Nano-Engineered Disruption of Heat shock protein 90 (Hsp90) Targets Drug-Induced Resistance and Relieves Natural Killer Cell Suppression in Breast Cancer

Abstract

Drug-induced resistance, or tolerance, is an emerging yet poorly understood failure of anticancer therapy. The interplay between drug-tolerant cancer cells and innate immunity within the tumor, the consequence on tumor growth, and therapeutic strategies to address these challenges remain undescribed. Here we elucidate the role of taxane-induced resistance on natural killer (NK) cell tumor immunity in triple-negative breast cancer (TNBC) and the design of spatio-temporally controlled nanomedicines, which boost therapeutic efficacy and invigorate ‘disabled’ NK. Drug tolerance limited NK cell immune surveillance via drug-induced depletion of the NK-activating ligand receptor axis, natural killer group 2 member D (NKG2D) and MHC class I polypeptide-related sequence A, B (MICA/B). Systems biology supported by empirical evidence revealed the heat shock protein 90 (Hsp90) simultaneously controls immune surveillance and persistence of drug-treated tumor cells. Based on this evidence, we engineered a ‘chimeric’ nano-therapeutic tool comprising taxanes and a cholesterol-tethered Hsp90 inhibitor, radicicol, which targets the tumor, reduces tolerance, and optimally re-primes NK cells via prolonged induction of NK-activating ligand receptors via temporal control of drug release in vitro and in vivo. A human ex vivo TNBC model confirmed the importance of NK cells in drug-induced death under pressure of clinically-approved agents. These findings highlight a convergence between drug-induced resistance, the tumor-immune contexture, and engineered approaches that considers the tumor and microenvironment to improve the success of combinatorial therapy.

Introduction

The high mortality in breast cancer is primarily due to most late-stage patients relapsing on chemotherapy and becoming resistant to other drugs (1). This is particularly true in breast cancers that are negative for the cell surface human epidermal growth factor receptor 2 (HER2) and estrogen and progesterone receptors (ER and PR, respectively), known as triple negative breast cancer (TNBC) (2). Indeed, the primary treatment for TNBC includes taxanes alone or combined with anthracyclines (3). Despite some success, recurrence and resistance happens at a substantially higher rate than other breast cancer subtypes, which associates significantly diminished likelihood of survival (4,5). The mechanisms of resistance in TNBC are poorly defined and even emerging modalities such as immunotherapy, in which drugs aim to re-activate immune cells to induce tumor rejection and eradication (6), have yet to markedly enhance duration of response (7–9). Elucidating the drivers and contributors of resistance and identifying modalities to target these mechanisms in TNBC is therefore a critical need towards achieving a sustainable cure.

Intratumor heterogeneity, cancer stem cells (CSC) and mutational evolution have long been implicated as the drivers of both intrinsic and acquired drug resistance(10). An emerging paradigm, however, is drug-induced resistance, or tolerance, which has been described as phenotypic transitions within subpopulations of cancer cells in the presence of drugs(11), which we previously showed can arise from non-CSC via protein expressions, kinase scaffolding and signaling activations(12). The heat shock protein 90 (Hsp90) plays a broad role in cellular signaling, including a direct effect on protein kinases, operating as an ATP-dependent dimeric molecular chaperone to form the core of large complexes with cochaperones and substrates (13). Indeed, combinations of Hsp90 inhibitors and chemotherapies have been studied (14) with the goal of targeting multiple pro-survival pathways including signal transducer and activator of transcription (STAT), extracellular signal regulated kinases (ERK), Src family kinases (SFK) and Phosphoinositide 3-kinases (PI3K) families of proteins, which are augmented under external stress(15). However, targeting Hsp90 in clinical studies has been somewhat lackluster(16) suggesting novel approaches that deploy rational combinations of drugs could help to address the existing challenges.

A concerted effort to understand the biological interaction between tumor, stroma and immune cells within the tumor immune microenvironment (TIME) will contribute to clinical treatment success (17). Not only are the activity and exhaustion status of cytolytic immune cells, such as CD8+ cytotoxic T-cells and natural killer (NK) cells, implicated in tumor rejection (18), their spatial arrangement and locations within the tumor are critical for prognostic benefit of anticancer cytotoxics and cancer immunotherapies(19). Attempts to improve tumor surveillance via augmenting immune cell activities (20) or suppressing the ‘don’t eat me signals’ on tumor cells have been tested (21). Few studies, however, have sought to increase tumor cell surface ligands that invigorate NK or T-cell surveillance such as MHC class I polypeptide-related sequence A, B (MICA/B) (22) to ‘unmask’ tumors from immune-evasion.

The goal of this study was to interrogate the TIME in drug-induced resistance and the role that chaperones contribute as druggable targets in this effect. Using in vitro co-culture experiments molecular and computational screening approaches, cancer nanomedicines as a tool and in vivo translational models, we describe a tumor-targeted, engineered therapeutic approach that re-invigorates NK cells to combat resistance phenotypes that emerge under drug pressure.

Materials and Methods

Animal welfare and human samples

All in vivo experiments were performed in compliance with active IACUC protocol approved through Harvard Medical School and Brigham and Women’s Hospital, and in accordance with institutional guidelines, supervised on-site by veterinary staff. Mice with tumors were closely monitored by careful clinical examination to detect deterioration of their physical condition and sacrificed at any sign of stress. Human samples for ex vivo experiments were obtained from patients clinically diagnosed with TNBC and were collected by Mitra Biotech under institutional review board (IRB) approval with written informed consent from each patient.

Materials

Radicicol was a kind gift from Dr. Leslie Gunatilaka (University of Arizona). All chemical reagents were of analytical grade, used as supplied without further purification and purchased from Signal-Aldrich, unless indicated. Recombinant human cytokines were reconstituted in a solution containing 0.1mM acetic acid and 0.1% BSA (Peprotech).

Cell culture and generation of drug tolerant cancer cells (DTCCs)

Human MDA-MB-231, MDA-MB-468, MDA-MB-436, MCF-7, mammary carcinoma 4T-1 cells (ATCC) and SUM159 (Bioivt) were purchased in the last 10 years and cultured in 10% fetal bovine serum in DMEM or RPMI media (Invitrogen, Carlsbad CA, USA)_. Cell lines were validated for absence of mycoplasma prior to use, by the sourcing agency. Cells were used within 10 passages from frozen stock vials obtained from the sourcing agency. NK-92MI (ATCC) were cultured according to manufacturer protocol. Primary human peripheral blood CD56+ natural killer cells (Stem Cell Technologies, catalog #70037) were cultured using Immunocult XF T-cell expansion media (Stem Cell Technologies) with 10% FBS and 100U/ml human recombinant IL-2. 3-dimensional tumor spheroid NanoCulture plates were used whenever indicated (MBLI, Woburn, MA).

