Identification of genes regulating breast cancer dormancy in 3D bone endosteal niche cultures

Abstract

Cancer tumor cell dormancy is a significant clinical problem in breast cancer (BrCa). We used a 3D in vitro model of the endosteal bone niche (EN), consisting of endothelial, bone marrow stromal cells and fetal osteoblasts in a 3D collagen matrix (GELFOAM™), to identify genes required for dormancy. Human triple-negative MDA-MB-231 BrCa cells, but not the bone-tropic metastatic variant, BoM1833, established dormancy in 3D-EN cultures in a p38-MAPK-dependent manner, whereas both cell types proliferated on 2D plastic or in 3D collagen alone. “Dormancy-reactivation suppressor genes” (DRSG) were identified using a genomic shRNA screen in MDA-MB-231 cells for gene knockdowns that induced proliferation in the 3D-EN. DSRG candidates enriched for genes controlling stem cell biology, neurogenesis, MYC targets, ribosomal structure and translational control. Several potential DRSG were confirmed using independent shRNAs, including BHLHE41, HBP1 and WNT3. Overexpression of the WNT3/a antagonists, secreted frizzled-related protein 2 or 4 (SFRP2/4), induced MDA-MB-231 proliferation in the EN. In contrast, overexpression of SFRP3, known not to antagonize WNT3/a, did not induce proliferation. Decreased WNT3 or BHLHE41 expression was found in clinical BrCa metastases compared to primary-site lesions, and the loss of WNT3 or BHLHE41 or gain of SFRP1, 2 and 4 in the context of TP53 loss/mutation correlated with decreased progression-free and overall survival.

INTRODUCTION

The progression of breast cancer (BrCa) at metastatic sites continues to be the largest contributor to patient mortality (1, 2). There has been renewed focus on identifying mechanisms governing the establishment of dormancy in specific peripheral sites and the reawakening of dormant cells as major contributors of this cancer’s lethal phenotype (3), especially because only a fraction of disseminated tumor cells (DTC), and even fewer circulating tumor cells (CTC), give rise to clinical macrometastases (4-6). Both estrogen receptor (ER)-positive and triple-negative BrCa (“TNBC”: ER⁻/PR⁻/Her2⁻) metastasize to and enter dormancy in the bone, with ER+ tumors often exhibiting a longer-lasting dormancy (7). Importantly, dormant vs. active growth depends on which bone niche is colonized (8). Colonization of the endosteal niche (EN), enriched in osteoblasts and marked by low oxygen and high calcium levels, results in dormancy, whereas colonization of the perivascular niche, enriched in hematopoietic stem cells, results in active proliferation and formation of macrometastases (7, 9). Signaling pathways in tumor cells define whether DTCs will remain dormant or become proliferative: activation of the p38 MAP-Kinase (MAPK) pathway in the absence of ERK1/2 MAPK activity favors quiescence (10). Additionally, long-term survival of dormant cells is likely to require adoption of stem-like quiescence properties through increased activity of lineage plasticity pathways. Although several groups identified genes and pathways differentially-regulated in dormant vs. proliferating BrCa metastatic cells (8, 11-16), few studies have addressed possible causative roles for these genes, especially in the context of p38 control (16, 17), confounding attempts at therapeutic targeting.

Using a novel 3D-EN culture system developed by Marlow et al. (18), in which otherwise aggressive MDA-MB-231 human BrCa cells become dormant for up to 60 days due to direct contact with EN environmental cells, we show here that knockdown of p38-MAPK induced proliferation, confirming the notion that dormancy is p38-dependent. In order to identify genes that promote or maintain dormancy, we used a high-stringency genomic shRNA screen to identify gene knockdowns that induced proliferation of MDA-MB-231 in 3D-EN cultures. Identification of the top hits, BHLHE41, a known p38 target required for induction of quiescence (19), and LSP1, a suppressor of ERK1/2 activity (20) increased confidence in the screen’s validity. Other gene hits grouped into several regulatory categories not previously identified as suppressors of dormancy. These include genes involved in neurogenesis, translation and non-canonical WNT signaling- all of which play roles in regulating the maintenance of cancer stem cells. These data strengthen the notion that BrCa dormancy in the EN is promoted by p38-MAPK-controlled stem cell pathways.

MATERIALS AND METHODS

Cell culture:

MDA-MB-231 (ATCC HTB-26), MDA-MB-231 BoM 1833 (gift from Joan Massague, Memorial Sloan Kettering Cancer Center (21), HEK293T (ATCC CRL-11268), Phoenix 293T (ATCC CRL-3213) and HS-5 (ATCC CRL-11882) were cultured in DMEM supplemented with 10% FBS and 1% penicillin-streptomycin (Pen/Strep) and incubated at 37^°C and 5% CO₂. hFOB (human fetal osteoblasts; ATCC CRL-11372) were cultured with DMEM/F12 (1:1) media without phenol red and supplemented with 15% FBS and G418 (0.3mg/ml) and incubated at 32^°C and 5% CO₂. HUVEC (Lonza C2517A) were cultured to no more than 5 passages with EBM-2 (Lonza CC-3156) supplemented with SingleQuot supplement pack (Lonza CC-4176) and grown at 37^°C and 5% CO₂.

