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. 2020 Mar 12;23(4):100979. doi: 10.1016/j.isci.2020.100979

Novel Aptamers Selected on Living Cells for Specific Recognition of Triple-Negative Breast Cancer

Simona Camorani 1,4, Ilaria Granata 2,4, Francesca Collina 3, Francesco Leonetti 1, Monica Cantile 3, Gerardo Botti 3, Monica Fedele 1, Mario Rosario Guarracino 2, Laura Cerchia 1,5,∗
PMCID: PMC7103779  PMID: 32222697

Summary

Triple-negative breast cancer (TNBC) is a high heterogeneous group of tumors with a distinctly aggressive nature and high rates of relapse. So far, the lack of any known targetable proteins has not allowed a specific anti-tumor treatment. Therefore, the identification of novel agents for specific TNBC targeting and treatment is desperately needed. Here, by integrating cell-SELEX (Systematic Evolution of Ligands by EXponential enrichment) for the specific recognition of TNBC cells with high-throughput sequencing technology, we identified a panel of 2′-fluoropyrimidine-RNA aptamers binding to TNBC cells and their cisplatin- and doxorubicin-resistant derivatives at low nanomolar affinity. These aptamers distinguish TNBC cells from both non-malignant and non-TNBC breast cancer cells and are able to differentiate TNBC histological specimens. Importantly, they inhibit TNBC cell capacity of growing in vitro as mammospheres, indicating they could also act as anti-tumor agents. Therefore, our newly identified aptamers are a valuable tool for selectively dealing with TNBC.

Subject Areas: Biochemistry, Cell Biology, Cancer

Graphical Abstract

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Highlights

  • •

    Six 2′FPy-RNA aptamers were obtained by TNBC Cell-SELEX/NGS

  • •

    They distinguish TNBC cells from non-malignant and non-TNBC breast cancer cells

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    They differentiate TNBC histological specimens by aptamer-based staining

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    They inhibit TNBC cell lines capacity of growing in vitro as mammospheres

Biochemistry; Cell Biology; Cancer

Introduction

Triple-negative breast cancer (TNBC) accounts for ∼15%–20% of breast cancers, and it annually affects approximately 170,000 patients worldwide (Newman et al., 2015). Compared with other breast cancers, TNBC generally arises at a younger age, is larger, of higher grade, and biologically more aggressive (Dent et al., 2007). Molecularly, TNBC is defined by the absence of estrogen receptor (ER), progesterone receptor (PR), and epidermal growth factor receptor 2 (HER2), excluding the possibility of using targeted therapies against these proteins. In the last two years, the first two targeted therapies have been approved by the Food and Drug Administration (FDA) for a very limited group of women with advanced TNBC: (1) PARP inhibitors, to treat patients with germline mutations in BRCA1/2 who have previously received chemotherapy (Beniey et al., 2019) and (2) the anti-programmed cell death ligand 1 (PD-L1) immunotherapy, used in combination with chemotherapy (Cortés et al., 2019). However, cytotoxic chemotherapy remains the mainstay treatment for the majority of patients (Gadi and Davidson, 2017), but, unfortunately, many patients with early stages of TNBC do not respond to treatment (Echeverria et al., 2019), and those who initially respond commonly develop metastatic and chemoresistant tumors showing a median overall survival of 13–18 months (Garrido-Castro et al., 2019).

One major obstacle to TNBC treatment is its high degree of heterogeneity, being characterized by different subtypes with unique gene expression profiles and distinct clinical behavior, including response to therapies (Lehmann et al., 2011, Lehmann et al., 2016, Burstein et al., 2015).

Therefore, it is fundamental to develop novel targeted treatment approaches able to control each individual TNBC subtype, diminish toxicity, and delay the onset of patient resistance to chemotherapy. Highly selective nucleic acids aptamers are emerging as the most promising candidates for targeting tumor cells. They have risen increasing attention for cancer diagnosis and therapy, as a result of their low molecular weight, low/no immunogenicity, and versatility to manipulation for improved stability and targeting efficacy (Zhou and Rossi, 2017, Camorani et al., 2018a).

Aptamers are isolated by the Systematic Evolution of Ligands by EXponential enrichment (SELEX) process (Zhou and Rossi, 2017, Camorani et al., 2018a, Camorani et al., 2018b, Keefe et al., 2010).

Although SELEX is typically carried out using purified target proteins, whole living cells are also employable as selection target (cell-SELEX) allowing to obtain aptamers against cell surface proteins in their native conformation. By cell-SELEX, target-binding aptamers are isolated from large libraries of randomized oligonucleotides over several rounds of selection against entire cells in culture, even without the prior knowledge of molecules present at the cell surface. Indeed, by altering target cell selection and off-target cell elimination, aptamers that may identify subtle differences existing among different cells, also belonging to the same tumor type, which drive important tumor cell behaviors, including resistance to therapy, tumorigenicity, stemness and capacity to metastasize, can be isolated (Tang et al., 2007, Camorani et al., 2014, Sefah et al., 2009, Chen et al., 2008, Shangguan et al., 2006, Esposito et al., 2011).

Also, cell-SELEX approaches for targeting of breast cancer cells have been reported to select aptamers for recognition of metastatic cancer cells (Li et al., 2014) and HER2-positive breast cancer cells (Liu et al., 2018). Furthermore, a SELEX method using genetic alphabet expansion allowed to select aptamers on breast cancer cells with some biological activities, such as internalization within the target cells and anti-proliferative activity (Futami et al., 2019).

Here, by integrating a cell-SELEX method for the specific recognition of TNBC cells with high-throughput sequencing technology, we identified a panel of nuclease-resistant RNA aptamers that bind to target cells, and their cisplatin- and doxorubicin-resistant derivatives, with an equilibrium dissociation constant (Kd) in the low nanomolar range, and distinguish TNBC cells from both normal breast cells and non-TNBC breast cancer cells. Importantly, the selected aptamers differentiate TNBC human samples when used for histochemical tissue staining, allowing to cluster them in molecular subtypes. Further, these aptamers interfere with the TNBC cells’ capacity of growing in vitro as mammospheres, a feature associated with the malignant phenotype, thus indicating they could be employed as important anti-tumor agents.

Results

TNBC Cell-SELEX

We reasoned that TNBC carries characteristic cell surface signatures that distinguish itself from the other breast cancer subtypes, thus allowing to identify, by a differential cell-SELEX screening, aptamers able to specifically bind to TNBC, eventually discriminating among different TNBC subtypes, over non-TNBC breast cancer expressing HER2, PR, and ER. To reach this goal, we started from a library of nuclease-resistant 2′fluoro-pyrimidines (2′F-Py) RNAs and performed a total of 14 consecutive rounds of positive selection on human MDA-MB-231 cells (ER−, PR−, HER2−), with increasing selection stringency (Figure 1A, Table S1). MDA-MB-231 cells represent an established model for aggressive TNBC cells and largely recapitulate the gene expression patterns and mutations found in vivo (Lehmann et al., 2011, Nguyen et al., 2014). These cells are characterized by the expression of epithelial-mesenchymal transition markers, highly malignant and invasive phenotype, and a strong tendency to form vasculogenic mimicry (Camorani et al., 2017a, Blick et al., 2008, Betapudi et al., 2006, Han et al., 2008, D'Ippolito et al., 2016, Camorani et al., 2017b, Camorani et al., 2018c). Starting from the second SELEX round (Figure 1A, Table S1), the positive selection was preceded by counterselection steps against the well-characterized BT-474 epithelial breast cancer cell line (ER+, PR+, HER2 over-expression) (Nowsheen et al., 2012, Dai et al., 2017, Pasleau et al., 1993) to deplete RNA molecules capable of recognizing non-TNBC cells. In order to avoid loss of specific sequences, the counterselection was not included in the first round. Additionally, because MDA-MB-231 cells express abundant levels of epidermal growth factor receptor (EGFR), a receptor frequently overexpressed in TNBC (Nair et al., 2018) and already used as target for aptamer recognition by our and other groups (Camorani et al., 2018a), we chose to include into the selection cycle, starting from the fifth SELEX round, a second counterselection against EGFR-overexpressing epidermoid carcinoma A431 cells (Ullrich et al., 1984) (Figure 1A, Table S1), to avoid that sequences against EGFR could dominate the selection. Importantly, an increase of MDA-MB-231 target cells’ recognition was observed as the selection progressed through additional rounds, whereas the enriched libraries did not interact with non-target BT-474 cells (Figure 1B), thus indicating that the counterselection strategy allowed to foster specificity toward TNBC cells.