Gene knockdown

siRNA gene knockdown was performed on cells at a concentration of 5 × 10⁴ cells ml⁻¹. Pre-validated Silencer Select siRNA targeting (sense sequences) MICA (siRNAs ID#1: s8772, ID#2: s458040; Thermo Fisher Inc., Rockford, IL, USA), and were transfected using lipofectamine 2000 (Invitrogen, Grand Island, NY, USA) following manufacturer’s protocol. Scrambled siRNA was used as a control.

Cell viability assays

Cell viability was measured as described using 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium (MTS; reagent, Promega, Madison, WI, USA) or water-soluble tetrazolium salts (WST reagent; Dojindo Molecular Technologies Inc., Rockville, MD, USA) following manufacturer’s protocol and absorbance was read at the recommended UV wavelength (450nm) using BioTek microplate reader (BioTek Instruments Inc., Winooski, VT, USA). To evaluate the pharmacological interaction of different combinations of drugs, we followed the method proposed by Chou et al. (23).

Phosphorylation arrays

The Proteome Profiler Human Phospho Array (R&D systems, Minneapolis MN, USA) was used to identify phosphorylated residues affiliated with different proteins. Following the Bradford protein analysis assay to normalize total protein content, cell lysate from the indicated cell line was applied to the phosphorylation membranes following manufacturer’s protocol. Optical densities were determined by Image J software (NIH.gov) and Adobe CS5. Reference spots were used to normalize between array membranes.

Immunoblotting

Protein samples were resolved by SDS-PAGE and transferred to PVDF membranes prior to incubation at 4°C with indicated primary antibodies, mTOR and pMTOR^Ser2448, pAKT^Thr308 and AKT, Phospho-p44/42 MAPK (Erk1/2)^{Thr202/Tyr204}, p44/42 MAPK (Erk1/2) pPRAS40^Thr246, pSTAT3^Tyr705, STAT3, PRAS40 and β-Actin antibodies were purchased from Cell Signaling Technology, pHck^Tyr410 (Thermo Fisher Scientific) MICA/B (R&D Systems, Minneapolis, MN) and HSF-1 and HSF-1^Ser326 (abcam). Western blotting images chosen as representative depictions in the figures demonstrate equivalent results taken from biological replicates (N≥3).

Flow cytometry

Cells were cultured as indicated, fixed with 4% paraformaldehyde, washed twice with PBS and blocked in 10% goat serum (v/v). Cells were incubated with fluorescently labeled antibodies for NKG2D, CD158a, NKB1, CD244, KLRG1 (BioLegend, San Diego, CA), or MICA/B (R&D Systems, Minneapolis, MN) overnight at 4 °C and analyzed (C6 Accuri cyomteters Inc. Ann Arbor, MI). Data analysis was performed using FlowJo software (Tree Star Inc., Ashland OR) and Accuri cFlow plus software to obtain and confirm mean fluorescent intensity (GNU.org). Isotype IgG control was used to subtract for background noise.

Ex vivo human tumor experiments

Human TNBC was collected immediately after surgical resection (See supplemental Table 2 for metadata). Matched-patient non-heparinized blood (5–10 mL) was also collected at the time of biopsy in BD-Vacutainer tubes (Becton Dickinson) following published protocol and established quality control criteria (24). Tissue slices were maintained in customized tumor matrix protein (TMP) coated plates as described in prior report (25). Tissue fragments (approximately 300 μm - 2 mm in size) were treated with the indicated drugs at the clinical max concentration (Cmax) for 72 hours as determined by published literature on each drug pharmacokinetic profile (See supplemental Table 3 for related drug concentrations used). DMSO was used as a vehicle control.

Cytokine analysis

Media was collected from parental cell lines or DTCCs cultured for indicated time points, centrifuged (‘neat’) and the resulting supernatant was aliquoted and stored at −80 °C. Using thawed conditioned media (25 μL ‘neat’ media), a panel of 41 cytokines and chemokines were profiled using the MILLIPLEX MAP Human Cytokine/Chemokine Magnetic Bead Panel (MilliporeSigma, Burlington, MA, USA) according to the manufacturer’s instructions and plates were read on the Luminex200 (Luminex Corp., Austin, TX, USA). Analyte measurements were reported using the MILLIPLEX analyst software (MilliporeSigma, Burlington, MA, USA).

In vivo experiments

4T1 mouse mammary carcinoma cells (1×10⁶ cells) were injected into the mammary fat pads of 5–6-week-old female balb/c mice (BALB/cAnNCrl, Charles River, Strain Code: 028). Docetaxel (DTX) was dissolved in pure ethanol at a concentration of 50 mg/mL mixed 1:1 with polysorbate 80 (Tween 80) and brought to a final working concentration with 5% glucose in PBS. Once tumors became palpable (~100 mm³), docetaxel, radicicol, DocRad-NP or vehicle treatments were administered intravenously (i.v.). on indicated days at the indicated doses. Tumor volumes were quantified using digital calipers (Starlett, Athol, MA) by a third party unaware of treatment conditions.

Immunofluorescence and confocal microscopy

Cells were permeabilized by incubation with 0.5% Triton X-100 at 4°C for 10 min, washed three times with 1x PBS-T (1x PBS+0.05% Tween 20) and blocked using 10% BSA solution (dilution with PBS-T) at room temperature for 1 hour. The samples were stained with a primary antibodies: Hsp90 (Cell signaling technology), phosphorylated HSF-1^Ser326 (Novus Biological) and phosphorylated Hck^Tyr410 (Thermofisher Scientific). For nuclear staining, the samples were counter stained with 4′,6-diamidino-2-phenylindole (DAPI; Thermo Fisher Scientific). Images were taken on a Nikon Eclipse Ti camera (Nikon Instruments) with NIS Elements Imaging Software (3.10). Confocal fluorescence imaging was performed on Zeiss LSM 800, Airyscan Confocal Laser Scanner Microscope with Zen 2.3 software. Post processing of the images was completed either in Image J or Zen lite software.

