Dll1+ quiescent tumor stem cells drive chemoresistance in breast cancer through NF-κB survival pathway

Kumar, Sushil; Nandi, Ajeya; Singh, Snahlata; Regulapati, Rohan; Li, Ning; Tobias, John W.; Siebel, Christian W.; Blanco, Mario Andres; Klein-Szanto, Andres J.; Lengner, Christopher; Welm, Alana L.; Kang, Yibin; Chakrabarti, Rumela

doi:10.1038/s41467-020-20664-5

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Published: 18 January 2021

Dll1⁺ quiescent tumor stem cells drive chemoresistance in breast cancer through NF-κB survival pathway

Nature Communications volume 12, Article number: 432 (2021) Cite this article

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An Author Correction to this article was published on 07 July 2022

This article has been updated

Abstract

Development of chemoresistance in breast cancer patients greatly increases mortality. Thus, understanding mechanisms underlying breast cancer resistance to chemotherapy is of paramount importance to overcome this clinical challenge. Although activated Notch receptors have been associated with chemoresistance in cancer, the specific Notch ligands and their molecular mechanisms leading to chemoresistance in breast cancer remain elusive. Using conditional knockout and reporter mouse models, we demonstrate that tumor cells expressing the Notch ligand Dll1 is important for tumor growth and metastasis and bear similarities to tumor-initiating cancer cells (TICs) in breast cancer. RNA-seq and ATAC-seq using reporter models and patient data demonstrated that NF-κB activation is downstream of Dll1 and is associated with a chemoresistant phenotype. Finally, pharmacological blocking of Dll1 or NF-κB pathway completely sensitizes Dll1⁺ tumors to chemotherapy, highlighting therapeutic avenues for chemotherapy resistant breast cancer patients in the near future.

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Introduction

Breast cancer is one of the leading causes of cancer-related death among women¹. Despite recent progress in the field, clinical challenges including treatment resistance, recurrence, and metastasis still remain². Breast cancer is classified into three main subtypes, luminal (expressing estrogen receptor (ER) with or without co-expression of the progesterone receptor (PR) and HER2 gene), HER2-enriched without ER and PR, and Triple-negative breast cancer (TNBC), which does not express any of these receptors^3,4. Such intertumoral heterogeneity has so far precluded the identification of “universal” breast cancer therapeutics. Moreover, the known intratumoral heterogeneity of breast cancer is thought to directly contribute to chemotherapeutic resistance, as a small population of tumor cells known as tumor-initiating cells (TICs)/cancer stem cells (CSCs) often are endowed with quiescence, metastatic, and chemotherapy resistance capabilities^{5,6,7,8,9,10,11} that promote tumor recurrence and metastasis, even after chemotherapeutic treatment^12,13.

TICs are present in several solid tumors including breast tumors¹⁴, and Notch signaling has been specifically implicated in promoting TICs/CSC function in breast cancer^{15,16,17,18,19}. The Notch pathway is composed of four Notch receptors (Notch1-4) and five ligands (Jagged1, Jagged2, Delta-like1 (Dll1), Dll3, and Dll4). While Notch signaling is dysregulated in breast cancer, and extensive studies support the function of Notch receptors in breast cancer^20,21,22,23, the role of specific Notch ligands in promoting TIC function and chemoresistance remains unclear. While global Notch signaling inhibitors reduce tumor growth by targeting TICs, their side effects limit its therapeutic efficacy. Notably, our recent studies highlighted the tumor-promoting function of the Notch ligand Dll1 in luminal breast cancer²⁴. As specific ligand-based therapies are likely to provide a better safety profile than global Notch signaling inhibitors, determining the role of Dll1 in TIC generation/function and Notch-mediated chemotherapeutic resistance, may aid in the identification of effective and better-tolerated therapeutics for breast cancer patients.

In this study, we show that Dll1 is essential for tumor development and metastasis in an Polyoma Middle T (MMTV-PyMT) mammary tumor mouse model. Using reporter models, we further demonstrate a temporal increase of Dll1 expression in tumors as they progress from early to late stages, including metastasis. Mechanistically, we show that within luminal tumors, Dll1⁺ cells represent quiescent TICs, display a chemotherapeutic resistant gene signature enriched in Hypoxia and NF-κB signaling genes, and are resilient to chemotherapy. Most notably, pharmacological blocking of Dll1 or NF-κB pathway sensitizes tumor cells to chemotherapy and significantly abolishes tumor growth and metastasis in tumor initiation and progression study. Collectively, these data suggest that quiescent Dll1⁺ TICs are an important regulator of breast tumor progression and metastasis and are responsible for chemoresistance. As such, targeting Dll1 in combination with chemotherapy and NF-κB may be a promising therapeutic strategy for breast cancer patients who are resistant to chemotherapy.

Results

Dll1 conditional knockout reveals an oncogenic function for Dll1 in the luminal MMTV-PyMT but not in MMTV-Wnt1 model

To dissect the role of Dll1 in autochthonous mammary tumors, we crossed Dll1 conditional-knockout (Dll1^cKO) mice in which K14-Cre recombinase efficiently knocks out Dll1 in mammary epithelial cells^25,26 to MMTV-PyMT to generate PyMT-Dll1^cKO animals (Fig. 1a). The MMTV-PyMT model represents a luminal-like breast cancer model with high metastatic potency^27,28, thereby permitting analysis of progression from early to late stages of the disease. Dll1 expression was significantly downregulated in tumor cells of PyMT-Dll1^cKO mice (Fig. 1b). Moreover, we found that loss of Dll1 led to a dramatic reduction in hyperplasia in PyMT-Dll1^cKO compared to PyMT-Dll1^WT mammary glands at 8 weeks (Fig. 1c), suggesting that loss of Dll1 affects early events in tumorigenesis, potentially by impacting tumor-initiating populations. In control PyMT-Dll1^WT mice, mammary tumors occurred as early as 11 weeks of age, with almost 100% tumor penetrance. In contrast, tumors in PyMT-Dll1^cKO mice occurred at 12 weeks of age with a 75% delayed penetrance (Fig. 1d). Moreover, PyMT-Dll1^WT mice had a significantly increased tumor burden (Fig. 1e) and lung metastasis (Fig. 1f, g) compared to PyMT-Dll1^cKO mice.

**Fig. 1: Conditional knockout of Dll1 significantly delays tumor initiation and lung metastasis in MMTV-PyMT model.**

To determine if the function of Dll1 was specific to the luminal adenocarcinoma MMTV-PyMT model, we crossed the Dll1^cKO mice to MMTV-Wnt1 mice, which represents a basal/mixed tumor model²⁹, to generate Wnt1-Dll1^cKO mice (Supplementary Fig. 1a). In contrast to the PyMT model, mammary glands from both Wnt1-Dll1^cKO and Wnt1-Dll1^WT mice exhibited similar hyperplastic growth (Supplementary Fig. 1b). Furthermore, there was no significant difference in tumor onset in Wnt1-Dll1^cKO compared with Wnt1-Dll1^WT mice (p = 0.4) (Supplementary Fig. 1c), suggesting a specific tumorigenic function for Dll1 in luminal adenocarcinoma.

A dramatic increase of Dll1⁺ tumor cells during PyMT tumor progression

The dramatic effect of Dll1 deletion on PyMT tumor formation prompted us to investigate the spatial and temporal distribution of Dll1 expression during tumorigenesis. For this, we generated PyMT-Dll1^mCherry reporter mice^25,30 and evaluated the expression pattern of Dll1⁺ cells during PyMT-Dll1^mCherry tumor progression from normal mammary gland to late-stage tumor. We observed a substantial increase in the number of Dll1⁺ cells as the mammary gland advanced through the hyperplastic stage to the tumor (Fig. 2a, b), supporting an oncogenic function Dll1 in PyMT tumors. The mRNA levels of Dll1 in sorted mCherry positive and negative populations from hyperplasia and tumors indicated faithful expression of the Dll1 in reporter mice (Fig. 2c). To analyze the status of Dll1⁺ cells in basal and luminal cell compartments, we performed confocal microscopy using basal (K14 and K5) and luminal (K8) markers along with Dll1 mCherry staining. As previously reported, we found that Dll1 is predominantly expressed in the basal layer of the normal mammary gland²⁵ (Fig. 2d). However, there was a relative increase in the number of Dll1⁺ luminal (K8⁺) cells when compared to Dll1⁺ basal tumor cells (K14⁺ and K5⁺) as tumors progressed from hyperplasia stage to late tumor (Fig. 2d, e and Supplementary Fig. 2a, b).