Figure 1.

Figure 1

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TNBC Cell-SELEX

(A) Left: schematic protocol for the selection of TNBC-specific aptamers. A pool of 2′F-Py RNAs (contained a 40-mer random sequence region flanked by two constant sequence regions at 5′ and 3′ of 21-mer and 23-mer, respectively) was incubated with BT-474 cells (starting from second round) for a first counterselection step. Unbound sequences were recovered and incubated with MDA-MB-231 cells for the selection step (from second to fourth round) or with A431 cells (from fifth round up to the end of SELEX) for a second counterselection step. Unbound sequences from the second counterselection were recovered and incubated with MDA-MB-231 cells for the selection step. The counterselection was not included in the first round. Unbound sequences were discarded by several washings, and bound sequences were recovered by phenol extraction. Sequences enriched by the selection step were amplified by RT-PCR and in vitro transcribed before a new cycle of selection. Right: characterization of cells used for the SELEX scheme. Lysates from MDA-MB-231, BT-474, and A431 cells were immunoblotted with anti-HER2, anti-EGFR, anti-PR, and anti-ERα antibodies, as indicated. α-tubulin was used as an internal control.

(B) Assessment of the selection progress. The indicated selected pools or the TN0 starting library (200-nM final concentration) were incubated for 15 min at 37°C with MDA-MB-231 and BT-474 cells and binding was detected by RT-qPCR. The results are expressed relative to the background binding detected with the TN0 library. Bars depict mean ± SD of three independent experiments.

Identification of TNBC-Specific Candidate Aptamers

We implemented the SELEX approach by using next-generation sequencing (NGS) and bioinformatics analysis to monitor the evolution of the selection procedure along with the screening cycles and to support the choice of which aptamers were worth testing in binding assays. Thus, ten selection rounds (0, 3, 5, 7, 9, 10, 11, 12, 13, and 14) were analyzed by Illumina NGS, and data were compared with those obtained by classical cloning of the last selected pool. Figure 2A shows the workflow used in the present study for filtering, clustering, and identifying candidate aptamers to test in cell-binding assays. After quality filtering and primers trimming of the paired ends merged reads, we obtained reads quantity ranging from 2633075 to 7803708. Before proceeding with length filtering, we examined the overall variable region nucleotide length of unique reads along all the rounds (Figure 2B). As expected, the mostly frequent variable region length was 40 nucleotides for all the rounds, with low frequencies also of 38, 39, 41, and 42 nucleotides length. Based on these observations, we included sequences with variable region lengths ranging from 38 to 42 nucleotides in our subsequent analyses. As shown in Figure 2C, the enrichment of the libraries shows an increasing trend throughout the rounds, with the most pronounced changes in percentage enrichment starting from selection round 7, thus indicating the convergence of potential aptamers in the resulting pools.

Figure 2.

Figure 2

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High-Throughput Sequencing and Data Processing

(A) A schematic view of the pipeline used in this study is shown. Briefly, the processing of the raw fastq files involved several steps of filtering after the merging of paired ends, such as quality, length, and frequency filtering. The ranges of the reads number obtained after each step are shown. Finally, 16,500 unique sequences got through the downstream analyses: sequences and secondary structure clustering. The methods used are colored in blue.

(B) The plot shows the average frequency (mean ± SD) of the variable region length of the RNA sequences in all the rounds. As expected, the most frequent length is 40 nt. Few sequences show a length of 38, 39, 41, and 42 nt.

(C) The plot shows the percentage enrichment at each round (black circle), calculated by: % Enrichment = 1-Unique/Total. A sigmoidal curve fit was added to the plot.

(D) Nucleotide frequency distribution for four rounds (0, 3, 9, 14).

(E) The heatmap shows the matrix of secondary structure similarities computed by RNAsmc R package. The length of the branch and the colors of the heatmap correspond to degree of similarity between the structures predicted.

(F) K-Means clustering of the predicted secondary structures, considering five clusters according to the connectivity parameter. The different clusters are highlighted by the different shape of the points, whereas the color of the ellipses indicates the density of the cluster. The two components explain 66.27% of the point variability. In the boxes are highlighted the candidate sequences, and the arrows indicate their belonging to the specific clusters.

(G) Trend of the candidate sequences along the selection rounds. The counts are normalized over the total number of sequences at each round. The y scale is log transformed and the points equal to 0 are not displayed. The candidate aptamers TN1, TN2, and TN3 cover the first three positions according to the slope ranking. They are already present at the round 0 and their concentrations continue to increase till round 14 (TN3) or till round 10 and then show a stable concentration till round 14 (TN1 and TN2). At the round 3 all three of them show a concentration decrease. The candidate aptamers TN20, TN29, TN58, and TN145 start to appear at round 5 (except the TN29 and TN58 that have a weak concentration at round 0) and show an increasing trend till round 14. Sequence 9829 (out of the top 5,000 sequences) is reported as an example of a sequence that did not show up as an enriched sequence in our analysis.

The Figure 2D shows that at the beginning of the selection experiment (round 0) the frequency of the four nucleotides is equally distributed, whereas in the subsequent rounds the enrichment of particular sequences is highlighted by the change of this distribution along the sequence positions.

In order to keep only the enriched sequences and ranking them based on the increasing trend along with the rounds, we applied the slope formula to the sequences showing counts equal or greater than 20 in almost one of the last three rounds, thus obtaining 16,500 sequences, from almost 28 million obtained by combining the unique sequences of each round. Then, based on the slope scores, the top 5,000 sequences were selected, which, based on their identity-based distances from alignment, gave rise to 293 clusters (Figure S1). Although aptamers are typically categorized just by the analysis of sequence similarity, we considered the undoubted role of the secondary structures in defining their functionality and added a step of clustering based on structural similarity. From each alignment-based cluster, we selected the most enriched sequences and predicted their secondary structure using the Quikfold software. The heatmap obtained from the 293 secondary structure similarity is shown in Figure 2E. In order to statistically determine the most appropriate number of clusters to our data, we applied the K-Means clustering algorithm, taking in consideration internal measures such as Connectivity, Dunn Index (DI), and Silhouette Width (Handl et al., 2005) (Table S2). The connectivity indicates the degree of connectedness of the clusters. It has a value between 0 and infinity and should be minimized. The Silhouette Width is the average of each observation's Silhouette value. Well-clustered observations show values near 1, and poorly clustered observations show values near −1. The DI is the ratio between the minimum inter-cluster distance and the maximum cluster size. It has a value between 0 and infinity and a higher DI implies better clustering (Handl et al., 2005) (Table S2). We decided to use the best three k settings (5, 6, 7) and found that the most stable discrimination occurred setting five clusters (Figure 2F). The best candidate RNAs were chosen by the following criteria: sequences with an optimum enrichment score that were also cloned from the 14th pool and covering all the different clusters. Accordingly, we extracted seven sequences for testing by cell-binding assays: TN1, TN2, TN3, TN20, TN29, TN58, and TN145 (Figure 2F). The enrichment of the selected sequences is shown in Figure 2G. Accordingly, a sequence out of the top 5,000 sequences, sequence 9829, did not show up as an enriched sequence in our analysis. Noteworthily, four sequences were particular abundant (>50 counts) in the initial, unselected RNA library but, among them, TN1, TN2, and TN3 survived to our filtering criteria and were thus taken into account for further characterization. The theoretical secondary structures of the candidate aptamers are shown in Figure S2.