3D cultures:

3D cultures recapitulating growth in the endosteal niche (EN) or control 3D collagen matrix growth were produced as described (18). Briefly, GELFOAM™ (Pfizer) discs were cut (5mm diameter and 3mm thickness) using a sterile hole punch and scalpel and then UV treated for two hours (1.2 × 10⁵ μ J/CM²) using a UV cross linker. GELFOAM™ discs were placed into a low attachment 96-well plate (Eppendorf Microplate 96/F-PP, Sigma-Aldrich #C150179G) using sterile forceps. Discs were incubated in 200 μl of 1X Dulbecco’s PBS (DPBS) at 37°C/5% CO₂ for 20 min. EN cultures were produced by seeding HS-5, hFOB and HUVEC onto GELFOAM discs (4×10⁴ cells/5μl each) via capillary action, incubating for 2h and then topped off with 200 μl niche media. After 24 h, media was removed and MDA-MB-231 cells (transduced with RFP-expressing shRNA libraries) were seeded onto EN cultures (10⁴ cells/10μl) and grown for 7d, with niche media (100μl) replaced daily. MDA-MB-231 cells grown on tissue culture plates (2D) or in GELFOAM alone (3D) served as negative controls. Bone-tropic MDA-MB-231[BoM1833] cells, infected with pGIPZ (GFP-expressing) lentivirus vector, served as a positive control for 3D-EN growth. MDA MB-231 growth was monitored every other day via fluorescence microscopy using a Nikon Eclipse TS100 inverted microscope and SPOT Insight Fire Wire Camera and SPOT 5.2 software. Cell numbers were quantified using ImageJ software (NIH) from five different fields containing >100 cells/field.

shRNA and expression vectors:

Lentivirus shRNA clones (pGIPZ-based; Supplementary Table S2), Cellecta Human DECIPHER™ screening libraries (Table 1), and DNA expression vectors (ORFeome 8.1) were provided by the Roswell Park Comprehensive Cancer Center Gene Modulation Core (Irwin H. Gelman, Director). Plasmids were propagated in Stbl3 bacteria (ThermoFisher) in LB media supplemented with 100 μg/ml ampicillin for 18 hours at 37^°C at a constant speed of 200 rpm. Plasmids were extracted using QIAprep Spin Miniprep Kit (Qiagen cat# 27104) according to protocol. Relative plasmid concentrations were quantified using a Nanodrop 2000 (ThermoFisher Scientific).

TABLE 1:

shRNA library and screening criteria

Human Module	Gene Targets	# of target mRNAs	shRNA complexity
1	Signaling pathway targets	5,043	27,500
2	Disease-associated targets	5,412	27,500
3	Cell surface, extracellular, DNA-binding targets	4,922	27,500

Hit Criteria	Definition		Module #	# of hits
Fold increase	≥1.55		1	65
# clones	≥3		2	139
Replicates	≥2 of 3		3	198
p38 regulated	Up- or down-regulated
Metastasis	Involved?

Transfection:

HEK293T cells were transfected with LipoD293/DNA mixtures as described previously (22).

Lentivirus packaging and infection:

Polytropic lentiviruses were packaged in HEK293T cells using psPAX2 and pMD2.G packaging constructs as described previously (23). For infection of target cells plated the previous day at confluency in 6-well dishes, 150 μl of lentiviral supernatant and 4μg/ml polybrene (Sigma) was added to the cells along with 1-2 ml DMEM (10% FBS, 1% Pen/Strep) until cells were completely covered. Cells were then incubated for 30 min at 37^°C with 5% CO₂, centrifuged at 1800rpm for 45 min, and then incubated overnight again at 37^°C/5% CO₂. The cultures were split at a 1:5 ratio in DMEM (10% FBS, 1% Pen/Strep, 2mg/ml puromycin).

PCR:

RNA was extracted using TRIzol (Life Technologies) according to the manufacturer’s protocol. One μg of total RNA/reaction was used in qRT-PCR reactions (50 μl total volume) containing the High Capacity cDNA Reverse Transcriptase Kit reagents (Life Technologies/Applied Biosystems #4368814). qPCR was performed using Power SYBR green PCR Mastermix (Life Technologies #4367659) on a Step One Plus thermocycler (Applied Biosystems). GAPDH housekeeping gene was used as the loading control. qRT-PCR reactions were performed in triplicate and analysis was determined using the 2^−ΔΔCt method (StepOne™ software).

