Drug-induced phenotypic transition in explants

To elucidate the mechanisms underlying adaptive resistance to anticancer therapy, we used three-dimensional explants derived from fresh tumour biopsies from patients. Three-dimensional tumour explants are emerging as powerful models to study tumour biology, as they preserve the tumour heterogeneity and microenvironment15. In a recent study, we have observed that culturing the explants in autologous serum and in grade-matched tumour matrix conserves the parental tumour genotypic and phenotypic characteristics16. We included breast cancers of different stages and receptor status, including those that were taxanes-treatment naive (Supplementary Table 1). We used 200 μm tumour explants in this study as drugs can diffuse through such thickness17 (Fig. 1a). CD44, a membrane glycoprotein, has been associated with chemorefractory, more mesenchymal stem-like characteristics8,18. In contrast, CD24-positive breast cancer cells have been reported to be more of the differentiated luminal and a Her2+ subtype, whereas basal-like tumours were classified as CD24−/Lo (ref. 19). We observed a significant inter-tumoral heterogeneity in the baseline expression of CD44 and CD24, and the distribution was normal between tumours from taxane-treated and taxane-naive patients (Fig. 1b–d). Interestingly, incubating the explants with high-concentration DTX (3.4 μM)20 for 72 h resulted in an increase in the median expression of both CD44 and CD24 as compared with vehicle-treated explants (P<0.01) (Fig. 1b–d), irrespective of the tumour type. In addition, the DTX-induced increase in expression of CD24 and CD44 was similar in explants generated from tumours that had progressed clinically on taxanes and those generated from taxanes-treatment naive patients, indicating that the phenotypic plasticity did not rely on the acquisition of resistance. The upregulation of CD44 following DTX treatment was correlated with reduced apoptosis as seen in decreased cleaved caspase-3 levels compared with baseline (Fig. 1e,f). Treatment with doxorubicin, which is widely used in the adjuvant or metastatic settings in breast cancer, similarly induced CD44 expression with reduced cleaved caspase-3 levels. Interestingly, in contrast, treatment with carboplatin (100 μM) and gemcitabine (100 μM) induced apoptosis without any upregulation of CD44 expression (Fig. 1e,f). Indeed, in a recent study, a combination of gemcitabine and carboplatin was found to be effective for pretreated patients with metastatic breast cancer21.

Figure 1: Taxane chemotherapy induces phenotypic cell state transition and adaptive resistance. (a) Schematic of human explant model to evaluate response of refractory human tissue to anticancer agents. Tumour biopsies were cut into ~200 mM-thick sections and cultured in microwells coated with tumour matrix and media supplemented with autologous serum. (b) Representative immunohistochemistry (IHC) of primary human breast tumour explants shows induction of CD44 and CD24 cell surface expression following 72 h treatment with docetaxel versus vehicle. × 40 Scale bar, 50 μm inset show higher magnification, × 100 (c,d) Graph shows quantification of CD44 and CD24 levels in the primary tumour explant studies, (N=14 patients). Black and red points denote the protein levels measured by IHC score in a tumour explant in vehicle- and docetaxel-treated groups. Each number denotes a patient. The orange arrows denote patients who were taxane-treatment naive, whereas those denoted with black arrows received a taxane. (e) Representative IHC from explant culture shows effect of different drug treatments (3.4 μM docetaxel, 5.6 μM doxorubicin) on the expression of CD44 and cleaved (cl) caspase 3 in corresponding serial sections. Gem, gemcitabine. × 40 magnification Scale bar, 50 μm. Inset shows higher magnification × 100 (f) Graph shows the quantification of CD44 and cleaved caspase 3 expression in the explants treated with docetaxel (n=9) or a combination of gemcitabine+carboplatin (n=2). Data shows mean±s.e.m. (g) Schematic shows generation of drug-tolerant cells (DTCs) selected acutely using high-concentration docetaxel chemotherapy in vitro. Cells were cultured in 100 μM (~20X IC 50 ) docetaxel. Cells surviving by day 4 were quiescent and considered as drug-tolerant cells (DTCs). Growing out the DTCs over 35 days resulted in restoring parental properties. (h) Graph shows MTS cell viability analysis of parental cells and DTCs generated from of MDA-MB-231 breast cancer cells following incubation (48 h) with different tubulin-binding chemotherapeutics at indicated concentration range. (i) Confocal images show expression levels of CD44 and CD24 in parental cells and DTCs generated from MDA-MB-468s. Scale bar, 18 μm (j) The population percentage of CD44HiCD24Hi cells in parental and DTCs generated from an array of luminal and basal breast cancer cell lines. Data shown are mean±s.e.m., n=3 (P<0.01 other than T47D cells). (k) Representative FACS plot of CD44 and CD24 in MDA-MB-231 parent cells and DTC. (l) Graph shows quantification of CD44Hi/CD24Lo and CD44Hi/CD24Hi as % of total population of MDA-MB-231 parent cells and DTCs (Data shown are mean±s.e.m., n=8, ANOVA analysis *P<0.01, ***P<0.001). Full size image

