A combination approach of pseudotime analysis and mathematical modeling for understanding drug-resistant mechanisms

Magi, Shigeyuki; Ki, Sewon; Ukai, Masao; Domínguez-Hüttinger, Elisa; Naito, Atsuhiko T; Suzuki, Yutaka; Okada, Mariko

doi:10.1038/s41598-021-97887-z

Download PDF

Article
Open access
Published: 16 September 2021

A combination approach of pseudotime analysis and mathematical modeling for understanding drug-resistant mechanisms

Shigeyuki Magi^1,2,3,
Sewon Ki²,
Masao Ukai⁴,
Elisa Domínguez-Hüttinger⁵,
Atsuhiko T Naito³,
Yutaka Suzuki⁶ &
…
Mariko Okada^1,2,4,7

Scientific Reports volume 11, Article number: 18511 (2021) Cite this article

4827 Accesses
2 Citations
Metrics details

Subjects

Abstract

Cancer cells acquire drug resistance through the following stages: nonresistant, pre-resistant, and resistant. Although the molecular mechanism of drug resistance is well investigated, the process of drug resistance acquisition remains largely unknown. Here we elucidate the molecular mechanisms underlying the process of drug resistance acquisition by sequential analysis of gene expression patterns in tamoxifen-treated breast cancer cells. Single-cell RNA-sequencing indicates that tamoxifen-resistant cells can be subgrouped into two, one showing altered gene expression related to metabolic regulation and another showing high expression levels of adhesion-related molecules and histone-modifying enzymes. Pseudotime analysis showed a cell transition trajectory to the two resistant subgroups that stem from a shared pre-resistant state. An ordinary differential equation model based on the trajectory fitted well with the experimental results of cell growth. Based on the established model, it was predicted and experimentally validated that inhibition of transition to both resistant subtypes would prevent the appearance of tamoxifen resistance.

Identification of a transient state during the acquisition of temozolomide resistance in glioblastoma

Article Open access 06 January 2020

Identifying transcriptional programs underlying cancer drug response with TraCe-seq

Article 16 September 2021

Diverse clonal fates emerge upon drug treatment of homogeneous cancer cells

Article 19 July 2023

Introduction

Estrogen receptor (ER) is a hormone-dependent transcription factor that plays an important role in many physiological processes, including reproductive development, bone homeostasis, and cardiovascular remodeling. ER is also closely associated with breast cancer development^1,2. Approximately 75% of all breast cancer cases are categorized into ER-positive luminal subtypes³ and initially treated using an ER antagonist, tamoxifen (TAM). Unfortunately, around 40% of TAM-responsive tumors progress to resistant and metastatic tumors after long-term treatment⁴. The molecular mechanisms by which those tumors exhibit TAM resistance, have been shown to involve alterations in the estrogen–ER interaction-dependent gene expression⁵, cholesterol pathway⁶, and histone demethylase activity (which regulates cellular transcriptomic heterogeneity)⁷ resulting in hyperactivation of alternative signaling pathways including ErbB receptors^8,9, ERK1/2¹⁰, PI3K^11,12,13, and NF-κB signaling¹⁴. However, little is known about the dynamic process of TAM resistance acquisition, i.e., when or how the factors involved in resistance acquisition are altered, because most of the previous studies focused on the end-point comparison between resistant and nonresistant states.

Previously, we analyzed the changes in gene expression of TAM-treated MCF-7 cells, a human ER-positive breast cancer cell line, by bulk RNA-sequencing (RNA-seq) and reported several molecular changes that preceded full acquisition of TAM resistance¹⁵. However, our analysis might have overlooked the involvement of intracellular heterogeneity in the process of TAM resistance acquisition. In luminal breast cancer patients, single-cell transcriptome and epigenomic surveys revealed that non-genomic cell-to-cell variability generates phenotypic heterogeneity¹⁶. Therefore, understanding the process of TAM resistance acquisition at a single-cell resolution may be important to fully understand the process of TAM resistance acquisition and develop strategies for preventing cancer recurrence.

In this study, we sequentially analyzed transcriptomic profiles in MCF-7 cells treated with TAM by single-cell RNA-seq. We report that TAM-resistant cells can be subgrouped into two subgroups, one showing altered gene expression related to metabolic regulation and the other showing high expression of genes encoding adhesion molecules and histone-modifying enzymes. Pseudotime analysis of single-cell RNA-seq data revealed a cell transition trajectory to the two resistant subpopulations that stem from a shared pre-resistant state. An ordinary differential equation model based on the cell trajectory fitted well with the experimental results of cell growth. Finally, we experimantally validated our model prediction that the conbinatorial inhibition of two important molecules for each resistant subgroup could repress the growth of resistant cells.

Results

Time-series transcriptome profiles of MCF-7 cells during continuous TAM treatment

We first investigated the effect of continuous TAM treatment on human breast cancer MCF-7 cells, whose growth depends on ER signaling (Fig. 1a). Treatment with 1 µM TAM initially inhibited cell growth (Fig. 1b) through decreasing the number of cells in S phase whereas increasing that in G1 phase (Fig. 1c,d), suggesting that TAM treatment induced G1 arrest. The growth of cells was almost completely inhibited until week 5 (W5) but recovered thereafter (Fig. 1b). The cell cycle of TAM-treated cells was also dysregulated until W5 but became comparable to control cells after week 6 (W6) (Fig. 1c,d). These results showed the process by which the cells survived and restored their growth potential in the absence of ER signaling.

We next analyzed the bulk RNA-seq data of TAM-treated MCF-7 cells to identify the difference in the gene regulatory network from parental cells. Previously, we performed time-course bulk RNA-seq analysis of TAM-treated MCF-7 cells and non-treated MCF-7 cells, and identified gene sets that play critical role at the “tipping” point of resistance acquisition¹⁵. In this study, we re-analyzed the dataset in order to focus on time-dependent changes in the expression of each gene, correcting for the effect of the difference in library preparation processes between samples (“Methods” and Supplementary Fig. S1). A total of approximately 6000 differentially expressed genes (DEGs) were identified between TAM-treated and control cells, of which approximately 3000 were up-regulated (Supplementary Fig. S2).

Gene expression in TAM-treated cells was normalized to control cells at each time point, and log2 fold change (log2FC) values were calculated. The log2FC values of all genes at week 0 were set as a theoretical value of zero. We then obtained time-course patterns of the log2FC values of 6982 DEGs at 13 time points and analyzed the similarity of the log2FC values of all DEGs in each week using cluster analysis (Fig. 1e). The expression patterns of DEGs at each time point were classified into four stages; week 0 (W0), from week 1 (W1) to week 4 (W4), W5 to week 8 (W8), and week 9 (W9) to week 12 (W12). The Pearson’s correlation distance from a previous week was the largest at W5, which preceded the recovery of cell growth (Supplementary Fig. S3). In addition, the distance at W1 to W9 was larger than that at week 10 (W10) to W12, indicating that DEG expression patterns became stable at the later stage.