Immunoblot analysis:

Immunoblot analysis was performed as described previously (24) using the following antibodies: primary- V5 tag (ThermoFisher #37-7500), GAPDH (Santa Cruz #sc-25778), α-tubulin (Santa Cruz, #sc-5286), p38-MAPK (Cell Signaling, #9212), p38^poT180/Y182 (Cell Signaling, #9211), ERK1/2 (Cell Signaling, #9102S), ERK1/2^poT202/Y204 (Cell Signaling, #9101S); secondary- AlexaFluor700 anti-mouse Ig (1:1000) or AlexaFluor800 anti-rabbit Ig (1:10,000).

High-throughput sequencing and gene identification:

DNA was isolated from 7d 2D and 3D-EN cultures using phenol/chloroform/isoamyl (25:24:1). Cells in the 3D culture were isolated by removing GELFOAM™ discs with cells and incubating on a rotator for 1h at 37°C in 5 ml 1X collagenase/hyaluronidase solution (STEMCELL Technologies). After vigorous mixing, cells were pelleted and washed three times with PBS. First-round and nested PCR were performed according to the Cellecta shRNA library manual (http://www.cellecta.com/resources/product-manuals-and-certificates/) as we described previously (23) using primers described in Supplementary Table S3. All experiments were conducted in triplicate. The PCR products were cleaned using QIAquick Gel Extraction Kits, and then subjected to for single-end Rapid Mode sequencing on an Illumina HiSeq2500 as performed by the Roswell Park Comprehensive Cancer Center Genomics Shared Resource (Sean Glenn, Director). Using FASTQ sequencing data files, barcodes were trimmed from flanking sequence using the ShortRead package from Bioconductor (25). The isolated barcode sequences were aligned to a reference file matching shRNA clones to gene targets using the DECIPHER BarCode Deconvoluter program (Cellecta), that allows for up to 2 incorrect base changes for accurate barcode identification. Individual sequence read counts were normalized by total reads sequenced, and top hits were filtered based on a threshold determined by luciferase shRNA negative controls (21 clones). An analysis of row sums was performed to identify genes targeted by multiple shRNA clones and across replicates.

Statistical analysis:

Statistical analysis was performed on the fold change between the cell counts from Day 1 to Day 7 using the student’s two-tailed t test. Error bars indicate standard error of the mean (S.E.M.). Significant differences between experimental groups had a p value lower than 0.05.

RESULTS and DISCUSSION

Using a novel 3D model of dormancy for bone metastatic BrCa (18), we endeavored to identify genes that suppress tumor cell quiescence in a cultured microenvironment recapitulating bone EN. In this model, the human TNBC cell line MDA-MB-231 proliferates in a GELFOAM™ biomatrix whereas it is growth-arrested in EN conditions (human hFOB osteoblasts, HUVEC endothelial cells and HS-5 diploid fibroblasts in GELFOAM™)(Fig. 1A). Importantly, the inclusion of bone marrow origin fibroblasts (HS-5) and human endothelial cells (HUVEC) promoted the long-term survival of hFOB osteoblasts even after these cells reached initial confluence after 24 h of growth. This EN culture condition was previously shown to induce growth arrest of ER-positive (MCF7, T47D, ZR75-1, and BT474) and ER-negative (SUM149, SUM159, MDA-MB-231, and MDA-MB-453) human BrCa cell lines, whereas these lines could proliferate in either GELFOAM™ alone, or in GELFOAM™ seeded with primary human bone marrow stem cells, representing a perivascular niche (18). In contrast, the bone-metastatic MDA-MB-231 variant, BoM1833, which was selected in vivo for increased bone growth (26), proliferates in either niche (Fig. 1B). Consistent with the notion that activated p38 MAPK in the absence of MEK-ERK activation favors dormancy, we showed that the knockdown of p38 by shRNA (shRNA clones #15 and #18) also induced MDA-MB-231 proliferation in the EN (Fig. 1C), consistent with previous data (18) using the p38 kinase inhibitor, SB203580.

Figure 1. Dormancy induction in 3D-EN is p38-MAPK-dependent.

Relative cell numbers of MDA-MB-231 (A), MDA-MB-231[BoM1833] (B) or MDA-MB-231 cells with p38 knockdown (vs. shCont.) (C) grown for either 1 or 7 d in 3D-EN or 3D, or in 2D (control) conditions. N = independent replicates; error bars, SEM; **, p <0.001.

To identify suppressors of tumor cell proliferation in a bone niche, MDA-MB-231 cells were transduced with a genomic shRNA library (Cellecta DECIPHER^® library covering 15,377 human genes with 82,500 independent shRNA clones, divided into 3 modules; Table 1) and clones that proliferated in EN cultures were enriched. Genes that are potentially required for MDA-MB-231 dormancy within the EN were identified by performing next-gen-sequencing (NGS) of shRNA clone barcodes from DNA taken from triplicate screen aliquots of freshly infected cells (24h) and from infected cells incubated for 7d in 3D-EN. The barcode sequences were trimmed from flanking sequences and shRNA-targeted genes then identified using Cellecta’s BarCode Deconvoluter software. We selected gene targets (shRNA bar-codes) that were found in ≥2 of 3 independent screens, identified by ≥3 independent shRNA clones/gene, each at >1.5-fold increase over background (normalized against the relative abundance of each clone in the library) (Table 1). This analysis identified 416 potential “dormancy-reactivation suppressor” genes (DRSG) in the 3 shRNA clone modules (Table 1).