**Fig. 2: Number of Dll1⁺ luminal cells increases during PyMT tumor development.**

To further investigate the distribution of Dll1⁺ cells during tumor development, we used PyMT-Dll1-GFP mouse model. We crossed Dll1^{GFP-IRES-Cre-ERT2} knockin mice (Dll1^GFP)³¹ with MMTV-PyMT mice to generate PyMT-Dll1^GFP mice. The GFP expression faithfully recapitulated Dll1 expression (Supplementary Fig. 2c), and similar to PyMT-Dll1^mCherry model, the number of Dll1⁺ cells (GFP⁺) in PyMT-Dll1^GFP mice was significantly enhanced, and Dll1⁺ cells acquired expression of the luminal marker (K8) as tumors progressed in this luminal model (Supplementary Fig. 2d–f and Supplementary Fig. 3a, b). These studies identified the cellular and temporal distribution of Dll1 at different stages of PyMT tumor development using multiple reporter models.

Metastatic lung nodules show increased expression of Dll1 compared to primary tumors

The MMTV-PyMT mouse model develops lung metastasis at a high frequency²⁸. To determine if there is an association between the expression of Dll1 and metastasis, we assessed Dll1 expression in PyMT-Dll1^mCh and PyMT-Dll1^GFP lung metastatic nodules and compared them to primary tumors. We found higher levels of Dll1 expression in metastatic nodules in both reporter models as compared with the primary tumors (Supplementary Fig. 4a–f). Collectively, our studies from Dll1 conditional knockout (Fig. 1) and reporter mice suggest that Dll1 may be a critical determinant of metastasis in breast cancer.

Dll1⁺ tumor cells display TIC characteristics

Increased metastasis is often linked to the presence of Tumor-initiating cells (TICs)/Cancer stem cells (CSCs) in cancer^32,33,34. To determine if Dll1⁺ cells display a TIC phenotype in breast tumors, we performed an in vitro tumorsphere assay to assess the tumorigenic potential of Dll1⁺ and Dll1⁻ tumor cells isolated from PyMT-Dll1^mCh tumors. Sorted single PyMT-Dll1⁺ tumor cells formed a significantly higher number of tumorspheres compared with PyMT-Dll1⁻ tumor cells, suggesting PyMT-Dll1⁺ cells are enriched in TICs (Fig. 3a). To directly test the tumor-initiating function of Dll1, we transplanted sorted PyMT-Dll1⁺ or PyMT-Dll1⁻ tumor cells into the mammary fat pad (MFP) of syngeneic C57BL/6 mice. We found that transplanted Dll1⁺ tumor cells initiated tumor growth before Dll1⁻ tumor cells, suggesting that Dll1⁺ cells display characteristics of TICs (Fig. 3b). In limiting dilution assay, Dll1⁺ tumor cells showed a higher capability (1/2791) to initiate xenograft growth compared to Dll1⁻ tumor cells (1/27,341) (Fig. 3c). Intriguingly, once Dll1⁻ tumors started growing, they grew faster than Dll1⁺ tumors, suggesting a higher proliferative potential of Dll1⁻ tumor cells (Fig. 3d, e). As TICs have a slow proliferating phenotype³⁵, we used in vivo EdU labeling³⁶ to assess tumor cell proliferation. Notably, we found that Dll1⁺ cells were EdU negative, indicating reduced proliferation compared to Dll1⁻ cells (Fig. 3f), further supporting the TIC-like characteristics of Dll1⁺ cells. Together, our studies identify Dll1⁺ cells as potential TICs with reduced proliferative potential in breast cancer.

**Fig. 3: Dll1⁺ cells are quiescent TICs and are highly metastatic compared to Dll1⁻ tumor cells.**

Dll1-mediated Notch signaling activates the NF-κB pathway in breast tumors

To better understand the molecular signatures between Dll1⁺ and Dll1⁻ tumor cells in breast cancer, we performed RNA-seq analysis using Dll1⁺ and Dll1⁻ tumor cells from both PyMT-Dll1^mCh and PyMT-Dll1^GFP tumors (Supplementary Fig. 5a). As expected, signatures linked to stem cells, invasion and quiescent cells were enriched in Dll1⁺ tumor cells (Fig. 4a, b and Supplementary Fig. 5b, c), supporting our experimental data that Dll1⁺ cells have stem-like properties that increase their invasive properties. NF-κB and Hypoxia signaling are intimately linked to chemoresistance through regulation of cell proliferation and survival in several cancers, including breast cancer^37,38,39,40. Next, we found activation of NF-κB, hypoxia and Notch pathways enriched in Dll1⁺ tumor cells (Fig. 4c and Supplementary Fig. 5d, e), which may contribute to the chemoresistance. Finally, we also found increase in metastasis signature to be enriched in Dll1⁺ tumor cells (Fig. 4d), supporting our in vivo metastasis data (Supplementary Fig. 4a–f). As expected, RNA-seq data demonstrated a ~7.5 higher level of DLL1 expression in this experimental setup. Considering a stringent cut off of >5.5 fold change gene expression in Dll1⁺ vs. Dll1⁻ tumor cells, we identified a set of 10 genes that are strongly expressed in Dll1⁺ cells as compared to Dll1⁻ cells (Supplementary Fig. 5f). Notably, 8 out of these 10 genes are related to lung and bone metastasis^{41,42,43,44,45,46,47,48}, suggesting that they could represent potential biomarkers.

**Fig. 4: RNA-seq and ATAC-seq shows that Dll1⁺ tumor cells have enriched signatures for NF-κB pathway.**

To further investigate the mechanistic basis of Dll1-mediated effect on metastasis and cell survival of tumor cells, we performed ATAC-seq to examine the chromatin accessible and restricted regions in Dll1⁺ and Dll1⁻ tumor cells (Supplementary Fig. 5a). On a very global level, ATAC-seq data do not indicate any dramatic, epigenome-wide changes in chromatin accessibility patterns between Dll1⁺ and Dll1⁻ cells. This conclusion is based on finding similar numbers of UTR, exon, intergenic, intron, and promoter peaks in both Dll1⁺ and Dll1⁻ tumor cells (Supplementary Fig. 5g). This finding is rather expected, given that these cells are of the same lineage, and as Dll1 is not itself a chromatin-modifying protein. We next performed an integrated analysis with ATAC-seq and RNA-seq data. For the integrated analysis, we took the “ATAC-seq Up” signature (all genes with a proximal ATAC-seq peak increasing significantly at p-adjusted < 0.05 in Dll1⁺ samples) and tested it for enrichment in the GFP/mCherry Up (Dll1⁺) combined RNA-seq ranked list. We found that this ATAC-seq signature is strongly enriched in the RNA-seq data (NES = 2.24, p < 0.001) (Fig. 4e). We then focused analyses on the 23 GSEA “Core Enrichment” genes (Fig. 4e) that are both in the ATAC-seq Up signature, and transcriptionally upregulated in Dll1⁺ cells using EnrichR online software. Our data shows that of these 23 common upregulated/increased accessibility genes from Dll1⁺ cells, there are significant enrichment of both Notch signaling and NF-kB signaling (e.g., RelA) (Supplementary Fig. 5h), suggesting that Dll1-mediated Notch signaling may regulate NF- kB1 activity and signaling.

