Background
Neoadjuvant chemotherapy (NAC), based on a sequential treatment with anthracyclines/cyclophosphamide and taxanes, has become the standard of care for high-risk patients with early-stage breast cancers (BC) [
1]. Indeed, NAC favors breast-conserving surgery (by reducing tumor size and down-staging lymph node status), allows the evaluation of tumor sensitivity to chemotherapeutic agents, and eradicates micro-metastases [
2]. Unfortunately, clinical response to NAC is usually incomplete, and about 60% of patients with early-stage BC have substantial residual cancer burden (RCB) after NAC, as detected by pathological evaluation of the breast and axillary nodes, at the time of surgical resection [
3]. Among the different subtypes, triple negative BC (TNBC), lacking expression of estrogen receptor (ER), progesterone receptor (PR), or HER2 amplification, displays the highest risk of relapse (between 40 and 80%), resulting in the progression towards incurable, advanced disease, and subsequent death for most patients [
4,
5].
Genomic profiling of serial biopsies, before and after NAC, has provided important insights into the clonal and mutational evolution of TNBC [
6‐
9]. However, the straightforward contribution of intratumoral genomic heterogeneity to therapy resistance and tumor progression remains unclear, and it has not modified the routine clinical care of TNBC patients. Instead, several studies have shown that NAC resistance is mediated by a reversible drug-tolerant persister (DTP) cell state, with chemoresistant tumors being able to return to a chemosensitive state after drug withdrawal [
10]. In particular, chromatin modifications and differentiation-state plasticity were reported as non-genetic drug tolerance mechanisms in several cancer types, including triple negative and HER2-amplified BC [
11‐
15].
Dysregulated metabolism is a common hallmark of cancer and it has emerged as an important factor that contributes to disease progression and clinical relapse for a variety of cancers, including TNBC [
16,
17]. In particular, metabolic needs/preferences of DTP cells evolve to maintain redox homeostasis and adapt therapy-induced stress [
18,
19]. For instance, several studies have reported the initiation of an antioxidant transcriptional program in DTP cells to regulate nucleotide synthesis and glutathione metabolism, thereby facilitating the recurrence of dormant BC cells [
20,
21]. Furthermore, mitochondrial oxidative metabolism has been identified as a dependency in residual TNBC, upon NAC treatment [
22]. On the contrary, the role of glycolysis, which is frequently dysregulated in cancer, remains poorly understood in the context of NAC resistance in TNBC.
Here, we reason that deciphering NAC-induced changes in glucose metabolism is critical to understand the molecular bases of drug tolerance in TNBC and to propose novel therapeutic approaches in order to limit residual disease and delay the development of resistance. By using early TNBC cell lines, patient-derived organoids and clinical samples, we show that TNBC cells surviving initial treatment with NAC exhibit an increased glycolysis activity, with hexokinase 2 (HK2) as a critical metabolic actor. Indeed, we report that blocking HK2 enzymatic activity significantly improves response to NAC by preventing metabolic adaptation in TNBC cells. In clinical samples, we also document that exacerbated glycolysis is a predictive marker of non-response to NAC in TNBC patients. Overall, our study positions the rewiring of glucose metabolism upon NAC in TNBC cells as a druggable target that may be therapeutically exploited in order to fulfil the current clinical need for patients with NAC-refractory TNBC.
Methods
Cell culture
Human TNBC cell lines HCC38 (#CRL2314), HCC1143 (#CRL2321) and HCC1937 (#CRL2336) were purchased from ATCC. Cell lines were stored according to the supplier’s instructions and used within 6 months after resuscitation of frozen aliquots. All cells were routinely cultured in RPMI 1640 medium (#61870-010, Thermo Fisher Scientific) containing 11.1 mM d-glucose and 2 mM GlutaMAX™, and supplemented with 10% heat-inactivated fetal bovine serum (FBS; #F7524, Sigma-Aldrich) and 1% penicillin/streptomycin (#15140-122, Thermo Fisher Scientific), and maintained in exponential growth in 5% CO2/95% air in a humidified incubator at 37 °C. All cell lines were tested for potential mycoplasma contamination with the PCR-based MycoplasmaCheck service from Eurofins Genomics. For drug testing, cells (10,000 cells/well) were seeded in 96-well plates and treatment was performed, 24 h later, in a full culture medium with 2-deoxy-glucose (2-DG; #D8375, Sigma-Aldrich), paclitaxel or epirubicin at different concentrations, as indicated in the figure legends. Chemotherapeutic agents were obtained, as ready-to-use solutions, from the central pharmacy of Cliniques universitaires Saint Luc (CUSL, Brussels, Belgium). After 72 h, cell growth was assessed by using the Presto Blue reagent (#A13262, Thermo Fisher Scientific) according to manufacturer’s instructions. To assess the capacity of TNBC cells to resume growth upon NAC treatment, Presto Blue reagent was also used at different timings: baseline (no NAC treatment), after a 24 h-treatment with paclitaxel or epirubicin, and two days after drug withdrawal.