We then investigated the relationships between dynamic gene expression patterns and gene functions by evaluating the similarity of the time-course patterns among all DEGs. Cluster analysis of the z-score of log2FC values revealed six groups of genes with distinct expression patterns: cluster A, rapidly decreasing expression; cluster B, initially down-regulated and recovered at W5; cluster C, rapidly increasing before cell growth rate recovery; cluster D, a gradual increase in expression concomitant with growth rate recovery; cluster E, initially up-regulated and then down-regulated; and cluster F, gradually decreasing (Fig. 1f and Supplementary Table S1). The enrichment analysis of each group was carried out using the Reactome pathway (Fig. 1g) and KEGG (Fig. 1h) databases¹⁷. Cluster A (rapidly decreasing expression pattern) was enriched in genes related to both receptor tyrosine kinase signaling and fatty acid metabolism. Genes in cluster C (rapid up-regulation) were related to TGFβ signaling or extracellular matrix–receptor interaction, such as TGFB2, SMAD3, and MMP9, or encoded collagen or laminin proteins. This implies that the reorganization of the gene network regulating epithelial–mesenchymal transition or extracellular matrix secretion, both of which contribute to cancer malignancy, occurred before cell growth recovery in the TAM-treated condition. Genes related to ribosomes were also enriched in cluster C, indicating that ribosomal biogenesis may be up-regulated during continuous TAM treatment. On the other hand, cluster D, in which gene expression level increased gradually, was enriched in genes functioning in the thyroid hormone signaling pathway, HIF1 pathway, and glycolytic process, and downstream signaling of RAS, among others. These data show that a Warburg-like effect co-occurs with TAM resistance acquisition. Genes in cluster B showed a non-monotinic dynamic expression pattern characterized by a transient decline from week 1 to week 4, followed by recovery to the basal level. This trend was similar to the growth rate pattern observed in the TAM-treated condition. Cluster B contained numerous genes involved in cell cycle regulation such as CCND1 and E2F1, and DNA replication such as RAD51, DNA damage (DNA repair, transcriptional regulation of TP53, and base excision repair), RNA metabolism, kinesins, beta-catenin degradation, generic transcription pathways, and the Fanconi anemia pathway. Genes in cluster E were up-regulated only when cell growth was effectively inhibited by TAM; this pattern was opposite to that observed in cluster B. Cluster E was enriched in genes involved in interferon signaling, FoxO signaling, autophagy, as well as transcription pathways, exytocin signaling pathways, and the MAPK signaling pathway. The interferon and FoxO signaling pathways exhibit anti-survival functions in cancer cells exposed to anti-cancer agents¹⁸, whereas autophagy contributes to cell survival under normal conditions¹⁹, suggesting that genes in cluster E reflect both antitumor as well as adaptation mechanisms triggered by the TAM treatment. Cluster F was the largest dynamic cluster containing 2088 genes and was characterized by a consistent decrease in gene expression. Enrichment analysis showed that cluster F contained genes related to multiple cellular functions including energy metabolism, growth hormone synthesis, Rho GTPase (Rap1) signaling, and crucially, the estrogen signaling pathway, suggesting that TAM treatment inhibits the estrogen-dependent gene expression mechanism, and TAM resistance observed in our experiment may be supported by an estrogen/ER-independent mechanism.

Single-cell RNA-seq analysis of MCF-7 cells under continuous TAM treatment

Because cell-to-cell heterogeneity of phenotypic features is a key mechanism of drug resistance²⁰, we investigated TAM-induced changes in gene expression profiles at a single-cell level. On the basis of the results of the cell growth assay and bulk RNA-seq data, we focused on four time points: W0 (just before starting TAM treatment), week 3 (W3, at the beginning of the complete cell growth inhibition period), W6 (at the end of the complete cell growth inhibition period), and W9 (at the acquisition of TAM resistance) (Fig. 2a). RNA-seq analysis of 1,108 single cells yielded 577 high-quality single-cell gene expressions (Fig. 2a and Methods section). Averaged single-cell expression profiles were correlated with bulk expression despite the zero-inflated distribution (Supplementary Fig. S4). The Pearson correlation coefficient of 11,413 gene expression values between individual cells increased at W3, then gradually decreased at W6 and W9 (Fig. 2b). This changing pattern of correlation coefficient might suggest the selection of cells that can survive the TAM treatment and subsequently transition into multiple stable states.

To visualize cell-to-cell diversity in detail, we conducted uniform manifold approximation and projection (UMAP), one of the standard methods of dimensional reduction. Before drawing the UMAP plot, we calculated the probability score of cell cycle progression in each cell using Seurat 3 software²¹ to correct for the bias caused by the difference in the cell cycle stage (Fig. 2c). All cells were mapped on a three-dimensional UMAP plot and projected in two dimensions (Fig. 2d–f, and Supplementary Fig. S5). Single-cell data were roughly divided into two groups: W0 and the others (W3, W6, and W9). Cells were widely distributed in space at W3 and W6 but were localized in two separate regions at W9. These cells could be clustered into six subpopulations in the UMAP plot (Fig. 2f). Cells in subgroups 1 and 6 belonged to the W0 group, and these cells were strongly diminished by W3. By contrast, cells in subgroups 2 and 3 newly emerged at W3 and prevailed in those clusters until W6. Finally, at W9, these cells were split into two groups: one containing subgroup 4, and the other containing subgroup 5.

Subgroup-specific gene modules and their functions

We first investigated marker genes in each subgroup. The top five genes showing the highest specificity scores were selected in each subgroup (Supplementary Fig. S6a). Subgroups 1 and 6 were the major groups at W0, and marker genes in these subgroups included typical ER pathway target genes such as AREG²² and GREB1²³. This result showed that the transcriptional activity of ER for typical target genes was down-regulated in the other subgroups. Comparing with the marker genes in subgroups 1 and 6, the expression level of marker genes in other subgroups, especially that in subgroup 2, does not clearly distinguish the cells into the subgroups. Interestingly, almost all marker genes of subgroups 4 and 5 also showed high expression in subgroup 3, suggesting that the pre-resistant subgroup 3 could potentially mature into distinct resistant subgroups by rewiring the genetic network.

Next, we analyzed the genetic modules specifically expressed in each subgroup or each week (Fig. 2g, Supplementary Fig. S6b, and Supplementary Table S2). Subgroups 1 and 6 contained highly expressed gene modules j, k, and l, which are enriched in ESR-mediated signaling, unfolded protein response, and amino acid and nucleotide metabolism. On the other hand, the gene expression of module a was particularly low for these subgroups. Subgroups 2 and 3 were the major subpopulations in W3 and W6. Both these subgroups showed high expression levels of genes in module a, some of which are involved in interferon signaling, TGFβ signaling, and tight junctions. These enriched terms showed a strong resemblance to the early responsive cluster C in the bulk RNA-seq experiment (Fig. 1g,h). Subgroup 4, whose population was increased at W9, showed high expression levels of genes in modules g, h, and i, as shown in the heatmap. These genes encoded cell adhesion-related molecules such as integrin β4 (ITGB4), laminin β2 (LAMB2), and zyxin (ZYX), and some genes were involved in ROCK activation mechanisms. These modules also include several terms related to signal transduction, such as the VEGF pathway and thyroid hormone signaling. In addition, some chromatin remodeling enzymes and lysine-specific histone demethylases were included in module h. These results indicate that TAM-resistant cells in subgroup 4 showed higher activities of cell adhesion and migration, with an altered signaling pathway and epigenetic status. Subgroup 5, which represents another major population during W9, contained highly expressed genes in modules b, c, d, and e compared to that of subgroup 4. This result indicates that genes related to innate immune responses, oxidative phosphorylation, and translation are highly expressed in the cells in subgroup 5. In addition, module c contained genes related to carbon metabolism, especially the glycolysis/glycogenesis pathway, suggesting that cells in subgroup 5 exhibit unique metabolic adaptation to TAM-induced stress. Based on the aforementioned results, we found that TAM-resistant ER-positive breast cancer cells obtained from the same parental cell line could be divided into two types, one of which acquired the re-wired metabolic network (subgroup 5) and another acquired high expression levels of adhesion molecules with changing epigenetic status (subgroup 4).