ATAC-seq data also identified the presence of enriched peaks of several of the prometastatic genes identified in the RNA-seq data from Dll1⁺ cells (volcano plot, Fig. 4f). Dll1⁺ tumor cells also have NF-kB1 specific open chromatin region/enriched peak compared to Dll1⁻ cells (Fig. 4g). Together, these data indicate that Dll1 activates NF-kB1 signaling in Dll1⁺ tumor cells, potentially contributing to protumor survival and associated chemoresistance. This observation was further confirmed by an IHC analysis, where Dll1⁺ tumors showed greater nuclear translocation of NF-kB compared with Dll1⁻ tumors suggesting activation of NF-kB signaling (Fig. 4h, i).

To explore the clinical correlation of our mouse data, we examined our Dll1⁺ mouse signature with METABRIC, a patient database to analyze correlation of DLL1 to NF-kB in luminal A and B breast cancer patients^49,50. Notably, we found that genes from DLL1^high patient tumors show strong enrichment to Dll1⁺ signature from PyMT^GFP tumors or PyMT^mCh (top 100 upregulated genes) (Fig. 4j and Supplementary Fig. 6a), highlighting the close resemblance of the mouse model to patients. Also, when patients were stratified based on DLL1 expression, the DLL1^high tumors from patients had upregulated NF-kB signatures similar to mouse of RNA-seq data (Fig. 4k, l and Supplementary Fig. 6b, c). Moreover, tumors from luminal A and B patients show a strong positive correlation between DLL1 and NF-kB (R = 0.185, p < 0.001, Fig. 4m). Finally, we found NF-kB motif genesets to be significantly enriched in DLL1⁺ patient tumors (Luminal A and B) compared to DLL1⁻ patients (Supplementary Fig. 6d) using GSEA C3 compendium. Together, these observations suggest that Dll1-mediated Notch signaling induces activation of NF-kB signaling by regulating NF-kB1, which may contribute to tumor cell survival.

Dll1⁺ TICs are chemotherapy resistant

TICs are thought to be responsible for resistance to chemotherapies in various cancers^51,52. Therefore, we next assessed sensitivity to doxorubicin, an anthracycline chemotherapeutic drug effective in the treatment of a wide range of malignancies, including breast cancer^53,54. To functionally test whether Dll1⁺ cells are resistant to doxorubicin in the PyMT-Dll1^mCh reporter model, we sorted out Dll1⁺ and Dll1^- tumor cells and treated them in vitro with doxorubicin. We found that Dll1⁺ cells were resistant to doxorubicin-induced cell death compared to Dll1⁻ cells (Supplementary Fig. 7a, b). Similar to our in vitro studies, treatment of transplanted Dll1⁺ and Dll1⁻ tumors with doxorubicin revealed that Dll1⁺ tumors were resistant, whereas Dll1⁻ tumor growth was significantly impaired upon doxorubicin treatment (Fig. 5a–d). Particularly, doxorubicin treatment significantly increased the number of quiescent/slow growing Dll1⁺ TICs in tumors (Fig. 5e and Supplementary Fig. 7c), potentially due to their evasion of cell death and/or DNA damage. Indeed, further analysis of transplanted tumors revealed that Dll1⁺ tumors were less prone to cell death compared to Dll1⁻ tumors as seen by cleaved-caspase-3 and TUNEL staining (apoptotic cells) in the tumor cells (Fig. 5f–h). Cell cycle analysis further demonstrated a strong increase in a sub-G1 population (apoptotic cells) of Dll1⁻ cells compared to Dll1⁺ tumor cells (Supplementary Fig. 8a, b). Finally, γH2AX staining (green staining indicates DNA damage) shows reduced doxorubicin-induced DNA damage in Dll1⁺ tumor cells compared to Dll1⁻ tumors (Supplementary Fig. 8c–e), potentially explaining the chemotherapeutic resistance of Dll1⁺ tumors (Fig. 5b–d).

**Fig. 5: Dll1⁺ tumor cells are chemoresistant in PyMT tumors.**

Pharmacological inhibition of Dll1 sensitizes Dll1⁺ tumor cells to chemotherapy

Next, we investigated whether targeting Dll1 in vivo could impact the chemoresistant phenotype of Dll1⁺ TIC/CSCs. Dll1⁺ tumor-bearing mice were given either the control vehicle or anti-Dll1-blocking antibody (αDll1)⁵⁵ or doxorubicin (Dox), or a combination of both (Supplementary Fig. 9a). In this tumor initiation and progression study, Dll1 antibody alone had a modest inhibitory effect on tumor growth, whereas, the combination treatment dramatically reduced tumor growth (Fig. 6a, b), suggesting that Dll1-blocking sensitizes Dll1⁺ TICs to chemotherapy. Moreover, metastatic events were significantly decreased in mice treated with anti-Dll1 antibody and chemotherapy compared with untreated controls (Fig. 6c–e and Supplementary Fig. 9b). This decrease in tumor growth and progression was associated with reduced nuclear translocation of NF-kB (Fig. 6f, g), suggesting that Dll1-mediated chemoresistance is potentially driven by NF-kB signaling.

**Fig. 6: The Dll1-blocking antibody treatment sensitizes the Dll1⁺ tumor cells to the chemotherapy.**

There were no observable side effects of Dll1 blocking in mono or in combination with doxorubicin, as body weight of these mice were unaltered (Supplementary Fig. 10a). The activity of the Dll1 antibody was further confirmed by examination of depletion of marginal B-cells in the spleen that depend on Dll1 activity (Supplementary Fig. 10b, c)^56,57,58. Taken together, these results delineate that Dll1 targeting may be effective for patients failing chemotherapy and may have a better safety profile during treatment.

Pharmacological inhibition of NF-kB and Dll1 resensitizes chemoresistant Dll1⁺ luminal tumor cells, phenocopying the effects of Dll1-blocking antibody

Since NF-kB was found to be downstream of Dll1-mediated Notch signaling, we next sought to determine if the NF-kB inhibitor IMD-0354 (IMD) would resensitize tumor cells to chemotherapy, phenocopying the effect of the Dll1-blocking antibody. IMD is a selective IKK2 inhibitor, which disrupts NF-κB signaling, which showes activities in several preclinical models including cancer^59,60,61,62. An in vitro tumorsphere assay with Dll1⁺ tumor cells demonstrated a significant decrease in the number and size of tumorspheres with a combination treatment of IMD and doxorubicin, similar to effects seen with Dll1-blocking antibody and doxorubicin (Fig. 7a–c). Especially, combination treatment of Dll1 antibody, IMD and chemotherapy further decreased the number and size of tumorspheres (Fig. 7a–c), while either IMD or chemotherapy alone had minimum effect on tumor cells. To complement the in vitro data, we treated Dll1⁺ tumor cells with doxorubicin, Dll1-blocking antibody, IMD-0354 treatment alone or in combinations (Supplementary Fig. 10d). Similar to in vitro results, NF-kB inhibition along with αDll1 ab or doxorubicin was very effective to inhibit tumor growth with no significant toxicity (Fig. 7d, e and Supplementary Fig. 10e). Furthermore, combination treatment targeting Dll1, NF-kB and doxorubicin significantly abolished tumor growth of Dll1⁺ tumor cells. Thus, our results suggest that activation of NF-kB downstream of Dll1 signaling in tumor cells promotes chemoresistance and blocking all these components together can lead to significant response in thes tumors.

**Fig. 7: Inhibition of NF-κB sensitizes Dll1⁺ tumor cells to chemotherapy.**

Discussion

Notch signaling, specifically the Notch receptors, has been associated with chemoresistance in several cancers, including breast cancer, most markedly in TNBC^{63,64,65,66,67,68}, suggesting that targeting this pathway may improve clinical outcome. Our study reveals a central role for the Notch ligand Dll1 in promoting tumor progression, metastasis and chemoresistance in aggressive luminal breast tumors. Identification of a ligand-based therapy to block tumor-specific Notch signaling may provide a safer and effective therapeutic option for breast cancer patients.