Immunohistochemistry (IHC) and multiplex IHC (mIHC) analysis

For murine tissue IHC, FFPE sections were incubated with the following primary antibodies; phosphorylated PRAS40^Thr246 (clone C77D7), STAT3^Tyr705 (D3A7) (Cell Signaling Technology, Danvers MA), CD49b (PA5–87012, ThermoFisher), MULT-1 (ABIN966609, antibodies online) and Rae-1 (PA5–93166, Invitrogen). Sections were then incubated with a HRP-conjugated secondary antibody (SignalStain® Boost IHC Detection Reagent; Cell Signaling Technology). Chromogenic development of signal was performed using 3,3’-diaminobenzidine (DAB Peroxidase Substrate Kit; Vector Laboratories). Terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) was used following manufacturer protocol (FITC kit, Genscript, Piscataway NJ). For ex vivo human tumor experiments, tissue was prepared from FFPE in serial 4μm sections and cut onto charged slides, which were stained with hematoxylin and eosin (H&E) for pathological determination of tumor viability and area (determined by a clinical pathologist), cleaved caspase-3 in vitro diagnostic (IVD) antibody (Cat#229, Biocare) or stained with a 4-plex panel of fluorophore dyes (Opal DAPI (Cat#FP1490), Discovery FAM (Cat#760–243,green), Discovery Cy5 (Cat#760–238)) with corresponding primary marker antibodies (FAM-CD56 IVD antibody (Clone# MRQ-420, Ventana,Cat#790–4596), Anti-CD3 IVD antibody (Clone#2GV6, rabbit monoclonal, Ventana, Cat#790–4341), anti-pan keratin (PanCK; clone AE1/AE3/PCK26, Ventana, Cat#760–2595)) selected for profiling natural killer cells (DAPI⁺PanCK⁻CD56⁺CD3⁻).

Multiplex IHC (mIHC) image analysis

H&E stains were annotated digitally by a clinical pathologist (David Goldman, MD, co-author) to designate tumor tissue, non-tumor tissue and stromal areas using the HALO™ digital image analysis software version 2.3.1.2089.70 (Indica Labs, Corrales, NM, USA) to establish tumor, non-tumor and stroma ROI (regions of interest). ROI groups were then trained based on ‘ground truth’ and cell populations were segmented and optimized using the DAPI stain. Once all algorithms had been fully developed, there were bulk applied to the appropriate patient, establishing a data set identifying the absolute count and spatial distribution of DAPI⁺PanCK⁻CD56⁺CD3⁻ cells in tumor, non-tumor and stromal ROI. HALO Spatial analysis (Indica Labs, Corrales, NM, USA) module was used for plotting the NK data set containing the requisite X and Y coordinate map. Computer software settings and details are provided in the supplemental information file. For spearman correlation analysis, data were normalized and read into the R statistical computing package “car”. Data tables were created caspase 3 high and caspase 3 low samples (respectively). These data were fed into a variety of visualization packages (GGPlot, GGPairs, scatterplotMatrix, and corr). For Correlation analysis - The R corrplot package was utilized to visually interpret the output from the above analysis. Spearman output was visualized by creating a heatmap to express the individual correlation values that were observed.

Synthesis of Radicicol-cholesterol conjugate

Cholesterol (500 mg, 1.29 mmol) was dissolved in 5 mL of anhydrous pyridine. Succinic anhydride (645 mg, 6.45 mmol) and catalytic amount of DMAP was added to the reaction mixture to form a clear solution. The reaction mixture was stirred for 12 hours under argon atmosphere. Removal of pyridine was carried out under vacuum and the crude residue was diluted in 30 mL DCM, washed with 1N HCl (30 mL) and water (30 mL) and the organic layer was separated and dried over anhydrous sodium sulfate, filtered and concentrated in vacuo. Completion of the reaction was confirmed by TLC in 1:99 Methanol: DCM solvent mixture. Radicicol (25 mg) was dissolved in 2 mL anhydrous DCM followed by addition of 1.2 M equivalent of cholesterol hemisuccinate, EDC and DMAP. The reaction mixture was stirred at room temperature for 48 hours under argon. Upon completion of reaction as monitored by TLC, the solvent was evaporated under vacuum and the crude product was purified by column chromatography, eluting with DCM: methanol gradient, to give radicicol-cholesterol conjugate as a yellow solid. The obtained conjugate was analyzed by ¹H NMR and Mass spectrometry. Further details on synthesis, characterization and release kinetics of nanoparticles can be found in the supplemental information. See supplemental figure S1 for reaction schemas (Suppl. Fig. S1).

Synthesis and characterization of SNPs

Docetaxel, Radicicol-cholesterol conjugate, L-α-phosphatidylcholine, and DSPE-PEG2000 at 0.01:0.09:0.6:0.3 molar ratios were dissolved in 1.0 mL DCM. Resulting clear solution was evaporated and thoroughly dried. The resulting thin film was hydrated with PBS with constant rotation at 60°C for 1 hours to get white turbid solution containing supramolecular nanoparticles (SNPs). SNPs were eluted through a Sephadex column and extruded through 0.4 μm polycarbonate membrane using mini-extruder. 10 μL of nanoparticles solution was diluted to 1 mL using DI water and 3 sets of 10 measurements each were performed at 90 degree scattering angle to get the average particle size by Dynamic Light Scattering method using Zetasizer Nano ZS90 (Malvern, UK). The zeta potential was measured using a Zetasizer ZS90 with the nanoparticles diluted in water for measurement according to the manufacturer’s manual.

Release kinetics studies

Drug loaded nanoparticles (1 mg drug/mL, 5 mL) were suspended in PBS buffer (pH 7.4), 4T1 cell lysate and sealed in a dialysis tube (MWCO= 3500 Dalton, Spectrum Lab). The dialysis tube was suspended in 1 L PBS (pH 7.4) with gentle stirring to simulate the infinite sink tank condition. A 100 μL portion of the aliquot was collected from the incubation medium at predetermined time intervals and replaced by equal volume of PBS buffer, and the released drug was quantified by HPLC and plotted as cumulative drug release.

Statistics

Statistical analysis was performed using Prism software (GraphPad) determined by ANOVA analysis followed by a Newman-Keuls post hoc test when values were represented between multiple groups, and, unless otherwise noted, two-tailed Student’s t-test used to identify statistical significance between individual groups. 2-way ANOVA was employed to track significance between groups from in vivo tumor volume experiments.

