
==== Front
Redox Biol
Redox Biol
Redox Biology
2213-2317
Elsevier

S2213-2317(24)00282-9
10.1016/j.redox.2024.103304
103304
Research Paper
SLC7A11 protects luminal A breast cancer cells against ferroptosis induced by CDK4/6 inhibitors
Cui Yingshu ab1
Li Yi a1
Xu Yuanyuan b1
Liu Xinxin c
Kang Xiaofeng d
Zhu Junwen d
Long Shan b
Han Yuchen d
Xue Chunyuan d
Sun Zhijia a
Du Yimeng d
Hu Jia d
Pan Lu d
Zhou Feifan zhouff@hainanu.edu.cn
e⁎⁎
Xu Xiaojie miraclexxj@126.com
d⁎⁎⁎
Li Xiaosong lixiaosong301@163.com
b⁎
a Medical School of Chinese PLA, Beijing, 100853, China
b Department of Oncology, the Fifth Medical Center, Chinese PLA General Hospital, Beijing, 100071, China
c Department of General Surgery, Peking University First Hospital, Beijing, 100034, China
d Department of Genetic Engineering, Beijing Institute of Biotechnology, Beijing, 100071, China
e State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Haikou, 570100, China
⁎ Corresponding author. Department of Oncology, the Fifth Medical Center of PLA General Hospital, Beijing, 100071, China. lixiaosong301@163.com
⁎⁎ Corresponding author. zhouff@hainanu.edu.cn
⁎⁎⁎ Corresponding author. miraclexxj@126.com
1 The authors contributed equally to the article.

10 8 2024
10 2024
10 8 2024
76 10330411 6 2024
26 7 2024
5 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cyclin-dependent kinase 4 and 6 inhibitors (CDK4/6 inhibitors) can significantly extend tumor response in patients with metastatic luminal A breast cancer, yet intrinsic and acquired resistance remains a prevalent issue. Understanding the molecular features of CDK4/6 inhibitor sensitivity and the potential efficacy of their combination with novel targeted cell death inducers may lead to improved patient outcomes. Herein, we demonstrate that ferroptosis, a form of regulated cell death driven by iron-dependent phospholipid peroxidation, partly underpins the efficacy of CDK4/6 inhibitors. Mechanistically, CDK4/6 inhibitors downregulate the cystine transporter SLC7A11 by inhibiting SP1 binding to the SLC7A11 promoter region. Furthermore, SLC7A11 is identified as critical for the intrinsic sensitivity of luminal A breast cancer to CDK4/6 inhibitors. Both genetic and pharmacological inhibition of SP1 or SLC7A11 enhances cell sensitivity to CDK4/6 inhibitors and synergistically inhibits luminal A breast cancer growth when combined with CDK4/6 inhibitors in vitro and in vivo. Our data highlight the potential of targeting SLC7A11 in combination with CDK4/6 inhibitors, supporting further investigation of combination therapy in luminal A breast cancer.

Graphical abstract

Image 1

Highlights

• CDK4/6 inhibitors promote ferroptosis partially by suppressing SLC7A11-mediated GSH synthesis.

• SLC7A11 is overexpressed in luminal A breast cancer and affects intrinsic sensitivity of luminal A subtype to CDK4/6 inhibitors.

• CDK4/6 inhibitors inhibit SLC7A11 expression largely through SP1 transcription.

• SP1 inhibition promotes ferroptosis and synergistically sensitizes luminal A breast cancer cells to CDK4/6 inhibitors.

• Ferroptosis correlates with abemaciclib-mediated tumor suppression in vivo.

Keywords

CDK4/6 inhibitors
Ferroptosis
SLC7A11
SP1
Synthetic lethal
==== Body
pmc1 Introduction

Breast cancer is highly heterogeneous encompassing a group of genetically and epigenetically distinct diseases exhibiting diverse clinical features [1]. Prognosis and treatment strategies for breast cancers are guided by immunohistochemistry (IHC) markers such as estrogen receptor (ER), progesterone receptor (PR), human epidermal growth receptor 2 (HER2), and Ki67 (a proliferation index marker). Tumors were then commonly classified as luminal A (ER+, PR−/PR+, HER2−), luminal B (ER+, PR−/PR+, HER2+), HER2 (ER−, PR−, HER2+), and TNBC (triple-negative breast cancer) (ER−, PR−, HER2−) based on receptor status [2]. Luminal A breast cancer, as the largest subtype of breast cancer, has been characterized by alterations in the p16-Cyclin D1-CDK4/6-Rb pathway, which regulates cell cycle progression. Recent advancements in biologically targeted therapies against cyclin-dependent kinase 4/6 (CDK4/6) have proven successful in delaying progression when added to endocrine therapy [3,4]. Multiple clinical trials have established the efficacy of the ATP competitive CDK4/6 inhibitors, PD0332991 (palbociclib) [5], LEE011 (ribociclib) [6], and LY2835219 (abemaciclib) [7]. Despite the substantial improvements in activity afforded by these drugs, approximately 25–35 % of patients do not respond, and most patients ultimately experience disease progression without any improvement in long-term survival [8], which limits their efficacy and scope of therapeutic applications. Many efforts have been devoted to overcoming intrinsic or acquired drug resistance to CDK4/6 inhibitors in the past few years [9,10]. Although there currently remains no known consensus on the pathway of CDK4/6 inhibitors resistance, drugs could synergize with other agents for more durable responses and therapeutic index improvement.

Ferroptosis is a form of cell death that is caused by the iron dependent accumulation of lipid peroxide and membrane damage [11,12]. The past few years have seen advanced understanding of the role of ferroptosis in tumor biology and cancer therapy [7,[13], [14], [15]]. Extensive preclinical evidence suggests that the induction of ferroptosis might be an effective therapeutic strategy to alleviate acquired resistance to chemotherapy and targeted therapy [7,14,16]. Solute carrier family 7 member 11 (SLC7A11; also known as xCT) is the transporter subunit in system xc− and mediates cysteine uptake from the extracellular space [17]. The SLC7A11–GSH–GPX4 axis is believed to constitute the major cellular system defending against ferroptosis, and dysfunction of this system by identification of FDA-approved drugs as ferroptosis inducers (FINs) is a potential therapeutic strategy for cancers. Thus, the precise mechanism by which this system is regulated in luminal A breast cancer progression requires further elucidation [12].

Specificity protein 1 (SP1) was the first identified and characterized transcription factor in the SP/Krüppel-like family and has a wide range of important biological functions [18]. It binds to GC-rich sequences and is involved in regulating the expression of many genes in organisms that are involved in many aspects of cell life, such as metabolism, cell growth and differentiation, angiogenesis, and apoptosis [19,20]. In collected or established patient samples and tumor models, SP1 expression levels are closely related to tumor stage, invasive potential, and patient survival, and high levels of SP1 often predict a poor patient prognosis [21]. Many studies have also shown that SP1 is also closely involved in oxidative responses and may be a potential regulator of ferroptosis in ischemia‒reperfusion diseases [[22], [23], [24]]. However, whether SP1 activates or inhibits ferroptosis and how it regulates ferroptosis in luminal A breast cancer tumors have not been determined, and systematic research is lacking.

In this study, it was found that CDK4/6 inhibitors transcriptionally inactivated SLC7A11 expression to induce ferroptosis by regulating SP1 activity in a proteasome-dependent manner. SP1 inhibition resulted in a decrease in SLC7A11 expression to promote ferroptosis and enhance the sensitivity of cells to CDK4/6 inhibitors. Furthermore, the combination of SP1 inhibitors or ferroptosis inducers with CDK4/6 inhibitors may be a promising therapeutic strategy for luminal A breast cancer.

2 Materials and methods

For Studies in humans, we ensure that the work described has been carried out in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki) for experiments involving humans. The manuscript should be in line with the Recommendations for the Conduct, Reporting, Editing and Publication of Scholarly Work in Medical Journals.

All animal experiments have followed the ARRIVE guidelines and should be carried out in accordance with the U.K. Animals (Scientific Procedures) Act, 1986 and associated guidelines, EU Directive 2010/63/EU for animal experiments, or the National Research Council's Guide for the Care and Use of Laboratory Animals.

2.1 Cell lines and transfection

Breast cancer cell lines MCF-7 (HTB-22) and T-47D (HTB-133) and HEK293T (CRL-3216) were obtained from ATCC (American Type Culture Collection). Other breast cancer cell lines were gifted from Beijing Keystone Life Technology Co., Ltd. All the cells were cultured at 37 °C and 5 % CO2. MCF7 and T-47D cells were maintained in Dulbecco modified Eagle medium (DMEM) (Gibco) supplemented with 10 % fetal bovine serum (FBS) (Gibco), 1 % Penicillin-Streptomycin (P/S) (Pricella) and 10 μg/ml insulin (Pricella). CAMA1, KPL1, EFM-192A, HCC2218, HCC202, HCC1187, MDAMB231, CAL51 and HS578T were maintained in DMEM supplemented with 10 % FBS and 1 % P/S, as above. EFM19, HCC1500, BT-474, HCC-1419, ZR7530, HCC1569, BT549 HCC1937, HCC1143, HCC70 and DU4475 cells were maintained in RPMI1640 medium (Gibco) supplemented with 10 % fetal bovine serum (FBS) and 1 % P/S, as above. SKBR3 cells were maintained in McCoy's 5A (Gibco) supplemented with 10 % FBS and 1 % P/S, as above. BT20 cells were maintained in MEM (Gibco) supplemented with 10 % fetal bovine serum (FBS) and 1 % P/S, as above. All cell lines were routinely tested for mycoplasma using a PCR-based method and cell identity was confirmed using GenePrint® 10 STR Analysis for Human Cell Line Authentication (Promega).

For the described assays, cell lines were used within 4 weeks of thawing. In the experiments involving cystine depletion, fresh DMEM was used as the control, and the conditioned DMEM (21013024, Gibco) supplemented with 1 mM sodium pyruvate, 0.2 mM l-methionine, 4 mM l-glutamate, and 10 % nondialyzed FBS was used as low-cystine treatment. All transient transfections of plasmids into cell lines followed the standard protocol for Lipofectamine 3000 (Invitrogen).