3D spheroid growth assay
For 3D spheroid initiation, HCC38 and HCC1937 cells (5000 cells/well) were seeded in ultra-low attachment round-bottom 96-well plates (Corning) before centrifugation [500 rpm, 5 min at room temperature]. Drug testing was carried out 96 h post-initiation by removing 100 µL of initial medium and adding 100 µL of new medium containing 2-DG alone or in combination with chemotherapeutic drugs at different concentrations, as indicated in the figure legends. Cell viability was assessed 72 h later using CellTiter-Glo 3D cell viability assay (#G9681 Promega) according to manufacturer’s instructions.
Patient-derived TNBC organoid initiation
BCO17 and IDC031 organoid models were established from freshly resected tumor tissues obtained from treatment-naive TNBC patients, at CUSL or Antoni van Leeuwenhoek Hospital, respectively. The study was approved by the hospital-faculty ethical committee (UCL-ONCO2015-02—2015/13AOU/445) and by the institutional review board (NKI-B17PRE) and all subjects provided informed consent. BC tissues were obtained from surgical pieces (mastectomy) and kept on ice in a transport medium (DMEM/F12 with HEPES, no phenol red, 1% penicillin–streptomycin). After washing (3 times with ice-cold PBS), tissues were minced with a scalpel and enzymatically digested using the tumor dissociation kit (#130-095-929, Miltenyi Biotec) in the gentleMACS™ octo dissociator with heaters (Miltenyi Biotec). After dissociation, samples were filtered through a 70-µm cell strainer and rinsed with DMEM/F12 containing 10% FBS, before centrifugation (1500 rpm, 5 min at 4 °C). Cell pellets were then resuspended in Cultrex reduced growth factor Basement Membrane Extract (BME) type 2 (#3532-005-02, R&D Systems) and plated as 50 µL droplets in pre-heated 24-well plates. Plates were incubated at 37 °C (5% CO
2) for 30 min to allow Cultrex BME solidification before adding BC organoid medium (500 µL/well) (see Additional file
1: Table S1 for medium formulation).
Organoid passaging and culturing
Organoid passaging was performed every 1–2 weeks, depending on the proliferation rate, and medium was changed twice a week. Briefly, each dome of Cultrex BME was dissolved with ice-cold PBS, collected in a tube, and then centrifuged (1500 rpm, 5 min at 4 °C). Trypsin–EDTA 0.05% was then added to the pellet, before incubation at 37 °C for 15–30 min, with frequent pipetting, to facilitate the dissociation into single cells. After addition of DMEM/F12 supplemented with 10% FBS, and centrifugation (1500 rpm, 5 min at 4 °C), single cells were resuspended in Cultrex BME, overlaid with fresh BC organoid medium. BCO17 and IDC031 organoid lines were cryopreserved with 45% medium/45% FBS/10% DMSO.
Organoid viability assays
After organoid dissociation, single cells were counted with a LUNA-II™ automated cell counter (Logos Biosystems), and then seeded (10,000 cells/well) in 96-well plates pre-coated with Cultrex BME (20 µL/well), in 100 µL of BC organoid medium supplemented with 2% Cultrex BME. After 7 days, BC organoids were treated with paclitaxel or epirubicin at different doses for 7 additional days. Next, organoid viability was assessed by using the CellTiter-Glo 3D cell viability assay (#G9681, Promega) according to manufacturer’s instructions, and luminescence was read with a GloMax microplate reader (Promega).