Results

Drug-induced resistant cancer cells diminish immune surveillance and cytolytic activity of NK cells following induction of granulocyte stimulating cytokines, in vitro

To interrogate the activity of NK cells in the presence of drug naïve vs. drug-induced resistant (i.e. tolerant) TNBC cells, we deployed an in vitro co-culture model. We generated a population of drug tolerant cancer cells (hereafter referred to as DTCCs) that temporarily display a hybrid epithelial-mesenchymal cell state implicated in therapy failure in multiple humanized models (12). Briefly, the parental TNBC cell line, MDA-MB-231, was exposed to docetaxel, a routine cancer chemotherapy for TNBC (26), at >20x the published IC₅₀ (Figure 1A). We co-cultured constitutively active NK cells (NK-92MI) or CD56+ primary human peripheral blood NK cells with either parental cells or DTCCs to assess cytolytic activity (Figures 1B). We determined that NK cells were incapable of killing DTCCs compared to parental cells across multiple TNBC models tested with different genetic backgrounds (Figures 1C and S2A). To study NK cell activity, we used a co-culture experimental design in which tumor spheroids, generated using a NanoCulture system (27), are separated from NK cells by a 0.2μm porous membrane that restricts diffusion to secreted factors such as growth factors, cytokines, chemokines and lipids (Figure 1D). Using flow cytometry, we evaluated several NK inhibitory and activating biomarkers including the well-established activation marker, natural killer group 2 member D (NKG2D), which plays a key role in NK activity (18). Notably, the expression of NKG2D and minimally-expressed NKG2C, which bind and elicit immune responses on ligand receptors such as MICA/B (28), were significantly diminished on NK cells in co-culture with DTCCs vs. the parental cells (Figures 1E and S2B). In parallel, we noted MICA/B was diminished on cancer cells in response to taxanes and on DTCCs (Figure S2C). To elucidate a mechanism underlying the decrease of NKG2D on NK cells, we isolated cell culture supernatant from parental cells or DTCCs and interrogated the excreted cytokine milieu using multiplex Luminex cytokine analysis over the course of 24 hours (Figure 1F). Clusters of cytokines that affiliated with DTCCs vs. parental cells emerged (Figure 1G), yet a smaller cohort was found to overlap among two independent TNBC cell lines tested, which included regulated on activation, normal T cell expressed and secreted (RANTES), granulocyte colony stimulating factor (G-CSF) and granulocyte macrophage colony stimulating factor (GM-CSF) (Figure S2D). Based on these results, we attempted to phenocopy the DTCC microenvironment in parental cells by introducing the top-induced cytokines or a cocktail of those that clustered together by Euclidean distance in the Luminex array (i.e. VEGF, G-CSF, GRO, RANTES and IL1α). Cell viability analysis confirmed that G-CSF, GM-CSF alone and in combination with other cytokines were the only ones tested to recapitulate the DTCC TIME in a parental cell line (Figures 1H and S2E). Indeed, using flow cytometry we confirmed a reduction of NKG2D expression on NK cells (Figure S2F), which is consistent with reports in other physiologic contexts (29,30).

Figure 1. Drug-induced resistant cancer cells diminish immune surveillance of local NK via release of inhibitory cytokines, in vitro.

(A) Schematic overviews the experimental design to generate drug tolerant cancer cells (DTCCs).

(B) Schematic overviews the experimental design for co-culture of natural killer cells with parental or DTCCs.

(C) Cell viability analysis of parental or DTCCs in the presence of varying concentrations of NK92-MI n>9 (left panel) or CD56+ primary human peripheral blood NK n=3 (right panel). Data represent mean ± SEM, ***p<0.001, **p<0.01. Bar graph represents mean ± SEM

(D) Experimental design for 0.2μm pore-separated co-culture of NK-92MI with parental cells or DTCCs.

(E) Quantification of cell surface biomarkers on NK-92MI following co-incubation with the indicated breast cancer cells for 24h. N≥3 in biological replicate, bar graph represents mean ± SEM, *p<0.05 by t-test.

(F) Schematic overviews the experimental design to isolate cytokines from parental or DTCCs following 4hour (4h), 8hour (8h) or 24hour (24h) culture in fresh media.

(G) Heat map of cytokine expression at different time intervals displayed as the log₂ fold change comparing DTCCs to parental. Hierarchical clustering was performed using Euclidean distance.

(H) Cell viability analysis in parental cells co-cultured with NK-92MI cells (1:1 population ratio) in the presence of absence of the indicated cytokines (10ng/ml) or a cocktail containing: VEGF, G-CSF, GRO, RANTES and IL1a. Cell viability for each cytokine-NK cell combination was normalized to a cancer-only control in which cancer cells were treated with cytokines in the absence of co-culture with NK cells. N=8 in biological replicate, bar graph represents mean ± SEM, *p<0.05.

Hsp90 simultaneously suppresses NK cell recognition and cancer cell survival axes in drug-induced resistant cancer cells

Next, we deployed systems biology and computational modeling to establish a chemical reaction network that integrated drug-induced protein kinetics with a systems biology approach. We used this strategy to infer drug effect on the DTCC phenotype using a framework that provided some mathematical certainty in which we could ‘toggle’, in silico, the effect of pathway perturbations. To do this, we interrogated phosphorylation status of proteins in DTCCs compared to parental cells and integrated these observations with empirical evidence from a kinetic analysis of drug-induced protein phosphorylation to establish the system of proteins and parameters that are induced by therapy, rather than ‘drug selected’. This approach implicated multiple protein families and pathways that are induced in the DTCCs and activated in discrete, time-dependent patterns following drug pressure in drug naïve parental cells (Figures 2A, S3A and Suppl. table S1). The systems biology and chemical reactions network that was built from these empirical observations, and from a review of the literature, identified Hsp90 as a potential ‘node’ with the closest determined relationship between a pro-survival phenotype in DTCCs as well as modulator of NK cell recognition of tumor cells, which functions via suppression of MICA/B through sequestration of the heat shock factor 1 (HSF-1) (31) (Figures 2B and S3B). We confirmed an increased expression of Hsp90 in the DTCCs compared to parental cells using fluorescent microscopy and western blot (Figures 2C,D and S3C,D) and found that docetaxel drug pressure induced the active form of HSF-1 (Ser326) (Figure S3E) (32), which appeared to sequester in the perinuclear space of DTCCs vs. parental cells (Figure 2E,F). Disruption of Hsp90 using the macrocyclic anti-fungal antibiotic, radicicol (33), or various other small molecule inhibitors including ganetespib (34) and PU-H71 (35), reversed cytoplasmic sequestration of HSF-1^Ser326 and activation of pro-survival proteins in DTCCs as evidenced by confocal microscopy and western blot, respectively (Figure 2E,F and S4A–C).