Trajectory analysis of TAM resistance

To confirm the cell transition trajectory into two different types of resistant subgroups, we conducted pseudotime analysis (Fig. 3a–c). The pseudotime of each cell calculated from the gene expression data was correlated with the sampling time after starting the continuous TAM treatment (Fig. 3d). The pseudotime of cells in subgroup 4 was higher than that of cells in subgroup 5, suggesting that cells in subgroup 4, showing high expression of epigenetic modulators, are more divergent from parental cells than cells in subgroup 5 (Fig. 3e). To indetify important molecules involved in the emergence of subgroups 4 and 5, we analyzed DEGs along the estimated cell trajectory. A total of 273 and 79 genes were detected as highly expressed genes in subgroups 4 and 5, respectively (Fig. 3f, Supplementary Tables S3 and S4). Then, we investigated the transcriptional regulators of the highly expressed genes in subgroup 4 and 5 using the ChIP-Atlas database²⁴, which covers approximately all public ChIP-seq data (Fig. 3g), and verified the expression patterns of genes based on our analysis (Fig. 3h and Supplementary Fig. S7). The detected genes and expression patterns in bulk (Fig. 3h) and single-cell RNA-seq (Supplementary Fig. S7) were not completely correlated. These differences may be attributed to technical reasons, including the difference in sequencing protocols, depth per sample, and the lack of non-treatment control conditions in each week for single-cell data.

Prediction of proteins that bind near the transcriptional start site of DEGs in the trajectory to subgroup 4 showed that only 4 of the top 10 factors represented ChIP-seq data from MCF-7 cells, and most of the others were obtained from the triple-negative breast cancer (TNBC) cell line (Fig. 3g, left, shown in green). These results also suggest that most up-regulated genes in subgroup 4 are controlled by bromodomain-containing proteins, BRD4 and BRD2, which recognize acetylated histones and act as super enhancers^25,26. In addition, our results also suggest the possible involvement of oncogenic transcription factors SMAD3 and ERG in the trajectory to subgroup 4. RNA expression levels of SMAD3 and ERG were up-regulated before W4 in bulk RNA-seq data (Fig. 3h, top and middle) and related molecular terminology (“Signaling by TGF-beta receptor complex” and “MAPK singaling pathway”) were detected in cluster C and E in bulk RNA-seq data, respectively (Fig. 1g,h). For single-cell data, the expression levels of BRD2, BRD4, and SMAD3 increased after TAM treatment (Supplementary Fig. S7). These results indicate that cells in subgroup 4 have different statuses due to epigenetic alteration, which is clearly distinct from that of parent MCF-7 cells; this result was consistent with the enrichment analysis of specific gene modules (Fig. 2g).

In subgroup 5, 7 of the top 10 factors were obtained from ER-positive breast cancer or normal cells (Fig. 3g, right), suggesting that subgroup 5 retained the transcriptional network of parental MCF-7 cells. ChIP-seq data from anti-WDR5 antibody showed the best q-value and fold enrichment score. Although the expression profiles of WDR5 were not consistent between bulk and single-cell RNA-seq data (Fig. 3h, bottom, Supplementary Fig. S7), various binding molecules of WDR5, such as methylated histone H3 lysine 4 or MYC²⁷, might regulate genes related to subgroup 5. Moreover, MYC was detected as a candidate estimated from ChIP-Atlas database for the upstream regulating of increased genes in subgroup 5. Among other candidate genes, BRD4, TAF1, and PML were up-regulated after TAM treatment in both bulk and single-cell data. These data indicate that MYC, TAF1, and PML may contribute to one of the emerging TAM-resistant subpopulations. Taken together, our analysis revealed key molecular candidates that drive two different TAM-resistant subgroups.

Mathematical modeling of the TAM resistance acquisition process

We constructed a phenomenological mathematical model that reproduces the population-level dynamics of TAM resistance, based on cell trajectories obtained using pseudotime analysis, to estimate the relative contribution of TAM-mediated cell growth- and differentiation to the acquisition of resistance (Fig. 4a). This model comprises cell transformation among four major cell subpopulations as estimated by single-cell gene expression profiles: cells initially sensitive to TAM (X_S, subgroups 1 and 6 in Fig. 3a), pre-resistant cells (X_P, subgroups 2 and 3), resistant cells showing altered expression of metabolism-related genes (X_R1, subgroup 5), and resistant cells with highly adhesive phenotype with changing epigenetics (X_R2, subgroup 4). The state transitions between the four subpopulations were assumed to follow the graph structure estimated by pseudotime analysis (Fig. 3c): X_S cells change to X_P in the presence of TAM, and cells in X_P change to the two resistant states X_R1 and X_R2. The model also enables cell transitions in the reverse direction. In addition, we assumed that continuous TAM treatment accelerates the rate of forward cell transition rate in response to a cumulative history of TAM treatment and describe this acceleration as a sigmoid function of the integral of TAM. This assumption is based on previous findings that cell state transitions require the accumulation of genetic or epigenetic changes, which from the tipping point of resistance¹⁵. We explicitly considered extrinsic noise-mediated cell-to-cell variability by fitting 20 independent model parameter sets to two experimental time-course datasets describing cell proliferation and differentiation dynamics using the BioMASS computational framework²⁸. Specifically, our model reproduced the experimentally observed dynamic distributions of both total cell growth rate in the presence of TAM (Fig. 1b) and the four different cell subpopulation proportions (Fig. 2g, heatmap in green) simultaneously (Fig. 4b,c, and Supplementary Fig. S8a).

We found two remarkable features of the well-fitting parameter distributions. First, the growth rate of subpopulation X_R2 (rate constant of reaction v₁₂) was significantly greater than that of X_R1 (v₉) (Supplementary Fig. S8b). This finding is consistent with the result that the subpopulation of X_R2 expressed some cell division-related genes (Fig. 2g). Second, the parameter determining the steepness of the rection rate mediating the acquisition of TAM resistance was greater for v₁₀ than that for at in v₇ (Supplementary Fig. S8c). This implies that the cell transition from X_P to X_R2 is more sensitive to cumulative TAM exposure time, which may be caused by the accumulation of epigenetic alterations. The finding is substantiated by the results of single-cell RNA-seq analysis, which showed that the genetic feature of X_R2 displayed high expression levels of chromatin-modifying enzymes (Fig. 2g), and pseudotime analysis, in which X_R2 was the most differentiated subtype compared with other subtypes (Fig. 3e). These results indicate that parameter inference using our phenomenological model, which is based on cell population dynamics, can help to successfully pinpoint underlying mechanisms (differential subpopulation-specific rates) which are consistent with empirical observations.