Clonogenic assay
For colony-forming ability assessment, HCC38 and HCC1143 cells were seeded in 6-well plates (1000 cells/well) and treated with 2-DG alone or in combination with paclitaxel, for 72 h, at concentrations indicated in the figure legends. Seven days after drug withdrawal, colonies were labelled with a mix of 0.5% brilliant blue (#B0149, Sigma-Aldrich), 50% ethanol and 5% acetic acid for 1 h under gentle agitation. After rinsing with ultrapure distilled water, culture wells were scanned with an Epson V600 scanner and images were processed with ImageJ to count the number of cell colonies larger than a defined threshold (30 pixels).
Western blot analysis
Subconfluent cancer cells were washed twice with ice-cold PBS and lysed in a RIPA buffer supplemented with a protease inhibitor cocktail (#P8340, Sigma-Aldrich) and a phosphatase inhibitor cocktail (#4906837001, Roche). Cell lysates were then cleared by centrifugation (10,000 rpm, 10 min, 4 °C) and stored at − 80 °C until analysis. After determination of protein concentration using a bicinchoninic acid-based assay (#23225, Thermo Fisher Scientific), samples were denaturated (5 min, 95 °C) with Laemmli sample buffer containing 100 mM dithiothreitol. Protein samples (20 µg/well) were then separated by SDS-PAGE (8 to 15% acrylamide/bis-acrylamide gels) and transferred to PVDF membranes. Membranes were blocked with 5% bovine serum albumin in TBS-0.1% Tween 20 (TTBS) and subsequently immunoblotted overnight at 4 °C with specific primary antibodies against β-actin (1:10,000; #A5441, Sigma-Aldrich), GAPDH (1:1000; #2118, Cell Signaling Technology), HK2 (1:1000; #2867, Cell Signaling Technology), LDHA (1:1000; #3582, Cell Signaling Technology) and HSP90 (1:7,500; #610419, BD Biosciences). After several washes with TTBS, membranes were then incubated (1 h, room temperature) with horseradish peroxidase-conjugated secondary antibodies (Jackson Immunoresearch) and chemoluminescent signals were revealed by using ECL Western Blotting Detection Kit (GE Healthcare) either on X-ray films in a dark chamber, or with an Amersham Imager 600 (GE Healthcare). Films were scanned with an Epson V600 scanner and images were processed with ImageJ.
Cancer cells (2 × 105 cells/well; 3 wells/condition) were seeded in 12-well plates with 2 mL of their routine culture medium, while organoid-derived single cells (5 × 105 cells/wells; 6 wells/condition) were seeded in 24-well plates with 1 mL of the BCO culture medium. After 1 or 7 days of incubation for cancer cells and TNBC organoids respectively, medium was replaced by 500 µL of DMEM containing 10 mM d-glucose, 2 mM L-glutamine and supplemented with 10% dialyzed FBS (#F0392, Sigma-Aldrich). Initial concentrations of glucose and lactate in the experimental medium were also assessed by including control wells containing only culture medium (no cells) on each plate. After cell incubation for 24 to 48 h, extracellular media were collected and deproteinized by centrifugation (15 min, 10,000 rpm, 4 °C) in 10 kDa cut-off filter tubes (VWR). Glucose and lactate concentrations were measured in the samples (50 µL) by using specific enzymatic assays and an ISCUSflex microdialysis analyzer (M Dialysis). Data analysis was done by calculating the difference in glucose and lactate concentrations between the control and experimental wells. Data were then normalized by the protein content in each well and expressed in µmol/hr/mg proteins.