Drug-induced phenotypic plasticity and tolerance

The results from explant studies suggested that taxane and anthracycline chemotherapy induce a phenotypic transition to a chemotherapy-refractory CD44HiCD24Hi state, rather than selecting for ‘privileged’ subsets. We recapitulated these findings using an array of established luminal and basal-like breast cancer cell lines. Although the IC 50 values of DTX in cancer cells typically range between high pM and low nM range20, a subset of the treatment-naive parent cell population was found to survive at supramaximal concentrations ≥100 nM of DTX. These persister cells were termed as drug-tolerant cells (DTCs) (Fig. 1g,h), and were characterized by low baseline apoptosis (Supplementary Fig. 1a,b). The DTCs showed cross-tolerance to other cytotoxics, including doxorubicin, vincristine and cabazitaxel (Fig. 1h). Cabazitaxel, a recently approved taxane for treatment of hormone-resistant prostate cancer, has poor affinity for drug-efflux p-glycoproteins, suggesting that the resistance of DTCs to the cytotoxics is independent of drug-efflux22. This was further validated by treating the cells with elacridar, a p-glycoproteins-transport inhibitor, which failed to reverse the resistance to the cytotoxics (Supplementary Fig. 1c,d). Furthermore, no changes in MDR1 expression were noted between parent cells and DTCs (Supplementary Fig. 1e).

We next explored whether the DTCs exhibit higher levels of CD44 and CD24. As shown in Fig. 1i, confocal imaging revealed an enhanced membrane expression of CD44 and CD24 in the DTCs derived from the basal-like breast cancer cell line MDA-MB-468 as compared with treatment-naive parent cells, which was validated using fluorescence-activated cell sorting (FACS) (Fig. 1j). In the context of breast cancer, a CD44HiCD24−/Lo cell has classically been defined as a belonging to the cancer stem cell population that confers intrinsic resistance8. Interestingly, consistent with the observation in the MDA-MB-468s, we observed an increase in the CD44HiCD24Hi population in the DTCs that were derived from MDA-MB-231 (basal), SUM159 (basal) or 4T1 (basal, murine) cells. A similar increase in the CD44HiCD24Hi population was also observed in the DTCs generated from the luminal cell lines, MCF7 and SKBr3, but not in the T47D cell line (Fig. 1j–l). Additional studies revealed an increase in CD44 expression in the DTCs generated from melanoma cell line MDA-MB-435 and murine ovarian cell lines, 4306 and 4412 (Supplementary Fig. 2a), suggesting that this phenomenon is not restricted only to breast cancer. CD44 was also found to be elevated following treatment with doxorubicin (Supplementary Fig. 2b). We did not observe an increase in the percentage of CD44HiCD24Lo population in the DTCs compared with the parent cells (Fig. 1l).

As the high concentration of DTX also induced cell death in parent cells, it was not evident whether the increase in the CD44HiCD24Hi phenotype was a true induction or just an enrichment of the subtype as in the case of CSCs. To dissect this, we treated parent cells acutely (24 h) with low-dose chemotherapy (10 nM for MDA-MB-468 and 25 nM for MDA-MB-231, respectively) that had no effect on cell viability, that is, did not select for ‘privileged’ cells (Supplementary Fig. 3a,c). Interestingly, as shown in Supplementary Fig. 3b,d, this resulted in an increase in the CD44Hi and CD24Hi populations. Furthermore, we observed a dose-dependent induction towards the CD44HiCD24Hi phenotype (Supplementary Fig. 3e,f). Taken together, these results suggest that the observed chemotherapy tolerance could potentially arise from drug-induced phenotypic cell state transition, distinct from the established models of clonal selections of privileged subsets.