Characterization of Aptamers' Binding Affinity to MDA-MB-231 Target Cells

The seven candidate aptamers were tested in binding assays to evaluate their ability to specifically bind to MDA-MB-231 target cells with respect to the cells used for the counterselections (BT-474 and A431). To this aim, three different methods were harnessed to detect cell binding of the chosen sequences: reverse transcription-quantitative polymerase chain reaction (RT-qPCR)-based, streptavidin-biotin-based (colorimetric), and flow cytometric assays. The last two approaches, involving the use of 3′-biotinylated and Alexa 647-labeled aptamers, respectively, were applied for binding affinity (Kd values) calculation. Results from RT-qPCR-based assay are shown in Figure S3. The curves of binding to target cells for each aptamer, by both colorimetric and flow cytometric assays, are shown in Figures S4 and S5, respectively, and their Kd for each cell line are listed in Table 1. As shown, except for aptamer TN1 that did not bind to either the target cell line (Figures S3–S5) or the cells of the counterselections (Figures S3 and S4), the other six aptamers bound with high affinity to MDA-MB-231 target cells (Figures S4 and S5) and comparable Kd values, in the low nanomolar range (from 9.84 ± 1.63 nM to 59.12 ± 12.93 nM and from 10.77 ± 2.41 nM to 73.02 ± 12.08 nM by colorimetric and flow cytometric assays, respectively), were obtained for each sequence by the two applied methods (Table 1). Additionally, the sequence 9829, which we chose among those filtered out according to frequency before the slope calculation, gave no binding to MDA-MB-231 target cells (Figure S3, Table 1), therefore confirming the validity of our approach for the identification of the TNBC-specific candidate aptamers. Importantly, none of the six aptamers bound to BT-474 cells (Figures S3 and S4), demonstrating the successful use of these cells for the counterselection. Further, only TN3 bound to the cell line of the second counterselection, A431, even if at a much lesser extent than MDA-MB-231 cells, indicating that the counterselection step did not completely abrogate its binding to these cells (Figure S4). Noteworthily, TN3 is among the top sequences in the initial library and started to enrich very early on, before the introduction of the counterselection against A431 cells (Figure 2G), possibly explaining why this sequence shows some binding to A431. However, TN3 did not bind to mouse NIH3T3 fibroblasts engineered to overexpress human EGFR (Camorani et al., 2015), thus excluding EGFR as its target (data not shown).

Table 1.

Dissociation Constant Values for the Candidate Aptamers to Target Cells (MDA-MB-231) and Cells of Counterselections (BT-474 and A431)
MDA-MB-231
 BT-474
 A431
Aptamer Sequence Random Region Kd, nM Biotin-RNA Kd, nM Alexa-RNA Kd, nM Biotin-RNA Kd, nM Biotin-RNA
TN1 GUGUCAGACGUAAUGUGUCGC
ACAUCUUGUCAUGCUACUG		 ND ND ND ND
TN2 AAGGCCGACGUAAUGUGUC
GGUCGUUACGCGUCGUGCACG		 59.12 ± 12.93 73.02 ± 12.08 ND ND
TN3 CCGAUCUCACGCGCACCUU
CUCUUCAGCGCGCGACUGGCA		 21.91 ± 3.08 28.93 ± 9.72 ND 62.98 ± 23.61
TN20 CGAUGCGCACCGAUCUCUC
UUCUGCACGUCCUUCGGCACA		 24.30 ± 4.99 23.92 ± 3.59 ND ND
TN29 CCUGCCCCAACCAUCGCUU
CCUCGACGCGCGUUGUCGGCA		 9.84 ± 1.63 10.77 ± 2.41 ND ND
TN58 GCAACGUUGUGGUCCCGUUUGC
ACUUUGUUUACGCGCGCA		 17.17 ± 2.87 26.09 ± 3.20 ND ND
TN145 CCUCAGCGCGCAACUUCCCUCCGU
UCCCUGCCACGCGUCA		 26.88 ± 3.68 37.36 ± 3.50 ND ND
Seq9829 GUCUUUUGCUCGUCCUUCCACCG
CUCACGUCCUUCCGACA		 ND ND – –
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The random sequence of each aptamer is reported; for simplicity fixed-primer sequences at 5′ and 3′ are not shown. Kd values were calculated by streptavidin-biotin colorimetric and flow cytometric assays. The mean values of three independent measurements are presented. ND, not detectable; "-", not determined. As expected, sequence 9829, which was filtered out according to lack of enrichment along the cell-SELEX rounds, did not bind to target cells by both the binding assays.

To confirm the validity of the above results, we compared the binding of one of the six candidate aptamers (TN20) with that of the non-binder TN1 aptamer to MDA-MB-231 cells by confocal imaging. As shown, TN20 aptamer specifically co-localized at the membrane level with the Wheat Germ Agglutinin (WGA) surface marker on MDA-MB-231 cells after 10-min incubation at RT (Figure 3A). Conversely, very little to no signal was observed with TN1 sequence (Figure 3A). As a further control experiment, both TN20 and TN1 aptamers were incubated with BT-474 cells, resulting in no binding (Figure 3A). It is well known that many aptamers internalize upon binding to cell surface targets, making them useful targeted delivery vehicles for therapeutic secondary reagents. Preliminary experiments, carried out so far only with TN20, show that it internalizes within the target cells and accumulates in compartments positive for LysoTracker, which stains acidic compartments in live cells, following 30-min incubation at 37°C (Figure 3B).

Figure 3.

Figure 3

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Target-Type Analysis of TNBC Aptamers

(A) Following 10-min incubation at RT with 500 nM Alexa 647-labeled TN20 or TN1 aptamers, MDA-MB-231 and BT-474 cells were fixed and incubated with WGA, visualized by confocal microscopy, and photographed. Alexa-labeled aptamer, WGA, and nuclei are visualized in red, green, and blue, respectively. Co-localization results appear yellow in the merged images. All digital images were captured at the same setting to allow direct comparison of staining patterns.

(B) Internalization of TN20 into MDA-MB-231 cells. Cells were incubated for 30 min at 37°C in the presence of 500 nM Alexa 647-labeled TN20, after the incubation with LysoTracker to detect acidic organelles inside the cells, visualized by confocal microscopy, and photographed. Alexa-labeled aptamer, WGA, LysoTracker, and nuclei are visualized in red, green, blue, and gray, respectively. Co-localization results appear purple in the merged images.