Using the fitted parameter sets (Supplementary Table S5) as nominal values, we then performed local sensitivity analysis to examine how much a given change in each single reaction affects the mean-over-time growth rate after the 3rd week (Fig. 4d). The results indicated that v₁₂, the growth rate of X_R2, was the most critical factor affecting the mean-over-time growth rate. On the contrary, neither the reverse transition from resistant cell types to X_P (or from X_P to X_S) nor cell death caused by TAM was found to significantly affect the growth rate. Finally, we examined the effect of combination inhibition on two key biological processes, cell growth and forward state transition to two different subtypes, on TAM-resistant cell growth (Fig. 4e). The simulation results show that the mean-over-time growth rate was lower when the two parameters related to both subgroups were inhibited than when only the parameter related to one subgroup was inhibited. Additionally, combined inhibition of cell growth rate of both resistant subtypes inhibited growth rate < 1 at broader inhibitory ranges than the other intervention pairs (Fig. 4e, blue line). However, under the conditions in which the growth of one resistant subtype is repressed, complete inhibition of transition to another resistant subtype showed stronger regression than complete cell growth inhibition of the same subtype with statistical significance (Fig. 4e, comparison of the gray box). We cannot determine whether the difference is biologically significant, but the result may indicate that inhibition of cell state transition by, for example, epigenetic inhibitors, has the potential to be more effective than targeting the growth of the resistant subpopulation alone.

Inhibition of molecules mediating the generation of two resistant subpopulations induces regression in the pre-resistant stage

Finally, we experimentally confirmed the hypothesis derived from pseudotime analysis and mathematical modeling that simultaneous intervention for the proliferation or transitions in subgroup 4, which undergoes epigenetic alterations via chromatin modification, and subgroup 5, wherein PML acts as an upstream regulator by effectively inhibiting the growth of the resistant cell population. Histone demethylase KDM5B is a candidate epigenetic modulator for subgroup 4, which reportedly modulates resistance to endocrine therapies by increasing transcriptional heterogeneity⁷. The combination of KDM5 inhibition and PML knockdown suppressed the cell growth of TAM-treated cells (W3, W6, and W9) but not that of cells not treated with TAM (W0) (Fig. 5). Particularly in W3 and W6, the combination inhibition was prone to suppress the cell growth more than PML knockdown or KDM5 inhibitor treatment alone. In addition, the growth inhibition in W3, the timing when the cell growth was completely inhibited by TAM (Fig. 1b), indicates that combined inhibition induces a decrease in cell numbers. Together, these results demonstrate that the inhibition of molecules important for resistant cell subgroups could induce regression before complete acquisition of TAM resistance.

Discussion

In this study, we analyzed transcriptional changes in MCF-7 cells during continuous TAM treatment using both bulk and single-cell RNA-seq. The results of bulk RNA-seq analysis revealed several time-course patterns of gene expression during the continuous TAM treatment. A subset of genes, including clusters B and E, showed low or high expression immediately before acquiring the growing ability in the presence of TAM, respectively. It is reasonable to speculate that the recovery of gene expression levels in cluster B is accompanied by the recovery of growth rate because these genes included positive cell cycle regulators. The expression levels of these genes may be regulated by E2F families, suggesting that the growth of TAM-resistant cells also depends on the CDK4–E2F cell cycle machinery, supporting the effect of the CDK4/6 inhibitor on ER cells²⁹. Combined with the expression pattern of ESR1, our results implied that the expression levels of E2F gene families are maintained by estrogen–ESR1-dependent signaling in the absence of TAM; however, this was superseded by other signaling pathways, such as central carbon metabolism-related HIF1 machinery, in TAM-resistant cells (Fig. 1g).

On the other hand, the significance in cluster E is rather difficult to interpret. Some groups have previously reported that interferon regulatory factor-1 (IRF1) is critical for TAM-mediated apoptosis, and its related pathway is also up-regulated in TAM-treated cells³⁰. IRF1 was shown to induce apoptosis in breast cancer cells³¹. However, another group showed that interferon-responsive genes are up-regulated in both TAM-resistant and radioresistant MCF-7 cells and contribute to cross resistance³². These previous reports imply that the bilateral function of interferon signaling may accelerate the adaptation of cancer cells to the TAM-treated condition by increasing cell-to-cell variability (Fig. 2b). Non-genetic cell-to-cell variability, believed as a major contributor to the production of outlier cells and can adapt to severe conditions^33,34, could play an important role in the acquisition of TAM resistance under our experimental conditions because few genes were mutated at the time when genes in cluster E were up-regulated¹⁵ (Fig. 1f). Previous studies show contradictory results on the relationship between chemosensitivity and FoxO-autophagy signaling. It has been reported that 4-hydroxytamoxifen induces autophagic cell death^35,36; however, another group reported that inhibition of autophagy restored the responsiveness to anti-estrogen therapy. In our single-cell RNA-seq analysis, cells in subgroup 4 showed high expression levels of autophagy-related genes (Supplementary Fig. S6b). Our results suggest the possibility that the modulation of autophagy and interferon signaling in early phases of endocrine therapy prevents the transition of cells to resistant types.

Several studies showed that TAM is localized to mitochondria and endoplasmic reticulum and shows non-genomic toxicity by inhibiting the electron transport chain complexes^37,38. Some results in our transcriptomic analysis can be explained by such estrogen-independent mode of action of TAM. The overrepresentation of genes related to translation in cluster C and that of genes involved in the detoxification of reactive oxygen species, which are mainly produced in mitochondria, in cluster D (Fig. 1g,h) can be interpreted as a protective response for the dysfunction of these organelles. High expression levels of ribosomal and mitochondrial genes (modules d and e) were also detected in TAM-resistant subgroup 5 by single-cell RNA-seq analysis (Fig. 2g). Another phenomenon related to mitochondrial dysfunction was the up-regulation of glycolytic pathway enzymes induced by the HIF1 signaling pathway (Fig. 1h), which was coincident with the growth ability of cells in the presence of TAM (Fig. 1b). We detected the overexpression of genes encoding glycolytic and gluconeogenetic enzymes in subgroups 3 and 5. Indeed, MYC, which drives a gene expression of ribosomal proteins³⁹, hexokinase 2, and lactate dehydrogenase⁴⁰ was increased during the time-course, and was predicted as one of the main regulators of resistant subgroup 5 (Fig. 3g). These results suggest that subgroup 5 genes overcome the non-genomic toxicity of TAM by up-regulating the ribosomal and mitochondrial functions via HIF1 or MYC activity.

Our single-cell RNA-seq analysis suggested the existence of two different resistant subpopulations and the role of important molecules in the emergence of each resistant subpopulation. Subgroup 5 was predicted to be initiated by the activity of TAF1 and PML molecules, in addition to MYC (Fig. 3g). Interestingly, the second bromodomain-specific inhibitor of TAF1 represses MYC expression, and its effect is synergistic to the BRD4 inhibitor⁴¹. On the basis of the results of this and previous studies, we infer that the differentiation of pre-resistant cells to resistant subgroup 5 requires TAF1 and BRD4 activity for up-regulating MYC gene expression. PML is believed to possess tumor-suppressing activity by controlling the cell cycle and apoptosis⁴²; however, recent studies revealed that PML is overexpressed and promotes metastasis, especially in TNBC^43,44. This bimodal character of PML was also detected in our experiments; PML knockdown decreased cell growth in TAM-treated cells but increased it in cells without TAM treatment (Fig. 5). Although the overexpression of PML in luminal types is uncommon, silencing PML functions elicits not only growth suppression in TNBC⁴⁵ but also oncosphere formation, a readout of self-renewal potential, in PML-overexpressing luminal type breast cancers⁴⁶. In addition, PML overexpression in MCF-10A cells promotes fatty acid oxidation and ATP production via the tricarboxylic acid cycle⁴⁴. Taken together, these results suggest that TAM-resistant cells in subgroup 5 are similar to proliferative cancer stem cells, which exhibit self-renewal potential and rely on both oxidative phosphorylation and glycolytic metabolism⁴⁷. The second resistant subgroup (subgroup 4) showed high expression levels of prostate cancer-related genes and chromatin-modifying enzymes (Fig. 2g and Supplementary Fig. S6b, module h). Some of the TAM-resistant specimens showed the overexpression of androgen receptor (AR), and exogenously AR-overexpressed MCF-7 cells resistant to TAM-induced growth inhibition⁴⁸. This study is consistent with our results that one resistant subpopulation acquired new TAM-resistant features by AR signaling and histone-modifying enzymes.