One of the ways we established statistical significance for potential DSRG candidates was to compare the relative frequency of shRNA clones to the 16 luciferase shRNA controls (shLuc) contained within each module. For example, two module-2 genes, DOLK and MICALL2, had at least 2 independent shRNA clones with fold-change sequence reads over the 1.55-fold shLuc threshold (Supplementary Fig. S1A), yet when compared between screening replicates, only the MICALL2 clones showed consistent statistical significance over the luciferase clones (Supplementary Fig. S1B) in ≥2 replicates. Indeed, the knockdown of DOLK using independent shRNAs failed to induce increase MDA-MB-231 proliferation in 3D-EN cultures (Table 2).

TABLE 2:

DSRG candidates subjected to secondary validation

Gene Targets	Human Module	Fold increase in proliferation at d7	P value
MAPK14	--	2.4 +/− 0.4	<0.001
HBP1	1	1.8 +/− 0.16	<0.01
WNT3	1	2.9 +/− 0.4	<0.001
NES	1	0.9 +/− 0.2	N.S.
BHLHE41	1	2.2 +/− 0.2	<0.001
TIAL1	2	1.1 +/− 0.2	N.S.
HTATIP2	2	1.06 +/− 0.032	N.S.
DOLK	2	1.04 +/− 0.06	N.S.

Many studies have shown that metastatic dormancy is controlled by the simultaneous upregulation of p38MAPK and downregulation of ERK activation (27), yet little is known about mediators of dormancy downstream of p38MAPK. Thus, DSRG were subjected to Ingenuity pathway analysis and Pubmed search to bin them based on a predicted or known relationship to p38MAPK signaling and metastasis. An example is shown in Table 3 for the 65 potential DSRG from module-1, relative to their relationship to p38MAPK signaling and metastasis. Of these candidates, 14 genes were associated with both p38MAPK signaling and metastasis (ADRB2, BHLHE41, CASR, CD63, CDC2L1, FLT1, HBP1, KEAP1, LSP1, NOB1, NRG1, P11, TTF1, WNT3), 12 genes were associated with metastasis but not with p38MAPK signaling (BIK, BRD4, CLDN2, EIF4A1, FUBP1, HSPD1, KIF11, NES, NOX1, RELN, SERPIN1, WNT8A), and 5 genes were associated with p38MAPK signaling alone (CACNB3, EHF, NNAT, OASL, TAC4). As a whole, there was a selection for DSRG candidates in module-1 that are involved in the regulation of neurogenesis or stem cell biology (Table 3), and/or protein translation (Tables 2&3). Loss of differentiation (neurogenesis) or stem cell induction genes resulting in active BrCa cell growth in the 3D-EN is consistent with the notion that disseminated tumor cells exhibit stem cell-like properties (28, 29). Differential expression of genes controlling ribosome biogenesis are known to control stem cell homeostasis (30), and indeed, antagonism of this process was shown to inhibit tumor formation induced by CD44⁺/CD24⁻ human BrCa stem cells (31).

TABLE 3:

Module-1 DSRG candidates

Gene	Fold increase*	# of hits	p38- related**	metastasis	pathway
ADCY8	1.81	5	no	no	neuro/stem
ADK	1.90	4	no	no	neuro/stem
ADRB2	2.19	5	yes	yes	neuro/stem
ARG1	1.64	4	no	no	neuro/stem
ATG2A	2.00	4	no	no	autophagy
ATP2B1	1.91	4	no	no	Ca transport
BHLHE41 ‡	2.3	5	yes	yes	p38/dormancy
BIK	1.56	4	no	yes	apoptosis
BRD4	1.78	7	no	yes	neuro/stem
CACNA1B	1.75	4	no	no	neuro/stem
CACNB3	2.70	5	yes	no	neuro/stem
CASR	1.76	5	yes	yes	neuro/stem
CD63	1.62	4	yes	yes	β-catenin signal
CDC2L1	1.84	5	yes	yes	translation
CFD	2.23	5	no	no	?
CHRND	1.80	4	no	no	neuro/stem
CIP29	2.31	6	no	no	translation
CLDN2	1.88	4	no	yes	neuro/stem
CLK3	1.64	4	no	no	translation
EHF/ESE3	2.33	4	yes	no	neuro/stem
EIF2S3	2.45	4	no	no	translation
EIF4A1	1.82	4	no	yes	translation
EME2	1.71	5	no	no	DNA repair
F10	1.68	4	no	no	coagulation
FLT1 (VEGFR1)	1.74	4	yes	yes	met. promoter
FUBP1	2.39	6	no	yes	translation
GABRG3	1.70	5	no	no	neuro/stem
GCGR	1.60	4	no	no	neuro/stem
GFPT2	1.82	5	no	no	metabolism
GHRL	1.70	4	no	no	metabolism
GPD2	1.85	4	no	no	metabolism
HBP1 ‡	1.85	5	yes	yes	neuro/stem
HMGB2	1.84	4	no	no	neuro/stem
HSPD1	1.71	4	no	yes	chaperone
IL2RG	1.84	4	no	no	survival
IREB2	1.60	4	no	no	Iron metabolism
KEAP1	2.26	5	yes	yes	neuro/stem
KIF11	2.22	11	no	yes	met. promoter
KREMEN1	1.69	5	no	no	survival
LSP1	2.20	5	yes	yes	ERK1/2 supp
NEDD8	1.81	4	no	no	neuro/stem
NES	1.76	5	no	yes	neuro/stem
NNAT	2.63	7	yes	no	neuro/stem
NOB1	2.67	6	yes	yes	translation
NOX1	1.86	7	no	yes	neuro/stem
NRG1	1.97	4	yes	yes	neuro/stem
OASL	2.84	3	yes	no	translation
OGG1	1.85	6	no	no	DNA repair
P11 (s100A10)	1.79	4	yes	yes	BrCa adhesion
PDE6H	1.91	4	no	no	cAMP metabol
PLLP	2.20	4	no	no	metabolism
PSMB4	2.03	4	no	no	neuro/stem
PSMC6	2.29	4	no	no	neuro/stem
PSMD7	1.68	6	no	no	neuro/stem
RELN	1.96	4	no	yes	neuro/stem
SCN7A	1.63	4	no	no	Na channel
SERPIN1	1.55	4	no	yes	invasion
SHC3	2.02	6	no	no	neuro/stem
SUOX	1.98	4	no	no	metabolism
TAC4	2.27	7	yes	no	neuro/stem
TTF1	2.26	5	yes	yes	neuro/stem
VARS	2.02	5	no	no	translation
WNT3 ‡	1.91	5	yes	yes	β-catenin signal
WNT8A	2.23	4	no	yes	β-catenin signal
YARS	1.62	4	no	no	translation

^‡Confirmed by independent shRNA knockdown

In contrast, several had roles that might directly control known dormancy functions. For example, BHLHE41 has been reported to play a role in p38MAPK-mediated dormancy (19), ADRB2 suppresses prostate cancer proliferation in bones by downregulating osteoblast-expressed GAS6 (32), LSP1 negatively controls ERK signaling (20), and P11 (S110A10) controls breast cancer adhesion to endothelial cells in the metastatic niche (33). Lastly, Gene Set Enrichment Analysis of all the module-1 genes showed that 18/65 genes (27.7%) were likely MYC targets (Fig. 2A). Although a role of MYC as a driver of dormancy reawakening has not been addressed, MYC amplification is associated with high-grade BrCa and worse prognosis (34), and in several non-BrCa models, the forced re-expression of MYC rescues proliferation in dormant tumor cells (35).

Figure 2. Analysis of HBP1 and WNT3 as potential DSRG.

(A) Gene Set Enrichment Analysis of module-1 DSRG candidates identified 18 of 65 hits as being MYC target genes. (B) qRT-PCR showing knockdown of HBP1 in MDA-MB-231 cells. (C and D) Knockdown of HBP1 (C) or WNT3 (D) induces proliferation in 3D-EN vs. 3D (“C”) or 2D cultures. Error bars, SEM of three independent replicates; *, p < 0.01; **, p <0.001. (E) Confirmation of WNT3 knockdown by qRT-PCR. Error bars, SEM of three independent replicates. (F) Immunoblot of lysates of MDA-MB-231 or BoM1833 transduced with scramble shRNA (“shCont”), or WNT3-knockdown MDA-MB-231 cells probed for total or activated (poT202/Y204) ERK1/2, total or activated (poT180/Y182) p38-MAPK or α-tubulin (as a loading control). Digital quantifications are shown as normalized to the shControl. This blot is typical of three independent experiments. (G) Overexpression of SFRP2 or 4, but not SFRP3, in MDA-MB-231 induces proliferation in 3D-EN cultures, whereas the overexpression of WNT3 in BoM1833 suppresses 3D-EN proliferation. Error bars, SEM of three independent replicates; *, p < 0.01; **, p <0.001. (H) Immunoblot of MDA-MB-231 lysates transduced with lentivirus expressing V5-tagged SFRP2, 3, or 4 (or empty vector), or BoM1833 (“1833”) cells transduced with WNT3 (or empty vector), probed for V5 or GAPDH. Molecular weight markers are at right.