(A and B) Magnification 63×, 0.5× digital zoom, scale bar = 20 μm. Inset: 2× digital zoom, scale bar = 5 μm.

(C) Binding of TNBC aptamers to proteinase K-treated MDA-MB-231 (blue) and untreated (red) cells. The concentration of the aptamers in the binding buffer was 200 nM.

(A–C) At least three independent experiments were performed.

To test preliminarily whether the targets of the six aptamers are cell surface proteins, we treated MDA-MB-231 target cells with proteinase K and then analyzed cell binding with Alexa-labeled aptamers using flow cytometry. Importantly, the treatment prevented the binding of the six aptamers to MDA-MB-231 cells (Figure 3C), indicating that the target molecules are most likely cytomembrane proteins.

Aptamers' Binding to Chemoresistant MDA-MB-231 Cells

Chemoresistance of TNBC is a major reason for treatment failures and contributes to metastasis, which further decreases the prognosis of patients (Nabholtz et al., 2014). Accumulating evidences indicate that certain cell surface proteins display differential expression in chemoresistant cancer cells compared with chemosensitive cells (O'Reilly et al., 2015, Ziegler et al., 2014). By treating MDA-MB-231 cells with either doxorubicin or cisplatin, both used in clinic for TNBC, over a period of about five months (see Methods), we generated MDA-MB-231 derivative cells (MDA-MB-231/dox and MDA-MB-231/cis) with increased resistance to doxorubicin and cisplatin compared with the parental cells (Figure S6A). Consistent with the acquired resistance, these cells showed a significant enhanced expression of platelet-derived growth factor receptor β (PDGFRβ), integrin αv, and PD-L1 (Figure S6B), three cell surface proteins related to chemoresistance. We then tested whether our novel TNBC aptamers preserve the ability to bind to chemoresistant cells. Importantly, binding curves obtained by flow cytometry analysis show that all the selected aptamers are capable of targeting both MDA-MB-231/dox (Figure S7A) and MDA-MB-231/cis (Figure S7B) cells with Kd values in the low nanomolar range (Figure 4A), thus suggesting that changes in protein expression under therapeutic selection pressure may not limit the effectiveness of targeting. Interestingly, as shown in Figure 4B, the TN58 aptamer displayed a higher affinity for chemoresistant cells with respect to the parental counterpart, thus suggesting that its target is a protein enriched in chemoresistant cells.

Figure 4.

Figure 4

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Targeting Specificity of TNBC Aptamers

(A) TNBC aptamers target chemoresistant MDA-MB-231/dox and MDA-MB-231/cis cells with nanomolar Kd values.

(B) Binding affinity (1/Kd) of each TNBC aptamer to MDA-MB-231/dox and MDA-MB-231/cis cells is expressed relative to the corresponding binding affinity to MDA-MB-231 cells. Note that the Kd values obtained on parental and chemoresistant cells by using the flow cytometric assay were taken into account for this analysis.

(C) Binding of the indicated aptamers (200-nM final concentration, colorimetric assay) on TNBC cells, covering different subtypes (Lehmann et al., 2011), non-TNBC breast cancer cells, human lung fibroblasts (HLF), and MCF10A cells, chosen as non-malignant breast cellular model. The results are expressed relative to the background binding detected with the TN0 negative control. The binding capacity of the aptamers to the cells is reported as ″++″ for high binding (more than 3.5-fold), ″+″ middle binding (between 1.5- and -3.5-fold), and ″−″no binding (less than 1.5-fold).

(B and C) Bars depict mean ± SD of three independent experiments. (B) ∗∗p< 0.01; ∗p< 0.05 (one-way ANOVA followed by Tukey's multiple comparison test).

Overall, these findings imply that, based on protein expression, personalizing aptamer-based tumor treatments might become possible.

Selected Aptamers Specifically Bind to Different TNBC Cell Lines

Next, in addition to the cells used in the selection (MDA-MB-231) and counterselection (BT-474), binding analyses were extended to other human breast cancer cell lines, TNBC and non-TNBC, to validate binding specificity for the desired cell type. All these cell lines have been extensively characterized and show gene expression profiles similar to the respective tumor subtypes (Lehmann et al., 2011, Lehmann et al., 2016, Ross and Perou, 2001, Sandberg and Ernberg, 2005). Importantly, tested at the same concentration, none of the selected aptamers bound to non-TNBC breast cancer cell lines representative of luminal A (MCF-7 and T47D, ER+, PR+, HER2−) and HER2-positive (SK-BR-3, ER−, PR−, HER2+) molecular categories (Figure 4C). Conversely, they bound to most TNBC cells tested (BT-549, MDA-MB-436, DU4475, MDA-MB-468) (Lehmann et al., 2011), except only for TN2 aptamer that gave no appreciable binding on the latter cell line. Noteworthy, MDA-MB-453 cells, which even if classified among TNBC cell lines have been proven to express HER2 (Garnett et al., 2012), were moderately or even not recognized by the six aptamers. Importantly, none of the selected aptamers bound to normal human lung fibroblasts and the human mammary epithelial MCF10A cells, chosen as non-malignant breast cellular model (Figure 4C).

These results clearly demonstrated that the aptamers selected by our cell-SELEX procedure bind to TNBC cells and discriminate them from normal cells or breast cancer cells expressing HER2 and/or ER and PR, suggesting that they may interact with unique surface-binding entities expressed on TNBC cells.

Aptamer-Based Histochemical Staining of Human TNBC Samples

In order to translate our results to the clinics, we evaluated the capacity of the six identified aptamers to be a new tool for the characterization of TNBC tissues. To this aim, we tested ex vivo the selected aptamers by setting up the best binding conditions on deparaffinized tissue sections, of biotinylated aptamers used for histochemical staining of 18 human TNBC samples included in a TMA. The main clinicopathological data of the cases are set out in Table S3. As shown in Figure 5, each aptamer showed a distinct pattern of binding on different tumors, which we scored as absent, low, moderate, or high, based on both staining intensity and cell percentage, thus highlighting the TNBC heterogenicity (Figure 5A). Interestingly, it is possible clustering TNBC samples according to a signature of aptamer staining (Figure 5B). For instance, samples #6, #10, and #14 (marked in red) on one side, and #16 and #18 (marked in green) on another side, appear to belong to two different clusters according to the same pattern of binding by all the six selected aptamers (Figure 5B). Further, the staining of four aptamers (TN2, TN3, TN20, and TN145) is common to samples #2 and #3 (marked in purple), and the other aptamers (TN29 and TN58) stained the two tumors at high or moderate intensity (score), respectively, suggesting they may represent another TNBC cluster (Figure 5B). For each aptamer the differences observed in the staining extent likely reflect the relative concentrations of the same target molecules in the different tumors. Importantly, when applied to breast cancer samples expressing ER, PR, and HER2, the aptamers resulted in negative or low staining (Figures 5B and 5C). Also, accordingly to binding analyses on human cell lines, none of the six aptamers bound to normal samples dissected adjacent to the tumors (Figures 5B and 5C).

Figure 5.

Figure 5

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Human Tissue Staining by TNBC Aptamers

(A) Images of representative human TNBC cases stained with the six candidate aptamers or TN0 starting library. The images were specifically chosen to represent a variation in staining pattern for each aptamer. Negative/low (left panels) and high (right panels) staining scores are shown.