In summary, our time-series single-cell sampling and multidimensional data analysis highlighted that the acquisition of drug resistance relies on heterogeneity and emphasized the importance of multiple molecules in phenotype transitions. Our approach reproduces the characteristics of the emergence of TAM-resistant cell populations by proposing a mathematical model of subpopulation dynamics based on cell trajectories obtained by single-cell analysis. Further, analysis of our phenomenological model allowed us to pinpoint key mechanisms differentiating the two TAM-resistant subpopulations (differential proliferation rates) and to devise combinatorial intervention strategies that effectively halt the progression to resistant states. Moreover, we empirically confirmed these two model predictions in our bulk dynamic gene expression data set by the double inhibition of two molecular mediators of TAM resistance acquisition.

The combination of enrichment (Fig. 3g) and sensitivity analyses (Fig. 4d) would enable the prediction of target subpopulations and important molecules for tumor growth inhibition and aid in prioritizing the predicted molecular targets. However, there are some limitations to our research. First, the model does not completely reproduce the in vivo environment of breast cancer because all experimental data in this study were obtained from two-dimensional cell culture experiments. In a three-dimensional in vivo environment, cell growth is spatially restricted and cell-to-cell communication within the microenvironment must also be considered. To understand the mechanism of TAM resistance in vivo, it will be necessary to combine our approach with modeling studies of spatially constrained three-dimensional cancer cell growth in breast ducts⁴⁹ and maintenance of cancer cells in their niche⁵⁰. Second, the generalizability of our findings is unclear because the single-cell sequencing data correspond to a single experimental condition using MCF-7 cells and TAM simultaneously. Validation experiments with KDM5 inhibitor and PML knockdown (Fig. 5) suggest that the two resistant states we observed are reproducible, but our results do not preclude the existence of other resistant subpopulations. In addition, data analysis using multiple cell line experiments or patient-derived samples is necessary to discuss the similarities and diversity of resistance acquisition processes in breast cancer. Accordingly, our approach combining scRNA-seq with mathematical modeling should be extended to address other data derived from different backgrounds, such as different concentrations of estrogenic compounds to account for pre/menopausal status⁵¹ and various breast cancer cells to account for the diversity of genetic mutations. Future studies also should address drug-independent fluctuation of subpopulations. Despite these issues, the combination of single-cell RNA-seq analysis of cancer cells with mathematical modeling could contribute to the understanding of the process of drug resistance acquisition and to designing novel treatment strategies.

Methods

Cell culture

Human breast adenocarcinoma MCF-7 cells were cultured in Dulbecco's modified Eagle's medium supplemented with 10% fetal bovine serum and antibiotics, as described previously¹⁵.

Cell growth assay

Approximately 1 × 10⁶ MCF-7 cells were seeded in a 100-mm dish containing 10 mL of culture medium supplemented with or without 1 µM TAM. After a week, cells were detached and collected with trypsinization, and the concentration of the cell suspension was measured using a hemocytometer. The cell growth rate per week was calculated by dividing 1 × 10⁶ with the total number of cells in each cell suspension.

Cell cycle analysis by flow cytometry

MCF-7 cells were trypsinized, washed with phosphate-buffered saline (PBS), and fixed with 80% ethanol. Subsequently, the fixed cells were washed with PBS and stained with PI staining solution (BD bioscience, CA, U.S.A.) for 15 min. The PI-stained cells were subjected to flow cytometry using the FACSCanto II Flow Cytometer (BD bioscience), and the number of cells at each cell cycle stage was analyzed using the FlowJo 7.6.5 software.

Gene silencing with siRNA

Gene silencing in MCF-7 cells performed by reverse transfection of 30 nM of SMARTpool ON-TARGETplus siRNA (Horizon Discovery, UK), targeting PML (L-006547-00) and Non-targeting Pool (D-001810-10) in 96-well-plates, as described previously⁵².

MTT assay

Cells were seeded at 3 × 10⁴ cells per 96-well-plate with reverse transfection of siRNA. After overnight incubation, cells were treated with compounds at indicated concentration. After 96 h, cells were treated with 0.5 mg/ml of MTT, and incubated for 4 h. Then, the medium was completely removed, and the insoluble formazan was resuspended with 100 µl of DMSO. Finally, absorbance at 570 nm (objective) and 650 nm (reference) was measured using a microplate reader.

Western blotting

Cells were collected and lysed with RIPA buffer (50 mM Tris–HCl pH 7.4, 150 mM NaCl, 1% NP-40, 0.1% w/v SDS, and 0.5% w/v sodium deoxycolate) containing both protease inhibitors and a phosphatase inhibitor cocktail (Nacalai Tesuque, Japan). The lysates were centrifuged and supernatants were recovered. After determining the protein concentration in each lysate, and boiling in a quarter volume of loading buffer (125 mM Tris–HCl pH 6.8, 25% v/v glycerol, 5% SDS, 0.25% w/v bromophenol blue, and 5% v/v 2-mercaptoethanol), samples were then electrophoresed in a polyacrylamide gel. Proteins were transferred onto a PVDF membrane, and immunoblotted. Antibodies employed for immunoblotting were anti-PML antibody (ab72137, Abcam, UK) and (sc-47778, Santa Cruz Biotechnology, TX, U.S.A.).

Bulk RNA-seq analysis of TAM-resistant cells

Bulk RNA-seq data of MCF-7 cells have been published previously¹⁵. Briefly, RNA was extracted from MCF-7 cells treated with or without 1 µM TAM using QIAshredder (QIAGEN, Netherlands) and RNeasy Mini Kit (QIAGEN) every week up to 12 weeks, and then used for RNA-seq analysis. Different sequencing methods were used, which resulted in either 100-bp paired-end reads or 36-bp single-end reads (Supplementary Fig. S1). To remove the influence of different sequencing methods, we used only the first 36 bp of the first single-end read of paired-end data. After removing adaptor sequences and checking sequence quality using Trim Galore (https://github.com/FelixKrueger/TrimGalore/tree/0.6.7), the reads were aligned to the human reference genome (version GRCh38), and the read number counted by featureCounts⁵³ without multi-mapping and multi-overlapping. The expression level of each gene was quantified as transcripts per million (TPM). TPM data of each sample were used for PCA to analyze the variability and reproducibility of the data (Supplementary Fig. S1). Comparing gene expression profiles between TAM-treated and control condition at each time point, DESeq2⁵⁴ were used for calculating fold change (FC). In order to reduce data dimensions, genes which did not show significant changes (determined by the following cutoff: q-value < 0.001 and |log2FC| > 0.5) at least three time point were filtered out for further analysis. As a consequence, 6982 genes were used for cluster analysis. Then, the log2FC values of all genes at W0 were set as a theoretical zero value. Hierarchical clustering of z-score of log2FC values (13 time points) in those genes was performed by using the method of Ward’s linkage based on the Pearson’s correlation distance (1 − Pearson’s correlation coefficient).