Earlier studies from our group have demonstrated that the Notch ligand Dll1 is important for normal mammary stem cell development²⁵. We further showed that Dll1 is preferentially upregulated in luminal breast cancer compared to TNBC/basal cancer and regulates CSC function in these breast cancer cells²⁴. However, at that time, we could not evaluate the spatial and temporal distribution of Dll1 expression during tumor progression and metastasis. Furthermore, we could not determine if Dll1⁺ cells themselves had TIC/CSC properties due to the lack of appropriate mouse models. In this study, we used Dll1 conditional knockout and reporter models crossed with spontaneous murine models (MMTV-PyMT and MMTV-Wnt1) to determine the spatial and temporal distribution of Dll1 and also determine the function of Dll1⁺ tumor cells in tumor progression. We found that Dll1⁺ cells increase dramatically from normal mammary gland to hyperplasia and tumor, suggesting a tumor supporting function of Dll1. We found that Dll1⁺ tumor cells increase in K8⁺ luminal compartment compared to basal cells as tumor progresses from hyperplasia to tumor stage in MMTV-PyMT model. Future studies on lineage tracing starting at earlier age in mice, will help to decipher the mechanism behind this increase in K8⁺ Dll1⁺ luminal cells in tumors. Using conditional-knockout mice of Dll1, we further demonstrated that Dll1 has a significant tumor-promoting function in MMTV-PyMT tumors but not in MMTV-Wnt1 tumors. It is possible that Dll1 may have tumor-promoting function in other basal tumors, which needs further explorations. We further show that Dll1⁺ tumor cells are quiescent TICs and Dll1-mediated Notch signaling is one of the major drivers of tumor progression and metastasis in the the MMTV-PyMT model using both reporter and conditional-knockout mouse models. These data are in accordance with the Metastatic Breast Cancer project database (Broad Institute), which highlights the amplification of Dll1 in metastatic breast cancer patients.

Tumor heterogeneity is currently a clinical challenge for breast cancer therapy^11,69,70,71. To determine if the Dll1⁺ tumor cells resemble TICs that could increase tumor burden, metastasis, and chemotherapeutic resistance, the transcriptional signatures of Dll1⁺ and Dll1⁻ tumor cells were further evaluated by RNA-seq analysis. These data show that Dll1⁺ tumor cells are associated with increased invasiveness and metastatic signatures in GSEA, corroborating experimental data. The RNA-seq data indicated that Notch target genes are activated in Dll1⁺ tumor cells compared with Dll1⁻ tumor cells. However, we need further evaluations to understand Notch signalling between Dll1⁺ cells and Notch receptor-expressing cells. Moreover, Dll1⁺ tumor cells are enriched for stem cell genes and as expected, the Dll1⁺ cells showed reduced expression of cell cycling genes, while Dll1⁻ cells were highly enriched, highlighting the relative quiescent properties of Dll1⁺ tumor cells, which is confirmed by EdU assay. Previous studies suggest that CD44^hi/CD24^lo epithelial cells retain classic TIC features in breast cancer, including tumor-initiating capacity in vivo, tumorsphere formation, and resistance to standard chemotherapy¹³. We found that Dll1⁺ tumor cells are resistant to the chemotherapeutic drug, doxorubicin. Mechanistically, we showed that Dll1⁺ tumor cells resist chemotherapy by avoiding drug-induced cell death and DNA damage as seen by cleaved-caspase-3 assay, γH2AX staining, TUNEL and cell cycle assays. Interestingly, Dll1⁺ cells were also enriched for signatures of NF-κB signaling and hypoxia, common features of cancer progression and chemoresistance^72,73,74,75. This observation was further confirmed in human patients data using METABRIC dataset, where we found strong positive correlation between DLL1 and NF-κB1. Moreover, Dll1^high gene signatures (top 100 genes) from our mouse models closely resemble genes of DLL1^high Luminal A and B patients supporting the clinical significance of our model to human patients. Indeed, the NF-κB pathway can crosstalk with Notch signaling in lung cancer to promote chemoresistance⁷⁶. Moreover, Hypoxia has long been associated with chemoresistance to several drugs, including doxorubicin, in multiple cancers such as lung, prostate, and breast cancer^39,77,78,79. ATAC-seq corroborates RNA-seq data and identified several open chromatin regions for the NF-κB1 gene and other protumorigenic and prometastatic genes, indicating that Dll1-mediated Notch signaling may regulate these pathways to resist chemotherapy. Furthermore, increased nuclear translocation of NF-κB was observed in tumor cells expressing higher levels of Dll1. Blocking of NF-kB activity using the IMD inhibitor in combination with doxorubicin significantly decreased tumor cells viability and stem cells activity in tumorsphere assays, thus phenocopying the effects of the Dll1-blocking antibody. Combination treatment blocking NF-kB activation, Dll1 and chemotherapy shows almost complete respone in tumor growth in vivo.

Together, our data suggest that Dll1-mediated Notch signaling drives metastasis and chemoresistance and may therefore be responsible for higher mortality in breast cancer (Fig. 7f). Mechanistically, our data suggest that Dll1 increases resistance to DNA damage and cell death through the NF-κB pathway. Remarkably, combination therapy targeting Dll1 ligand and NF-κB pathway shows complete response to the doxorubicin in Dll1⁺ tumor cells. Altogether, our findings demonstrate a promising therapeutic target in breast cancer patients with advanced disease and metastasis, particularly for those who are chemoresistant. Further preclinical studies will assess the potential efficacy of this strategy in the clinics.

Methods

Animal studies

Animal housing, handling, and procedures were conducted in compliance with the Institutional Animal Care and Use Committee (IACUC) of the University of Pennsylvania. The health status of all mice was normal, and they were not involved in any previous procedure. For Dll1 in vivo loss-in-function studies, Dll1^cKO mice²⁵ were crossed with MMTV-PyMT or MMTV-Wnt1 to obtain PyMT-Dll1^cKO or Wnt1-Dll1^cKO mice. To obtain PyMT-Dll1^mCh and PyMT-Dll1^GFP mice, the Dll1^mCherry BAC-based mice³⁰ and Dll1^GFP knockin²⁵ mice were crossed with MMTV-PyMT mice. All mice were in C57BL/6 background for tumor studies. Six-week-old female wild-type C57BL/6 mice for mammary fat pad injections were obtained from Jackson Laboratory. For mammary fat pad injections, all mice were anesthetized, and tumor cells were injected into the mammary fat pad following established protocols⁷¹. Tumors were detected by palpation and measured twice a week. The experiment was terminated when tumors reached certain size as mentioned in the figure legend, or the experiment was terminated before if the tumors were ulcerated (humane endpoint). Mice were injected with 0.2 mg/10 g body weights EdU (Invitrogen, A10044) intraperitoneally 12 h before sacrifice to study in vivo proliferation of tumor cells.

Metastatic lung nodule count

The tumor-bearing mice were sacrificed by cervical dislocation to harvest the metastatic lungs. For PyMT-Dll1^cKO and PyMT-Dll1^WT mice, the lungs were fixed with Bouin’s solution for 24 h and then washed with 70% ethanol and then counted for nodules based on whole lung images. For PyMT-Dll1^mCherry and PyMT-Dll1^GFP mice, after a brief wash with PBS, the mCherry⁺ lung nodules were counted under Leica fluorescent dissection microscope. Lungs were also sectioned and the lungs sections were stained for H&E as per described protocol^24,80. The H&E slides were evaluated by a board certified pathologist to confirm metastatic nodules.

In vivo cell proliferation assay

We intraperitoneally injected EdU (0.2 mg/10 g body weight) 12 h before harvesting the tumors. EdU-labeled proliferating cells were detected on Formalin-fixed tissues by Click-iT EdU Alexa Fluor 488 imaging kit (Thermo Fisher Scientific) following the manufacturer’s protocol.