Figure 2. Hsp90 controls survival and NK cell recognition axes in drug tolerant cancer cells, which can be reversed using radicicol.

(A) Representative protein phosphorylation array of parental cells and DTCCs. Color coded blocks indicate inter-related pathways that are qualitatively increased in DTCCs compared to drug naïve parent cells. Data are representative of n=3 in biological replicate. See supplemental table 1 for coordinates of each phosphorylation site corresponding to the respective protein.

(B) Systems biology network interconnects Hsp90 with proteins involved in cell survival, via suppression of caspase-3 (red box) or regulation of NK cell recognition via expression of MHC class I polypeptide-related sequence A (MICA), a ligand for NKG2D (blue box).

(C) Representative immunofluorescent image of Hsp90 in MDA-MB-231 parental and DTCCs. Image is representative of 3 biological replicates producing similar results. Scale bar = 50 μm

(D) Representative western blot showing expression of Hsp90 in MDA-MB-231 parental and DTCCs. N=3 in biological replicate.

(E) Representative immunofluorescent images of activated HSF1 in MDA-MB-231 parental and DTCCs with the indicated treatments. Image is representative of 3 biological replicates producing similar results. Scale bar = 20 μm.

(F) Representative confocal microscopic image of phosphorylated HSF1 in parental or DTCCs confirms evidence from immunofluorescence of protein cellular localization. Image is representative of 3 biological replicates producing similar results. Scale bar = 10μm.

Ordering taxanes before Hsp90 inhibitors augments anti-cancer effects and re-invigorates NK cell surveillance and cytolysis, in vitro

The timing and sequence of anticancer drug combinations is an important consideration, which influence responses to therapy (36). Indeed, simultaneous administration of taxanes in combination with targeted inhibitors was previously determined to be suboptimal (12). We tested the hypothesis that sequencing Hsp90 inhibitors and docetaxel can optimize anticancer effects while also improving immune detection by NK cells via NKG2D receptor ligand expression. We tested the anticancer effect of different dosing schedules on cancer cell viability, timing the separation of docetaxel and radicicol in discrete order (Figure 3A). Values from cell viability analysis were used to plot the fraction affected (F(a)), which indicates the fraction of cells inhibited by treatment administered. A combination index (CI) was calculated at each F(a), CI below 1 indicates synergism and above 1 indicates antagonism (23). Schedule 2 (radicicol → docetaxel) resulted in antagonism. In contrast, schedule 1 (docetaxel → radicicol) resulted in synergism across all cell lines (Figures 3B and S5A). To validate these results, we simulated protein signaling and cell death by considering the reaction rates of the chemical reaction network, which were used to construct a system of ordinary differential equations to represent the protein and drug dynamics. The genetic algorithm in MATLAB was then used to explore our multi-dimensional parameter space and find a local minimum for the error between the simulation results and the in vitro data. With the given parameter fit, in silico experiments confirmed a direct correlation between docetaxel and radicicol sequencing and the effect on Hsp90 pathway induction and perturbation when drugs are administered in discrete sequence (Figure S5B).

Figure 3. Sequencing the combination of taxanes and radicicol reduces the proportion of drug tolerant cancer cells and increases NK cell surveillance and cytolysis via MICA expression in residual populations, in vitro.

(A) Drug treatment schematic overviews the in vitro approach to sequence docetaxel or radicicol in different, time separated order.

(B) In vitro drug synergy was determined using the Chau-Talalay method. Two schedules of drugs in combination were administered to the indicated TNBC parental cell lines (A). Schedule 1: docetaxel followed by radicicol; Schedule 2: radicicol followed by docetaxel. Plots generated using constant ratio drug combination. Values falling below 1.0 combination index = synergy.

(C) Representative flow cytometry graphs show MICA/B expression in DTCCs treated with vehicle control or radicicol (5μM) overnight. Values represent the percent (%) positively expressing cells over negative control threshold ± SEM, ***p<0.001. N=11 in biological replicate.

(D) Quantification of MICA/B mean fluorescence, corrected for background by negative control. Data is expressed as the fold change vs. vehicle control and bar graph represents mean ± SEM, ***p<0.001. N=11 in biological replicate.

(E) Schematic overviews the experimental design to study the effect of radicicol on NK cytolysis of DTCCs. Note: NK cells are not exposed directly to radicicol in this experimental design.

(F) Quantification of cell viability in DTCCs exposured to radicicol and then co-cultured with NK-92MI (n>10) or primary human NK cells (n=3) in increasing population density. Bar graph represents mean ± SEM, ***p<0.001.

(G) Schematic describes the experimental design for NK cytolysis in DTCCs following siRNA gene knockdown of MICA. Note: NK cells are not exposed directly to radicicol in this experimental design.

(H) Quantification of cell viability of MICA depleted DTCCs following the schematic outlined in panel G. Bar graph represents mean ± SEM, *p<0.05. N≥3 in biological replicate.

We next tested how Hsp90 disruption affects NK cell recognition and cytolysis in cells using the optimized temporal schedule (i.e. docetaxel→radicicol). Indeed, Hsp90 inhibition ‘primed’ tumor cells, significantly increasing both the intensity of expression and percent (%) positive-expressing MICA/B cells, (Figures 3C,D and S6). Moreover, co-cultures of NK cells with DTCCs that had been ‘primed’ were significantly more sensitive to NK cytolysis, as determined by cell viability analyses (Figures 3E–F and\ S7A\-\C). We confirmed this effect was indeed tumor cell-dependent by treating NK cells with Hsp90 inhibitors and determining there was no change in tumor cell cytolysis vs. vehicle control (Figure S7D,E). Finally, we used siRNA gene knockdown of MICA (Figure S7F) and determined that NK cells lost a significant proportion of their cytolytic capacity in MICA-knockdown DTCCs (MICA^KD), which we had ‘primed’ by overnight inhibition of Hsp90 (Figure 3G,H). The in silico modeling data together with in vitro evidence support a rationale for sequencing docetaxel prior to radicicol, but not inversely, as a means to improve antitumor effects while simultaneously re-priming tumor cells for NK cell cytolysis.