Seahorse analysis
Oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) were measured by using the Seahorse XFe96 plate reader (Agilent). For 2D cell lines, assays were carried out using a seeding density of 20,000 cells/well (pre-treated or not with NAC for 24 h or 7 days) in non-buffered DMEM, adjusted at pH 7.4 and supplemented with specified metabolic substrates. Mitochondrial respiration was assessed in DMEM medium containing 10 mM d-glucose and 2 mM l-glutamine, at baseline conditions and after sequential treatment with 1 µM oligomycin, 2 µM FCCP and 0.5 µM rotenone/antimycin A. Glycolytic function was evaluated in DMEM medium supplemented with 2 mM L-glutamine, at baseline conditions and after sequential treatment with 10 mM d-glucose, 1 µM oligomycin and 50 mM 2-DG. For organoids, 10,000 cells/well were seeded in XF96 culture plates, pre-coated with Cultrex BME (20 µL/well), and incubated for 7 days in the BC organoid culture medium, before starting the experiments. Cells were then incubated in non-buffered DMEM, adjusted at pH 7.4 and supplemented with specified metabolic substrates. Mitochondrial and glycolytic activities were assessed, as described above for 2D cell cultures, except for glucose concentration set at 20 mM for the glycolysis test. For all assays, three cycles (3 min mixing/3 min measuring) were carried out between each treatment. Glucose-dependent ECAR was determined by calculating the difference between the values before (measurements 1–3) and after (measurements 4–6) addition of the substrate. Data were normalized by the protein content in each well and expressed in mpH/min/µg proteins (ECAR) or pmoles/min/µg proteins (OCR).
Immunohistochemical staining
Organoids were fixed in 4% paraformaldehyde (20 min at room temperature) before embedding (first in a 2% agar solution, and then in paraffin). Organoid sections (5-µm width), as well as sections from matching primary BC tissues, were prepared and stained for ER (#13258, Cell Signaling Technology), PR (#8757, Cell Signaling Technology), HER2 (#2165, Cell Signaling Technology) and Ki67 (#MA5-14520, Thermo Fisher Scientific) with a VENTANA automated staining system (Roche) at CUSL. Image acquisition was performed using an AxioImager Z1 microscope equipped with an Apotome1 (Zeiss).
Collection of TNBC clinical samples
Patients with early TNBC (diagnosed between 2012 and 2022), eligible for NAC, were recruited, retrospectively and prospectively, at CUSL (NCT03314870, ethics committee number 2017/25JUL/376). For the retrospective cohort, recruited from 2012 to 2017, no informed consent was needed while for the prospective cohort, recruited from 2018 to 2022, subjects gave informed consent. Formalin-fixed, paraffin-embedded (FFPE) tissue slides from diagnostic biopsies (pre-NAC) were obtained from the Department of Pathology from CUSL. Pathological and clinical information from this cohort was collected and managed using the REDCap (Research Electronic DataCapture) secured database hosted at CUSL.
Patients received NAC with sequential therapy of anthracyclines and taxanes, with 4 cycles of epirubicin (90 mg/m
2)/cyclophosphamide (600 mg/m
2) intravenously (iv)) followed by 12 cycles of weekly paclitaxel (80 mg/m
2 iv), and 2 patients out of the 29 TNBC had the same regimen with the addition of 3-weekly carboplatin (AUC 5, iv). Response to NAC was evaluated by histological examination of the surgical resection specimen, sliced at 5 mm intervals. Pathological complete response was defined as the absence of invasive tumor in the breast and in the axilla, with the calculation of the RCB score, as previously described [
23]. The responding tumors obtained an RCB of 0 (no residual disease) or I (minimal residual disease) while the non-responding patients had an RCB of II (moderate residual disease) or III (extensive residual disease).
FFPE slides from diagnostic biopsies were cut with a depth of 5 µm. Delimitation of the tumoral zone was done by the pathologist after hematoxylin/eosin staining on one slide, and then reported on the other slides. On average, the tumoral zones of 15–20 slides were scratched. After a brief centrifugation, deparaffinization and extraction steps were carried out using the AllPrep DNA/RNA FFPE kit (#80234, Qiagen), according to manufacturer’s recommendations and DNA was removed from the samples with the TURBO DNA-free™ kit (#AM1907, Thermo Fisher Scientific), by following manufacturer’s instructions. Total RNA samples were then quantified by using Quant-it™ RiboGreen RNA Assay kit (#R11490, Thermo Fisher Scientific), before ribosomal RNA depletion with the NEBNext rRNA Depletion kit (Human/Mouse/Rat) with sample purification beads (#E6350, New England Biolabs). Libraries were prepared with the NEBNext Ultra II Directional RNA Library Prep with Beads kit (#E7765, New England Biolabs). Adaptors and primers used for the library preparation were from NEBNext Multiplex Oligos for Illumina (Dual Index Set 1), 96 rxns (#E7760, New England Biolabs). The quality of the libraries was evaluated using High Sensitivity DNA kit (chips and reagents) (#5067 and #4626, Agilent) with a 2100 Bioanalyzer system (Agilent). Sequence reads were generated on the Illumina NovaSeq 6000 sequencer, on an S4 cartridge (300 bp). RNA sequencing was performed in 3 distinct batches.