Enrichment analyses

All enrichment analyses except Fig. 3g were carried out using the Targetmine platform⁵⁵. Redundant enrichment terms, shared by > 70% of the genes of interest, were removed from the results, and the term with the lowest q-value was retained. The enrichment analysis of upstream transcriptional regulators (Fig. 3g) was performed using the ChIP-Atlas database (https://chip-atlas.org)²⁴ under the following settings: antigen class, “TFs and others” and cell type class, “Breast.”

Single-cell RNA-seq analysis of TAM-resistant cells

Single-cell RNA-seq in this study was performed in single replicate. Single cells were separated using the ICELL-8 system (Takara Bio, Shiga, Japan). MCF-7 cells treated with or without continuous TAM were trypsinized and collected following dilution with the culture medium. The cells were then washed twice with cold PBS and stained with Hechest33342 (5 µg/mL) and PI (1 µg/mL) for 15 min. After staining, the cells were diluted to a concentration of 20,000 cells/mL and loaded into the ICELL-8 single-cell system. Then, cDNA was prepared using 3′DE reagents (Takara Bio), according to the manufacturer's instructions, and subjected to 100-bp paired-end sequencing on the Illumina HiSeq 3000 platform (Illumina, CA, U.S.A.). Mapping of sequence reads to the human reference genome sequence and counting genes were carried out using the mappa and hanta software (Takara Bio). The gene count data of individual cells were cleaned using the Seurat 3 software²¹. A series of quality controls were implemented. First, any gene expressed in < 5 cells at < 5 counts per million was removed. Cells with < 1500 detected genes and > 25% mitochondrial genes were filtered out. After filtering, the count data matrix consisting of 11,413 genes and 186, 189, 118, and 84 cells at weeks 0, 3, 6, and 9, respectively, was obtained. Next, any bias due to differences in the cell cycle stage was removed using the function CellCycleScoring and cell cycle gene set, and the effect of cell cycle phases on gene expression data was regressed. The data were imported into the Monocle 3 software⁵⁶, and the data dimensions were reduced to three with UMAP. Then, cells were categorized into multiple classes. Gene module and pseudotime analyses were carried out using the Monocle 3 software, according to the developer's instructions (https://cole-trapnell-lab.github.io/monocle3/). Marker genes in each subgroup were calculated with the “top_markers” function (Supplementary Fig. S6). The pseudotime of each cell was calculated on the basis of the relative distance from open circle #1 (set as pseudotime = 0) (Fig. 3c). DEGs along with an estimated cell trajectory were calculated by applying the “graph_test” and “find_gene_modules” functions to cell subsets of groups 2, 3, 4, and 5 (Fig. 3f).

Mathematical simulation

The mathematical model comprised 12 ordinary differential equations with 19 parameters. In this model, cell growth was assumed to follow logarithmic growth, and cell state transitions were assumed to follow the trajectory predicted by pseudotime analysis (Fig. 4a). Details of the equations are summarized in Supplementary Table S5. Mathematical simulation and parameter search were performed using the BioMASS platform²⁸. During the parameter search process, we attempted to minimize the weighted sum of squared percentage errors (wSSPE):

$$wSSPE= \sum_{i=1}^{n}{\left(\frac{{x}_{sim, i}-{x}_{exp, i}}{{x}_{exp, i}+0.1}\right)}^{2}$$

where wSSPE is an objective function; n is the number of obtained data points to be fitted such as growth rate and rate of subpopulation at each time point and treatment; x_sim,i and x_exp,i are the ith simulation and experimental data, respectively. Importantly, “weighted” SSPE (calculated by adding 0.1 to the denominator of objective function) was used instead of normal SSPE to achieve two purposes simultaneously: escaping division by zero and fitting the simulation results to two experimental datasets with different range limits. In this study, a total of 38 (n = 38) data points, with 11 from growth rate of TAM-treated cells (W0–W11), 11 from parental cells, and 16 from the ratio of four subgroups in four time points (W0, W3, W6, and W9) were analyzed. Note that growth rate data in parental cells do not constrain parameters working in TAM-treated conditions and the degree of freedom of the subpopulation rate at each time point is 3. Therefore, the parameters in the model are practically constrained by 23 data points.

Sensitivity analysis

The single parameter sensitivity of each reaction is defined as follows:

$${s}_{i}\left(q\left({\varvec{v}}\right),{v}_{i}\right)= \frac{\partial \mathrm{ln}(q\left({\varvec{v}}\right))}{\partial \mathrm{ln}({v}_{i})}= \frac{\partial q\left({\varvec{v}}\right)}{\partial {v}_{i}}\cdot \frac{{v}_{i}}{q\left({\varvec{v}}\right)},$$

where v_i is the ith reaction; v is a reaction vector (v = v₁, v₂, …); and q(v) is a target function. q(v) we considered in this study is the mean-over-time growth rate after W3 described below:

$$\frac{\sum_{t=4}^{10}{X}_{total}(t)/ {X}_{total}(t-1)}{7},$$

where t denotes the time (week), X_total(t) means the whole cell number at the time t. The sensitivity of each reaction was calculated with 1% increase in the reaction rate using the BioMASS platform²⁸.

Statistics and reproducibility

The number of replicates were as follows: Bulk RNA-seq, 5; single-cell RNA-seq, 1; and cell growth and cell cycle experiment, 2 or more than 2 (depending on experiments and described in the corresponding figure legend). The statistical comparisons between two samples in cell-biological experiments observing the representative value of a cell population, i.e., growth assay, cell cycle assay, and MTT assay, were performed using independent two-tailed Welch’s test. Those from single-cell data not showing Gaussian distributions were comapred using Wilcoxon rank-sum test. The comparison between simulation parameters were performed using Wilcoxon signed-rank test. For multiple test correction, the Benjamini–Hochberg method was used and adjusted p-values were presented as q-values.

Data availability

The raw bulk RNA-seq and single-cell RNA-seq data are deposited in the DNA Data Bank of Japan (DDBJ) and available under the accession numbers DRA004349 and DRA009126, respectively.