2.2 Indicated genes overexpression and shRNA knockdown cell lines establishment and cell transfection

We screened four hairpin shRNAs targeting CDS (Coding sequence) of human SLC7A11 transcripts and found two independent sequences that reduced mRNA levels by > 70 %. These shRNAs were in the pLKO.1vector (shSLC7A11-1 and shSLC7A11-2). For SP1, we constructed two efficient hairpin shRNAs (shSP1-1 and shSP1-2). The targeting sequences used were show in Supplementary Table 1. To produce lentiviral particles, 1 × 107 HEK293T cells in a 55 cm2 dish were co-transfected with 10 μg pLKO.1 shRNA construct, 5 μg psPAX2, and 5 μg pMD2G using Lipofectamine 3000 (Invitrogen). The supernatant containing viral particles was harvested at 48 and 72h after transfection and was filtered through Millex-GP Filter Unit (0.45 μm pore size, Millipore). To infect cancer cells with lentivirus, cells were infected twice with culture medium containing 2 mL lentivirus, 200 μL FBS and 5 mg/mL polybrene (Sigma) at 37 °C for 24 and 48h. To increase the knockdown efficiency, infected cells were under several days of puromycin (Beyotime) selection, followed by western blotting examination. To establish SLC7A11 overexpression cells, vectors containing SLC7A11 cDNA were used. Lentiviral transduction, puromycin selection and western blotting examination were performed as described above. ShRNAs targeting SLC7A11 and SP1 were listed in Supplementary Table 2.

For RNA interference, breast cancer cells with 80 % confluence in 6-well plates were transfected with control siRNA or GPX4 siRNA using Lipofectamine 2000 (11668027, Invitrogen) according to the manufacturer's protocol. A nonspecific oligonucleotide without complementary to any human gene was used as a negative control. The sequences of two sense strands of siRNA targeting GPX4 are listed in Supplementary Table 2. All siRNAs were synthesized by JTSBIO Company (Beijing, China).

2.3 Chemicals

Mithramycin A (HY-A0122), Z-VAD-FMK (HY–16658B), Necrostatin-1s (HY-14622A), NAC (HY–B0215), RSL3 (HY-100218A) and sulfasalazine (HY-14655) were obtained from MedChemExpress (Shanghai, China). Ferrostatin-1 (S7243), Palbociclib (S1579), Ribociclib (S5187), Abemaciclib (S7158) and Erastin (S7242) were obtained from Selleckchem. Palbociclib, Ribociclib and Abemaciclib were dissolved in double distilled water, and the rest were dissolved in dimethyl sulfoxide (DMSO) (Sigma).

2.4 Clonogenic survival assay

To determine drug effect on colony formation (clonogenic assay), 400–2000 cells were plated in triplicate in each well of 6-well plates, treated with the indicated agents for 24 h and allowed to recover in the absence of drug until 14 days. Cells were then washed with 1X PBS and stained with a 0.5 % crystal violet (Solarbio) solution in 25 % methanol for 10 min. Plates were then scanned to obtain pictures.

2.5 Cell viability assay and treatment combination analysis

For cell viability assays, 7000 T-47D or MCF-7 cells per well were plated in triplicate in 96- well plates and allowed to adhere for 24 h. To assess the rescue effect of Necrostatin-1s (Nec-1S), Z-VAD-FMK (Z-VAD), N-Acetylcysteine (NAC) or Ferrostatin-1(Ferr-1) on cells treated with abemaciclib, cells were treated with ddH2O, abemaciclib, or abemaciclib in combination with cell death inhibitors at indicated concentrations for 24 h. To assess the sensitization of erastin, RSL3, SAS or Mithramycin A to abemaciclib, cells were treated with ddH2O or abemaciclib for 24 h before being co-treated with erastin, RSL3, SAS or Mithramycin A for 24 h. Then, the medium was replaced with 100 μL fresh medium containing 10 μL Cell Counting Kit-8 (CCK8) reagent (Dojindo Molecular Technologies, CK04), and cells were incubated in a humidified incubator at 37 °C, 5 % CO2 for 1.5 h. Cell viability was measured at 450 nm absorbance using a Spectra Max 250 spectrophotometer (Molecular Devices, Sunnyvale, CA, USA). The percentage of growth was calculated as Cell viability (%) = [OD (Compound +) – OD (Blank)]/[OD (Compound -) – OD (Blank)] × 100 %. Each experiment was repeated in triplicate independently. Values were normalized to those of no treatment controls and analyzed in GraphPad Prism 9.

To evaluate the combination effect of abemaciclib and ferroptosis inducers or Mithramycin A at indicated concentrations, a series of doses and effects (FA-fraction affected) were entered into the CompuSyn software tool for treatment combination analysis. For each treatment alone and their combinations (nonconstantratio combinations), the software automatically calculated combination index (CI) values at different FA levels based on the CI algorithm. The resulting CI theorem of Chou–Talalay offered quantitative definition for additive effects (CI = 1), synergism (CI < 1), and antagonism (CI > 1) of drug combinations.

2.6 Western blotting and antibodies

Proteins were lysed from cells or tumor tissues using RIPA buffer (Solarbio) containing 1 % PMSF (Solarbio, Beijing, China). Protein concentration was determined using BCA Protein Assay Kit (ab102536, Abcam, UK) before proteins were equally loaded and separated by polyacrylamide gel. Protein lysates were separated by Western blot on 12 % SDS-polyacrylamide gels (Invitrogen) and transferred onto PVDF membranes (Millipore). Membranes were probed with primary antibodies: anti-β-Actin (1:2000, #3700S, Cell Signaling Technology, USA), anti-SP1 (1:1000, 9389S, Cell Signaling Technology, USA), anti-SLC7A11(1:1000, 12691S, Cell Signaling Technology, USA), anti-GPX4 (1:2000, ab125066, Abcam, USA), anti-ACSL1(1:1000, 9189S, Cell Signaling Technology, USA), Phospho-Rb (Ser807/811) (1:1000, 8516S, Cell Signaling Technology, USA), Rb (1:2000, 9309, Cell Signaling Technology, USA). HRP-conjugated secondary antibodies were bought from Proteintech. Inc and signals were detected on the Bio-rad chemidoc MP system after incubation with ECL solution.

2.7 Quantitative RT-PCR

Total RNA was extracted from cultured breast cancer cells using Trizol (Invitrogen) and reverse transcribed into cDNA by using HifairTM II 1st Strand cDNA Synthesis SuperMix Kit (11123ES60, YEASEN, Shanghai, China) according to the manufacturer's protocol. Quantitative PCR reactions were performed by a BIO-RAD CFX96™ (Bio-rad, San Diego, USA) in the presence of SYBR Green (11201ES08*, YEASEN, Shanghai, China) and the amplification of the desired products was monitored and recorded using CFX96TM Real-Time PCR Detection System (BIO-RAD). Reactions were carried out in triplicate. The fold difference in transcripts was calculated using the ΔΔCt method with β-actin as a control. All the above primers were synthesized by Biomed Company (Beijing, China). Primer sequences are listed in Supplementary Table 3.

2.8 Transmission electron microscopy

Briefly, cells with indicated treatments were fixed with 2.5 % (vol/vol) glutaraldehyde with Phosphate Buffer (PB) (0.1 M, pH 7.4) two times in PB and two times in ddH2O at 4 °C. Then cells were post-fixed with 1 % (wt/vol) OsO4 and 1.5 % (wt/vol) potassium ferricyanide aqueous solution at 4 °C for 2 h, dehydrated through a graded ethanol series (30,50,70,80,90,100 % × 2, 6 min) into pure acetone (2 × 6 min). Samples were infiltrated in graded mixture (3:1, 1:1, 1:3) of acetone and SPI-PON812 resin (21 ml SPI-PON812, 13 ml DDSA and 11 ml NMA), then changed pure resin. Finally, cells were embedded in pure resin with 1.5 % BDMA and polymerized for 12h at 45 °C, 48 h at 60 °C. The ultrathin sections (70 nm thick) were sectioned with microtome (Leica EM UC6), double-stained by uranyl acetate and lead citrate, and examined by a transmission electron microscope (FEI Tecnai Spirit120kV) with the EMSIS CCD camera (VELETA) at the Center for Biological Imaging (CBI), Institute of Biophysics, Chinese Academy of Science.

2.9 Lipid peroxidation assay

Briefly, cells were seeded in triplicate in 6-well or 12-well plates for 24 h, followed by treatment with test compounds for the indicated times as shown in the figures. BODIPY 581/591C11 dye (Invitrogen, D3861) for lipid peroxidation (total) was added at a concentration of 5 μM at 37 °C for 30 min in the dark. After trypsinization into a cell suspension, cells were washed with PBS by centrifugation. The fluorescence intensity of cells with BODIPY 581/591C11 staining was measured by flow cytometry (Beckman Coulter) on the FL1 detector that recorded only live cells using a gating technique. Oxidation of BODIPY™ 581/591C11 resulted in a shift of the fluorescence emission peak from 590 nm (FL2 or FL3) to 510 nm (FL1). The relative lipid peroxidation level was indicated by the percentage of cells gated by the black solid line based on the fluorescence intensity in FL1 channel, and the values obtained from three independent replicates for each condition per cell line were used to generate bar graphs as quantitative data. A minimum of 5 × 103 cells was analyzed for each sample, and each experiment was independently performed at least three times. Representative experimental results are shown, and data were collected and analyzed using the BD FACSuite software, guava v.3.1.1 software and FlowJo 10 software.

For Liperfluo assay, MCF-7 and T-47D cells were seeded into 15 mm glass-bottom cell culture dishes and incubated overnight. Then, cells were treated with Abemaciclib, Mithramycin A or vehicle (DMSO) for 24 h. Cells were stained in 10 μM Liperfluo (L248, DOJINGO, Japan) in RPMI-1640 medium for 30 min at 37 °C incubator with 5 % CO2 and imaged immediately. Treatments were staggered to ensure precise staining duration. Images were captured by Laser Scanning Confocal Microscope FV3000 (Olympus, Japan). Six representative fields were captured for each condition under identical exposure times.