We then sought to independently confirm that the downregulation of several DSRG candidates leads to MDA-MB-231 proliferation in 3D-EN cultures. Thus, MDA-MB-231 cells were transduced with two independent shRNA clones/gene, and following confirmation of gene knockdown by either qRT-PCR or immunoblotting (IB), the cells were assessed for proliferation (vs. scrambled shRNA controls) in 3D-EN cultures as in Fig. 1. For this analysis, we chose three predicted DSRG from module-1, BHLHE41, HBP1 and WNT3, which were both p38- and metastasis-associated (Table 3), and one gene, NES, not known to be p38-regulated. As well, we chose two negative controls (not predicted to be DSRG): DOLK, that was neither p38- nor metastasis-associated (Supplementary Table S1) and that was likely not significant due to lack of replicate hits (Supplementary Fig. S1B), and HTATIP2, a module-2 gene that failed to make the cut because it had only 2 shRNA hits in one of three replicates. BHLHE41, HBP1 and WNT3 were validated as DSRG, i.e.- knockdown resulted in significantly increased proliferation in EN over controls (BHLHE41: Table 2, HBP1 and WNT3: Fig. 2C&D), whereas DOLK, NES and HTATIP2 knockdown failed to induce MDA-MB-231 proliferation in the 3D-EN (Table 3).

WNT signaling largely has been linked to metastatic progression, especially in models of TNBC (36). However, recent data suggest that specific WNT family members, such as WNT5A, might promote either metastatic progression or dormancy, depending on whether signaling is through canonical or non-canonical pathways (37). Therefore, we sought to follow-up our finding that WNT3 knockdown induced MDA-MB-231 proliferation in our 3D-EN assay. First, we confirmed that WNT deficiency caused proliferation (Fig. 2D), using two independent WNT3-specific shRNAs, which knocked down WNT3 expression in MDA-MB-231 cells ~2.5-fold over scrambled controls (“shCont”; Fig. 2E). Interestingly, WNT3 levels were relatively decreased in BoM1833 cells, consistent with the notion that WNT3 loss facilitates proliferation in the 3D-EN (Fig. 2F). However, WNT3 knockdown in MDA-MB-231 cells had no effect on relative p38^poT180/Y182 levels, indicating that the increased proliferation of MDA-MB-231 cells after WNT3 deficiency was not a result of loss of p38 activation. Although this finding would be consistent with WNT3 being a downstream mediator of p38 signaling, we cannot rule out that the WNT3 effect is p38-independent. In contrast, the BoM1833 variant, which failed to growth-arrest in the 3D-EN cultures (Fig. 1B), exhibited decreased relative p38 activation levels.

Secreted forms of Frizzled-related proteins (SFRP) are thought to antagonize WNT/β-catenin signaling by directly binding WNT members (38), and more specifically, SFRP2 and 4, but not SFRP3, are known to bind WNT3 at high affinity (39). We therefore transduced MDA-MB-231 cells with V5-epitope tagged SFRP2, 3 or 4, confirmed ectopic expression vs. an empty vector control (Fig. 2H), and tested these cells for proliferation in 3D-EN cultures. Fig. 2G shows that SFRP2 and 4, but not 3, could release MDA-MB-231 cells from dormancy. Similarly, the over-expression of WNT3 in BoM1833 suppressed proliferation in 3D-EN, but not in 3D-control cultures (Fig. 2G). Taken together, these data strongly suggest that WNT3 promotes dormancy in our 3D-EN model. This is consistent with a report showing that increased Hedgehog-mediated Sfrp1 expression in liver stroma increased the metastatic potential of human Capan-1 pancreatic tumor cells through the suppression of WNT3 signaling, and that the overexpression of WNT3A in Capan-1 cells decreased experimental metastasis formation (40).