(B) Aptamer-staining scores (calculated based on both staining intensity and cell percentage) on the 18 TNBC samples, a representative triple-positive (ER+, PR+, HER2+) breast cancer, and a representative normal breast sample. Different scores are highlighted by different colors: white (negative, score 0), light blue (low, score 1), dark blue (moderate, score 2–4), and black (high, score 6–9). Groups of samples with same/similar pattern of aptamer staining are marked in red (#6, #10, and #14), in green (#16 and #18), or in purple (#2 and #3).

(C) Breast cancer samples expressing ER, PR, and HER2 (upper panels) and breast normal samples dissected adjacent to TNBC cases (lower panels) were stained with TNBC aptamers or TN0 starting library. Images of representative samples of breast cancers and normal tissues stained with a representative TNBC aptamer and TN0 starting library are shown.

(A and C) Magnification 20×, scale bar = 100 μm.

Overall, we validated the use of these aptamers in human histologic sections and propose their combined application as a potential tool to allow simple sub-classification of TNBC subtypes.

In Vitro Functional Efficacy of TNBC Aptamers

In most cases, by binding to their proper protein target, aptamers strongly interfere with its biological activity, thus exerting significant anti-tumor activity in the case the target is linked to cancer cell growth and progression (Camorani et al., 2018a, Camorani et al., 2018b). Therefore, we asked whether the selected TNBC aptamers affect cancer cells malignancy. Both MDA-MB-231 and BT-549 cells have the propensity to form mammospheres under non-adherent and non-differentiated culture conditions, a cancer-stem-like trait that is associated with aggressive and metastatic nature of TNBC (Oh et al., 2018). Based on this, as readout of their anti-tumor functionality, the TNBC-specific aptamers were tested for inhibition of in vitro mammosphere-forming ability of both cell lines (Figure 6). Importantly, the mammosphere formation was drastically impaired in the presence of each of the six aptamers, while resulting indifferent to the starting library negative control (Figures 6A and 6B), as evidenced by a significant reduction in both number and size of mammospheres, after four days of treatment. Moreover, according to the role of CD44 in enhancing self-renewal and mammosphere growth (To et al., 2010), both MDA-MB-231 and BT-549 cells strongly upregulated the CD44 gene, when grown in stem-permissive conditions, and the aptamers counteracted its induction of expression (Figure 6C).

Figure 6.

Figure 6

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TNBC Aptamers Affect Mammosphere-Forming Efficiency of ML TNBC Cells

(A and B) (A) MDA-MB-231 and (B) BT-549 cells were plated in ultralow attachment 24-multiwell-plates and grown in stem-permissive conditions in the presence of 400 nM indicated aptamers or TN0 starting library for four days. Representative phase-contrast images are shown. Magnification 10×, scale bar = 250 μm. Cell treatment with specific aptamers, but not the TN0, inhibits both the number, expressed as percentage with respect to mock-treated cells (left), and diameter (right) of mammospheres.

(C) RT-qPCR analysis of CD44 gene expression in MDA-MB-231 (left) and BT-549 (right) cells grown in adherent two-dimensional condition (2D) and stem condition in the absence (Mock) or presence of indicated aptamers or TN0 library.

(A–C) Bars depict mean ± SD of three independent experiments. ∗∗∗p< 0.001; ∗∗p< 0.01; ∗p< 0.05 relative to mock-treated cells; ##p< 0.01 (unpaired t test). No statistically significant variations among TN0 and mock treatment were obtained.

The presence of 2′F-Py allows extending half-lives in serum of unmodified aptamers that can be as short as few seconds with RNA aptamers (Chen et al., 2017, Dassie et al., 2014). Accordingly, we found that TN2 aptamer, chosen among the six aptamers because it contains the lowest number of 2′F-Py, is almost fully stable up to 8 h in 80% human serum and then slowly degrades.

These findings suggest a potential therapeutic role for the selected aptamers, which likely target important surface TNBC biomarkers.

Discussion

The development of selective TNBC targeting agents, which might ensure therapeutic applications, would greatly advance the development of personalized cancer therapy.

Here, we performed a new TNBC cell-SELEX approach, combined with high-throughput NGS and bioinformatics, which applied stringent criteria for identifying the best candidate aptamers to be tested for targeting efficacy. This strategy allowed us to restrict the efficacy testing to a small panel of potential candidates starting from 16,500 analyzed sequences. Noteworthily, among the seven aptamer candidates, TN1, TN2, and TN3 had unusual high frequencies in the initial RNA library probably due to some biased sequences in the custom library, as previously reported (Takahashi et al., 2016). Importantly, one of these biased sequences was selected out because it was not enriched along the cell-SELEX selection rounds. However, consistently with Takahashi et al. (2016), the three selected top sequences did not limit the identification of several high-affinity aptamers (TN20, TN29, TN58, and TN145), and also, among them, TN2 and TN3 showed optimal performance in all the assays (targeting and functional), whereas TN1 was discarded in binding analyses.

The six aptamers bind to the TNBC MDA-MB-231 target cells with Kd values in the low nanomolar range. In agreement with the principles of the differential cell-SELEX approach, able to provide multiple ligands that recognize molecular differences (e.g. protein expression level or protein conformation) between the target cell population and the cells used for counterselection (Camorani et al., 2018a, Camorani et al., 2018b, Keefe et al., 2010), the selected RNA aptamers efficiently discriminate the target cells from triple-positive (HER2+, ER+, PR+) breast cancer cells (BT-474) used in the counterselection. Furthermore, the six aptamers have unequivocal efficacy in targeting TNBC cells lines, representative of different molecular subtypes, distinguishing them from non-TNBC breast cancer cells and non-malignant cells. Despite the yet limited published body of work regarding aptamer-based histochemistry, aptamers have the potential to revolutionize the field of histopathological diagnostics, and this is one of the most intense research areas by the aptamer scientific community (Bauer et al., 2016). Noteworthy, we succeed in using all the six aptamers in a biotinylated form for histochemical staining of 18 human TNBC samples in a TNBC tissue microarray. Importantly, they are not or poorly recognized samples from triple-positive (ER+, PR+, HER2+) breast cancers and did not bind to normal adjacent tissues, thus encouraging further analyses by extending the aptamer-based staining to a larger cohort of tumors. Noteworthily, each TNBC sample has its own unique molecular expression signature revealed by the combination of staining intensity of the six aptamers. Therefore, we believe that the aptamers identified in this study may help to advance the needs of precise molecular subtyping of breast cancers. Also, they could provide new biomarkers for detection and delivery of personalized medicine and, hopefully, reducing time and effort required for conventional immunohistochemistry.

Our results suggest that the targets of the TNBC-specific aptamers are cell surface proteins with an important role in cancer cell malignancy. Importantly, aptamer's binding is able to disrupt such function, as revealed by their action to interfere with TNBC cell growth as mammospheres, opening up a potential therapeutic role for these aptamers, with the possibility of using them in different combinations depending on distinct molecular and/or clinical TNBC phenotypes. It is notable that the six selected aptamers have different sequences and predicted secondary structures, suggesting they likely hit different targets on TNBC cells. Although we are aware that the RNA structural complexity represents a big challenge for computational modeling, in our approach we used the secondary structure prediction to further define similarity among sequences and to select aptamers mostly different from each other. Consistently, each of them has a unique pattern of intensity of staining onto the panel of TNBC tissues analyzed in our study. We are currently investigating the minimized versions for each aptamer that retain the cell targeting properties of the full-length sequence to be used for target-identification studies.