2.10 Glutathione and MDA measurement

For glutathione measurement, indicated cells were seeded into a 6 cm2 tissue culture plate and treated indicated measurements. After 24 h, cells were collected by scraping and the levels of GSH (reduced glutathione)/GSSG (oxidized glutathione disulfide) were measured using a GSH/GSSG ratio detection assay kit (S0053, Beyotime, Shanghai, China) according to the manufacturer's instructions. The GSH and GSSG concentrations were calculated using the standard curve and normalized to the total protein level. Three independent biological replicates were performed.

For MDA measurement, indicated cells were seeded into a 55 cm2 culture plate and were treated indicated measurements. After 24 h, cells were collected and the level of MDA was using the Lipid Peroxidation MDA Assay Kit (S0131, Beyotime, Shanghai, China) according to the manufacturer's instructions. The samples and standards were prepared and the OD value was measured at 532 nm. MDA concentrations (nmol/ml) were expressed as μ mol/mg protein.

2.11 Reporter constructs and luciferase reporter assay

Transient transfections were performed using Lipofectamine 2000 (1168019, Invitrogen, Carlsbad, CA, USA). SLC7A11 reporter-gene assays were performed by co-transfecting ZR75-1 cells pGL4.10-SLC7A11, pcDNA3-SP1 and respective control vector plasmids, and TKRL plasmid was used as an internal control. The SLC7A11 mutant form (SLC7A11 Mut) contained sequences mutated from TGTGAGGAGGAAAAT to TGTAGAAGAAGGAAT. Briefly, MCF-7 cells seeded in 24-well plates in DMEM medium were transfected the indicated plasmid DNA. 24 h after transfection, transfected cells were treated with abemaciclib for another 24 h. And then, cells were lysed with the lysis buffer (25 mM Tris-Cl (pH 7.8), 2 mM 1,2-diaminocyclo-hoxane N,N,N,N-tetracetic acid, 25 mM dithiothreitol (DTT), 1 % Triton X-100 and 10 % glycerol), and Luciferase assays were performed using the dual-luciferase reporter assay system (Promega, E1960).

2.12 Chromatin immunoprecipitation (ChIP) analysis

ChIP assay was performed according to the manufacturer's instructions (CST, 9004). Briefly, indicated MCF-7 cells were cross-linked with 1 % formaldehyde for 10 min and terminated by glycine. The extracted chromatin was digested and fragmented to 0 to 90 bp and then immunoprecipitated using Anti-SP1 antibody-ChIP Grade (Abcam, ab125066) or normal IgG with protein A/G agarose beads. The complexes were then uncross linked to obtain pure DNA fragments. The primers for the SP1 binding site of SLC7A11 promoter were then used for PCR. The following primers? pairs were used:

SLC7A11-ChIP-F: 5′-CCTATCTGAGTGGGGTCTTTGG-3’;

SLC7A11-ChIP-R: 5′-GGAATCTCAGGGAACAGCAACT-3’.

2.13 Cell line-derived xenograft model

Animal studies were approved by the Research Ethics Committee of the Chinese PLA General hospital (2019-x15-68), and performed in accordance with guidelines established by NIH Guide for the care and use of laboratory animals. All animals were housed on a 12 light/12 dark cycle, temperature 70° ± 5F, and humidity 40–60 %. For the MCF-7 cell subcutaneous xenograft model, four-week-old BALB/c-nu/nu mice (female, weighing 16–18 g, SPF grade, certification No. SCXK (Beijing) 2019–0010) were achieved from SPF (Beijing) Biotechnology Co., Ltd. (Beijing, China). Briefly, 7 × 106 MCF-7 cells were suspended in a total of 100 μL serum-free medium DMEM and Matrigel (ABW BIO, China) (1:2, v/v) and implanted subcutaneously into the dorsal flank of the mice. When the tumor volume reached about 100 mm3, the mice were grouped randomly and divided into six groups (n = 6) randomly and treated with 100 μL of either vehicle (i.p.), abemaciclib alone (150 mg/kg, i. g.), sulfasalazine alone (250 mg/kg, i. p.), Mithramycin A (0.3 mg/kg, i. p.) and abemaciclib in combination with sulfasalazine or Mithramycin A. Every 5 days was a treatment cycle until experimental endpoints, in which drug treatment was administered daily for the first 3 days, and the last 2 days were rest days. Tumor volume and body weight were measured once every five days unless otherwise specified. The volume was calculated with Eq: V = [length (mm) × width [2](mm)]/2. The mice were sacrificed at the end of the studies. Tumors were harvested, weighed, and analyzed by immunohistochemistry. Survival curves were plotted by using Graphpad Prism 10.

2.14 Histology and immunohistochemistry

Xenograft tumor samples were collected and immediately fixed in 10 % neutral-buffered formalin (ThermoFisher Scientific) overnight. After being washed once with PBS, samples were transferred into 70 % ethanol and subjected to embedding, sectioning, and hematoxylin and eosin staining. For immunohistochemical staining, the formalin-fixed and paraffin-embedded tissue sections were subjected to immunohistochemistry (IHC). And then, tissue sections were deparaffinized, rehydrated, and treated with 3 % H2O2 for 15min to inhibit endogenous peroxidase activity. After heat-induced epitope recovery in 10 mM citrate buffer (pH 6.0) in microwave for 30min, pre-diluted primary antibodies were incubated overnight at 4 °C [anti-4-HNE (1:100, MAB3249, R&D Systems), anti-Ki-67 (1:200, Abcam, ab16667), anti-SP1(1:600, 9389S, Cell Signaling Technology) and anti-SLC7A11(1:50, NB300–318SS, Novus)]. After incubation with a secondary antibody, the signal was developed with 3,3′-diaminobenzidine tetrachloride. Images were obtained at 400 × magnification using an Olympus BX43 microscope. Immunohistochemical staining was semiquantitatively analyzed using the immunoreactive score (IRS) system. The percentage of positive cells was scored as follows: no stained cells: 0; 1–10 % staining: 1; 10–50 % staining: 2; 51–80 % staining: 3; and 81–100 % staining: 4. The staining intensity was scored as follows: no color reaction: 0; mild reaction: 1; moderate reaction: 2; and intense reaction: 3. Final IRS scores of immunohistochemistry = (scores of staining intensity) × (scores of percentage of positive cells). Slides were assessed by 2 pathologists. Values were expressed as mean ± SD.

2.15 Patients and tissue samples

We obtained paraffin-embedded samples of primary breast adenocarcinomas (prepared as Tissue Microarray, TMA) from the Department of Oncology at the Seventh medical center of Chinese PLA General Hospital. The original immunohistochemistry slides were scanned by Aperio Versa (Leica Biosystems) which captured digital images of the immunostained slides. The Genie calculates an H-score for regions selected by the pathologist. The receiver operating characteristic curve (ROC) was used to define the cut-off point. All samples were collected with the patients’ written informed consent and approval from the Seventh medical center of Chinese PLA General Hospital Review Board (ethics code: LZEC2013-YW-004).

2.16 Bioinformatics analysis

The RNA Seq data of hormone receptors-positive breast cancer tissue in TCGA database and normal breast tissue or adjacent breast tissue in GTEX database were downloaded from the UCSC Xena website (http://xena.ucsc.edu/) and subsequently analyzed by R (Version 3.4, http://www.bioconductor.org) with edgeR package using GSVA method. GSEA analysis was performed using the Java desktop software (http://www.broadinstitute.org/gsea/index.jsp), genes were ranked according to the shrunken limma log2 fold changes, and the GSEA tool was used in the ‘pre-ranked’ mode with all default parameters. Bubble chart and volcano plot analysis was performed using the OmicShare tools, an online platform for data analysis (http://www.omicshare.com/tools). A web server for cancer and normal gene expression profiling and interactive analyses, the publicly available Gene Expression Omnibus (GEO) Databases (https://www.ncbi.nlm.nih.gov/), GEPIA2 (http://gepia.cancer-pku.cn/index.html), GSCA (https://guolab.wchscu.cn/GSCA), and Kaplan-Meier Plotter (https://kmplot.com/analysis/) were recruited to determine the expression of related genes in breast cancer and the clinical survival of the related genes. The online database of R2: Genomics Analysis and Visualization Platform (https://hgserver1.amc.nl) was applied to determine the correlation between SP1 and related genes. The open-access database of transcription factor binding profiles-JASPAR 2020 (http://jaspar.genereg.net/) was recruited to predict related motifs.

2.17 RNA-seq data analysis

Total RNA was extracted by TRizol (ThermoFisher, USA) from MCF-7 cells treated with abemaciclib or vehicle (ddH2O) for 24 h and RNase-free DNase I to remove genomic DNA contamination. RNA integrity was evaluated with a 1.0 % agarose gel. Thereafter, the quality and quantity of RNA were assessed using a NanoPhotometer® spectrophotometer (IMPLEN, CA, USA). The high-quality RNA samples were subsequently submitted to the Novogene Biotech (Beijing, China) for library preparation and sequencing. Sequencing libraries were generated using VAHTSTM mRNA-seq V2 Library Prep Kit for Illumina® following the manufacturer's recommendations and index codes were added to attribute sequences to each sample. The libraries were then quantified and pooled. Paired-end sequencing of the library was performed on the HiSeq XTen sequencers (Illumina, San Diego, CA). FastQC (version 0.11.2) was used for evaluating the quality of sequenced data. Clean reads were mapped to the reference genome by HISAT2 (version 2.0) with default parameters. RSeQC (version 2.6.1) was used to analyze the alignment results. Gene expression values of the transcripts were computed by StringTie (version 1.3.3b). Library preparation and high-throughput sequencing were performed by Novogene Biotech (Beijing, China).