References

Aranda, A. & Pascual, A. Nuclear hormone receptors and gene expression. Physiol. Rev. 81, 1269–1304 (2001).
Article CAS PubMed Google Scholar
Burns, K. A. & Korach, K. S. Estrogen receptors and human disease: An update. Arch. Toxicol. 86, 1491–1504 (2012).
Article CAS PubMed PubMed Central Google Scholar
Allred, D. C., Brown, P. & Medina, D. The origins of estrogen receptor alpha-positive and estrogen receptor alpha-negative human breast cancer. Breast Cancer Res. 6, 240–245 (2004).
Article CAS PubMed PubMed Central Google Scholar
Ring, A. & Dowsett, M. Mechanisms of tamoxifen resistance. Endocr. Relat. Cancer 11, 643–658 (2004).
Article CAS PubMed Google Scholar
Jeselsohn, R., Buchwalter, G., De Angelis, C., Brown, M. & Schiff, R. ESR1 mutations—a mechanism for acquired endocrine resistance in breast cancer. Nat. Rev. Clin. Oncol. 12, 573–583 (2015).
Article CAS PubMed PubMed Central Google Scholar
Hultsch, S. et al. Association of tamoxifen resistance and lipid reprogramming in breast cancer. BMC Cancer 18, 850–914 (2018).
Article PubMed PubMed Central CAS Google Scholar
Hinohara, K. et al. KDM5 histone demethylase activity links cellular transcriptomic heterogeneity to therapeutic resistance. Cancer Cell 35, 330–332 (2019).
Article CAS PubMed PubMed Central Google Scholar
Knowlden, J. M. et al. Elevated levels of epidermal growth factor receptor/c-erbB2 heterodimers mediate an autocrine growth regulatory pathway in tamoxifen-resistant MCF-7 cells. Endocrinology 144, 1032–1044 (2003).
Article CAS PubMed Google Scholar
Fan, P., Wang, J., Santen, R. J. & Yue, W. Long-term treatment with tamoxifen facilitates translocation of estrogen receptor alpha out of the nucleus and enhances its interaction with EGFR in MCF-7 breast cancer cells. Cancer Res. 67, 1352–1360 (2007).
Article CAS PubMed Google Scholar
Gee, J. M., Robertson, J. F., Ellis, I. O. & Nicholson, R. I. Phosphorylation of ERK1/2 mitogen-activated protein kinase is associated with poor response to anti-hormonal therapy and decreased patient survival in clinical breast cancer. Int. J. Cancer 95, 247–254 (2001).
Article CAS PubMed Google Scholar
Kirkegaard, T. et al. AKT activation predicts outcome in breast cancer patients treated with tamoxifen. J. Pathol. 207, 139–146 (2005).
Article CAS PubMed Google Scholar
Shoman, N. et al. Reduced PTEN expression predicts relapse in patients with breast carcinoma treated by tamoxifen. Mod. Pathol. 18, 250–259 (2005).
Article CAS PubMed Google Scholar
Tokunaga, E. et al. Activation of PI3K/Akt signaling and hormone resistance in breast cancer. Breast Cancer 13, 137–144 (2006).
Article PubMed Google Scholar
Zhou, Y., Eppenberger-Castori, S., Eppenberger, U. & Benz, C. C. The NFkappaB pathway and endocrine-resistant breast cancer. Endocr. Relat. Cancer 12(Suppl 1), S37-46 (2005).
Article CAS PubMed Google Scholar
Liu, R. et al. Hunt for the tipping point during endocrine resistance process in breast cancer by dynamic network biomarkers. J. Mol. Cell Biol. 31, 166 (2018).
Google Scholar
Patten, D. K. et al. Enhancer mapping uncovers phenotypic heterogeneity and evolution in patients with luminal breast cancer. Nat. Med. 24, 1469–1480 (2018).
Article CAS PubMed PubMed Central Google Scholar
Ogata, H. et al. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 27, 29–34 (1999).
Article CAS PubMed PubMed Central Google Scholar
Lindner, D. J., Kolla, V., Kalvakolanu, D. V. & Borden, E. C. Tamoxifen enhances interferon-regulated gene expression in breast cancer cells. Mol. Cell. Biochem. 167, 169–177 (1997).
Article CAS PubMed Google Scholar
Doherty, J. & Baehrecke, E. H. Life, death and autophagy. Nat. Cell Biol. 20, 1–8 (2018).
Article CAS Google Scholar
Huang, S. Genetic and non-genetic instability in tumor progression: Link between the fitness landscape and the epigenetic landscape of cancer cells. Cancer Metastasis Rev. 32, 423–448 (2013).
Article PubMed CAS Google Scholar
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888-1902.e21 (2019).
Article CAS PubMed PubMed Central Google Scholar
Ciarloni, L., Mallepell, S. & Brisken, C. Amphiregulin is an essential mediator of estrogen receptor alpha function in mammary gland development. Proc. Natl. Acad. Sci. USA 104, 5455–5460 (2007).
Article ADS CAS PubMed PubMed Central Google Scholar
Rae, J. M. et al. GREB 1 is a critical regulator of hormone dependent breast cancer growth. Breast Cancer Res. Treat. 92, 141–149 (2005).
Article CAS PubMed Google Scholar
Oki, S. et al. ChIP-Atlas: A data-mining suite powered by full integration of public ChIP-seq data. EMBO Rep. 19, e46255 (2018).
Article PubMed PubMed Central CAS Google Scholar
Verdin, E. & Ott, M. 50 years of protein acetylation: From gene regulation to epigenetics, metabolism and beyond. Nat. Rev. Mol. Cell Biol. 16, 258–264 (2015).
Article CAS PubMed Google Scholar
Cheung, K. L. et al. Distinct roles of Brd2 and Brd4 in potentiating the transcriptional program for Th17 cell differentiation. Mol. Cell 65, 1068-1080.e5 (2017).
Article CAS PubMed PubMed Central Google Scholar
Guarnaccia, A. D. & Tansey, W. P. Moonlighting with WDR5: A cellular multitasker. J. Clin. Med. 7, 21 (2018).
Article PubMed Central CAS Google Scholar
Imoto, H., Zhang, S. & Okada, M. A computational framework for prediction and analysis of cancer signaling dynamics from RNA sequencing data-application to the ErbB receptor signaling pathway. Cancers 12, 2878 (2020).
Article CAS PubMed Central Google Scholar
Finn, R. S. et al. PD 0332991, a selective cyclin D kinase 4/6 inhibitor, preferentially inhibits proliferation of luminal estrogen receptor-positive human breast cancer cell lines in vitro. Breast Cancer Res. 11, 1–13 (2009).
Article CAS Google Scholar
Fang, Q., Yao, S., Luo, G. & Zhang, X. Identification of differentially expressed genes in human breast cancer cells induced by 4-hydroxyltamoxifen and elucidation of their pathophysiological relevance and mechanisms. Oncotarget 9, 2475–2501 (2018).
Article PubMed Google Scholar
Stang, M. T. et al. Interferon regulatory factor-1-induced apoptosis mediated by a ligand-independent fas-associated death domain pathway in breast cancer cells. Oncogene 26, 6420–6430 (2007).
Article CAS PubMed Google Scholar
Post, A. E. M. et al. Interferon-stimulated genes are involved in cross-resistance to radiotherapy in tamoxifen-resistant breast cancer. Clin. Cancer Res. 24, 3397–3408 (2018).
Article CAS PubMed Google Scholar
Brock, A., Chang, H. & Huang, S. Non-genetic heterogeneity–a mutation-independent driving force for the somatic evolution of tumours. Nat. Rev. Genet. 10, 336–342 (2009).
Article CAS PubMed Google Scholar
Pisco, A. O. & Huang, S. Non-genetic cancer cell plasticity and therapy-induced stemness in tumour relapse: “What does not kill me strengthens me”. Br. J. Cancer 112, 1725–1732 (2015).
Article CAS PubMed PubMed Central Google Scholar
Bursch, W. et al. Active cell death induced by the anti-estrogens tamoxifen and ICI 164 384 in human mammary carcinoma cells (MCF-7) in culture: The role of autophagy. Carcinogenesis 17, 1595–1607 (1996).
Article CAS PubMed Google Scholar
Kohli, L. et al. 4-Hydroxytamoxifen induces autophagic death through K-Ras degradation. Cancer Res. 73, 4395–4405 (2013).
Article CAS PubMed PubMed Central Google Scholar
Theodossiou, T. A., Yannakopoulou, K., Aggelidou, C. & Hothersall, J. S. Tamoxifen subcellular localization; observation of cell-specific cytotoxicity enhancement by inhibition of mitochondrial ETC complexes I and III. Photochem. Photobiol. 88, 1016–1022 (2012).
Article CAS PubMed Google Scholar
Theodossiou, T. A., Wälchli, S., Olsen, C. E., Skarpen, E. & Berg, K. Deciphering the nongenomic, mitochondrial toxicity of tamoxifens as determined by cell metabolism and redox activity. ACS Chem. Biol. 11, 251–262 (2016).
Article CAS PubMed Google Scholar
Golomb, L., Volarevic, S. & Oren, M. p53 and ribosome biogenesis stress: The essentials. FEBS Lett. 588, 2571–2579 (2014).
Article CAS PubMed Google Scholar
Li, B. & Simon, M. C. Molecular pathways: Targeting MYC-induced metabolic reprogramming and oncogenic stress in cancer. Clin. Cancer Res. 19, 5835–5841 (2013).
Article CAS PubMed Google Scholar
Sdelci, S. et al. Mapping the chemical chromatin reactivation landscape identifies BRD4-TAF1 cross-talk. Nat. Chem. Biol. 12, 504–510 (2016).
Article CAS PubMed Google Scholar
Mazza, M. & Pelicci, P. G. Is PML a tumor suppressor?. Front. Oncol. 3, 174 (2013).
Article PubMed PubMed Central Google Scholar
Ponente, M. et al. PML promotes metastasis of triple-negative breast cancer through transcriptional regulation of HIF1A target genes. JCI Insight 2, e87380 (2017).
Article PubMed PubMed Central Google Scholar
Carracedo, A. et al. A metabolic prosurvival role for PML in breast cancer. J. Clin. Investig. 122, 3088–3100 (2012).
Article CAS PubMed PubMed Central Google Scholar
Arreal, L. et al. Targeting PML in triple negative breast cancer elicits growth suppression and senescence. Cell Death Differ. 98, 10869–10914 (2019).
Google Scholar
Martín-Martín, N. et al. Stratification and therapeutic potential of PML in metastatic breast cancer. Nat. Commun. 7, 12595 (2016).
Article ADS PubMed PubMed Central CAS Google Scholar
Chae, Y. C. & Kim, J. H. Cancer stem cell metabolism: Target for cancer therapy. BMB Rep. 51, 319–326 (2018).
Article CAS PubMed PubMed Central Google Scholar
De Amicis, F. et al. Androgen receptor overexpression induces tamoxifen resistance in human breast cancer cells. Breast Cancer Res. Treat. 121, 1–11 (2010).
Article CAS PubMed Google Scholar
West, J., Schenck, R. O., Gatenbee, C., Robertson-Tessi, M. & Anderson, A. R. A. Normal tissue architecture determines the evolutionary course of cancer. Nat. Commun. 12, 2060 (2021).
Article ADS CAS PubMed PubMed Central Google Scholar
Stiehl, T., Wang, W., Lutz, C. & Marciniak-Czochra, A. Mathematical modeling provides evidence for niche competition in human aml and serves as a tool to improve risk stratification. Cancer Res. 80, 3983–3992 (2020).
Article CAS PubMed Google Scholar
Fan, P. et al. Mechanisms of resistance to structurally diverse antiestrogens differ under premenopausal and postmenopausal conditions: Evidence from in vitro breast cancer cell models. Endocrinology 150, 2036–2045 (2009).
Article CAS PubMed PubMed Central Google Scholar
Magi, S. et al. Transcriptionally inducible Pleckstrin homology-like domain, family A, member 1, attenuates ErbB receptor activity by inhibiting receptor oligomerization. J. Biol. Chem. 293, 2206–2218 (2018).
Article CAS PubMed Google Scholar
Liao, Y., Smyth, G. K. & Shi, W. featureCounts: An efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).
Article CAS PubMed Google Scholar
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Article PubMed PubMed Central CAS Google Scholar
Chen, Y.-A. et al. The TargetMine data warehouse: Enhancement and updates. Front. Genet. 10, 934 (2019).
Article CAS PubMed PubMed Central Google Scholar
Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).
Article ADS CAS PubMed PubMed Central Google Scholar