RNA-sequencing data analysis
Raw sequences were filtered to remove low-quality reads. The quality of analyzed data was checked using FastQC and QualiMap, while trimming was carried out by Trimmomatic. Filtered data were then mapped by CLC Genomics v22 software (Qiagen) to the Homo sapiens genome hg38 and the RNA database v91. On average, 2 × 106 reads could be mapped to the human genome. Samples with a mapping lower than 500,000 reads were excluded. The read count expression matrix was analyzed using the edger v3.40.2 Bioconductor package in order to detect differentially expressed genes (DEGs). Briefly, a filtering strategy was first applied in order to keep genes having sufficiently large counts to be retained in statistical analyses. Scaling factors were then computed with the trimmed mean of M-values method. A quasi-likelihood negative binomial generalized log-linear model was built on the resulting data. In addition to the variable of interest (e.g. RCB), a batch effect, a DNA contamination effect (estimated by the percentage of intergenic mapping), as well as the age of the patients were explicitly incorporated in the models in order to correct for the potential confounding effects of these factors. This modeling strategy was applied to detect DEGs between non-responding and responding tumors (i.e. RCB II-III vs. 0-I). Finally, based on the resulting gene expression modulations, gene set enrichment analysis (GSEA) was performed using the fgsea v1.24.0 bioconductor packages on hallmark gene sets of the MSigDB collection.
Two publicly available transcriptomic datasets, namely GSE25066 and GSE123845, were analyzed to confirm our findings in TNBC patients. Gene expression profiles from the GSE25066 dataset were obtained using an Affymetrix Human Genome U133A Array while profiles from the GSE123845 dataset were obtained using expression profiling by high-throughput RNA sequencing. For both transcriptomic datasets, metadata were imported using the GEOquery v.2.66.0 Bioconductor package. Regarding the GSE25066 dataset, we selected 146 TNBC patients for which pathological response information (i.e. RCB class) was available from the full dataset of 508 patients. The limma Bioconductor package was employed to estimate fold-changes and p-values associated with pathological response (RCB), while adjusting for patient age. In the GSE123845 dataset, we selected 33 samples, corresponding to tumor biopsies from TNBC patients collected before treatment with NAC, from the full dataset of 227 samples for which information about pCR was available. The edgeR v.3.42.4 Bioconductor package was employed to estimate fold-changes and p-values associated with pCR, while adjusting for patient age. Subsequently, GSEA was conducted for both datasets using the fgsea v1.24.0 Bioconductor package on hallmark gene sets from the MSigDB collection.
Statistical analysis
Data are expressed as mean ± SEM of at least three independent experiments unless otherwise indicated. Statistical analyses were performed through GraphPad Prism 9 by using Student’s t test, one-way or two-way ANOVA with Tukey’s multiple comparison test when appropriate. Statistical significance is indicated in the figures as follows: *p < 0.05; **p < 0.01; ***p < 0.001; ns, not significant.