2.18 Statistical analysis

Statistical analyses were performed as described in the figure legend for each experiment. Results were represented as mean ± SD unless otherwise noted in the figure legend. Statistical tests were selected based on appropriate assumptions with respect to data distribution and variance characteristics. Differences were considered statistically significant at P ≤ 0.05. Statistical significance (P values) was calculated using unpaired Student's t-tests, two-way ANOVA, two-sided Pearson Chi-Square Test, Fisher's Exact Test, χ2 test or log-rank test by SPSS 26.0 (SPSS Inc.) and plotted by GraphPad Prism 9.0 (GraphPad Software, Inc.) or. All data shown are representative of two or more independent experiments with similar results, unless indicated otherwise.

3 Results

3.1 CDK 4/6 inhibition promotes ferroptosis in luminal A breast cancer cells

Inhibitors targeting CDK4/6 kinases (CDK4/6 inhibitors) have shown promising clinical prospects in treating ER+/HER2-breast cancers when combined with letrozole or fulvestrant [25,26]. Previous studies have focused on the function of inducing cell cycle blockade and cell cycle senescence by CDK4/6 inhibitors, however, few studies have explored whether ferroptosis may be involved in the tumor inhibition of CDK4/6 inhibitors. To explore the role of ferroptosis in the antitumor effects of palbociclib, cells were treated with abemaciclib or ddH2O for 24 h before DNA was extracted and subjected to RNA-seq transcriptome and gene set enrichment analysis (GSEA). Subsequent results showed that the signaling pathways involved in the cell cycle and DNA replication were significantly downregulated as expected (Supplementary Fig. S1C), while the ferroptosis pathway was notably upregulated by abemaciclib (Fig. 1B). A total of 585 differentially expressed genes (DEGs) between the control and abemaciclib treatment groups were identified in the volcano plot (Fig. 1A). Further GSEA indicated that ferroptosis signature enrichment was increased by abemaciclib (Fig. 1C). Collectively, ferroptosis was closely related to the abemaciclib-induced response.Fig. 1 CDK 4/6 inhibition promotes ferroptosis in ER + breast cancer cells.

A, Volcano plot showing genome-wide mRNA expression in MCF-7 cells treated with abemaciclib (1 μM) for 24 h, as compared to control (ddH2O) (Padj ≤0.05; log2 (Fold Change)| ≥1).

B, Bubble chart displaying the upregulated gene enrichment analyses, as detected by RNA-seq in MCF-7 cells treated with abemaciclib (1 μM) for 24 h, as compared to control (ddH2O)·

C, Gene set enrichment pathway analysis (GSEA) of ferroptosis signature in MCF-7 cells treated with abemaciclib (1 μM) for 24 h, as compared to control (ddH2O) revealed that abemaciclib treatment is positively correlated with ferroptosis.

D, C11-BODIPY 581/591 (5 μM) probe was used to detected lipid peroxidation level in in MCF-7 and T-47D cells treated with ddH2O or abemaciclib (0.8 μM) in the absence or presence of ferrostatin-1(5 μM) for 24 h by flow cytometry. Quantification of C11-BODIPY 581/591(FL1) fluorescence was shown. The data are presented as the means ± SD, n = 3 biologically independent experiments.

E, Lipid ROS detected by confocal microscopy performed at ddH2O or abemaciclib (1 μM) in MCF-7 and T-47D cells stained with Liperfluo probe (10 μM). Shown is one of six representative fields illustrating fluorescence intensity taken at identical exposures for each condition. Scale bars = 50 μm. The data are presented as the means ± SD, n = 3 biologically independent experiments.

F, Relative GSH/GSSG ratio were assayed in T-47D and MCF-7 cells treated with ddH2O or abemaciclib (1 μM). The data were normalized to those of control cells treated with ddH2O in respective cell lines. The data are presented as the means ± SD, n = 3 biologically independent experiments.

G, Intracellular MDA were assayed in T-47D and MCF-7 cells treated with ddH2O or abemaciclib (1 μM). The data were normalized to those of control cells treated with ddH2O in respective cell lines. The data are presented as the means ± SD, n = 3 biologically independent experiments.

H, Transmission electron microscopy images of T-47D cells treated with ddH2O or abemaciclib (1 μM)for 24 h. Scale bars: left, 2 μm; right, 0.5 μm. Shown is one of representative fields taken at identical exposures for each condition. n = 3 biologically independent experiments.

I, Cell viability in T-47D and MCF-7 cells lines treated with 1.5 μM abemaciclib in the absence or presence of 2 μM necrostatin-1s, 5 μM Z-VAD-fmk, 5 μM ferrostatin-1, 5 mM N-acetyl-l-cysteine, or DMSO, solely or combinedly for 24h. The survival data were normalized to those of untreated control cells. The data are presented as the means ± SD, n = 3 biologically independent experiments.

J, Cell viability in T-47D and MCF-7 cells treated with indicated concentrations of abemaciclib with or without ferrostatin-1 (5 μM). The survival data were normalized to those of untreated control cells. The data are presented as the means ± SEM, n = 3 biologically independent experiments.

P values were determined by unpaired two-tailed unpaired Student's t-test for panels F, G, I, and two-way ANOVA followed by Tukey test for panels J. (ns, P > 0.05; *, P < 0.05; **, P ≤ 0.01; ***, P < 0.001; ****, P ≤ 0.0001.)

Fig. 1

Given that ferroptosis is driven by the accumulation of lipid peroxidation products [11,27], the BODIPY™ 581/591C11 staining was used to quantify the percentage of lipid peroxidation in tested cells via flow cytometry. Results showed that CDK4/6 inhibitors treatment with abemaciclib markedly induced lipid peroxidation in the luminal A breast cancer cell lines T-47D and MCF-7, and that the increased accumulation of lipid peroxides following abemaciclib treatment was abolished by concurrent treatment with ferrostatin-1, a potent ferroptosis inhibitor (Fig. 1D). It was worth pointing out that lipid peroxidation is measured by BODIPY™ 581/591C11 only in living cells. Therefore, cells were treated with relatively low doses of abemaciclib to prevent excessive cell death. Liperfluo is another lipophilic ROS sensor that provides a rapid, indirect approach to detect lipid ROS [11,12]. The results revealed that abemaciclib also increased lipid ROS in luminal A breast cancer cells by measuring the fluorescence in a confocal microscope (Fig. 1E). Next, transmission electron microscopy (TEM) was used to examine the morphological changes in luminal A breast cancer cells treated with abemaciclib. The treated cells exhibited obvious changes in the mitochondrial morphology consisting of size shrinkage, thickening of the double bilayer membranes and disappearance of mitochondrial cristae, which are morphologic features of ferroptosis [[27], [28]] (Fig. 1H). Moreover, abemaciclib reduced the GSH/GSSG ratio considered as a readout for intracellular oxidative damage and upregulated the level of MDA (malondialdehyde), which is the end product of lipid peroxidation in luminal A breast cancer cells (Fig. 1F and G). To find out more about the potential role of ferroptosis in the abemaciclib-induced cell death response, the impacts of some cell death inducers, including the ferroptosis inhibitor ferrostatin-1, the apoptosis inhibitor Z-VAD-fmk, the necroptosis inhibitor necrostatin-1s, and the ROS scavenger N-acetyl-l-cysteine (NAC), were examined through the viability of abemaciclib-treated cancer cells. It was observed that the restoration of cell survival induced by Z-VAD-fmk was mildly stronger than that of ferrostatin-1 or NAC in T-47D cells but weaker than that of ferrostatin-1 or NAC in MCF-7 cells. Consistently, previous studies suggested that abemaciclib induced ROS production and apoptosis by arresting the cell cycle, so NAC, as an ROS scavenger, also partially restored the antitumor effect of abemaciclib (Fig. 1I). Additionally, after treating cells with Ferr-1 and Z-VAD in combination with abemaciclib, cell viability was further restored, showing a stronger effect compared to the use of a single drug combined with abemacicilib. It was indicated that abemaciclib exerted its effects through at least two pathways: ferroptosis and apoptosis. However, when Ferr-1, Z-VAD, and NAC were used together, there was no further improvement in cell viability. This suggests that NAC, as an ROS scavenger, may inhibit the function of abemaciclib by ferroptosis or apoptosis pathways, thus not further restoring cell viability. These data further support our finding that ferroptosis partially contributes to the efficacy of the CDK4/6 inhibitor abemaciclib and that this effect does not completely overlap with apoptosis. Further analysis revealed that abemaciclib treatment expectedly inhibited clonogenic survival, while the combination of abemaciclib and ferrostatin-1 partially restored survival in T-47D and MCF-7 cells (Supplementary Fig. S1A). Similarly, ferrostatin-1 treatment increased cell viability upon treatment with various concentrations of abemaciclib (Fig. 1J). Furthermore, abemaciclib treatment also sensitized T-47D and MCF-7 cells to low-cystine-medium–induced ferroptosis, and this sensitivity was fully rescued by Ferr-1 (Supplementary Fig. S1D). Taken together, our data strongly suggest that abemaciclib induces ferroptosis in cancer cells and that ferroptosis is partially responsible for the efficacy of the CDK4/6 inhibitor abemaciclib.

3.2 Abemaciclib promotes ferroptosis partially by suppressing SLC7A11-mediated GSH synthesis

To gain more insights into which genes involved in ferroptosis regulation may modulate the anti-tumor efficacy of CDK4/6i in breast cancer, we verified the impact of ferroptosis-related genes under abemaciclib treatments and performed analysis of mRNA expression data obtained from the BC datasets GSE243154 and GSE125215 in GEO and our own RNA-seq (Fig. 2A and B; Supplementary Fig. 1E). The repeated genes were finally identified as SLC7A11, GPX4 and ACSL1. To further confirm the results, we performed Western blot analysis and found that the lower protein levels of SLC7A11 were also observed in cells treated with three kinds of CDK4/6 inhibitors. Moreover, various concentrations of abemaciclib could influence SLC7A11 translation (Fig. 2G and H). The transcription of ACSL1 and GPX4 were obviously increased while the protein level wasn't (Fig. 2D–F). Notably, abemaciclib-reduced SLC7A11 expression preceded the induction of GPX4 (Supplementary Fig. 1F), suggesting that abemaciclib-induced changes in GPX4 might function as an adaptive response to combating abemaciclib-mediated ferroptosis stress to restore cell survival. Besides, the analysis of SLC7A11 expression in breast cancer from GSCA database demonstrated the potential activated effects of SLC7A11 mRNA on cell cycle activity [29] (Fig. 2C).Fig. 2 CDK4/6 inhibitor promotes ferroptosis partially through suppressing SLC7A11-mediated GSH synthesis.