Quantitative model of phenotypic cell state transitions

To theoretically test the drug-induced phenotypic cell state transition versus clonal selection, we developed a phenotype switching model consisting of three cellular compartments, describing the population dynamics of CSCs (CD44HiCD24Lo), the induced cells (CD44HiCD24Hi) and non-stem cells (CD44LoCD24Hi and CD44LoCD24Lo). Experimental data for the population dynamics were obtained from FACS data describing the re-equilibration kinetics of both the parental cells as well as the DTC, generated using the experimental design shown in Fig. 2a. The obtained parameter sets for the cases of the parental population and the DTC populations are summarized in Supplementary Table 2, using the methods described in detail in the Supplementary Information. In addition, Fig. 2b depicts the curves describing the time-evolution of the system composition from an arbitrary steady state, and highlights the system dynamics as it reaches equilibrium. In both cases, the model was able to fit well to the experimental data, implying that the model is versatile enough to describe the system dynamics of both treatment naive and post-chemotherapy cases, although, given the phenomenological nature of the model, we note that the derived parameter sets are useful only in a comparative sense, and are not necessarily precisely representative of the underlying biology for individual cases. Interestingly, the parameter values for either system was found to be quantitatively distinct, giving rise to different system saturations in equilibrium, that is, after induction of chemotherapy, there is a deterministic shift in the parameters governing the growth and switching rates of the subpopulations of cells, such that different steady states are observed. The model predicted that within the DTC population, the rates of proliferation of CSCs and induced CD44HiCD24Hi cells are significantly increased, whereas the rate of proliferation of non-stem cells decreases to a negligible value. The rate of transition from stem to non-stem cells remains the same in both environments, but the rate of transformation directly from non-stem to stem cells does not occur to a great degree in the chemoresistant cells. In addition, in the DTC, we observe high rates of transition between the induced CD44HiCD24Hi cells and non-stem cell compartments in both directions, indicating high inter-conversion (with no net direction), whereas in the case of the parental cells, the transition rate between the CD44HiCD24Hi cells and non-stem cells is highly skewed in the direction of the former, predominantly switching into the latter and not in the reverse direction. Finally, in the parental population, the CD44HiCD24Hi cells and CSCs are able to transition between each other. In contrast, in the DTCs, CSCs do not appear to transition into CD44HiCD24Hi cells, which are however able to transition into CSCs (Fig. 2c).

Figure 2: Modelling the induction of CD44HiCD24Hi cells. (a) Schematic shows experimental design used to derive mathematical parameters of population dynamics. Treatment of MDA-MB-231 breast cancer cells with 25 nM docetaxel (DTX) for 24 h induces phenotype plasticity rather than providing a selection pressure. In parallel, starting cells with different permutations and combinations of CD24 and CD44 expression levels were used, and the expression of CD44CD24 was monitored over defined time points. (b) Population dynamics modelling derived from experimental data indicates temporal kinetics of breast cancer cells in distinct compartments over 5 days (CD44Lo described as non-CSC). Left panel shows dynamics of distinct phenotypes under basal conditions, right panel demonstrates population dynamics under chemotherapy pressure. (c) Schematic shows subpopulation transition dynamics and predictive contribution of each population under chemotherapy pressure or basal state, saturated to equilibrium. Arrow weights denote prevalence of conversion. Loops indicate propensity to replicate or transition. (d) Treatment-naive 231-parental cells were sorted into CD44HiCD24Hi, CD44HiCD24Lo, CD44LoCD24Hi and CD44LoCD24Lo subpopulations, which were subsequently exposed to high-dose docetaxel (100 nM) for 48 h and re-analyzed by FACS for CD44HiCD24Hi subset expressed as % of total population. ‘Basal’ denotes the change in % of CD44HiCD24Hi subset in parental cells treated with vehicle. Data are mean±s.e.m. (ANOVA analysis, N=7, #P<0.05, *P<0.05 **P<0.01 versus basal group). (e) Depletion of intrinsic CSC population with salinomycin (5 μM, 48 h) was confirmed by reduction of a CSC (CD44Hi/CD24Lo) and enrichment of a non-CSC phenotype (CD44LoCD24Hi) expressed as fold change from vehicle-treated cells (error bars indicate s.e.m., N=5, *P<0.05 **P<0.01). (f) Chemo-tolerant cells generated from parent (DTC) and salinomycin-selected (Sal-DTC) MDA-MB-231 cells were analyzed by FACS for CD44Hi/CD24Hi, and results are expressed as % of total population (Data shown are mean±s.e.m., n=8, ANOVA analysis ***P<0.001, NS, not significant). (g) Graph shows mean fluorescent intensity (MFI) from FACS analysis of CD44 and CD24 expression in MDA-MB-231-parent, -DTC, -Sal-DTC or in a population of -expanded (E)-DTC and -Sal-DTC, demonstrating a reversal to parental phenotype when the chemotolerant cells are expanded over time (Data shown are mean±s.e.m. n=5, *P<0.05 **P<0.01). (h) Graph shows cell viability of each indicated population to docetaxel or doxorubicin, quantified by MTS cytotoxicity assay as % of viability in vehicle-treated control. All data shown are mean±s.e.m. from independent replicates (ANOVA analysis, n=8, ***P<0.001 versus parent cells). Full size image