To address this in a clinical context, we compared relative WNT3 expression levels in primary-site vs. metastatic breast cancers in TCGA and Radvanyi Oncomine datasets (41). These data show lower levels of WNT3 in clinical macrometastases compared to primary-site BrCa (Fig. 3A), suggesting that WNT3 deficiency promotes active metastatic progression. One drawback, however, is that both studies have very few metastatic cases (3 each), with none derived from bone, confounding determination of statistical significance. We then analyzed how the loss of WNT3 correlates with either progression-free or overall survival using the TCGA Breast and METABRIC (42, 43) datasets in cBioPortal (http://www.cbioportal.org). We noted that WNT3 loss, either due to gene deletion or transcriptional downregulation, occurred in 31% and 21% of all BrCa cases in TCGA and METABRIC, respectively, and that these cases showed strong co-occurrence with the loss/mutation of TP53 (q-values of 1.36e-21 and 2.98e-58, respectively). Indeed, clinical cases of TNBC are marked by specific TP53 mutations (44). We then sought to determine if the loss of WNT3 and/or TP53 predicted poorer clinical survival, or whether any survival correlation associated with WNT3 loss was potentiated in a background of ER, PR and HER2 loss (encoded by ESR1, PGR and ERBB2, respectively), reflecting TNBC. Indeed, a large portion of the WNT3-deficient cases in the TCGA Breast dataset had coincident losses of ESR1, PGR and ERBB2 (Fig. 3B). Fig 3C shows that in the TCGA dataset, poorer progression-free survival was only detected in cases with combined losses of WNT3, TP53, ESR1, PGR and ERBB2; the loss of any of these genes alone or the combined loss of ESR1, PGR and ERBB2 did not affect survival (WNT3 loss alone: p = 0.652; TP53 loss alone: p = 0.125; WNT3 plus TP53 loss: p = 0.0855; ESR1, PGR and ERBB2 combined loss: p = 0.300). It is noteworthy that BRCA1 mutational status, which represents fewer than 3% of all the WNT3-deficient cases in the TGCA Breast database, has no effect on survival (WNT3 loss + BRCA1 mutation: p = 0.693). In contrast, loss of only WNT3 and TP53 in the METABRIC dataset showed poorer survival (Fig. 3D). Taken together, these data show that WNT3 loss contributes to poorer survival, especially in the context of TP53 loss. The superior powering of the METABRIC dataset, and the fact that it includes many more cases of disease recurrence/progression associated with metastasis, allows for the conclusion that WNT3/TP53 loss is sufficient for poorer survival, a value that worsens in the context of what are likely triple-negative cases (ESR1, PGR and ERBB2 loss). These data correlate with the fact that TNBC dormancy in the bones is shorter in duration than that of ER-positive BrCa (7), suggesting that the combined loss of ER, PR and HER2 might sensitize dormant BrCa cells towards WNT3 loss. It is important to note that we only studied the role of WNT3 signaling in tumor cells in the context of a 3D-EN microenvironment. Although secreted factors are likely to play an important role, it should be noted that cell-cell contact was required for MDA-MB-231 dormancy in this 3D-EN (18). The exact roles played by each EN niche cell type, whether in its direct interaction to BrCa cells or through its secretome, remains to be elucidated. Additionally, the role of WNT3 in controlling tumor dormancy in the bone may be cancer type-dependent because Nandana et al. (45) showed that prostate cancer invasiveness and bone colonization required TBX2-regulated WNT3A expression.

Figure 3. WNT3 expression in clinical BrCa datasets and correlation with survival.

(A) Oncomine TCGA Breast and Radvanyi datasets showing relative WNT3 expression in primary (1°) vs. metastatic BrCa cases. N = number of cases. (B) Copy number variations, mutations and expression changes of TP53, WNT3, ERBB2, PGR and ESR1 in the TCGA Breast dataset produced through cBioPortal, with vertical bars representing a single patient, and the percentages representing the total changes for a given gene. (C and D) Progression-free (C) and overall survival (D) for TGCA Breast and METABRIC datasets, respectively, based on combined TP53, WNT3, ERBB2, PGR and ESR1 losses in the TGCA data, and TP53 and WNT3 losses in the METABRIC dataset. The number of cases with or without these gene changes, as well as the median number of disease-free months, are shown below. (E) Copy number variations, mutations and expression changes of TP53, SFRP1, 2 and 4 in the TCGA Breast dataset produced through cBioPortal. (F and G) Progression-free (F) and overall survival (G) for TGCA Breast and METABRIC datasets, respectively, based on combined TP53, SFRP1, 2 and 4 losses, with numbers of cases with or without changes (below).

We performed a similar analysis on the two other validated DSRG, which indicated that BHLHE41, but not HBP1, exhibited decreased expression in BrCa metastases compared to levels in primary tumors (Fig. 4A; Supplementary Fig. S1C). In the TCGA Breast dataset, 11% of cases exhibited partial or full loss of BHLHE41 (Fig. 4B). As with WNT3 loss, the loss of BHLHE41 had a statistical co-occurrence with mutation/loss of TP53 (q = 0.0242) in this dataset. Moreover, the combined loss of BHLHE41 and TP53 correlated with decreased progression-free or overall survival, respectively, in the TCGA Breast and METABRIC datasets (TCGA: p = 0.0379; METABRIC: p = 6.669e-5). In contrast, loss of BHLHE41 alone did not correlate with decreased survival in TCGA Breast (p = 0.568). Interestingly, the combined loss of WNT3, BHLHE41 and TP53 did significantly change the rate of progression-free survival in TCGA Breast cases, strongly suggesting that the WNT3/TP53 and BHLHE41/TP53 loss cohorts were independent groups, and that either loss of WNT3 or BHLHE41 was individually capable of initiating reawakening in the context of TP53 loss. This also suggests that TP53 loss is the main driver of the poorer prognosis. In regards to possible mechanisms underlying BHLHE41 as a DSRG, the gene product, BHLHE41 (a.k.a.- DEC2 and SHARP1), functions as a transcriptional repressor of epithelial-to-mesenchymal transition and invasion factors, SNAI1, SNAI2 and TWIST (46). Additionally, Adorno et al. (47) showed that BrCa cases with higher levels of BHLHE41 and CCNG2, two p63-induced genes, correlated with lower metastatic risk. Interestingly, specific sets of p53 mutations abrogate p63 activity, likely leading to BHLHE41 loss (48).