A, Heatmap of ferroptosis related genes with data normalized from −1 (blue) to 1 (Red) in RNA-seq results. The color scale indicates fold change of log2 signal intensities of indicated genes. Those genes in red are significantly (P ≤ 0.05) enriched in ferroptosis pathway.

B, Venn diagram illustrating the overlap among the top 100 enriched pathways of RNA-seq and two BC databases.

C, Activity of cell cycle pathway in high and low SLC7A11 expression data from GSCA database.

D, Gene abundance of ferroptosis pathway after treated with palbociclib (1 μM), ribociclib (1 μM), or abemciclib (1 μM), for 24 h, as compared to control (ddH2O). The data are presented as the means ± SD, n = 3 biologically independent experiments.

E, Immunoblotting analysis of SLC7A11, GPX4 and ACSL1 protein levels in MCF-7 and T-47D cells treated with indicated concentrations of abemciclib for 24 h

F, Quantification of relative protein expression levels of SLC7A11, GPX4 and ACSL1 in (E). The data are presented as the means ± SD, n = 3 biologically independent experiments.

G, Immunoblotting analysis of SLC7A11, GPX4 and ACSL1 protein levels in MCF-7 and T-47D cells treated with indicated CDK4/6 inhibitors (1 μM)for 24 h

H, Quantification of relative protein expression levels of SLC7A11, GPX4 and ACSL1 in (G). The data are presented as the means ± SD, n = 3 biologically independent experiments.

P values were determined by unpaired two-tailed unpaired Student's t-test for panels F and H.(ns, P > 0.05; *, P < 0.05; **, P ≤ 0.01; ***, P < 0.001; ****, P ≤ 0.0001.). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 2

3.3 SLC7A11 affect cell susceptibility to CDK4/6 inhibitors through ferroptosis and ferroptosis inducers synergistically sensitize luminal A breast cancer cells to CDK4/6 inhibitors

SLC7A11 mediates cystine (oxidized dimeric form of cysteine) uptake and promotes GSH biosynthesis by providing its precursor cysteine [17]. Consistent with our results, abemaciclib treatment indeed reduced the GSH/GSSG ratio in luminal A breast cancer cells (Fig. 1F; Supplementary Fig. S1B), suggesting that abemaciclib might cause the functional inhibition of SLC7A11. To collect more evidence, SLC7A11-overexpressing T-47D cells (Fig. 3B) were also generated. It was observed that while abemaciclib decreased GSH levels and promoted lipid peroxidation in control cells, overexpression of SLC7A11 obviously restored GSH levels and mitigated abemaciclib-induced lipid peroxidation (Fig. 3C and D). Consistently, SLC7A11 overexpression significantly restored cell viability in abemaciclib-treated MCF-7 cells to a level similar to that after ferrostatin-1 treatment (Fig. 3A), suggesting weakened cellular sensitivity to abemaciclib treatment. It should be noted that SLC7A11 overexpression failed to promote resistance to abemaciclib under ferroptosis perturbation by ferrostatin-1 treatment (Fig. 3A), suggesting that overexpression of SLC7A11 attenuated the efficacy of abemaciclib mainly by blocking ferroptosis. Next, SLC7A11 knockdown (KD) via short hairpin RNA (shRNA) transfection exerted a more significantly negative effect on MCF-7 cell proliferation under abemaciclib treatment, suggesting that SLC7A11-KD rendered cells more sensitive to abemaciclib (Fig. 3G). We also showed that GSH/GSSG ratios were further reduced while the lipid peroxidation levels had increased, and the phenotypes were more obvious in SLC7A11-KD cells treated with abemaciclib (Fig. 3F–H and I). Together, these results revealed that the CDK4/6 inhibitors promoted ferroptosis at least partly by inhibiting SLC7A11-mediated GSH synthesis and that the efficacy of abemaciclib can be modulated by genetically altering SLC7A11 expression or pharmacologically modulating GSH levels in luminal A breast cancer cells. To further link SLC7A11-mediated GSH synthesis to abemaciclib-induced ferroptosis, MCF-7 cells were cotreated with N-acetyl cysteine (NAC) to promote GSH synthesis upon abemaciclib treatment. As with SLC7A11 overexpression, NAC treatment increased GSH levels in MCF-7 cells under both basal and abemaciclib-treated conditions (Supplementary Fig. S1B), suggesting that SLC7A11-promoted GSH biosynthesis represented an important mechanism in abemaciclib-induced ferroptosis.Fig. 3 SLC7A11 affect cell susceptibility to CDK4/6is and ferroptosis inducers synergistically sensitize LA breast cancer cells to CDK4/6is.

A, Relative cell viability of T-47D cells treated with flag-SLC7A11 and vector, and cultured with indicated concentrations of either abemciclib or both of abemciclib and ferrostatin-1 (5 μM) for 24 h. The Data are presented as the means ± SEM, n = 3 biologically independent experiments.

B, Western blot detected the expression of SLC7A11 in MCF-7 cells treated with flag-SLC7A11 and vector. Quantification of relative protein expression levels of SLC7A11was shown below. The data are presented as the means ± SD, n = 3 biologically independent experiments.

C, Relative GSH/GSSG ratio were assayed in T-47D treated with flag-SLC7A11 and vector, and cultured with either ddH2O or abemciclib (1 μM). The data were normalized to those of control cells treated with ddH2O in respective cell lines. The data are presented as the means ± SD, n = 3 biologically independent experiments.

D, Intracellular MDA were assayed in T-47D treated with flag-SLC7A11 and vector, and cultured with either ddH2O or abemciclib (1 μM). The data were normalized to those of control cells treated with ddH2O in respective cell lines. The data are presented as the means ± SD, n = 3 biologically independent experiments.

E, C11-BODIPY 581/591 (5 μM) probe was used to detected lipid peroxidation level in in T-47D cells treated with flag-SLC7A11 and vector, and cultured with either abemciclib (1 μM) or both of ferrostatin-1 (5 μM) by flow cytometry. Quantification of C11-BODIPY 581/591(FL1) fluorescence was shown. The data are presented as the means ± SD, n = 3 biologically independent experiments.

F, Relative cell viability of MCF-7 cells treated with SLC7A11 shRNA or scrambled shRNA, and cultured with indicated concentrations of abemciclib for 24 h. The Data are presented as the means ± SEM, n = 3 biologically independent experiments.

G, Western blot detected the expression of SLC7A11 in MCF-7 cells treated with SLC7A11 shRNA or scrambled shRNA. Quantification of relative protein expression levels of SLC7A11 was shown below. The data are presented as the means ± SD, n = 3 biologically independent experiments.

H, Relative GSH/GSSG ratio were assayed in MCF-7 cells treated with SLC7A11 shRNA or scrambled shRNA, and cultured with abemciclib (1 μM). The data were normalized to those of control cells treated with ddH2O in respective cell lines. The data are presented as the means ± SD, n = 3 biologically independent experiments.

I, Intracellular MDA were assayed in MCF-7 cells treated with SLC7A11 shRNA or scrambled shRNA, and cultured with abemciclib (1 μM). abemciclib (1 μM). The data were normalized to those of control cells treated with ddH2O in respective cell lines. The data are presented as the means ± SD, n = 3 biologically independent experiments.

J, C11-BODIPY 581/591 (5 μM) probe was used to detected lipid peroxidation level in MCF-7 cells treated with SLC7A11 shRNA or scrambled shRNA, and cultured with either abemciclib (1 μM) or both of ferrostatin-1 (5 μM) by flow cytometry. Quantification of C11-BODIPY 581/591(FL1) fluorescence was shown. The data are presented as the means ± SD, n = 3 biologically independent experiments.

K, Cell viability in MCF-7 cells treated with abemaciclib and/or erastin at indicated concentrations. The data are presented as the means ± SEM, n = 3 biologically independent experiments.

L, The Chou-Talalay plot showing the combination effect of indicated treatments. The purple or blue or orange dots in the plot represent the combination of abemaciclib and erastin at indicated concentrations. CI values less than, equal to, or greater than 1 indicate synergistic, additive, or antagonistic effects, respectively.

M, Clonogenic survival assay and representative images in MCF-7 cell lines treated with DMSO, abemaciclib(1 μM), erastin(4 μM) or abemaciclib in combination with erastin. The survival data were normalized to those of control cells treated with DMSO. The data are presented as the means ± SD, n = 3 biologically independent experiments.

N, Relative GSH/GSSG ratio were assayed in MCF-7 cells treated with abemaciclib (1 μM) and/or erastin(4 μM). The data were normalized to those of control cells treated with DMSO in respective cell lines. The data are presented as the means ± SD, n = 3 biologically independent experiments.

O, Intracellular MDA were assayed in MCF-7 cells treated with abemaciclib (1 μM) and/or erastin(4 μM). The data were normalized to those of control cells treated with DMSO in respective cell lines. The data are presented as the means ± SD, n = 3 biologically independent experiments.

P, C11-BODIPY 581/591 (5 μM) probe was used to detected lipid peroxidation level in MCF-7 cells treated with abemaciclib (1 μM) and/or erastin(4 μM) by flow cytometry. Quantification of C11-BODIPY 581/591(FL1) fluorescence was shown. The data are presented as the means ± SD, n = 3 biologically independent experiments.