Histological analysis and immunofluorescence (IF)

For histological analysis, mouse tumor and mouse lung specimens were processed as previously described^81,82. Antibodies information and dilutions are listed in Supplementary Table 2. DAPI was used to stain nuclei. IF Images were taken using a Leica SP5 FLIM Confocal microscope.

Flow cytometry analysis and sorting

The single-cell suspension for flow cytometry analysis of normal mammary glands, hyperplasia and tumors was obtained following a published protocol^71,82. Single cells were stained with a combination of antibodies (listed in Supplementary Table 1) for 30 min on room temperature in the dark. Live cells were gated out using either DAPI or PI. Flow cytometry analysis was performed using the LSRII and LSRFortessa Flow Cytometers (BD Biosciences), and data were analyzed using FlowJo software (TreeStar, Inc). We used the Aria II instrument for sorting mammary tumors.

Protein extraction and western blot analysis

We digested MMTV-PyMT tumors into single cells to isolate protein. We performed western blot analysis as described in previous articles²⁴. The antibodies details and dilutions are listed in Supplementary Table 3.

RNA extraction and quantitative PCR (qRT-PCR)

Total RNA was isolated from sorted tumor cells or hyperplasia using the Invitrogen RNA extraction kit in accordance with the manufacturer’s instructions. The amount of isolated RNA was quantified using a Nanodrop spectrophotometer (Thermo Scientific). Complementary DNA was synthesized from 1 µg of total RNA using SuperScript (Invitrogen). Real-time RT-PCR was performed on the Applied Biosystems StepOne Plus PCR machine (Thermo Fisher) using SYBR Green Power (Life Science Technologies). We performed all qRT-PCR assays in duplicate at least three independent experiments. The gene-specific primer sets were used at a final concentration of 0.2 μM and their sequences are listed in Supplementary Table 4.

ATAC-seq library preparation and analysis

Single-cell digestion from freshly isolated spontaneous mammary tumors from PyMT-Dll1^mCh tumor-bearing mice was performed followed by cell sorting. Lineage-negative epithelial tumor cells, which are Dll1⁺ and Dll1⁻ cells (based on mCherry expression) were sorted. 20,000 Dll1⁺ and Dll1⁻ lineage-negative tumor cells from four different tumors were used. Previously described established protocols for ATAC-Seq transposition assay and library preparation were used^83,84.

Raw sequence (fastq) files were processed in genotype-specific replicate batches to find open chromatin regions using the ENCODE ATAC-seq pipeline https://github.com/ENCODE-DCC/atac-seq-pipeline. Peaks enumerated in the optimized peak files for the Dll1⁺ and Dll1⁻ replicate groups were merged with bedops⁴¹, and the nucleotide sequence underlying each of the resulting regions was extracted to a fasta file using bedtools (https://bedtools.readthedocs.io/en/latest/). Reads for each sample were mapped to the sequences for all regions using salmon⁸⁵, and the read matrix was normalized and analyzed for differential peak depth between the two genotypes using DESeq2⁸⁶. Peak locations were annotated for nearby genes using homer (http://homer.ucsd.edu/homer/⁸⁷ and merged into the RNA-seq dataset based on gene symbol. Data were visualized using the Integrative Genomics Viewer⁸⁸.

RNA-sequence analysis and geneset enrichment analysis (GSEA)

Raw sequence files (fastq) for eight samples were mapped using salmon (https://combine-lab.github.io/salmon/) against the mouse transcripts described in genecode (version M20, built on the mouse genome GRCm38.p6, https://www.gencodegenes.org), with an average of 51.3 M total input reads for each sample, and an 80.1% average mapping rate. Transcript counts were summarized to the gene level using tximport (https://bioconductor.org/packages/release/bioc/html/tximport.html), and normalized and tested for differential expression using DESeq2 (https://bioconductor.org/packages/release/bioc/html/DESeq2.html). A 2-factor statistical model was used including Dll1 status (+/−) and mouseID. Gene Set Enrichment Analysis (GSEA, http://software.broadinstitute.org/gsea/index.jsp) was run for the contrast in pre-ranked mode using the DESeq2 statistic of the ranking metric. Mouse gene symbols were mapped to human gene orthologs using Ensembl’s BioMart (http://www.ensembl.org/biomart/martview/). Where there were redundant mappings, the statistic with the highest absolute value was chosen. Geneset enrichment analysis (GSEA) was conducted using GSEA2.2.4 software to generate enrichment scores for genesets in Hallmark, C2.all and C5.all, datasets with default settings.

METABRIC dataset analysis

RNA expression data from the METABRIC dataset^49,50 were downloaded from the cBio portal at http://download.cbioportal.org/brca_metabric.tar.gz, and corresponding patient clinical data were downloaded from https://www.cbioportal.org/study/clinicalData?id=brca_metabric. Luminal A and Luminal B patient subtypes were identified using the Pam50 + Claudin-low subtype parameter included in the clinical data. Pearson correlation was used to determine statistical significance of correlation of DLL1 expression and expression of NF-kB1 in the Luminal A and Luminal B combined patient cohort. The combined cohort was then stratified by median DLL1 expression, and GSEA was performed by testing all genesets within the GSEA C2 and C3 MSigDB compendia for enrichment in the list of genes ranked by magnitude of overexpression in DLL1^high (above median) vs. DLL1^low (below median) patients. The signal-to-noise (S2N) metric was used for gene ranking, and permutations (n = 1000) were performed on genesets. Default settings were used for all other GSEA parameters. To derive Dll1^high and Dll1^low experimental gene expression signatures from sorted mouse tumor cells, genes were ranked by magnitude of overexpression in RNA-seq data from Dll1^high (GFP⁺ or mCherry⁺) vs. Dll1^low (GFP⁻ or mCherry⁻) samples. The S2N metric used for ranking genes, and the top 100 most overexpressed genes were used for Dll1^high genesets.

In vivo Dll1-blocking antibody, doxorubicin and IMD treatments

The mice were randomly assigned to the cohorts for drug treatments. Treatments were started when tumor size reached 3 × 3 mm. Supplementary Figs. 9a and 10d provide complete experimental strategies. The Dll1-blocking antibody (18 mg/kg thrice a week), doxorubicin (1 mg/kg twice a week), and IMD (0.5 mg/kg once a week) were given until the endpoint of the experiment.

MTT assay

Dll1⁺ and Dll1⁻ tumor cells were obtained form PyMT-Dll1^mCherry spontaneous tumors. The 10,000 cells were seeded in each well of 96-well plate. Forty-eight hours after seeding, the cells were treated with the 100 ng/mL of Doxorubicin for 4-days. After incubation, we added 20 uL of the MTT reagent (5 mg/mL) with 80 µL of MEGM media to each well. We incubated the plate in cell-culture incubator for 4 h. We added 100 µL of DMSO to solubilize the purple formazan crystals and the absorbance was observed at 600 nm wavelength and reference wavelength at 750 nm as described⁸⁹.

Tumorsphere assay

We digested the PyMT-Dll1^mCh tumors into single cells and sorted Lin⁻ Dll1⁺ and Dll1⁻ tumor cells. The tumor cells were cultured for tumorsphere formation, as previously described⁸¹. Briefly, cells were grown in Corning low adherent plates in tumorsphere media for 5–7 days⁸¹. The tumorsphere media includes 50 ng/mL EGF, 150 ng/mL FGF, 1× B27 in DMEM:F12 (1:1) media. The number of total tumorspheres and diameter were counted/measured using a microscope. The single cells from tumors were made following a published protocol⁸⁰.

Quantification and statistical analysis

Sample size and replicates

We repeated our in vitro experiments at least three times in triplicate. The exact number of mice for each group is mentioned in the corresponding figure legends. For in vivo treatments of Dll1-blocking antibody, doxorubicin and IMD, mice were randomized based on their age and body weight, before the start of the experiment.