Figure 4. BHLHE41 expression in clinical BrCa datasets and correlation with survival.

(A) Oncomine Bittner, TCGA Breast and Radvanyi datasets showing relative BHLHE41 expression in primary (1°) vs. metastatic BrCa cases. N = number of cases. (B) Copy number variations, mutations and expression changes of TP53 and BHLHE41 in the TCGA Breast dataset produced through cBioPortal, with vertical bars representing a single patient, and the percentages representing the total changes for a given gene. (C and D) Progression-free (C) and overall survival (D) for TGCA Breast and METABRIC datasets, respectively, based on combined TP53 and BHLHE41 losses in the METABRIC dataset. The number of cases with or without these gene changes, as well as the median number of disease-free months, are shown below.

In conclusion, the current study marks a novel method to identify and validate potential DSRG based on an in vitro 3D-EN dormancy model for BrCa. Our data suggest several therapeutic avenues, but these would likely be divided into treatments that either secure dormancy, i.e.- antagonize reawakening, or that induce large scale reawakening in a neoadjuvant setting, linked to standard chemotherapies prescribed for TNBC. Examples of reawakening suppressors might include small molecule inhibitors of MYC (49) or Nutlin-3a to normalize mutant p53 function (50), whereas inducers of reawakening might include inhibitors of WNT3 signaling (51, 52) or p38 kinase activity (53).

Table 4:

Translation-controlling potential DSRG

Gene	Gene Name	Function
60S subunit
RPL3	Ribosomal protein L3	Ribosome, structural
RPL4	Ribosomal protein L4	Ribosome, structural
RPL6	Ribosomal protein L6	Ribosome, structural
RPL7	Ribosomal protein L7	Ribosome, structural
RPL8	Ribosomal protein L8	Ribosome, structural
RPL10	Ribosomal protein L10	Ribosome, structural
RPL12	Ribosomal protein L12	Ribosome, structural
RPL13A	Ribosomal protein L13A	Ribosome, structural
RPL14	Ribosomal protein L14	Ribosome, structural
RPL21	Ribosomal protein L21	Ribosome, structural
RPL23	Ribosomal protein L23	Ribosome, structural
RSL24	Ribosomal protein L24	Ribosome, structural
RPL32	Ribosomal protein L32	Ribosome, structural
RPL37A	Ribosomal protein L37A	Ribosome, structural
40S subunit
RPS4X	Ribosomal protein S4X	Ribosome, structural
RPS14	Ribosomal protein S14	Ribosome, structural
RPS15AP17	Ribosomal protein S15a pseudogene 17
RPS20	Ribosomal protein S20	Ribosome, structural
RPS26	Ribosomal protein S26	Ribosome, structural
RPS27AP5	Ribosomal protein S27a pseudogene 5
RPL36P14	Ribosomal protein L36 pseudogene 14
Elongation factors
EIF2B5	Eukaryotic translation initiation factor 2B subunit ε	Elongation factor
EIF2S2	Eukaryotic translation initiation factor 2B subunit β	Elongation factor
EIF2S3	Eukaryotic translation initiation factor 2B subunit γ	Elongation factor
EIF3A	Eukaryotic translation initiation factor 3 subunit A	Elongation factor
EIF4A1	Eukaryotic translation initiation factor 4A1	Elongation factor
Misc. regulators
CLK3	CDC-like kinase 3	Regulates splicing factors
FCF1	rRNA-processing protein	Ribosome formation
KARS	Lysyl tRNA synthase	Codon usage
NARS2	Asparaginyl-tRNA synthetase 2	Codon usage
NOB1	NIN1 binding protein 1	rRNA processing
TSFM	Ts translation elongation factor, mitochondrial	Mitochondrial translation
VARS	Valyl-tRNA synthetase	Codon usage
YARS	Tyrosyl-tRNA synthetase	Codon usage

Implications:

These data describe several novel, potentially targetable pathways controlling BrCa dormancy in the EN.

ABBREVIATIONS

BrCa

breast cancer

EN

endosteal niche

FBS

fetal bovine serum

GFP

green fluorescent protein

HUVEC

human umbilical vascular endothelial cells

Luc

luciferase

RT-PCR

reverse transcriptase-polymerase chain reaction

SEM

standard error of the mean

TCGA

The Cancer Gene Atlas

TNBC

triple-negative breast cancer

WT

wild-type