P values were determined by unpaired two tailed T test for panels B–E, G-J, M−P, and two-way ANOVA followed by Tukey test for panels A, F, K. (ns, P > 0.05; *, P < 0.05; **, P ≤ 0.01; ***, P < 0.001; ****, P ≤ 0.0001). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 3

The aforementioned data not only revealed that ferroptosis significantly contributed to the efficacy of abemaciclib (Fig. 1A and E), but also led us to explore whether abemaciclib in combination with ferroptosis inducers (FINs), such as erastin, sulfasalazine (SAS), or RSL3, could synergistically potentiate ferroptosis and thus further sensitize luminal A breast cancer cells to abemaciclib. Since knockdown of SLC7A11 enhanced the efficacy of abemaciclib (Fig. 3F–I), there was reason to assume that erastin, which targets SLC7A11 to knowdown, would deliver a similar effect. We treated T-47D and MCF-7 cells with various concentrations of abemaciclib, erastin, or both. Consistent with our hypothesis, erastin treatment reduced cell viability and clonogenic survival, and dramatically sensitized MCF-7 and T-47D cells to abemaciclib (Fig. 3K and M; Supplementary Figs. S2C and E). To confirm that the combination effect of abemaciclib and erastin was synergistic rather than additive or antagonistic, the combination index was calculated using CompuSyn software based on the Chou-Talalay methodology. It should be noted that the combination indexes at the indicated concentrations of FINs and abemaciclib were less than 1 in both T-47D and MCF-7 cells (Fig. 3L; Supplementary Fig. S2D), suggesting that erastin synergizes with abemaciclib. It was also observed that the combination of abemaciclib and erastin further decreased GSH levels and promoted lipid peroxidation than single drug. Similar observations with SAS treatment were also made, an SLC7A11 inhibitor commonly used in the clinic [30,31]: SAS also sensitized T-47D cells to abemaciclib in a synergistic manner (Supplementary Figs. S2F–H).

The aforementioned data showed that GPX4 mRNA level was moderately induced in T-47D and MCF-7 cells upon abemaciclib treatment (Fig. 2D). Actually, The GPX4 dependent system, widely regarded as a cornerstone in defense against ferroptosis, hinges on the uptake of extracellular cystine mainly mediated by SLC7A11. Intracellular cystine imported by SLC7A11 is subsequently reduced to cysteine. Cysteine, in turn, serves as the essential precursor for the synthesis of GSH. GSH is harnessed by GPX4 to catalyze the reduction of harmful lipid hydroperoxides into harmless lipid alcohols, and thus it effectively suppresses ferroptosis [32,33]. However, as research progresses, there still exsists additional mechanisms to regulate GPX4 synthesis. Therefore, GPX4 induction may represent an adaptive response to combating abemaciclib-mediated ferroptosis stress, which may weaken the anticancer effect of abemaciclib to some extent. We transfected cells with siGPX4. As shown in Supplement Figs. S1G–1I, cells transfected with siGPX4 showed higher cell sensitivity to abemaciclib, stronger ferroptotic effects induced by abemaciclib. Whether the combination of FINs targeting GPX4 and abemaciclib would overcome this adaptive response was also studied. Results demonstrated that RSL3 (which inactivates GPX4) treatment significantly sensitized MCF-7 cells to abemaciclib in a synergistic manner (Supplementary Figs. S2I–K). Together, our data support that FINs synergize with abemaciclib to kill luminal A breast cancer cells, suggesting that the applications of abemaciclib might be extended to luminal A breast cancer patients when combined with FINs.

3.4 SLC7A11 is overexpressed in luminal A breast cancer and affects intrinsic sensitivity of luminal A subtype to CDK4/6 inhibitors

To further explore the potential oncogenic role of SLC7A11 in breast cancer, we interrogated the TCGA dataset and GTEx dataset and then tissues diagnosed as luminal A subtype were selected, including 681 tumor tissues and 67 tumor-adjacent tissues. The analysis showed that SLC7A11 was highly elevated in both a total of breast cancer without distinguishing subtypes and single luminal A subtype (Fig. 4D; Supplementary Fig. S2A). Moreover, higher expression of SLC7A11 was more significantly associated with low survival rates in luminal A breast cancer patients (HR = 5.33), compared with a total of breast cancer patients (HR = 2.45) (Fig. 4A and B).Fig. 4 SLC7A11 are associated with progression in luminal A breast cancer.

A, Kaplan-Meier analysis of breast cancer patients whatever subtypes from TCGA project with high or low expression levels of SLC7A11, and log-rank analysis was used to test for significance.

B, Kaplan-Meier analysis of luminal A breast cancer patients from TCGA project with high or low expression levels of SLC7A11, and log-rank analysis was used to test for significance.

C, Kaplan-Meier curve showing progression free survival of luminal A cancer patients with high or low SLC7A11 expression from tissue microarray, and log-rank analysis was used to test for significance.

D, Gene expression level of SLC7A11 in luminal A breast tumor and matched normal breast tissues from TCGA along with GTEx data, and Wilcox test was used to test for significance.

E, SLC7A11 expression in four subtypes of breast cancer data from GSCA database.

F, Representative images from immunohistochemical staining of SLC7A11 in luminal A cancers (n = 41) and normal tissues (n = 41). Scale bars: 100 μm (left); 50 μm (right).

G, The quantification of SLC7A11 protein level in breast cancer and adjacent normal breast tissues. The SLC7A11 levels were classified into 3 grades (weak positive/negative, media positive, strong positive) based on quantification of immunohistochemical staining and plotted.

H, Tumor tissue identified as luminal A or other subtypes were classified into 3 grades as panel G had mentioned and plotted.

I, Luminal A tumor tissue cancer patients with high or low SLC7A11 expression were classified into another 3 grades based on pathological grade and plotted.

J, Luminal A tumor tissue cancer patients with high or low SLC7A11 expression were classified into 2 subgroups

K, The up line of heatmap was plotted based on SLC7A11 relative mRNA expression in four subtypes of breast cancer by RT-PCR. The down line of heatmap was plotted based on median lethal concentration (IC50) to abemaciclib by cell viability assays.

Fig. 4

Given the important role of SLC7A11 in breast cancer has remained largely unexplored, we sought to directly evaluate its clinical importance in breast cancer patients. We examined the expression of SLC7A11 by immunohistochemistry (IHC) using serial sections of tissue microarrays containing breast cancer tissues (n = 90) and the matched adjacent breast tissues (n = 90; Fig. 4F). Specifically, only 35.6 % of breast cancer samples exhibited weak or negative staining for SLC7A11, whereas 90 % of adjacent lung tissues displayed weak or negative staining for SLC7A11 (Fig. 4G). Notably, forty-one out of ninety breast cancer tissues were luminal A subtype. We next investigated the role of SLC7A11 in luminal A breast cancer. In the results analyzed from both the tumor tissue microarray and public database, it was demonstrated that patients with a higher expression level of SLC7A11 usually had a poorer pathological stage, higher clinical stage and worse progression free survival (Fig. 4C–J), pointing to the important role of SLC7A11 in luminal A breast cancer malignancy. Therefore, our clinical observations indicate that SLC7A11 is a potential prognostic factor in luminal A breast cancer.

Additionally, we next explore the relationship between SLC7A11 and cell cycle. As shown in Fig. 4E and H, we found that luminal A subtype presented relative lower SLC7A11 mRNA expression, compared with other subtypes, which was similar to the results achieved from the GSCA database and the tumor tissue microarray. Intriguingly, the previous research had identified that cell lines representing luminal subtype was most sensitive to growth inhibition by palbociclib while nonluminal/basal subtypes were most resistant [5]. In order to further verify relative results, a total of 24 human breast cancer cell lines were categorized as representing luminal A, luminal B, Her-2 amplified or Her-2 amplified breast cancer subtypes. The calculated IC50 for each cell line and its molecular classification were determined (Fig. 4K). There was a statistically significant correlation between molecular subtype and sensitivity to Abemaciclib (χ2 < 0.05). The subtypes most sensitive to growth inhibition by Abemaciclib were luminal A subtype. Consisting with them, our aforementioned data were also shown that knockdown or inhibition of SLC7A11 was associated with an increased susceptibility to CDK4/6 inhibitors (Fig. 3A and F). The association between SLC7A11 expression and half maximal inhibitory concentration (IC50) of abemaciclib in luminal A type of cell lines was also determined and showed the positive correlation (Supplementary Fig. S2B). Taken together, our data suggest that the expressional level of SLC7A11 might play an important role in the intrinsic sensitivity of luminal A subtype to CDK4/6 inhibitors.

3.5 CDK4/6 inhibitor inhibits SLC7A11 expression largely through SP1 transcription

Our aforementioned data revealed that abemaciclib treatment exerted antitumor effects at least partly by inhibiting SLC7A11 from promoting ferroptosis. Moreover, the mRNA and protein levels of SLC7A11 were changed in the same way after treatment with CDK4/6 inhibitors. Therefore, it was assumed that CDK4/6 inhibitors suppressed the expression of SLC7A11 partially through transcriptional regulation.