Cytotoxic chemotherapy is the mainstay treatment for patients with TNBC in both the early and advanced stages (Bianchini et al., 2016). However, the treatment is toxic and, after an initial response, a large percentage of tumors commonly relapse causing a death rate in the metastatic setting disproportionately higher than any other breast cancer subtypes (Dent et al., 2007, Abramson et al., 2015). Identification of novel drug targets, especially those that might impact chemotherapy resistance, is an important unmet medical need. Importantly, our selected TNBC aptamers bind at high affinity to TNBC cells resistant to cisplatin and doxorubicin, with TN58 aptamer displaying, approximately, a 3-fold higher affinity for chemoresistant cells with respect to their parental counterpart. Whether the aptamers interfere with aggressiveness of chemoresistant cells and/or revert their resistance to cisplatin and doxorubicin remains a not yet tested possibility.

Nevertheless, whether TNBC aptamers are able to readily internalize into target cells, as already assessed for TN20 aptamer and other aptamers targeting MDA-MB-231 cells (Futami et al., 2019), they may be explored as agents to deliver novel therapeutics and/or classical drugs to the tumor. This will allow to increase the concentration at the tumor site while dispensing lower absolute doses of the cargo (therefore possibly reducing systemic sideeffects) (Alshaer et al., 2018).

Nanoparticle technology for chemotherapeutic delivery has been approved by the FDA or are in clinical trials to treat patients with breast cancer (Tang et al., 2017), and several preclinical studies demonstrate that aptamer-conjugated nanoparticles can guide drugs to tumor sites with superior tumor penetration than antibodies (Xiao and Farokhzad, 2012). Recently, the anti-nucleolin AS1411 aptamer has been used to deliver the anti-cancer triptolide specifically to MDA-MB-231-derived tumors implanted in mice, which efficiently inhibited tumor growth without affecting healthy organs (He et al., 2020). These results highlight the power of aptamers to transport cytotoxic agents to TNBC overcoming limitation due to high toxicity, poor solubility, and poor bioavailability. In this frame, the identification of our multiple tumor-targeting aptamers opens the way to their use in different combinations depending on distinct molecular and/or clinical TNBC phenotypes.

In addition to therapeutic utility, TNBC aptamers may be useful as specific probes for molecular imaging, thus increasing a repertoire that is currently extremely scarce (Camorani et al., 2018b), as we recently demonstrated for the PDGFRβ aptamer that can act for both specific non-invasive imaging and suppression of lung metastases in mouse models of TNBC (Camorani et al., 2018c).

Limitations of the Study

Although the aptamers we identified in this study are highly selective binders of surface proteins of TNBC target cells and discriminate TNBC cell lines from both non-malignant cells and non-TNBC breast cancer cells, a great challenge is that their molecular targets need to be identified and validated. This aspect of the study is crucial to address the relevance and in-depth mechanism of aptamer binding to its own protein target. Moreover, as we aim to apply the proposed aptamer-based staining approach for advancing molecular subtyping of breast cancers, it is necessary to further extend the analyses to a larger cohort of tumors, correlating the expression of the target protein to clinical features. Finally, although the six selected aptamers show strong potential for TNBC diagnosis and therapy, additional studies are required to assess their efficacy in vivo (including stability and lack of immunogenicity). Future studies in animal models will help to determine whether this is a viable strategy for TNBC treatment.

Methods

All methods can be found in the accompanying Transparent Methods supplemental file.

Acknowledgments

This work was supported by Fondazione AIRC per la Ricerca sul Cancro (IG 18753 and IG 23052) to LC.

Author Contributions

S.C. performed the majority of the experiments, analyzed data, and assisted with manuscript preparation. I.G. and M.R.G. performed the bioinformatic analyses of NGS data and participated in editing of the manuscript. F.C. and M.C. performed the aptamer-based TMA staining and evaluated tumor morphology and histochemical staining. F.L. contributed to the cell-based assays experiments. G.B. provided the human TNBC samples. M.F. provided conceptual advice, analyzed the data, and participated in editing of the manuscript. L.C. conceived and designed the study, analyzed the data and coordinated the research, was responsible for funding, and wrote the manuscript. All authors read and approved the final manuscript.

Declaration of Interests

The authors declare no competing interests.

Published: April 24, 2020

Footnotes

Supplemental Information can be found online at https://doi.org/10.1016/j.isci.2020.100979.

Supplemental Information

Document S1. Transparent Methods, Figures S1–S8, and Tables S1–S3
mmc1.pdf (2.4MB, pdf)