Transient drug-tolerant phenotype originates from non-CSCs

To test the theoretical predictions that the CD44HiCD24Hi cells indeed arise from non-CSCs (that is, CD44Lo cells), we sorted the parental population into four subsets based on various permutations and combinations of CD44Hi, CD44Lo, CD24Hi and CD24Lo status (Fig. 2a). These fractionated cellular subsets were then treated with high-concentration DTX for 48 h, following which the percentage of CD44HiCD24Hi cells in each population was quantified using FACS. In the absence of DTX, the cells re-equilibrate to a heterogenous cellular population similar to the treatment-naive parent cells with CD44HiCD24Hi cells forming ~20% of the total population, which was used as the baseline to elucidate the effect of DTX on each starting cellular fraction. Interestingly, as shown in Fig. 2d, a statistically significant increase in the CD44HiCD24Hi population was evident when the starting population consisted of CD44LoCD24Hi or CD44LoCD24Lo subsets.

We next used small molecule salinomycin-selection against CSC23 to significantly deplete the parent MDA-MB-231 population of CD44HiCD24Lo cells (Fig. 2e, Supplementary Fig. 4a). The parent and salinomycin-selected cells were then treated with DTX to generate DTCs, that were then FACS analyzed to quantify the percentage of CD44HiCD24Hi cells. The fact that both parent-derived DTCs and DTCs that were generated from salinomycin-selected cells (Sal-DTCs) exhibited similar percentages of CD44HiCD24Hi cells (Fig. 2f), despite the salinomycin-treated cells having ~50% less CD44HiCD24Lo cells to start with, indicated that the CD44HiCD24Hi were indeed originating from a non CD44HiCD24Lo population. Interestingly, the chemotherapy-induced upregulation of CD44 and CD24 levels were only transient, and in both DTCs and Sal-DTCs, the cells recalibrated back to parental CD44 and CD24 basal expression phenotype when expanded (DTC-E or Sal-DTC-E) over 35 days in the absence of chemotherapy pressure (Fig. 2g, Supplementary Fig. 4b shows schematic for FACS isolation). Although the cells existed in the transient CD44HiCD24Hi state, they were found to be resistant to high concentration of both DTX and doxorubicin. The expanded cultures, however, regained drug sensitivity (Fig. 2h), suggesting that the acquired tolerance to chemotherapy is reversible. Cell cycle analysis of the DTCs revealed that the cells were primarily in the G2M phase, with a large subset also undergoing endoreduplication, consistent with previous observations24. Interestingly, endoreduplication, the replication of DNA without undergoing an intervening mitotic division, has been reported to result in chemoresistance 25. Cell cycle analysis of the expanded DTCs revealed reversion to the parental phenotype, although a remnant tail of endoreduplicating cells was still evident (Supplementary Fig. 4c).