Characterizing 2-in-1 nanomedicines with rapid release of docetaxel and sustained release of the Hsp90 inhibitor, radicicol

Cancer nanomedicines are useful tools to differentially release drug payloads in distinct, controlled, temporal constraints(37) or co-delivery of two drugs to control spatial distribution of drugs (38). In this study, our evidence suggested that (1) drug order was important to improve the anticancer effect of the combination of docetaxel and radicicol and (2) DTCCs suppress NK cells via prolonged secretion of extracellular factors, which can be remedied by sustained inhibition of Hsp90. Given the physiological limitations of drugs, which have shortened half lives in vivo compared to in vitro cell cultures, we hypothesized that a 2-in-1 drug delivery strategy (docetaxel and radicicol chimera) would achieve two goals: (1) fast release of docetaxel will eliminate the drug sensitive population in the tumor; (2) sustained release of radicicol suppresses survival and optimally improves tumor immunity in the residual tumor population by boosting expression of NK activity ligand receptors. In silico drug delivery comparisons predicted improved anticancer outcomes using time-delayed ‘chimeric’ approach (Figure S8A–B). Next, we engineered a nanoparticle (NP) containing both docetaxel and radicicol (DocRad-NP) wherein radicicol is conjugated to cholesterol and held in the lipid bilayer, designed for slow release, while free-form docetaxel was encapsulated for rapid release (Figures 4A–B). The dual payload NP was constructed using a thin film hydration followed by an extrusion approach. Dynamic light scattering confirmed the formation of a supramolecular nanostructure of 225 ± 42 diameter (Figure 4C) where the ζ-potential and size were consistent over time at 4°C (Figure 4D). Release kinetics show minimal release of radicicol NPs in PBS (20%) when compared to release in cell lysate (65%) over 125 hours (Figure 4E). Notably, differential release kinetics of docetaxel and radicicol were observed such that rapid release of docetaxel was seen within 4hours compared to radicicol (35% vs. 23%, respectively) with a clear separation in release kinetics followed by saturation of docetaxel (70%) observed at 96 hours compared to equivalent level of release of radicicol (p<0.05), which was not achieved until 120 hours (Figure 4E). We reasoned that DocRad-NP may serve as a suitable tool to selectively toggle the release of the chemotherapy agent (docetaxel) and the Hsp90 inhibitor (radicicol) in a time-dependent fashion to achieve optimal cell kill and sustained ‘priming’ of any residual cancer mass.

Figure 4. Characterization of a docetaxel-radicicol nanoparticle (DocRad-NP).

(A) Schematic of the docetaxel-radicicol nanoparticle (DocRad-NP) structure with respect to the location of docetaxel and radicicol in the lipid bilayer.

(B) Structural schematic to illustrate the synthesis of the radicicol-cholesterol compound which is inserted into the lipid bilayer of the nanoparticle.

(C) Quantification and distribution of the hydrodynamic diameter of DocRad-NP. Histogram is representative of three independent experiments producing similar results.

(D) Quantification of physical stability of DocRad-NP on storage at 4^○C measured as the difference in Zeta potential (mV) and size (nm). Line graph is representative of three independent experiments producing similar results.

(E) Release kinetics of Docetaxel and Radicicol from DocRad-NP in PBS (pH 7.4) or 4T1 mammary carcinoma cell lysate. N=3 in biological replicate. *p<0.05 comparing the % radicicol release and docetaxel release at the indicated time point.

Sustained release of radicicol primes drug-induced resistance via NKG2D ligand receptors in nanoparticle formulation vs. free drug, in vitro

Next, we characterized the pharmacodynamics and in vitro efficacy of DocRad-NP. DTCCs were treated with the radicicol-NP or free-form (free drug) followed by an immediate wash-out after 4 hours and analyzed at 16, 36 and 48 hours later by western blotting (Figure 5A). The radicicol-NP-treated cells showed sustained inhibition of phosphorylated proteins up to and beyond 48 hours compared to free drug, which showed rescue of signaling disruption by 36 hours in most cases (Figure 5B). Furthermore, cell viability analysis in drug naïve parental cells indicated that DocRad-NP achieved 50% cell killing at concentrations below those of the single drug-loaded NP individually or together (Figure 5C). Next, we interrogated MICA/B expression in the context of free drug radicicol or radicicol-NP. Transient (4h) treatment of the radicicol-NP resulted in a significant increase in the % positive expression of MICA/B on DTCCs compared to the free drug (p<0.001) and improved cytolytic capacity of NK cells in the residual tumor cell fraction (Figures 5D–F). These data suggested an improvement of antitumor effects while simultaneously priming of the residual tumor mass for NK surveillance (Figure 5G).

Figure 5. In vitro characterization of radicicol nanoformulation confirms increased anticancer effect, sustained inhibition of Hsp90-related survival axis and enhanced MICA/B expression, as compared to the free drug radicicol.

(A) Schematic overviews the in vitro experimental design DTCCs transiently treated with radicicol or Rad-NP.

(B) Western blot analysis of DTCCs following transient exposure to Rad-NP or radicicol free drug, as described in (A).

(C) Cell viability analysis of DTCCs derived from several luminal or TNBC cell lines following constant treatment with indicated concentration-equivalent doses of single-loaded NP and the dual loaded DocRad-NP.

(D) Quantification of MICA/B expression on DTCCs after a transient exposure (4hours) with radicicol free drug or equivalent dose of Rad-NP and following washout and incubation for an additional 20 hours (read out at 24 hours total incubation). N>3 in biological replicate Bar graph represents mean ± SEM, ***p<0.001 by one-way ANOVA, N=3 in biological replicate.

(E) Quantification of MICA/B fluorescence in the indicated treatment conditions by flow cytometry. Bar graph represents mean ± SEM, ***p<0.001 by one-way ANOVA, n=3 in biological replicate.

(F) Quantification of cell number following exposure to docetaxel nanoparticle (Doc-NP) or chimeric nanoparticle (DocRad-NP) for 48hours and sequential administration of NK-92MI (24hours).

(G) Schematic summarizes the effect of Rad-NP compared to free drug based on empirical data.

DocRad-NPs reduce tumor burden, in vivo, and prime residual tumor cells for NK cell surveillance via NKG2D ligand receptor expression.