Download references

Acknowledgements

We acknowledge the NGS core facility of the Genome Information Research Center at the Research Institute for Microbial Diseases of Osaka University, particularly Dr. Daisuke Motooka and Dr. Daisuke Okuzaki for their support in single cell RNA-sequencing and data analysis. The super-computing resource used for mathematical simulations was provided by the Human Genome Center, the University of Tokyo (http://sc.hgc.jp/shirokane.html). We would like to thank Enago (http://www.enago.jp) for the English language review. This study was supported by the Ministry of Education, Science, Sports and Culture, Grant-in-Aid for Young Scientists (B) (17K18359), and Scientific Research on Innovative Areas (19H04963).

Author information

Authors and Affiliations

Laboratory of Cell Systems, Institute for Protein Research, Osaka University, Osaka, 565-0871, Japan
Shigeyuki Magi & Mariko Okada
Laboratory for Integrated Cellular Systems, RIKEN Center for Integrative Medical Sciences (IMS), Yokohama, 230-0045, Japan
Shigeyuki Magi, Sewon Ki & Mariko Okada
Department of Physiology, Division of Cell Physiology, Faculty of Medicine, Toho University, Tokyo, 143-8540, Japan
Shigeyuki Magi & Atsuhiko T Naito
Graduate School of Medical Life Science, Yokohama City University, Yokohama, 230-0045, Japan
Masao Ukai & Mariko Okada
Departamento de Biología Molecular y Biotecnología, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Ciudad Universitaria, 04510, México, México
Elisa Domínguez-Hüttinger
Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, 277-8562, Japan
Yutaka Suzuki
Center for Drug Design and Research, National Institutes of Biomedical Innovation, Health and Nutrition, Ibaraki, Osaka, 567-0085, Japan
Mariko Okada

Authors

Shigeyuki Magi
View author publications
You can also search for this author in PubMed Google Scholar
Sewon Ki
View author publications
You can also search for this author in PubMed Google Scholar
Masao Ukai
View author publications
You can also search for this author in PubMed Google Scholar
Elisa Domínguez-Hüttinger
View author publications
You can also search for this author in PubMed Google Scholar
Atsuhiko T Naito
View author publications
You can also search for this author in PubMed Google Scholar
Yutaka Suzuki
View author publications
You can also search for this author in PubMed Google Scholar
Mariko Okada
View author publications
You can also search for this author in PubMed Google Scholar

Contributions

S.M. and M.O. are responsible for the entire research project. S.M., S.K., M.U., and Y.S. performed the experiments. S.M. analyzed the sequencing data. S.M. and A.T.N. constructed and simulated the mathematical model. S.M., A.T.N., E.D.H., and M.O. wrote the manuscript.

Corresponding authors

Correspondence to Shigeyuki Magi or Mariko Okada.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary Figures.

Supplementary Table S1.

Supplementary Table S2.

Supplementary Table S3.

Supplementary Table S4.

Supplementary Table S5.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Cite this article

Magi, S., Ki, S., Ukai, M. et al. A combination approach of pseudotime analysis and mathematical modeling for understanding drug-resistant mechanisms. Sci Rep 11, 18511 (2021). https://doi.org/10.1038/s41598-021-97887-z

Download citation

Received: 29 June 2021
Accepted: 31 August 2021
Published: 16 September 2021
DOI: https://doi.org/10.1038/s41598-021-97887-z

This article is cited by

Proteomic time course of breast cancer cells highlights enhanced sensitivity to Stat3 and Src inhibitors prior to endocrine resistance development
- Stephen F. Madden
- Mattia Cremona
- Jean McBryan
Cancer Gene Therapy (2023)

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.