Kinase library screening in DTCs

To identify whether DTCs are therapeutically tractable during this transient phase, we performed a drug screen with a library of kinase-targeted agents (Fig. 3a). Although some targeted therapeutics, such as the Akt inhibitor, PI103, or the pan-kinase inhibitor, sorafenib, were non-selective for DTCs over parent cells (Sensitivity index~1), others like the EGFR inhibitor, erlotinib, inhibited parent cells, whereas the DTCs continued to grow (sensitivity index (SI) <1) (Fig. 3b). Interestingly, dasatinib, a dual SFK/BCR-Abl inhibitor exerted greater cell killing of DTCs compared with the parental fraction (SI >1) (Fig. 3b–d). In contrast to dasatinib, imatinib, a selective BCR-Abl inhibitor, had no effect on the DTCs, suggesting that the activity of dasatinib could be attributed to its SFK-inhibiting property (Fig. 3c,d). Interestingly, RK20449, a selective inhibitor of the SFK protein Hck26, was found to be ~600% more selective in reducing the viability of DTCs as compared with parental cells (Fig. 3d). Furthermore, dasatinib was found to exert a synergistic outcome against DTCs selected with increasing concentrations of DTX, suggesting that the refractory cancer cells exert a DTX dose-dependent reliance on the SFK-signalling pathway to persist during chemotherapy (Fig. 3e).

Figure 3: SFK/hck inhibitors preferentially disrupt a drug-tolerant state (a) A kinase inhibitor array was tested for activity against MDA-MB-231-DTCs. Table shows kinase inhibitors tested. (b) Representative concentration-effect curves showing activity of erlotinib, sorafenib and dasatinib on parent cells versus DTCs. Error bars indicate s.e.m. (c,d) Values obtained from concentration-effect analyses of cell viability were used to generate the sensitivity index (data shown are mean±s.e.m., n>25 independent experiments per group, ***P<0.001). Upper panel shows representative bright field microscopy of residual population. Scale bar, 100 μm (e) Viability curve following dasatinib treatment in MDA-MB-231-DTCs selected with increasing doses of docetaxel (5 nM, 10 nM 100 nM). (f) Phosphorylation arrays (lower panel) and quantification by optical density (graph) show fold change in the phosphorylation of residues corresponding to Src Family Kinases (SFK) in the MDA-MB-231-DTC versus parent cells. Inset western blot of the inactivating SrcY527 residue in DTCs versus parent cells (ANOVA analysis, *P<0.05, n=4). Full western blot images can be found in Supplementary files. (g) DTCs generated from a panel of basal-like and luminal breast cancer cells were treated with dasatinib or RK20449 (48 h), and drug sensitivity index (SI) was determined as described in methods. SI>1 indicates greater drug sensitivity compared with an equivalently treated parent population of cells (that is, greater sensitivity to the SFK inhibitors in the DTCs compared with parent). Full size image

Consistent with the above results, a phosphorylation array revealed a global activation of the pro-oncogenic and pro-survival SFK family27 in the DTCs as compared with parent cells, with Hck as the predominant target (Fig. 3f). Western blotting revealed that the Src-activating residue (Y419) remained unchanged but the inactivating residue (Y527) was diminished in DTCs, indicating a gain-of-function mechanism underlying the activation of this pathway (Fig. 3f). Furthermore, Immunoprecipitation (IP) for phospho-Tyr Hck revealed a DTX concentration-dependent increase in phosphorylated Hck with maximal expression in the DTCs, which reverted back to parental levels by day 35 (in the expanded population) (Supplementary Fig. 5a). We next tested the efficacy of dasatinib and RK20449 against an array of basal and luminal parental breast cancer cell lines and the corresponding DTCs. As shown in Fig. 3g, the cell lines where treatment with DTX induced the CD44HiCD24Hi population were significantly more sensitive to SFK inhibition than the parent population. In contrast, this discrimination was lost in the luminal cancer cell line, T47D, which did not demonstrate an augmentation of the CD44HiCD24Hi population in the drug-tolerant subset. The addition of the BCR/ABL inhibitor, imatinib, with RK20449 did not further augment this sensitivity of DTCs to the latter, implicating only the SFK pathway in this response (Supplementary Fig. 5b). Furthermore, dasatinib treatment significantly inhibited the CD44HiCD24Hi population in the DTCs (Supplementary Fig. 5c). Taken together, these results suggested the involvement of the SFK pathway in mediating the transient chemotherapy tolerance arising owing to drug-induced phenotypic cell state transitions.