Next, we used an in vivo immune-competent orthotopic syngeneic mammary carcinoma model (4T-1 in Balb/C), and treated mice with either docetaxel, radicicol, a combination of individual compounds or DocRad-NP at equivalent drug concentrations. In a previous report we showed that maximum tolerated dose of docetaxel chemotherapy in this syngeneic model will induce the DTCC phenotypic transition, in vivo (12). First, we determined that DocRad-NP displayed superior anticancer efficacy compared to equivalently-dosed individual or combination drugs via significant reduction in tumor burden over the course of treatment (p<0.01 by two-way ANOVA) (Figure 6A). Using terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) we evaluated tumor-specific killing and organ toxicity, which confirmed DocRad-NP increased cell death in the residual tumor while avoiding other organs and systemic toxicities (Figures 6B and S9). Using IHC on the residual tumor tissue, we tested pathway activation of Hsp90 via expression of phosphorylated the proline-rich Akt substrate of 40 kDa (PRAS40) and STAT3. IHC indicated that DocRad-NP sustained pathway inhibition up to four days beyond the last dosing, evidenced by reduced antibody staining at day 16 compared to the free drug combination (Figure 6C). Blinded pathology assessment indicated over-expression of the NKG2D murine ligand receptor Murine ULBP-Like Transcript 1 (MULT-1) in the DocRad-NP cohort, while another receptor, retinoic acid early inducible gene 1 (Rae-1), remained of similar expression status across cohorts (Figure 6D). Serial sections confirmed that regions of high MULT-1 expression tended to localize with increased incidence of CD49b⁺ cells, an indication of NK cells (39), while treatment conditions with regions of low expressing MULT-1 showed minimal infiltration (Figure 6E).

Figure 6. DocRad-NP reduces tumor burden, sustains inhibition of pro-survival proteins and primes residual tumor cells for NK surveillance via upregulation of the NKG2D ligand receptor, MULT-1, in vivo.

(A-D) Orthotopic syngeneic mammary carcinoma model (4T-1) receiving the following treatments: vehicle, docetaxel, radicicol, docetaxel and radicicol, or a 2-in-1 docetaxel radicicol nanoparticle (DocRad-NP) delivered at equivalent doses. N=4 per group. Immunohistochemistry (IHC) images were determined by a clinical pathologist blinded to the treatment condition as a representation of the overall effect of treatment from each treatment group.

(A) Quantification of tumor growth curves from Arrows indicate specific days the mice were treated. (Top) Representative tumors from mice harvested at the end of treatment. **p<0.01 by two-way ANOVA (Doc+rad vs. DocRad NP). Animals were treated with docetaxel on days 1 and 3 (blue arrows). Black arrows indicate subsequent treatment regimens.

(B) Representative confocal microscopy shows fluorescence intensity of TUNEL (indication of apoptosis). Scale bar = 120 μm.

(C) Representative images from IHC. Scale bar =75 μm.

(D) Representative images from IHC. Inset of magnified representative section to show staining distribution and intensity. Scale bar =75 μm/.

(E) Representative images from IHC serial sections of the same tissue region. Scale bar =75μm

NK cells affiliate with drug-induced cell death in human tumor samples, ex vivo

Our data demonstrate an important dynamic relationship between NK cells and drug-induced death. We sought to confirm the important role of CD56⁺ NK cells in response to multiple clinically-approved drugs as they affiliate with anticancer response or resistance in human tumors. To elucidate this, we deployed a human autologous ex vivo tumor model using primary human TNBC (Suppl. table 2). Fragments of living, fresh tumor biopsies and autologous patient-derived peripheral blood mononuclear cells (PBMC) were cultured on a substrate of tumor matrix proteins following a previously published procedure (25) (Figure 7A). To this, we introduced clinically-approved (and off-label) anticancer drugs at their respective pharmacokinetic clinical max (C_max) (n=7, Suppl. table 3).

Figure 7. Confirmation of a dynamic role for tumor infiltrated NK cells in drug-induced cancer cell death using human TNBC samples.

(A-F) Ex vivo human tumor model system used to study spatial distribution of natural killer (NK) cells in the tumor and stroma under drug pressure. All tissue biopsies are from triple negative breast cancer patients, n=7 patient samples, fragments from each patient biopsy are plated into triplicate fragments per treatment ‘arm’. Patient demographic and metadata can be found in supplemental data files.

(A) Schematic overviews the ex vivo tumor model, comprising live human tissue fragments from biopsy plated into culture wells and treated with vehicle or drug as described in methods. Image was reproduced with permission. Inky Mouse Studios, 2018 all rights reserved.

(B) Schematic overviews the analytical process of using thin-cut serial FFPE sections to discern tumor vs. stroma (H&E), drug-induced cell death via immunohistochemistry (IHC) of apoptosis (cleaved caspase-3) and overlay multiplex IHC (mIHC) for identification of natural killer cells PanCK⁻CD3⁻ CD56⁺.

(C) Representative mIHC overviews the strategy to identify and quantify the spatial arrangement of NK cells (teal) vs. T-cells (red) in the stroma via measurement of distance to the tumor interface (red line; D_t).

(D) Quantification of cleaved caspase-3 presented as a waterfall plot. Histogram represents the log₂ fold change of drug vs. vehicle. A cut-off of 0.5 demarcated by the dashed red line separates samples as caspase-3 Hi vs. Lo.

(E) Spearman correlation rank order heatmap. Five various cellular localization, density and spatial arrangement metrics were analyzed for correlation within ‘caspase-3 Lo’ and ‘caspase-3 Hi’ samples. Positive correlations are displayed in red and negative correlations in blue color. Color intensity and the size of the circle are proportional to the correlation coefficients.

(F) Quantification of NK cell density and spatial arrangement represented as the log₂ fold change of drug vs. vehicle. Bar graph represents mean ± SEM, *p<0.05.

We developed a ‘sequential imaging’ strategy to study the TIME using 4μm serial sections from formalin fixed paraffin embedded (FFPE) tissue following drug treatment, ex vivo, to locate: (1) regions of tumor vs. stroma (hematoxylin and eosin; H&E), (2) drug-induced apoptosis by cleaved caspase-3 and (3) NK cells via multiplex immunohistochemistry (mIHC; PanCK⁻CD3⁻CD56⁺), which demarcate a population poised for activity, excreting high amounts of immune-stimulating cytokines (40) (Figures 7B and S10A–B). This imaging approach allowed us to identify regions of tumor that were positive for apoptosis while also allowing us to pinpoint the location of NK cells within tumor vs. stroma (see methods, Figure 7C).