Replicates statistical analysis

The results are reported as mean ± SEM (standard error of the mean). The significance of differences was calculated using two-tailed unpaired Student’s t-test for normally distributed datasets. One-way ANOVA with Tukey’s multiple-comparisons post-hoc test was used for multiple pairwise comparisons. The tumor growth and grouped datasets were analyzed using the Bonferroni corrected two-way ANOVA to compute statistical significance. Differences in tumor initiation between groups via Kaplan–Meier plots were statistically evaluated using Log-rank tests. For limiting dilution assay, Chi-square test and ELDA online software was used. The number of animals and the statistical tests to compute p-value are reported in each of the corresponding figure legends.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

Sequencing data from RNA-sequencing and ATAC sequencing from tumor samples that support the findings of this study (Fig. 4 and Supplementary Figs. 5 and 6) are deposited in the Gene Expression Omnibus, reference GSE129198 (RNA-seq) and GSE133736 (ATAC-seq). Source data are provided with this paper.

Change history

07 July 2022
A Correction to this paper has been published: https://doi.org/10.1038/s41467-022-30946-9

References

Bray, F. et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 68, 394–424 (2018).
Article PubMed Google Scholar
Chun, K. H., Park, J. H. & Fan, S. Predicting and overcoming chemotherapeutic resistance in breast cancer. Adv. Exp. Med. Biol. 1026, 59–104 (2017).
Article CAS PubMed Google Scholar
Sorlie, T. et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc. Natl Acad. Sci. USA 98, 10869–10874 (2001).
Article ADS CAS PubMed PubMed Central Google Scholar
Parker, J. S. et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J. Clin. Oncol. 27, 1160–1167 (2009).
Article PubMed PubMed Central Google Scholar
Prieto-Vila, M., Takahashi, R. U., Usuba, W., Kohama, I. & Ochiya, T. Drug resistance driven by cancer stem cells and their niche. Int. J. Mol. Sci. 18, R135–R155 (2017).
Article Google Scholar
Wei, W. & Lewis, M. T. Identifying and targeting tumor-initiating cells in the treatment of breast cancer. Endocr. Relat. Cancer 22, R135–155 (2015).
Article CAS PubMed PubMed Central Google Scholar
Dean, M., Fojo, T. & Bates, S. Tumour stem cells and drug resistance. Nat. Rev. Cancer 5, 275–284 (2005).
Article CAS PubMed Google Scholar
Visvader, J. E. & Lindeman, G. J. Cancer stem cells: current status and evolving complexities. Cell Stem Cell 10, 717–728 (2012).
Article CAS PubMed Google Scholar
Nguyen, L. V., Vanner, R., Dirks, P. & Eaves, C. J. Cancer stem cells: an evolving concept. Nat. Rev. Cancer 12, 133–143 (2012).
Article CAS PubMed Google Scholar
Visvader, J. E. & Lindeman, G. J. Cancer stem cells in solid tumours: accumulating evidence and unresolved questions. Nat. Rev. Cancer 8, 755–768 (2008).
Article CAS PubMed Google Scholar
Koren, S. & Bentires-Alj, M. Breast tumor heterogeneity: source of fitness, hurdle for therapy. Mol. Cell 60, 537–546 (2015).
Article CAS PubMed Google Scholar
Kreso, A. & Dick, J. E. Evolution of the cancer stem cell model. Cell Stem Cell 14, 275–291 (2014).
Article CAS PubMed Google Scholar
Al-Hajj, M., Wicha, M. S., Benito-Hernandez, A., Morrison, S. J. & Clarke, M. F. Prospective identification of tumorigenic breast cancer cells. Proc. Natl Acad. Sci. USA 100, 3983–3988 (2003).
Article ADS CAS PubMed PubMed Central Google Scholar
Meacham, C. E. & Morrison, S. J. Tumour heterogeneity and cancer cell plasticity. Nature 501, 328–337 (2013).
Article ADS CAS PubMed PubMed Central Google Scholar
Harrison, H., Farnie, G., Brennan, K. R. & Clarke, R. B. Breast cancer stem cells: something out of notching? Cancer Res. 70, 8973–8976 (2010).
Article CAS PubMed Google Scholar
Shah, D. et al. Inhibition of HER2 Increases JAGGED1-dependent Breast Cancer Stem Cells: Role for Membrane JAGGED1. Clin. Cancer Res. 24, 4566–4578 (2018).
Article CAS PubMed PubMed Central Google Scholar
Rangel, M. C. et al. Developmental signaling pathways regulating mammary stem cells and contributing to the etiology of triple-negative breast cancer. Breast Cancer Res. Treat. 156, 211–226 (2016).
Article PubMed PubMed Central Google Scholar
Takebe, N. et al. Targeting Notch, Hedgehog, and Wnt pathways in cancer stem cells: clinical update. Nat. Rev. Clin. Oncol. 12, 445–464 (2015).
Article CAS PubMed PubMed Central Google Scholar
Espinoza, I. & Miele, L. Notch inhibitors for cancer treatment. Pharmacol. Ther. 139, 95–110 (2013).
Article CAS PubMed PubMed Central Google Scholar
Leontovich, A. A. et al. NOTCH3 expression is linked to breast cancer seeding and distant metastasis. Breast Cancer Res. 20, 105 (2018).
Article PubMed PubMed Central Google Scholar
Kontomanolis, E. N. et al. The notch pathway in breast cancer progression. ScientificWorldJournal 2018, 2415489 (2018).
Article PubMed PubMed Central Google Scholar
McGowan, P. M. et al. Notch1 inhibition alters the CD44hi/CD24lo population and reduces the formation of brain metastases from breast cancer. Mol. Cancer Res. 9, 834–844 (2011).
Article CAS PubMed PubMed Central Google Scholar
Stylianou, S., Clarke, R. B. & Brennan, K. Aberrant activation of notch signaling in human breast cancer. Cancer Res. 66, 1517–1525 (2006).
Article CAS PubMed Google Scholar
Kumar, S. et al. Estrogen-dependent DLL1-mediated Notch signaling promotes luminal breast cancer. Oncogene 38, 2092–2107 (2018).
Article PubMed PubMed Central Google Scholar
Chakrabarti, R. et al. Notch ligand Dll1 mediates cross-talk between mammary stem cells and the macrophageal niche. Science 360, eaan4153 (2018).
Article PubMed PubMed Central Google Scholar
Dassule, H. R., Lewis, P., Bei, M., Maas, R. & McMahon, A. P. Sonic hedgehog regulates growth and morphogenesis of the tooth. Development 127, 4775–4785 (2000).
Article CAS PubMed Google Scholar
Lin, E. Y. et al. Progression to malignancy in the polyoma middle T oncoprotein mouse breast cancer model provides a reliable model for human diseases. Am. J. Pathol. 163, 2113–2126 (2003).
Article PubMed PubMed Central Google Scholar
Guy, C. T., Cardiff, R. D. & Muller, W. J. Induction of mammary tumors by expression of polyomavirus middle T oncogene: a transgenic mouse model for metastatic disease. Mol. Cell Biol. 12, 954–961 (1992).
CAS PubMed PubMed Central Google Scholar
Herschkowitz, J. I. et al. Identification of conserved gene expression features between murine mammary carcinoma models and human breast tumors. Genome Biol. 8, R76 (2007).
Article PubMed PubMed Central Google Scholar
Tikhonova, A. N. et al. The bone marrow microenvironment at single-cell resolution. Nature 569, 222–228 (2019).
Article ADS CAS PubMed PubMed Central Google Scholar
van Es, J. H. et al. Dll1+ secretory progenitor cells revert to stem cells upon crypt damage. Nat. Cell Biol. 14, 1099–1104 (2012).
Article PubMed PubMed Central Google Scholar
Velasco-Velazquez, M. A., Popov, V. M., Lisanti, M. P. & Pestell, R. G. The role of breast cancer stem cells in metastasis and therapeutic implications. Am. J. Pathol. 179, 2–11 (2011).
Article CAS PubMed PubMed Central Google Scholar
Baccelli, I. & Trumpp, A. The evolving concept of cancer and metastasis stem cells. J. Cell Biol. 198, 281–293 (2012).
Article CAS PubMed PubMed Central Google Scholar
Li, F., Tiede, B., Massague, J. & Kang, Y. Beyond tumorigenesis: cancer stem cells in metastasis. Cell Res. 17, 3–14 (2007).
Article CAS PubMed Google Scholar
Moore, N. & Lyle, S. Quiescent, slow-cycling stem cell populations in cancer: a review of the evidence and discussion of significance. J. Oncol. 198, https://www.hindawi.com/journals/jo/2011/396076/ (2011).
Brown, J. A. et al. TGF-beta-induced quiescence mediates chemoresistance of tumor-propagating cells in squamous cell carcinoma. Cell Stem Cell 21, 650–664 e658 (2017).
Article CAS PubMed PubMed Central Google Scholar
Hoesel, B. & Schmid, J. A. The complexity of NF-kappaB signaling in inflammation and cancer. Mol. Cancer 12, 86 (2013).
Article CAS PubMed PubMed Central Google Scholar
Antoon, J. W. et al. Targeting NFkB mediated breast cancer chemoresistance through selective inhibition of sphingosine kinase-2. Cancer Biol. Ther. 11, 678–689 (2011).
Article CAS PubMed PubMed Central Google Scholar
Sullivan, R., Pare, G. C., Frederiksen, L. J., Semenza, G. L. & Graham, C. H. Hypoxia-induced resistance to anticancer drugs is associated with decreased senescence and requires hypoxia-inducible factor-1 activity. Mol. Cancer Ther. 7, 1961–1973 (2008).
Article CAS PubMed Google Scholar
Hu, R. et al. NF-kappaB signaling is required for XBP1 (unspliced and spliced)-mediated effects on antiestrogen responsiveness and cell fate decisions in breast cancer. Mol. Cell Biol. 35, 379–390 (2015).
Article PubMed Google Scholar
Lange, A. M. & Lo, H. W. Inhibiting TRK proteins in clinical cancer therapy. Cancers (Basel) 10, https://www.mdpi.com/2072-6694/10/4/105 (2018).
Ricciardelli, C. et al. Keratin 5 overexpression is associated with serous ovarian cancer recurrence and chemotherapy resistance. Oncotarget 8, 17819–17832 (2017).