CD44/CD24 clusters in lipid rafts with SFK/Hck

As the DTCs exhibited increased activation of the SFK signalling, we used a short interfering RNA (siRNA)-based approach to test if the increased expressions of CD44 and CD24 are directly linked with the activation of SFK (knockdown validation can be found in Supplementary Fig. 5d). A phosphorylation array-based analysis of DTCs generated from cells treated with DTX following siRNA-knockdown of CD44 revealed a reduction in the phosphorylation of Hck. SiRNA-mediated knockdown of CD24 also decreased the phosphorylation of Hck and additionally of Lyn (Fig. 4a). Indeed, previous studies have implicated SFK proteins in mediating signalling through CD24 (refs 28, 29). Immunoprecipitation studies validated that both CD44 and CD24 scaffold with Hck (Fig. 4b). A double knockdown of CD44 and CD24, however, did not exert any additive effect, suggesting that the both contribute to the activation of SFK but do so through the same machinery (Fig. 4a).

Figure 4: Mechanisms underlying SFK activation leading to adaptive resistance in DTCs. (a) Graph shows quantification of activated SFK in MDA-MB231 treated with high concentration docetaxel post siRNA knockdown of CD44, CD24 or both genes in (N=4, ANOVA **P<0.01). (b) Hck was immunoprecipitated from DTCs or parent MDA-MB-231s cell lysates followed by western blotting for CD44 and CD24 antibodies IgG input included for control. IgG HC indicates heavy chain (HC) bands. (c) Co-IP of Hck was performed from cell lysate of parent or DTCs generated from MDA-MB-231 cells transfected with siRNA targeting CD24 and CD44 or a combination of both. Western blotting indicates Cav-1 scaffolding to Hck. (d) Representative confocal images demonstrates colocalization of CD44, CD24 and Hck to lipid raft-rich regions of the cell membrane in DTC derived from MDA-MB-231 cells. Scale bar, 5 μm (e) Confocal microscopy identifies Caveolin 1 (Cav-1) colocalizing with CD24, CD44 and Hck in the MDA-MB-231 DTCs. Scale bar, 5 μm (f) Subcellular localization of Hck in DTC generated following siRNA-knockdown Cav-1. β-Actin and PARP indicate loading controls of cytoplasmic and nuclear compartments, respectively. (g) Confocal microscopy was used to identify subcellular localization in the nuclear plane of phosphorylated Hck (pHck) in the MDA-MB-231 parent compared with DTCs (upper panel). Dual staining shows a pattern of nuclear and perinuclear localized pHck and Cav-1 in the DTCs. Scale bar, 8 μm. (h) Representative confocal images reveal the expression of APAF-1 (Green signal) in MDA-MB-231 DTCs treated with vehicle or RK20449 (1 μM) for 24 h, and counterstained with DAPI. Scale bar, 8 μm. Full size image

To study the interactions between CD44, CD24 and SFK/Hck, we looked at the role of caveolins (Cav). Cav are major protein components of lipid rafts, and its upregulation is associated with poor prognosis in several human cancers30,31. Studies have implicated that engagement of CD44 and CD24 in lipid rafts can result in the activation of cortex kinases via clustering-mediated autophosphorylation32,33. Furthermore, recent studies have reported the activation of SFK by Cav-1 and vice versa34. Immunoprecipitation studies revealed an enhanced interaction between Hck and Cav-1 in the DTCs as compared with in the parents. Consistent with the earlier observation of the requirement of both CD44 and CD24 for the activation of Hck, we observed that siRNAs-mediated knockdown of either CD44 or CD24 is sufficient to inhibit this interaction (Fig. 4c). Interaction between Cav-1 and Hck was further evidenced by confocal microscopy (Supplementary Fig. 5b). Labelling the lipid rafts using a cholera toxin-based fluorescent tracer followed by confocal imaging revealed a robust colocalization of CD44, CD24 and Hck in the lipid rafts (Fig. 4d). Similarly, immunofluorescence imaging confirmed the colocalization of CD44, CD24 and Hck with Cav-1 in the DTCs. (Fig. 4e). Interestingly, the clustering of Hck with Cav-1 was found to facilitate a nuclear localization of the complex, which was augmented in the DTC compared with parent cells (Fig. 4f,g), and was blocked by a siRNA-mediated knockdown of Cav-1 (Fig. 4f). This is consistent with previous observations, where Cav-1 and SFK has been reported to facilitate stabilization and nuclear translocation of signalling proteins35. Nuclear translocation of activated Hck is reported to result in the inhibition of p73, resulting in a survival response via a reduced induction of the Caspase activation and recruitment domains 12/apoptotic protease-activating factor 1 (CARD-12/APAF1)36. Indeed, the inhibition of Hck with RK20449 released the suppression of proapoptotic CARD-12/APAF1 (Fig. 4h).