References

  1. Abramson V.G., Lehmann B.D., Ballinger T.J., Pietenpol J.A. Subtyping of triple-negative breast cancer: implications for therapy. Cancer. 2015;121:8–16. doi: 10.1002/cncr.28914. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Alshaer W., Hillaireau H., Vergnaud J., Mura S., Deloménie C., Sauvage F., Ismail S., Fattal E. Aptamer-guided siRNA-loaded nanomedicines for systemic gene silencing in CD-44 expressing murine triple-negative breast cancer model. J.Control Release. 2018;271:98–106. doi: 10.1016/j.jconrel.2017.12.022. [DOI] [PubMed] [Google Scholar]
  3. Bauer M., Macdonald J., Henri J., Duan W., Shigdar S. The application of aptamers for immunohistochemistry. Nucleic Acid Ther. 2016;26:120–126. doi: 10.1089/nat.2015.0569. [DOI] [PubMed] [Google Scholar]
  4. Beniey M., Haque T., Hassan S. Translating the role of PARP inhibitors in triple-negative breast cancer. Oncoscience. 2019;6:287–288. doi: 10.18632/oncoscience.474. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Betapudi V., Licate L.S., Egelhoff T.T. Distinct roles of nonmuscle myosin II isoforms in the regulation of MDA-MB-231 breast cancer cell spreading and migration. Cancer Res. 2006;66:4725–4733. doi: 10.1158/0008-5472.CAN-05-4236. [DOI] [PubMed] [Google Scholar]
  6. Bianchini G., Balko J.M., Mayer I.A., Sanders M.E., Gianni L. Triple-negative breast cancer: challenges and opportunities of a heterogeneous disease. Nat. Rev. Clin. Oncol. 2016;13:674–690. doi: 10.1038/nrclinonc.2016.66. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Blick T., Widodo E., Hugo H., Waltham M., Lenburg M.E., Neve R.M., Thompson E.W. Epithelial mesenchymal transition traits in human breast cancer cell lines. Clin. Exp. Metastasis. 2008;25:629–642. doi: 10.1007/s10585-008-9170-6. [DOI] [PubMed] [Google Scholar]
  8. Burstein M.D., Tsimelzon A., Poage G.M., Covington K.R., Contreras A., Fuqua S.A., Savage M.I., Osborne C.K., Hilsenbeck S.G., Chang J.C. Comprehensive genomic analysis identifies novel subtypes and targets of triple-negative breast cancer. Clin. Cancer Res. 2015;21:1688–1698. doi: 10.1158/1078-0432.CCR-14-0432. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Camorani S., Crescenzi E., Colecchia D., Carpentieri A., Amoresano A., Fedele M., Chiariello M., Cerchia L. Aptamer targeting EGFRvIII mutant hampers its constitutive autophosphorylation and affects migration, invasion and proliferation of glioblastoma cells. Oncotarget. 2015;6:37570–37587. doi: 10.18632/oncotarget.6066. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Camorani S., Crescenzi E., Fedele M., Cerchia L. Oligonucleotide aptamers against tyrosine kinase receptors: prospect for anticancer applications. Biochim.Biophys.Acta Rev. Cancer. 2018;1869:263–277. doi: 10.1016/j.bbcan.2018.03.003. [DOI] [PubMed] [Google Scholar]
  11. Camorani S., Crescenzi E., Gramanzini M., Fedele M., Zannetti A., Cerchia L. Aptamer-mediated impairment of EGFR-integrin αvβ3 complex inhibits vasculogenic mimicry and growth of triple-negative breast cancers. Sci. Rep. 2017;7:46659. doi: 10.1038/srep46659. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Camorani S., Esposito C.L., Rienzo A., Catuogno S., Iaboni M., Condorelli G., de Franciscis V., Cerchia L. Inhibition of receptor signaling and of glioblastoma-derived tumor growth by a novel PDGFRβ aptamer. Mol. Ther. 2014;22:828–841. doi: 10.1038/mt.2013.300. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Camorani S., Fedele M., Zannetti A., Cerchia L. TNBC challenge: oligonucleotide aptamers for new imaging and therapy modalities. Pharmaceuticals (Basel). 2018;11:123. doi: 10.3390/ph11040123. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Camorani S., Hill B.S., Collina F., Gargiulo S., Napolitano M., Cantile M., Di Bonito M., Botti G., Fedele M., Zannetti A. Targeted imaging and inhibition of triple-negative breast cancer metastases by a PDGFRβ aptamer. Theranostics. 2018;8:5178–5199. doi: 10.7150/thno.27798. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Camorani S., Hill B.S., Fontanella R., Greco A., Gramanzini M., Auletta L., Gargiulo S., Albanese S., Lucarelli E., Cerchia L. Inhibition of bone marrow-derived mesenchymal stem cells homing towards triple-negative breast cancer microenvironment using an anti-PDGFRβaptamer. Theranostics. 2017;7:3595–3607. doi: 10.7150/thno.18974. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Chen H.W., Medley C.D., Sefah K., Shangguan D., Tang Z., Meng L., Smith J.E., Tan W. Molecular recognition of small-cell lung cancer cells using aptamers. ChemMedChem. 2008;3:991–1001. doi: 10.1002/cmdc.200800030. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Chen Z., Liu H., Jain A., Zhang L., Liu C., Cheng K. Discovery of aptamer ligands for hepatic stellate cells using SELEX. Theranostics. 2017;7:2982–2995. doi: 10.7150/thno.19374. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Cortés J., André F., Gonçalves A., Kümmel S., Martín M., Schmid P., Schuetz F., Swain S.M., Easton V., Pollex E. IMpassion132 Phase III trial: atezolizumab and chemotherapy in early relapsing metastatic triple-negatve breast cancer. Future Oncol. 2019;15:1951–1961. doi: 10.2217/fon-2019-0059. [DOI] [PubMed] [Google Scholar]
  19. Dai X., Cheng X., Bai Z., Li J. Breast cancer cell line classification and its relevance with breast tumor subtyping. J. Cancer. 2017;8:3131–3141. doi: 10.7150/jca.18457. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Dassie J.P., Hernandez L.I., Thomas G.S., Long M.E., Rockey W.M., Howell C.A., Chen Y., Hernandez F.J., Liu X.Y., Wilson M.E. Targeted inhibition of prostate cancer metastases with an RNA aptamer to prostate-specific membrane antigen. Mol. Ther. 2014;22:1910–1922. doi: 10.1038/mt.2014.117. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Dent R., Trudeau M., Pritchard K.I., Hanna W.M., Kahn H.K., Sawka C.A., Lickley L.A., Rawlinson E., Sun P., Narod S.A. Triple-negative breast cancer: clinical features and patterns of recurrence. Clin. Cancer Res. 2007;13:4429–4434. doi: 10.1158/1078-0432.CCR-06-3045. [DOI] [PubMed] [Google Scholar]
  22. D'Ippolito E., Plantamura I., Bongiovanni L., Casalini P., Baroni S., Piovan C., Orlandi R., Gualeni A.V., Gloghini A., Rossini A. miR-9 and miR-200 regulate PDGFRβ-mediated endothelial differentiation of tumor cells in triple-negative breast cancer. Cancer Res. 2016;76:5562–5572. doi: 10.1158/0008-5472.CAN-16-0140. [DOI] [PubMed] [Google Scholar]
  23. Echeverria G.V., Ge Z., Seth S., Zhang X., Jeter-Jones S., Zhou X., Cai S., Tu Y., McCoy A., Peoples M. Resistance to neoadjuvant chemotherapy in triple-negative breast cancer mediated by a reversible drug-tolerant state. Sci. Transl Med. 2019;11 doi: 10.1126/scitranslmed.aav0936. eaav0936. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Esposito C.L., Passaro D., Longobardo I., Condorelli G., Marotta P., Affuso A., de Franciscis V., Cerchia L. A neutralizing RNA aptamer against EGFR causes selective apoptotic cell death. PLoS One. 2011;6:e24071. doi: 10.1371/journal.pone.0024071. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Futami K., Kimoto M., Lim Y.W.S., Hirao I. Genetic alphabet expansion provides versatile specificities and activities of unnatural-base DNA aptamers targeting cancer cells. Mol. Ther.Nucleic Acids. 2019;14:158–170. doi: 10.1016/j.omtn.2018.11.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Gadi V.K., Davidson N.E. Practical approach to triple-negative breast cancer. J. Oncol. Pract. 2017;13:293–300. doi: 10.1200/JOP.2017.022632. [DOI] [PubMed] [Google Scholar]
  27. Garnett M.J., Edelman E.J., Heidorn S.J., Greenman C.D., Dastur A., Lau K.W., Greninger P., Thompson I.R., Luo X., Soares J. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature. 2012;483:570–575. doi: 10.1038/nature11005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Garrido-Castro A.C., Lin N.U., Polyak K. Insights into molecular classifications of triple-negative breast cancer: improving patient selection for treatment. Cancer Discov. 2019;9:176–198. doi: 10.1158/2159-8290.CD-18-1177. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Han H.J., Russo J., Kohwi Y. SATB1 reprogrammes gene expression to promote breast tumour growth and metastasis. Nature. 2008;452:187–193. doi: 10.1038/nature06781. [DOI] [PubMed] [Google Scholar]