Article PubMed PubMed Central Google Scholar
Thangavelu, P. U., Krenacs, T., Dray, E. & Duijf, P. H. In epithelial cancers, aberrant COL17A1 promoter methylation predicts its misexpression and increased invasion. Clin. Epigenetics 8, 120 (2016).
Article PubMed PubMed Central Google Scholar
Amatu, A., Sartore-Bianchi, A. & Siena, S. NTRK gene fusions as novel targets of cancer therapy across multiple tumour types. ESMO Open 1, e000023 (2016).
Article PubMed PubMed Central Google Scholar
Galli, S. et al. Neuropeptide Y as a biomarker and therapeutic target for neuroblastoma. Am. J. Pathol. 186, 3040–3053 (2016).
Article CAS PubMed PubMed Central Google Scholar
Park, S. et al. Aberrant CDK4 amplification in refractory rhabdomyosarcoma as identified by genomic profiling. Sci. Rep. 4, 3623 (2014).
Article PubMed PubMed Central Google Scholar
Klopfleisch, R., Klose, P., da Costa, A., Brunnberg, L. & Gruber, A. D. HEPACAM1 and 2 are differentially regulated in canine mammary adenomas and carcinomas and its lymph node metastases. BMC Vet. Res. 6, 15 (2010).
Article PubMed PubMed Central Google Scholar
Kayed, H. et al. BGLAP is expressed in pancreatic cancer cells and increases their growth and invasion. Mol. Cancer 6, 83 (2007).
Article PubMed PubMed Central Google Scholar
Curtis, C. et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486, 346–352 (2012).
Article CAS PubMed PubMed Central Google Scholar
Pereira, B. et al. The somatic mutation profiles of 2,433 breast cancers refines their genomic and transcriptomic landscapes. Nat. Commun. 7, 11479 (2016).
Article ADS CAS PubMed PubMed Central Google Scholar
Chen, W., Dong, J., Haiech, J., Kilhoffer, M. C. & Zeniou, M. Cancer stem cell quiescence and plasticity as major challenges in cancer therapy. Stem Cells Int. 2016, 1740936 (2016).
Article PubMed PubMed Central Google Scholar
Lytle, N. K., Barber, A. G. & Reya, T. Stem cell fate in cancer growth, progression and therapy resistance. Nat. Rev. Cancer 18, 669–680 (2018).
Article CAS PubMed PubMed Central Google Scholar
Pathak, M. et al. Neoadjuvant chemotherapy regimens in treatment of breast cancer: a systematic review and network meta-analysis protocol. Syst. Rev. 7, 89 (2018).
Article PubMed PubMed Central Google Scholar
Anampa, J., Makower, D. & Sparano, J. A. Progress in adjuvant chemotherapy for breast cancer: an overview. BMC Med. 13, 195 (2015).
Article PubMed PubMed Central Google Scholar
Chung, J. et al. Early notch signals induce a pathogenic molecular signature during priming of alloantigen-specific conventional CD4(+) T cells in graft-versus-host disease. J. Immunol. 203, 557–568 (2019).
Article CAS PubMed Google Scholar
Moriyama, Y. et al. Delta-like 1 is essential for the maintenance of marginal zone B cells in normal mice but not in autoimmune mice. Int. Immunol. 20, 763–773 (2008).
Article CAS PubMed Google Scholar
Hozumi, K. et al. Delta-like 1 is necessary for the generation of marginal zone B cells but not T cells in vivo. Nat. Immunol. 5, 638–644 (2004).
Article CAS PubMed Google Scholar
Sheng, Y. et al. Expression of Delta-like 1 in the splenic non-hematopoietic cells is essential for marginal zone B cell development. Immunol. Lett. 121, 33–37 (2008).
Article ADS CAS PubMed Google Scholar
Inayama, M. et al. A novel IkappaB kinase-beta inhibitor ameliorates bleomycin-induced pulmonary fibrosis in mice. Am. J. Respir. Crit. Care Med. 173, 1016–1022 (2006).
Article CAS PubMed Google Scholar
Kinose, Y. et al. IKKbeta regulates VEGF expression and is a potential therapeutic target for ovarian cancer as an antiangiogenic treatment. Mol. Cancer Ther. 14, 909–919 (2015).
Article CAS PubMed Google Scholar
Lennikov, A. et al. Selective IKK2 inhibitor IMD0354 disrupts NF-kappaB signaling to suppress corneal inflammation and angiogenesis. Angiogenesis 21, 267–285 (2018).
Article CAS PubMed PubMed Central Google Scholar
Sugita, A. et al. Antiallergic and anti-inflammatory effects of a novel I kappaB kinase beta inhibitor, IMD-0354, in a mouse model of allergic inflammation. Int. Arch. Allergy Immunol. 148, 186–198 (2009).
Article CAS PubMed Google Scholar
Venkatesh, V. et al. Targeting Notch signalling pathway of cancer stem cells. Stem Cell Invest. 5, 5 (2018).
Article Google Scholar
Lamy, M. et al. Notch-out for breast cancer therapies. N. Biotechnol. 39, 215–221 (2017).
Article CAS PubMed Google Scholar
Zhu, H. et al. Elevated expression of notch1 is associated with metastasis of human malignancies. Int. J. Surg. Pathol. 21, 449–454 (2013).
Article PubMed Google Scholar
Giuli, M. V., Giuliani, E., Screpanti, I., Bellavia, D. & Checquolo, S. Notch signaling activation as a hallmark for triple-negative breast cancer subtype. J. Oncol. 2019, 8707053 (2019).
Article CAS PubMed PubMed Central Google Scholar
Nagamatsu, I. et al. NOTCH4 is a potential therapeutic target for triple-negative breast cancer. Anticancer Res. 34, 69–80 (2014).
CAS PubMed Google Scholar
Speiser, J. J., Ersahin, C. & Osipo, C. The functional role of Notch signaling in triple-negative breast cancer. Vitam. Horm. 93, 277–306 (2013).
Article CAS PubMed Google Scholar
Bedard, P. L., Hansen, A. R., Ratain, M. J. & Siu, L. L. Tumour heterogeneity in the clinic. Nature 501, 355–364 (2013).
Article ADS CAS PubMed PubMed Central Google Scholar
Polyak, K. Heterogeneity in breast cancer. J. Clin. Invest. 121, 3786–3788 (2011).
Article CAS PubMed PubMed Central Google Scholar
Kumar, S. et al. DeltaNp63-driven recruitment of myeloid-derived suppressor cells promotes metastasis in triple-negative breast cancer. J. Clin. Invest. 128, 5095–5109 (2018).
Article PubMed PubMed Central Google Scholar
Marignol, L., Rivera-Figueroa, K., Lynch, T. & Hollywood, D. Hypoxia, notch signalling, and prostate cancer. Nat. Rev. Urol. 10, 405–413 (2013).
Xia, L. et al. Role of the NFkappaB-signaling pathway in cancer. OncoTargets Ther. 11, 2063–2073 (2018).
Article Google Scholar
Xia, Y., Shen, S. & Verma, I. M. NF-kappaB, an active player in human cancers. Cancer Immunol. Res. 2, 823–830 (2014).
Article CAS PubMed PubMed Central Google Scholar
Zhong, H. et al. Overexpression of hypoxia-inducible factor 1alpha in common human cancers and their metastases. Cancer Res. 59, 5830–5835 (1999).
CAS PubMed Google Scholar
Huang, J. et al. Notch-1 confers chemoresistance in lung adenocarcinoma to taxanes through AP-1/microRNA-451 mediated regulation of MDR-1. Mol. Ther. Nucleic Acids 5, e375 (2016).
Article CAS PubMed PubMed Central Google Scholar
Mitani, T. et al. Resveratrol reduces the hypoxia-induced resistance to doxorubicin in breast cancer cells. J. Nutr. Sci. Vitaminol. (Tokyo) 60, 122–128 (2014).
Article CAS Google Scholar
Song, X. et al. Hypoxia-induced resistance to cisplatin and doxorubicin in non-small cell lung cancer is inhibited by silencing of HIF-1alpha gene. Cancer Chemother. Pharmacol. 58, 776–784 (2006).
Article CAS PubMed Google Scholar
Frederiksen, L. J. et al. Hypoxia induced resistance to doxorubicin in prostate cancer cells is inhibited by low concentrations of glyceryl trinitrate. J. Urol. 170, 1003–1007 (2003).
Article CAS PubMed Google Scholar
Chakrabarti, R. et al. DeltaNp63 promotes stem cell activity in mammary gland development and basal-like breast cancer by enhancing Fzd7 expression and Wnt signalling. Nat. Cell Biol. 16, 1001–1013 (2014). 1004-1015.
Article Google Scholar
Chakrabarti, R. et al. Elf5 inhibits the epithelial-mesenchymal transition in mammary gland development and breast cancer metastasis by transcriptionally repressing Snail2. Nat. Cell Biol. 14, 1212–1222 (2012).
Article CAS PubMed PubMed Central Google Scholar
Kumar, S. et al. Inducible knockout of Np63 alters cell polarity and metabolism during pubertal mammary gland development. FEBS Lett. 594, 973–985 (2020).
Article CAS PubMed Google Scholar
Giraddi, R. R. et al. Single-cell transcriptomes distinguish stem cell state changes and lineage specification programs in early mammary gland development. Cell Rep. 24, 1653–1666 e1657 (2018).
Article CAS PubMed PubMed Central Google Scholar
Buenrostro, J. D., Wu, B., Chang, H. Y. & Greenleaf, W. J. ATAC-seq: a method for assaying chromatin accessibility genome-wide. Curr. Protoc. Mol. Biol. 109, 21 29 21–29 (2015).
Article PubMed Google Scholar
Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).
Article CAS PubMed PubMed Central Google Scholar
Fakhr, M. M. & el-Bagoury, S. M. Epidemiology of surgical ward infection. J. Egypt Public Health Assoc. 45, 470–484 (1970).
CAS PubMed Google Scholar
Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).
Article CAS PubMed PubMed Central Google Scholar
Robinson, J. T., Thorvaldsdottir, H., Wenger, A. M., Zehir, A. & Mesirov, J. P. Variant review with the integrative genomics viewer. Cancer Res. 77, e31–e34 (2017).
Article CAS PubMed PubMed Central Google Scholar
Kumar, S. et al. IPS-1 differentially induces TRAIL, BCL2, BIRC3 and PRKCE in type I interferons-dependent and -independent anticancer activity. Cell Death Dis. 6, e1758 (2015).
Article CAS PubMed PubMed Central Google Scholar