Temporally sequenced SFK inhibitor and taxane in vivo

The in vitro results suggested a novel function of SFK signalling in breast cancer, driving a transient adaptive resistance during phenotypic cell state transitioning. We next investigated whether the inhibition of SFK could overcome adaptive resistance to taxanes in vivo. As the first step, we studied the tumorigenic ability of taxane-‘induced’ CD44HiCD24Hi cells compared with different phenotypic subpopulations of murine mammary carcinoma drug naive parental cells. The ‘induced’ cells were isolated using FACS based on the de novo appearance of a previously non-existent population (~2.2%) post acute cytotoxic pressure (Fig. 5a). We performed a dilution assay, where defined numbers of parental CD44Hi, CD44Lo or the induced cells were implanted in mice. As shown in Fig. 5b, all the phenotypes could contribute to tumour progression in 100% of the animals when implanted in >2,500 cells. However, at the lower dilutions of 1,000 and 100 cells, only 60% and 20% of the CD44Lo cells gave rise to tumours, respectively, consistent with previous observations that CD44Lo cells are less tumorigenic. In contrast, both CD44Hi and ‘induced’ CD44HiCD24Hi cells contributed to tumorigenesis in 100% of the animals even at the lowest dilution (100 cells). Monitoring the rate to tumorigenesis revealed that the parental CD44Hi cells led to faster tumour development as compared with the induced cells, which in turn led to tumour development faster than the CD44Lo cells (Fig. 5c). We next sorted the parental cells into CD44HiCD24Hi, CD44HiCD24Lo, CD44LoCD24Hi and CD44LoCD24Lo, which were subsequently implanted in mouse at a dilution of 5,000 cells each. ‘Induced’ CD44HiCD24Hi cells were also injected at the same dilution (Fig. 5d). As shown in Fig. 5e, tumours derived from CD44HiCD24Hi cells were most aggressive in terms of tumour growth followed by the ‘induced’ CD44HiCD24Hi cells, whereas the tumours derived from CD44LoCD24Lo cells were the slowest growing. These results implicated that phenotypically transitioned chemotherapy-refractory cells can potentially reinitiate tumour growth.

Figure 5: In vivo characterization of the tumorigenic property of chemotherapy-induced CD44HiCD24Hi cells. (a) Schematic shows the selection of ‘induced’ CD44Hi CD24Hi phenotype from 4T1 mammary carcinoma cells sorted by FACS. Cells that emerge de novo with a CD44Hi CD24Hi phenotype following chemotherapy treatment (50 nM docetaxel, 24 h) (~2.2% of the population that was not originally present in the parent population were considered an ‘induced’ subset. (b) Table shows the tumorigenicity of different subsets of breast cancer cells isolated on the basis of CD44, CD44 expression levels compared with the induced subset. Tumorigenicity was quantified by implanting different cell numbers in mice and monitoring the number of tumours developed. (c) Graph shows the temporal kinetics of tumour growth initiated from seeding CD44Hi, CD44Lo or induced cells. (d) Schematic illustrates experimental design of in vivo kinetic analysis of tumour growth of distinct subpopulations. Cells were isolated from each quadrant and an equal number of cells were implanted in mice in separate areas. An induced CD44HiCD24Hi group was run in parallel. (e) Graph shows tumour volumes from indicated phenotypic subsets over time. Data shown are mean±s.e.m., n=4. Full size image

We next studied whether the temporal induction of phenotypic cell state transition in response to chemotherapy can be recapitulated in vivo. Treatment-naive cells were implanted in a syngeneic 4T1 mammary carcinoma mouse model, which were then treated with a maximum-tolerated dose of docetaxel on days 2 and 5 post implantation. A control group was treated with the vehicle. As shown in Fig. 6a, a separation of the growth curves between treated and untreated group was evident by day 6, reaching a cutoff point by day 9 in the control group. The vehicle-treated animals and a batch of DTX-treated animals (during growth-plateau phase) were killed on day 9. The remaining drug-treated animals were subsequently killed on day 19, when the tumour growth rate (the slope of the curve) had reached the slope of growth observed in the vehicle-treated control group. Immunohistological and western blot analysis of the tumour tissue revealed a significant upregulation of CD44, phosphorylated Hck, as well as activated Src (via ablation of the inactivating residue at Y527) in the day 9 tumours from animals treated with DTX as compared with vehicle-treated controls. Furthermore, day 19 drug-treated tumours showed a reversal to the baseline (Fig. 6b, Supplementary Fig. 6a–d). This was consistent with the earlier in vitro observations, where chemotherapy induced a phenotypic switch towards a CD44Hi state, with an associated activation of Hck, resulting in adaptive resistance (that is, generation of DTCs), and the transiently acquired phenotype recalibrating to the parental state with time.