SP1 has a wide range of important biological functions, including the cell cycle and ferroptosis. In our study, we interrogated the information from four common database about transcriptional factors prediction targeted on SLC7A11 and combined with the Kaplan–Meier analysis results in TCGA-BRCA, which showed that luminal A breast cancer patients with higher levels of SP1 presented worse overall survival, which was similar to the results for SLC7A11 (Fig. 5A and B). Moreover, the expression of SP1 was positively associated with that of SLC7A11 in luminal A breast cancers (Fig. 5C), these results were further verified by the positive expression correlation between SLC7A11 and SP1, as well as between respective gene expressional levels and corresponding progression free survival of patients through the IHC analysis of luminal A breast tumor microarray (Fig. 5D; Supplementary Fig. S3G). Besides, to further confirm the relationship between SP1 and SLC7A11, we transfected SP1 shRNAs into human MCF-7 cells and performed RT-PCR analysis and Weston blot found that SLC7A11 was significantly down-regulated by SP1 knockdown at both mRNA and protein levels in MCF-7 cells (Fig. 5E and G). Intriguingly, the protein levels of both SP1 and SLC7A11 in MCF-7 cells were decreased under the treatments of three CDK4/6 inhibitors (Fig. 5F). We then further investigated how abemaciclib regulates SP1 protein levels. As shown in Supplement Figure S3K and S3L, we treated cells with abemaciclib in combination with a proteasome inhibitor (MG132), a lysosome inhibitor (chloroquine) or a caspase inhibitor (Z-VAD-FMK). We found that abemaciclib-induced degradation of SP1 was significantly hindered by MG132, but not by chloroquine (CQ), indicating that abemaciclib induces SP1 degradation in a proteasome-dependent manner. And then, to assess the regulation of SLC7A11 by SP1 with or without CDK4/6 inhibitors treatment, ChIP‒qPCR analysis was performed. It was found that SP1 was bound to SLC7A11 in luminal A breast cancer cells (Fig. 5H). Then, the promoter of SLC7A11 was cloned into a dual-luciferase reporter construct and luciferase reporter gene assays were performed. It was shown that SLC7A11 was highly responsive to SP1-mediated transactivation (Fig. 5I). Additionally, abemaciclib effectively diminished SP1-dependent activation in a dual-luciferase reporter assay (Fig. 5I). Finally, the mutant form of the SLC7A11 promoter was generated by replacing AA with GG in the core motif of human SP1. Mutations of the putative SP1 core motif effectively diminished the SP1-dependent activation effect with or without abemaciclib (Fig. 5J). Taken together, these data suggest that SP1 transcriptionally regulate of SLC7A11 in luminal A breast cancer, thus offering evidence that abemaciclib may downregulate SLC7A11 mediated by SP1.Fig. 5 Abemaciclib inhibits SLC7A11 expression largely through SP1 transcription.

A, Venn diagram illustrating the overlap among three public database(TRANSFAC, GTRD, UCSC and PROMO) about the prediction of transcriptional factors targeted on SLC7A11.

B, Kaplan–Meier analysis of the overall survival rate (log-rank test, two sides) of ER + breast cancer patients with low (n = 69) or high (n = 611) expression of SP1·

C, Pearson's correlation analysis of the mRNA levels of SP1 and SLC7A11 in TCGA ER + breast cancer.

D, Representative images showing the correlation of SLC7A11 and SP1 staining in human luminal A breast tumor tissue microarray samples. Scale bars represent 100 μm

E, Immunoblotting analysis of SP1 and SLC7A11 protein levels in MCF-7 cells treated with SP1 shRNA#1, SP1 shRNA#2 or scrambled shRNA. Quantification of relative protein expression levels of SP1 and SLC7A11 was shown below. The data are presented as the means ± SD, n = 3 biologically independent experiments.

F, Immunoblotting analysis of SP1 and SLC7A11 protein levels in MCF-7 cells treated with palbociclib and abemaciclib for 24 h. Quantification of relative protein expression levels of SP1 and SLC7A11 was shown below. The data are presented as the means ± SD, n = 3 biologically independent experiments.

G, The mRNA levels of SP1 and SLC7A11 were examined by RT-PCR in MCF-7 and T-47D cells transfected with SP1 shRNA#1, SP1 shRNA#2 or scrambled shRNA. The data are presented as the means ± SD, n = 3 biologically independent experiments.

H, ChIP-qPCR analysis of relative enrichment of SP1 at the indicated gene promoter in MCF-7 cells treated with vehicle or abemaciclib for 24 h. Fold change means the indicated enrichment on this gene under influence of abemaciclib compared to the IgG enrichment in cells treated with vehicle control set as 1. The data are presented as the means ± SD, n = 3 biologically independent experiments.

I, JASPAR (http://jaspar.genereg.net/) predicts that the SP1 binding element on the SLC7A11 promoter is conserved. MCF-7 cells were transfected with different SLC7A11 constructs or empty vector for 24h and treated with ddH2O or abemaciclib (1 μM) for 24 h. And then, the luciferase activity of the different SLC7A11 promoter-reporter genes was measured. The solid red circle shows the position of the putative STAT3 binding site. The data are presented as the means ± SD, n = 3 biologically independent experiments.

J, WT or mutated pGL4.10- SLC7A11 promoter reporter genes were co-expressed with SP1 or EV and then treated with ddH2O or abemaciclib (1 μM) for 24 h. The expression of luciferase was measured and normalized to Renilla. The red letters in each binding region indicate a putative SP1 binding sequence or a mutated SP1 binding sequence. The data are presented as the means ± SD, n = 3 biologically independent experiments.

P values were determined by unpaired two-tailed T test for panels E–J, and the log-rank test for panel B. (ns, P > 0.05; *, P < 0.05; **, P ≤ 0.01; ***, P < 0.001; ****, P ≤ 0.0001). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 5

3.6 SP1 inhibition promotes ferroptosis and synergistically sensitizes luminal A breast cancer cells to abemaciclib

In order to find out whether SP1 inhibition contributed to ferroptosis in luminal A breast cancer cells, flow cytometry and TEM were used to determine the signal of ferroptosis. Results showed that SP1 knockdown caused a major increase in the lipid peroxidation level using the C11-BODIPY and exhibited morphologic features of ferroptosis (Fig. 6D; Supplementary Fig. S3A). Moreover, SP1 knockdown could further decrease the GSH/GSSG ratio, increase MDA level, and enhanced the sensitivity to abemaciclib in cells treated with abemaciclib (Fig. 6A–D; Supplementary Fig. S3I). Importantly, SLC7A11 re-expression in SP1 knockdown cells abrogated these effects in luminal A breast cancer cells (Fig. 6A–C; Supplementary Fig. S3I). These results suggested that inhibition of SP1 could further trigger ferroptosis in luminal A breast cancer cells treated with CDK4/6 inhibitors.Fig. 6 SP1 inhibition promotes ferroptosis and synergistically sensitizes LA breast cancer cells to abemaciclib.

A, Cell viability in MCF-7 treated with SP1 shRNA or scrambled shRNA and transfected with flag-SLC7A11, and cultured with abemaciclib at indicated concentrations of abemciclib. The Data are presented as the means ± SEM, n = 3 biologically independent experiments.

B, Relative GSH/GSSG ratio were assayed in MCF-7 cells treated with SP1 shRNA or scrambled shRNA and transfected with flag-SLC7A11, and cultured with or without abemciclib (1 μM) for 24 h The data were normalized to those of scrambled shRNA cells treated with ddH2O in respective cell lines. The data are presented as the means ± SD, n = 3 biologically independent experiments.

C, Intracellular MDA were assayed in MCF-7 cells treated with SP1 shRNA or scrambled shRNA and transfected with flag-SLC7A11, and cultured with or without abemciclib (1 μM) for 24 h The data were normalized to those of scrambled shRNA cells treated with ddH2O in respective cell lines. The data are presented as the means ± SD, n = 3 biologically independent experiments.

D, C11-BODIPY 581/591 (5 μM) probe was used to detected lipid peroxidation level in MCF-7 cells treated with SP1 shRNA or scrambled shRNA and transfected with flag-SLC7A11, and cultured with or without abemciclib (1 μM) for 24 h by flow cytometry. Quantification of C11-BODIPY 581/591(FL1) fluorescence was shown. The data are presented as the means ± SD, n = 3 biologically independent experiments.

E, Immunoblotting analysis of SP1 and SLC7A11 protein levels in T-47D and MCF-7 cells treated with DMSO or the indicated concentrations of Mithramycin A for 24 h

F, C11-BODIPY 581/591 (5 μM) probe was used to detected lipid peroxidation level in in MCF-7 and T-47D cells treated with Mithramycin A (0.5 μM) in the absence or presence of ferrostatin-1(5 μM) for 24 h by flow cytometry. Quantification of C11-BODIPY 581/591(FL1) fluorescence was shown. The data are presented as the means ± SD, n = 3 biologically independent experiments.

G, Relative GSH/GSSG ratio were assayed in T-47D and MCF-7 cells treated with Mithramycin A (0.5 μM) in the absence or presence of ferrostatin-1(5 μM) for 24 h The data were normalized to those of control cells treated with ddH2O in respective cell lines. The data are presented as the means ± SD, n = 3 biologically independent experiments.

H, Intracellular MDA were assayed in T-47D and MCF-7 cells treated with ddH2O or abemaciclib (1 μM). The data were normalized to those of control cells treated with ddH2O in respective cell lines. The data are presented as the means ± SD, n = 3 biologically independent experiments.

I, Quantification of relative protein expression levels of SP1 and SLC7A11 in (E). The data are presented as the means ± SD, n = 3 biologically independent experiments.

J, Transmission electron microscopy images of MCF-7 cells treated with DMSO, Mithramycin A (0.5 μM) or Mithramycin A + ferrostatin-1(5 μM) for 24 h. Scale bars: left, 2 μm; right, 0.5 μm. Shown is one of representative fields taken at identical exposures for each condition. n = 3 biologically independent experiments.

K, Lipid ROS detected by confocal microscopy performed at ddH2O or abemaciclib (1 μM) in MCF-7 and T-47D cells stained with Liperfluo probe (10 μM). Shown is one of six representative fields illustrating fluorescence intensity taken at identical exposures for each condition. Scale bars = 50 μm. The data are presented as the means ± SD, n = 3 biologically independent experiments.

L, Cell viability in MCF-7 cells treated with indicated concentrations of abemaciclib and/or Mithramycin A at indicated concentrations. The survival data were normalized to those of untreated control cells. The data are presented as the means ± SEM, n = 3 biologically independent experiments.

M, The Chou-Talalay plot showing the combination effect of indicated treatments. The purple or blue or orange or green dots in the plot represent the combination of abemaciclib and Mithramycin A at indicated concentrations. CI values less than, equal to, or greater than 1 indicate synergistic, additive, or antagonistic effects, respectively.

N, Relative GSH/GSSG ratio were assayed in MCF-7 cells treated with abemaciclib (0.5 μM) and/or Mithramycin A (0.2 μM). The data were normalized to those of control cells treated with DMSO in respective cell lines. The data are presented as the means ± SD, n = 3 biologically independent experiments.