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Acknowledgements

We thank Dr. Leslie King (University of Pennsylvania, PA) for careful reading of the manuscript and helpful discussions. We thank Dr. Michael L. Atchison and Dr. Sarmistha Banerjee for generously providing flow cytometry antibodies of marginal B-cells. We thank the animal facility located in Hill Pavilion building for housing and maintenance of all mouse models. We thank the Center for Host-Microbial Interactions for running the ATAC-Sequencing libraries. We thank Penn Vet Comparative Pathology Core for embedding and sectioning of tumor and lung tissues. We thank Penn Vet imaging core for providing confocal microscopy service. We thank Flow Cytometry core at Children’s Hospital of Philadelphia and the University of Pennsylvania for FACS analysis and cell sorting. This work was supported by grants from Abramson Cancer Center Emerson Collective Fund, American Cancer Society, McCabe Fund Fellow Award from Perelman School of Medicine, UPENN and NCI-K22 grant to R.C. (K22CA193661) and American Cancer Society grant to R.C. (RSG DDC - 133604).

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Department of Biomedical Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA
Sushil Kumar, Ajeya Nandi, Snahlata Singh, Rohan Regulapati, Ning Li, Mario Andres Blanco, Christopher Lengner & Rumela Chakrabarti
Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
John W. Tobias
Department of Discovery Oncology, Genentech Inc., South San Francisco, CA, 94080, USA
Christian W. Siebel
Histopathology Facility, Fox Chase Cancer Center, Philadelphia, PA, USA
Andres J. Klein-Szanto
Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, 84112, USA
Alana L. Welm
Department of Molecular Biology, Princeton University, Princeton, NJ, 08544, USA
Yibin Kang

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Contributions

S.K. and R.C. conceptualized the study and designed all experiments. S.K. and A.N. performed all the experiments. R.R. helped with genotyping of mice. M.A.B., S.S., and J.W.T. analyzed the RNA-sequencing data. J.W.T. and M.A.B. also analyzed the ATAC-sequencing data. A.K-S. performed histopathological evaluation of lung metastasis in the manuscript. M.A.B. analysed METABRIC dataset. C.L. and N.L. provided the kit and the support for ATAC-sequencing library preparation. C.W.S. provided Dll1-blocking antibody. A.L.W. provided helpful discussions. Y.K. provided Dll1 reporters and knockout mice. S.K. and R.C. wrote the manuscript. All authors discussed the results and commented on the manuscript.

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Correspondence to Rumela Chakrabarti.

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Kumar, S., Nandi, A., Singh, S. et al. Dll1⁺ quiescent tumor stem cells drive chemoresistance in breast cancer through NF-κB survival pathway. Nat Commun 12, 432 (2021). https://doi.org/10.1038/s41467-020-20664-5

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Received: 13 January 2020
Accepted: 10 December 2020
Published: 18 January 2021
DOI: https://doi.org/10.1038/s41467-020-20664-5

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