plots (B, C, F, G, H–J, M) show median tumor weight at time of sacrifice. *, p < 0.05. See also Figure S6.

Figure 7. Proteasome inhibition selectively induces apoptosis in BPLER by promoting NOXA accumulation

(A) Immunoblot of cells treated for 16 hr with bortezomib (12.5 nM). Each sample was assessed in duplicate independent samples. (B) Mitochondrial depolarization, assessed by the percentage of DilC1(5)^low BPLER and HMLER cells after treatment with 12.5 nM bortezomib, was determined by flow cytometry. (C–E) Viability of BPLER cells transfected with the indicated siRNAs and then treated 24 hr later with 12.5 nM bortezomib or DMSO for 24 hr. zVAD-fmk was used as control. Data for each siRNA were normalized to viability of DMSO-treated cells. Data in (B–E) indicate mean +/− SD. See also Figure S7.

Figure 8. Bortezomib sensitivity of basal-like TNBC is linked to NOXA accumulation and MCL-1 dependency

(A–B) Immunoblot of breast cancer cell lysates treated or not with 12.5 nM bortezomib for 24 h. (C) Immunoblot of breast cancer cells 24 hr after transfection with an siRNA against MCL1 or a non-targeting siRNA (control). (D) Scatter plot showing cell viability 72 hr after MCL1 knockdown (x axis) and 24 hr after 12.5 nM bortezomib treatment (y axis) in TNBC cell lines and BPE cells. Color scheme in (A–D) as indicated in (B). All data are representative of at least 3 independent experiments. *, p<0.05. See also Figure S8.