We then performed several quantitative measurements: First, drug-induced cleaved caspase-3 in the tumor region determined as a log₂ fold change increase of 0.5 calculated between vehicle and drug (Figure 7D). To quantify the role of NK cells with high drug-induced death (i.e. caspase-3 Hi) we deployed the HALO multiplex IHC software platform to quantify five independent parameters related to the location and density of NK cells within the tumor vs. stroma as well as the proximity to the tumor interface following drug pressure, which are features of TIL that contribute an anticancer effect (19). We then used Spearman correlation rank order analysis to compare these with drug effect. We made two key observations: 1) we identified a higher correlation within the five cellular metrics in the caspase-3 Hi vs. Lo cohorts, with a direct affiliation between NK cells within the tumor vs. stroma and proximity to tumor interface in relationship to drug-induced caspase-3 (Fig. 7E), and 2) a significant increase in NK cell density within the tumor (p<0.05) as well as a trend towards diminishing of the distance between NK cells and tumor in the caspase-3 Hi vs Lo cohort when comparing the vehicle treatment to drug treatment (Fig. 7F). These preliminary observations support a critical role for the dynamics of NK cells as they are linked to drug-induced cell death in human cancers.

Discussion

Resolving drug resistance is penultimate to finding a sustainable cure for cancer. While conventional models of drug resistance rely on stochastic mutations conferred through Darwinian evolution, drug-induced resistance is seen as a measure of cellular ‘fitness’ wherein the entirety of the tumor ecosystem contributes to the effect while under drug pressure. There is a paucity of literature to support how drug-induced resistant cancer cells and other cells, such as NK, ‘cooperate’ or ‘compete’ to drive tumor growth. We focused on the role of innate NK in drug-induced resistance and based on this information we engineered potential therapeutic strategies and established Hsp90 as a putative ‘lynch pin’ in the survival signaling pathway while simultaneously ‘putting the brakes’ on the surveillance of NK cells for tumor cell clearance. To some degree, we relied on the systems biology model to establish the shortest molecular relationship among this effect. While this simplified the protein interactions involved and may overestimate the effect of Hsp90 due to its simplicity, it provided the necessary evidence that the effect of Hsp90 on Src, ERK, STAT3, and Akt are significantly changing the cell’s response to docetaxel while simultaneously depressing innate immune surveillance.

The role of Hsp90 in oncology as a drug target is not new – Hsp90 has been identified as an upstream regulator of many oncogenes, making it of long interest in cancer – and small molecule inhibitors have come and gone in the last two decades, with many failing in phase I trials due to lack of efficacy and poor bioavailability (16). More importantly, Hsp90 inhibition irreversibly downregulates expression of critical antigens and activating receptors on NK cells and T-cells (41). To address this, we developed a bioengineered strategy that takes advantage of the enhanced permeability and retention (EPR) to avoid cells in systemic circulation (42). Indeed, the nanoparticle formulation elicited death only in the tumor, improved therapeutic response and presence of CD49⁺ cells proximal to MULT-1, in vivo. We chose radicicol as the Hsp90 disruptor in our nanoparticle design for the following reasons: (1) our data to this point indicated that radicicol led to the greatest reduction of surviving cells after exposure to NK92 and greatest induction of MICA/B expression compared to the other inhibitors tested, (2) the free phenolic groups on radicicol enables a simple conjugation chemistry and (3) potential for rapid clinical translation, which may be inhibited by clinically-deployed agents protected by intellectual property. While the strategy of using nanomedicine to augment the efficacy of Hsp90 inhibitors has been used by other groups, we show the improved benefit of a rational combination of drugs in tandem with nanotechnology to support tumor immunity and drug resistance. Indeed, based on our evidences, sustained inhibition of Hsp90 using drug re-formulations in combination with other chemotherapeutic agents may be an approach to revitalize these failed clinical compounds. Future work to understand how nanotherapeutics can aid this effort is needed.

To examine the relevance of NK cell dynamics under drug pressure in human cancers we deployed the ex vivo tumor model. The data demonstrated that drug pressure affiliates with dynamic changes to the immune cell populations and their location within the tumor vs. stroma without changing the overall number of cells. These evidences have implications in vivo, which suggest a critical role for NK and potentially other cytolytic lymphocytes under drug pressure. However, it should be acknowledged that ex vivo human tumor models have their limitations and more work is needed to understand dynamics of the tumor-immune interface as it relates to response vs. resistance in humans. For example, controlling for the baseline level of TIL in each tumor sample and the degree to which lymphocytes (e.g. NK cells) infiltrate the tumor fragment during drug treatment prior to post-treatment analysis are not fully established here.

Finally, our findings may impact other drug modalities including immunotherapies. For example, our evidences suggest that engineered disruptors of Hsp90, cytotoxic drugs and potentially immune checkpoint blockade could act in-concert to improve immune recognition, diminish drug-sensitive clones and invigorate exhausted T-cells, respectively. NK cell therapy is another surging field in immuno-oncology (43). Not only are NK the focused target of interest for biological manipulation (44), they are being explored as tools for ‘off the shelf’ therapy with a chimeric antigen receptor (CAR), including the same NK-92 cell line we deployed in this study (45). An unexplored challenge in this space is the development of ‘boosters’ to augment activity of NK cell therapy. The data in our report suggests that Hsp90-inhibiting nanovehicles induce NK activating ligand receptors that have been previously un-reported to associate. For example, while MULT-1 has previously been connected to heat shock (46) and xenobiotic stress (47), there is no evidence linking it to Hsp90 inhibition. We hypothesize other stress-related NK activating ligands may also emerge under pressure of Hsp90 nanovehicles, in vivo. We acknowledge, however, there is more work to be done to understand how critical the NK population was, in vivo, to the anticancer effects that we observed. For example, we determined a correlation between the expression of MULT-1 and CD49b (pan-NK but also some non-NK targets). Definitive evidence that anticancer effects were mediated by NK cells could improve our understanding for clinical use of Hsp90 and chemotherapy combinations.

Taken together, these findings highlight a potentially novel role for the tumor-immune contexture in drug-induced tolerance. They further support a rational approach to sequence cancer therapies using advanced nanomedicines that can deliver drugs in temporal order and sustain inhibition of key signaling pathways to thwart resistance and improve tumor immunity.

Statement of significance:

This study uncovers a molecular mechanism linking drug-induced resistance and tumor immunity and provides novel engineered solutions that target these mechanisms in the tumor and improve immunity, thus mitigating off-target effects.