Discussion
In the current era of personalized medicine, therapeutic resistance remains a critical hurdle in our ambition for curative cancer treatment [
27]. For patients with early-stage TNBC, although NAC can lead to tumor regression, the effect is often temporary and the emergence of drug-resistant cells almost invariably leads to clinical relapse, still making the overall prognosis dismal [
28]. Expanding the molecular characterization of residual disease in TNBC is therefore crucial for understanding the resistance mechanisms to NAC and for the discovery of new actionable targets induced by the treatment. In this study, we aimed to tackle the issue of resistance to NAC in TNBC through a specific metabolic angle. Indeed, reprogramming of cellular metabolism has emerged as a pivotal mechanism for the adaptation and resistance to chemotherapy in cancer, thereby creating a new field of investigation for the development of novel anticancer agents with the potential to overcome therapeutic resistance [
29,
30]. Like most aggressive tumors, TNBC exhibits a high rate of glycolysis to meet its metabolic demands, with a strong correlation between elevated levels of tumor-derived lactate and high risk of metastatic spread in primary TNBC [
31,
32]. Here, we have shown that glycolytic activity is increased in TNBC cells surviving initial treatment with paclitaxel in both 2D cell lines and 3D PDO models, while mitochondrial oxidative metabolism is reduced upon treatment. These data are in line with previous studies that documented an enhanced glycolytic phenotype, with increased glucose uptake and lactate release, in chemoresistant BC cells [
33,
34]. Although another study reported the downregulation of glycolysis, and upregulation of oxidative phosphorylation, in residual TNBC upon NAC [
35], this was in the specific context of TNBC cell lines harbouring
MYC and
MCL1 gene co-amplification. Additionally,
18F-fluoro-2-deoxy-
d-glucose positron emission tomography (
18FDG-PET) signal has been shown to predict final NAC response in BC patients, especially for ER-positive/HER2-negative and triple negative tumors [
36‐
38]. However, it is worth mentioning that
18FDG-PET does not accurately reflect quantitative in vivo glucose utilization and is only a surrogate marker of glucose uptake, without considering its intracellular fate (e.g. through different metabolic pathways, including glycolysis, pentose phosphate pathway or mitochondrial tricarboxylic acid cycle) [
39]. In the quest for new reliable predictive biomarkers of NAC response in TNBC [
40], the identification of a glycolysis-related gene signature in non-responders, in the current study, is reminiscent to other observations made in a variety of therapy-resistant tumors [
41‐
43], and may help to better predict TNBC therapeutic responses by integrating metabolic reprogramming.
In the current study, we have also used patient-derived TNBC organoids to assess the response to NAC. PDO have emerged as relevant preclinical models since they recapitulate the genetic and histological features of their parental tumors and help predict response to therapy [
44‐
47]. In particular, recent studies have reported that human BC-derived organoids show strong biological fidelity to their original tumors with, for instance, concordant responses to a variety of anticancer treatments, including NAC [
48‐
51], making them amenable tools for drug discovery and precision oncology. Although organoids have been used to assess genomic, transcriptomic and proteomic alterations in a variety of pathophysiological situations, studies describing the use of (patient-derived) organoids to decipher cell metabolism are still scarce [
52]. Our data reveal that BC organoids may be used as relevant preclinical models to decipher metabolic preferences upon NAC treatment. Nevertheless, in our study, BC organoids were metabolically profiled as whole 3D cell cultures and fluorescence- and phosphorescence-based lifetime imaging microscopy may be now considered to better assess the cellular/metabolic heterogeneity (i.e. NAD(P)H and oxygenation levels) within 3D organoids and correlate it with response to NAC in specific local TNBC cell populations [
53,
54]. In particular, the mechanisms by which metabolic changes lead to chemoresistance and the origin of NAC-tolerant persister cells are critical points that still need to be addressed. While DTP cell phenotypes can be selected from pre-existing cell populations (likely located in permissive local microenvironmental niches) and/or be induced upon NAC application, the identification of a glycolysis-related gene signature as a marker of non-response to NAC in diagnostic biopsies from early TNBC patients, in our study, suggests that the basal metabolic state of the tumors strongly influences the response to NAC, with “glycolytic” tumors more prone to resist chemotherapy. Our data are also reminiscent of a previous study reporting the effect of paclitaxel on glucose uptake [
55]. Indeed, paclitaxel-induced tubulin polymerization was shown to stimulate GLUT1-dependent hexose uptake in C6 glioma cells, thereby suggesting that metabolic reprogramming towards glycolysis may be a direct consequence of NAC treatment.
From a therapeutic point of view, we have documented that treatment with 2-DG, a glycolysis inhibitor, greatly potentiates NAC-induced growth-inhibitory effects. Although several studies have also reported the use of glycolytic inhibitors to sensitize cancer cells to anticancer treatments, including chemotherapy [
56], it has never been successfully exploited clinically due to undesirable side effects and limited efficacy in humans [
57]. Further research is thus needed to develop safe glycolysis-interfering therapeutic strategies that may help improve response to NAC in TNBC patients. In conclusion, our study pinpoints a metabolic adaptation towards glycolysis as a non-genetic mechanism driving resistance to NAC in TNBC, thereby opening new perspectives for the development of metabolism-related approaches to limit residual disease and improve NAC efficiency in TNBC patients.
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