Figure 6: Sequential temporally constrained delivery of a taxane and SFK inhibitor regresses tumour growth rate and overcomes adaptive resistance. (a) Graph indicates tumour volume over time from heterotopic, syngeneic murine mammary carcinoma model (4T1). Groups were treated with docetaxel (DTX) or vehicle (black arrows show treatment days). Tumours were extracted on day 9 and 19 (Red arrows) corresponding to plateau or regrowth of tumour volume in docetaxel-treated arms, respectively (Data shown are mean±s.e.m., n=4). (b) Representative IHC of CD44 and pHCK following tumour extraction at indicated time points, H&E from serial sections confirm viable regions of tumour. Scale bar, 50 μm (c) Schematic shows experimental design for temporal delivery of dasatinib (10 mg kg−1) administered in two schedules, (1) 72 h or (2) 216 h post DTX treatment. The first schedule is designed to target the induction phase of chemotherapy-phenotypic transitioning and the second schedule targets the recalibration phase to parental state. (d) Histogram quantifies specific tumour growth rate. (e) Kaplan–Meier survival graph of an orthotopic syngeneic mammary carcinoma model treated as indicated (N=4 in all groups, N=3 for vehicle). (f) Representative confocal microscopy shows co-staining of CD44 and pHck. Regions of robust Hck activity correspond to regions of tumour with high expression of CD44 (areas outside yellow circumscription). Scale bar, 25 μm Full size image

Based on this understanding of the temporal induction of the phenotypic transition in vivo, we next explored whether a time-constrained administration of a SFK inhibitor could potentially reverse the adaptive drug-resistant state. The experimental design is outlined in Fig. 6c. The animals were treated with vehicle or DTX (at maximum-tolerated dose) on days 2 and 5 post implantation of tumour cells. The DTX-treated animals were then randomized into four groups. The first group was treated with four, once daily, doses of dasatinib, simultaneously administered with DTX between days 2 and 5. The second group was treated with dasatinib administered between days 8 and 11, that is, schedule 1, timed to target the induction phase of DTX-induced cell state transition. The third group was similarly treated with imatinib. The fourth group was administered with dasatinib between days 14 and 17 (Schedule 2), timed to target SFK during the reversion phase to parental phenotype. As shown in Fig. 6d (and Supplementary Fig. 6d), the simultaneous administration of DTX and dasatinib only marginally improved the antitumour efficacy compared with DTX alone treatment, whereas treatment with dasatinib as per schedule 2 had no statistically significant effect. Interestingly, dasatinib administered as per schedule 1 synergized with DTX in tumour growth inhibition. Imatinib, which was included as a negative control, had no effect on DTX-induced tumour growth inhibition. The Kaplan–Meier curves demonstrate that orthotopic tumour-bearing mice treated as per schedule 1 exhibited significantly superior survival than schedule 2, simultaneous administration and vehicle-treated controls (Fig. 6e). Consistent with the in vitro results, study of cross-sections of DTX-treated tumours revealed the localization of phosphorylated HCK with CD44Hi cells. Furthermore, cells that had lower CD44 expression also exhibited low phosphorylated Hck (Fig. 6f). Finally, as shown in Supplementary Fig. 6e, treatment of animals with the Hck inhibitor administered post treatment with DTX treatment resulted in increased APAF1 expression that overlapped with TdT-mediated dUTP nick end labelling positivity, validating the role of Hck in suppression of apoptosis in vivo.

At last, to study the clinical implications of these findings, we generated explants from primary tumour biopsies that were clinically resistant to DTX. The explants were treated with vehicle, DTX or a schedule of DTX followed by dasatinib. As shown in Fig. 7a, treatment with DTX did not cause a significant change in the percentage of apoptotic cells as compared with vehicle. In contrast, the sequenced dosing resulted in a marked increase in apoptosis. Taken together, these results suggest that targeting SFK/Hck during the chemotherapy-induced phenotypic cell state transition can overcome adaptive resistance.