  30. Handl J., Knowles J., Kell D.B. Computational cluster validation in post-genomic data analysis. Bioinformatics. 2005;21:3201–3212. doi: 10.1093/bioinformatics/bti517. [DOI] [PubMed] [Google Scholar]
  31. He J., Peng T., Peng Y., Ai L., Deng Z., Wang X.Q., Tan W. Molecularly engineering triptolide with aptamers for high specificity and cytotoxicity for triple-negative breast cancer. J. Am. Chem.Soc. 2020;142:2699–2703. doi: 10.1021/jacs.9b10510. [DOI] [PubMed] [Google Scholar]
  32. Keefe A.D., Pai S., Ellington A. Aptamers as therapeutics. Nat. Rev. Drug Discov. 2010;9:537–550. doi: 10.1038/nrd3141. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Lehmann B.D., Bauer J.A., Chen X., Sanders M.E., Chakravarthy A.B., Shyr Y., Pietenpol J.A. Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. J. Clin.Invest. 2011;121:2750–2767. doi: 10.1172/JCI45014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Lehmann B.D., Jovanović B., Chen X., Estrada M.V., Johnson K.N., Shyr Y., Moses H.L., Sanders M.E., Pietenpol J.A. Refinement of triple-negative breast cancer molecular subtypes: implications for neoadjuvant chemotherapy selection. PLoS One. 2016;11:e0157368. doi: 10.1371/journal.pone.0157368. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Li X., Zhang W., Liu L., Zhu Z., Ouyang G., An Y., Zhao C., Yang C.J. In vitro selection of DNA aptamers for metastatic breast cancer cell recognition and tissue imaging. Anal. Chem. 2014;86:6596–6603. doi: 10.1021/ac501205q. [DOI] [PubMed] [Google Scholar]
  36. Liu M., Wang Z., Tan T., Chen Z., Mou X., Yu X., Deng Y., Lu G., He N. An aptamer-based probe for molecular subtyping of breast cancer. Theranostics. 2018;8:5772–5783. doi: 10.7150/thno.28949. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Nabholtz J.M., Abrial C., Mouret-Reynier M.A., Dauplat M.M., Weber B., Gligorov J., Forest A.M., Tredan O., Vanlemmens L., Petit T. Multicentric neoadjuvant phase II study of panitumumab combined with an anthracycline/taxane-based chemotherapy in operable triple-negative breast cancer: identification of biologically defined signatures predicting treatment impact. Ann. Oncol. 2014;25:1570–1577. doi: 10.1093/annonc/mdu183. [DOI] [PubMed] [Google Scholar]
  38. Nair A., Chung H.C., Sun T., Tyagi S., Dobrolecki L.E., Dominguez-Vidana R., Kurley S.J., Orellana M., Renwick A., Henke D.M. Combinatorial inhibition of PTPN12-regulated receptors leads to a broadly effective therapeutic strategy in triple-negative breast cancer. Nat.Med. 2018;24:505–511. doi: 10.1038/nm.4507. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Newman L.A., Reis-Filho J.S., Morrow M., Carey L.A., King T.A. The 2014 society of surgical oncology Susan G. Komen for the cure symposium: triple-negative breast cancer. Ann. Surg. Oncol. 2015;22:874–882. doi: 10.1245/s10434-014-4279-0. [DOI] [PubMed] [Google Scholar]
  40. Nguyen L.V., Cox C.L., Eirew P., Knapp D.J., Pellacani D., Kannan N., Carles A., Moksa M., Balani S., Shah S. DNA barcoding reveals diverse growth kinetics of human breast tumour subclones in serially passaged xenografts. Nat. Commun. 2014;5:5871. doi: 10.1038/ncomms6871. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Nowsheen S., Cooper T., Bonner J.A., Lo Buglio A.F., Yang E.S. HER2 overexpression renders human breast cancers sensitive to PARP inhibition independently of any defect in homologous recombination DNA repair. Cancer Res. 2012;72:4796–4806. doi: 10.1158/0008-5472.CAN-12-1287. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Oh E., Kim Y.J., An H., Sung D., Cho T.M., Farrand L., Jang S., Seo J.H., Kim J.Y. Flubendazole elicits anti-metastatic effects in triple-negative breast cancer via STAT3 inhibition. Int. J. Cancer. 2018;143:1978–1993. doi: 10.1002/ijc.31585. [DOI] [PubMed] [Google Scholar]
  43. O'Reilly E.A., Gubbins L., Sharma S., Tully R., Guang M.H., Weiner-Gorzel K., McCaffrey J., Harrison M., Furlong F., Kell M. The fate of chemoresistance in triple negative breast cancer (TNBC) BBA Clin. 2015;3:257–275. doi: 10.1016/j.bbacli.2015.03.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Pasleau F., Grooteclaes M., Gol-Winkler R. Expression of the c-erbB2 gene in the BT474 human mammary tumor cell line: measurement of c-erbB2 mRNA half-life. Oncogene. 1993;8:849–854. [PubMed] [Google Scholar]
  45. Ross D.T., Perou C.M. A comparison of gene expression signatures from breast tumors and breast tissue derived cell lines. Dis. Markers. 2001;17:99–109. doi: 10.1155/2001/850531. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Sandberg R., Ernberg I. Assessment of tumor characteristic gene expression in cell lines using a tissue similarity index (TSI) Proc. Natl. Acad. Sci. U S A. 2005;102:2052–2057. doi: 10.1073/pnas.0408105102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Sefah K., Tang Z.W., Shangguan D.H., Chen H., Lopez-Colon D., Li Y., Parekh P., Martin J., Meng L., Phillips J.A. Molecular recognition of acute myeloid leukemia using aptamers. Leukemia. 2009;23:235–244. doi: 10.1038/leu.2008.335. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Shangguan D., Li Y., Tang Z., Cao Z.C., Chen H.W., Mallikaratchy P., Sefah K., Yang C.J., Tan W. Aptamers evolved from live cells as effective molecular probes for cancer study. Proc. Natl. Acad. Sci. U S A. 2006;103:11838–11843. doi: 10.1073/pnas.0602615103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Takahashi M., Wu X., Ho M., Chomchan P., Rossi J.J., Burnett J.C., Zhou J. High throughput sequencing analysis of RNA libraries reveals the influences of initial library and PCR methods on SELEX efficiency. Sci. Rep. 2016;6:33697. doi: 10.1038/srep33697. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Tang X., Loc W.S., Dong C., Matters G.L., Butler P.J., Kester M., Meyers C., Jiang Y., Adair J.H. The use of nanoparticulates to treat breast cancer. Nanomedicine (Lond). 2017;12:2367–2388. doi: 10.2217/nnm-2017-0202. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Tang Z., Shangguan D., Wang K., Shi H., Sefah K., Mallikratchy P., Chen H.W., Li Y., Tan W. Selection of aptamers for molecular recognition and characterization of cancer cells. AnalChem. 2007;79:4900–4907. doi: 10.1021/ac070189y. [DOI] [PubMed] [Google Scholar]
  52. To K., Fotovati A., Reipas K.M., Law J.H., Hu K., Wang J., Astanehe A., Davies A.H., Lee L., Stratford A.L. Y-box binding protein-1 induces the expression of CD44 and CD49f leading to enhanced self-renewal, mammosphere growth, and drug resistance. Cancer Res. 2010;70:2840–2851. doi: 10.1158/0008-5472.CAN-09-3155. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Ullrich A., Coussens L., Hayflick J.S., Dull T.J., Gray A., Tam A.W., Lee J., Yarden Y., Libermann T.A., Schlessinger J. Human epidermal growth factor receptor cDNA sequence and aberrant expression of the amplified gene in A431 epidermoid carcinoma cells. Nature. 1984;309:418–425. doi: 10.1038/309418a0. [DOI] [PubMed] [Google Scholar]
  54. Xiao Z., Farokhzad O.C. Aptamer-functionalized nanoparticles for medical applications: challenges and opportunities. ACS Nano. 2012;6:3670–3676. doi: 10.1021/nn301869z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Zhou J., Rossi J. Aptamers as targeted therapeutics: current potential and challenges. Nat. Rev. Drug Discov. 2017;16:181–202. doi: 10.1038/nrd.2016.199. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Ziegler Y.S., Moresco J.J., Tu P.G., Yates J.R., 3rd, Nardulli A.M. Plasma membrane proteomics of human breast cancer cell lines identifies potential targets for breast cancer diagnosis and treatment. PLoS One. 2014;9:e102341. doi: 10.1371/journal.pone.0102341. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Document S1. Transparent Methods, Figures S1–S8, and Tables S1–S3
mmc1.pdf (2.4MB, pdf)

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