O, Intracellular MDA were assayed in MCF-7 cells treated with abemaciclib (0.5 μM) and/or Mithramycin A (0.2 μM). The data were normalized to those of control cells treated with DMSO in respective cell lines. The data are presented as the means ± SD, n = 3 biologically independent experiments. P values were determined by unpaired two tailed T test for panels B–D, F–H, K, N, O, and two-way ANOVA followed by Tukey test for panels A, L. (ns, P > 0.05; *, P < 0.05; **, P ≤ 0.01; ***, P < 0.001; ****, P ≤ 0.0001). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 6

In addition to genetic inhibition of SP1, a pharmacological inhibitor of SP1 was also chosen to clarify our results. As a selective SP1 inhibitor approved by the FDA, Mithramycin A (MIT) inhibits SP1 binding to DNA. Next, several experiments were performed to provide further evidence for the regulation of ferroptosis by MIT in luminal A breast cancer cells. Consistent with observations of genetic inhibition of SP1, MIT treatment also resulted in a series of mitochondrial morphological changes associated ferroptosis and after treated with Ferr-1, these changes were attenuated, suggesting that MIT induced ferroptosis (Fig. 6J). As expected, it was observed that MIT promoted lipid ROS formation by the C11-BODIPY and Liperfluo probes, which was reversed by the ferroptosis inhibitor ferrostatin-1 (Fig. 6F and K). It was also shown that various concentrations of MIT could influence SLC7A11 transcription and translation (Fig. 6E and I; Supplementary Fig. S3B). We also evaluated the GSH/GSSG and MDA level in luminal A breast cancer cells and found that MIT treatment resulted in a decrease in GSH/GSSG ratio while an increase in MDA level (Fig. 6G and H). Collectively, these findings demonstrated that ferroptosis triggered by MIT played a critical role in the antitumor effects in luminal A breast cancer cells, and that the induction of ferroptosis suggested a new mechanism by which SP1 inhibitors functioned as anticancer agents.

Our aforementioned data revealed that FINs synergistically sensitized luminal A breast cancer cells to abemaciclib, which could help us explore whether abemaciclib in combination with MIT could also synergistically potentiate ferroptosis and sensitize cells to abemaciclib. Similar to the results for FINs, combined treatment with abemaciclib and MIT resulted in synergistic increases in antitumor effects in the tested cancer cells, including cell viability, GSH/GSSG ratio and MDA level (Fig. 6L–O; Supplementary Figs. S3C–F). Moreover, the combination index using CompuSyn software at the indicated concentrations of abemaciclib and MIT were less than 1 in both T-47D and MCF-7 cells (Fig. 6M; Supplementary Figure S3E). Taken together, these findings suggested that ferroptosis played an important role in the antitumor effect of MIT, and that the combination of MIT and abemaciclib exerted a synergistic ferroptosis effect.

3.7 Ferroptosis correlates with abemaciclib-mediated tumor suppression in vivo

To confirm the potential relevance of ferroptosis in the efficacy of abemaciclib and the possibility that inducing ferroptosis with FINs or MIT can sensitize tumors to abemaciclib in vivo, MCF-7 cells were inoculated into Balb/c nude mice. Given different kinds of FINs, class 1 FINs (which inhibit SLC7A11) were focused on in our vivo studies. However, erastin is not suitable for in vivo treatment because of its low solubility and poor metabolic stability. On the other hand, SAS, another class 1 FIN, has been used for in vivo treatment [17,34]. For these reasons, SAS was finally chosen in the following animal studies. We inoculated a mixture of MCF-7 cells and matrigel into nude mice and treated the mice with abemaciclib, SAS, MIT or both agents concurrently (Fig. 7A). As expected, a similar synergistic effect was also observed in the xenograft tumors (Supplementary Fig. S3M). The therapeutic potential of combining the ferroptosis inducer SAS or MIT with abemaciclib in MCF-7-cell-derived xenografts was further evaluated. Remarkably, the combinational treatments led to synergistic tumor growth regression in the luminal A breast cancer model (Fig. 7B and C), whereas single treatment with the SP1 inhibitor MIT, ferroptosis inducer SAS or abemaciclib alone showed only moderate and comparable inhibitory effects on tumor growth. In addition, IHC analysis of xenograft tumors demonstrated that the combined treatment significantly inhibited tumor growth as indicated by Ki67 staining (a marker of cell proliferation) and triggered ferroptosis as indicated by the staining of 4-hydroxy-2-nonenal (4-HNE), a lipid peroxidation marker [35] (Fig. 7D-H). Notably, compared with single drug, both SP1 and SLC7A11 staining were more significantly decreased under the combined treatment. Together, the results from the cell model and animal model suggested that either FINs or MIT significantly sensitized tumors to abemaciclib, likely by synergizing with abemaciclib-induced lipid peroxidation and ferroptosis, which can be a new strategy for luminal A breast cancer.Fig. 7 Ferroptosis correlates with abemaciclib-mediated tumor suppression in vivo.

A, Treatment schema for nude mice bearing MCF-7 xenograft. abemaciclib, sulfasalazine and/or Mithramycin A treatment initiated on day 1, and every 5 days was a treatment cycle until experimental endpoints, in which drug treatment was administered daily for the first 3 days, and the last 2 days are rest days.

B-C, BALB/c-nu/nu mice bearing the MCF-7 xenografts (n = 6 mice per group) received the vehicle, abemaciclib (i.g., 100 mg/kg), SAS (i.p., 250 mg/kg), Mithramycin A (i.p., 0.3 mg/kg) and their combination, as indicated. Mean tumor volume ± SD (B) andmean tumor weight ± SD (C) are shown.

D, Representative images of hematoxylin and eosin and immunohistochemical staining (Ki67, SLC7A11, SP1 and 4-HNE) of MCF-7 xenograft tumors with the indicated treatments. Scale bars, 50 μm.

E–H Immunochemistry scoring of Ki67, SLC7A11, SP1 and 4-HNE staining. Error bars are means ± SD, n = 6 randomly selected magnification fields. The data are presented as the means ± SD, n = 3 biologically independent experiments.

I, The working model depicting the role of ferroptosis in CDK4/6 inhibition-mediated tumor suppression in luminal A breast cancer. CDK4/6 inhibition can inactivate SP1 to repress SLC7A11 expression, leading to a decrease in GSH levels, thereby promoting ferroptosis. On this basis, CDK4/6 inhibitors in combination with ferroptosis inducers targeting SLC7A11 or SP1 inhibitors synergistically augment ferroptosis, resulting in potent tumor suppression in luminal A breast cancer. The data are presented as the means ± SD. P values were determined by unpaired two tailed T test for panels E-H, and two-way ANOVA followed by Tukey test for panels B, C. (ns, P > 0.05; *, P < 0.05; **, P ≤ 0.01; ***, P < 0.001; ****, P ≤ 0.0001).

Fig. 7

4 Discussion

Collectively, in the current study, the important relationships between CDK4/6 inhibition and SLC7A11 regulation and ferroptosis were uncovered (Fig. 7J). Our data suggested that ferroptosis represented a part of the CDK4/6 inhibitors-induced cell death response in luminal A breast cancer cells, which partly by targeting SP1-mediated SLC7A11. And the expressional level of SLC7A11 might play an important role in the intrinsic sensitivity of luminal A subtype to CDK4/6 inhibitors. Moreover, the combination of CDK4/6 inhibitors with ferroptosis inducers or SP1 inhibitor might be a more effective strategy than monotherapy to enhance antitumor effects in vivo.

CDK4/6 inhibitors have greatly altered the treatment landscape of luminal A subtype breast cancer patients, stimulating further explorations of combination therapy [36,37]. Cell cycle entry results in elevated rates of oxidative ATP synthesis, which generates reactive oxygen species (ROS) as a byproduct [38]. When ROS generation outpaces antioxidant capacity, damage to membrane lipids and cell lysis occur. In a number of biological systems, the process of ROS-mediated lipid peroxidation and cell lysis requiring iron has been called ferroptosis [[7], [12], [39], [40]]. Therefore, there is comprehensive crosstalk between the cell cycle and ferroptosis. Our results also demonstrated that abemaciclib could induce lipid peroxidation.

Ferroptosis, as a unique cell death mechanism, has been considered to play a significant role in the anticancer effects of many cancer therapies. Therapeutic efficacy can be further enhanced by combining FINs with conventional therapies that can induce ferroptosis [[37], [38], [41], [42]]. SLC7A11 is frequently overexpressed in multiple human cancer types, making it a weak spot for overcoming cancer by inducing ferroptosis. The expression and activity of SLC7A11 are precisely regulated at multiple levels, including transcription, epigenetic regulation, and posttranslational modification. Our resulted presented that SLC7A11 was transcriptionally downregulated in luminal A breast cancer cells under CDK4/6i treatment and directly reducing SLC7A11 levels or blocking the key steps of downregulating SLC7A11 under abemaciclib treatment may be a viable and important way to enhance the antitumor effects. Notably, our team also observed that despite the reduction of SLC7A11 following abemaciclib treatment, GPX4 mRNA levels increased and protein levels remained unchanged, indicating there exsists additional mechanisms to regulate GPX4. Similarly, Zhang Y et al. provided a novel propose that Rag-mTORC1-4EBP signaling axis could at least partially promote GPX4 protein synthesis [43]. We want to emphasize that other pathways regulating of GPX4 are not in conflict with our model, because we are not proposing that SLC7A11 or GSH synthesis is the only mechanism linking CDK4/6 inhibitors to ferroptosis regulation. Therefore, our study primarily focuses on how abemaciclib regulates SLC7A11 not GPX4. Additionally, the ubiquitin-proteasome system (UPS) is closely linked to cell cycle regulation, and many studies suggest that CDK4/6 inhibitors may potentially impact the ubiquitination-proteasome system [10,44,45]. Furthermore, as previously reported, GPX4 undergoes various ubiquitination modifications and regulatory mechanisms [[39], [46]]. For these reasons, CDK4/6 inhibitors may activate the proteasomal system to degrade proteins, which could explain why GPX4 mRNA levels increase despite stable protein levels.

Recent data suggest that the specificity protein (SP) transcription factor family member SP1 plays a pivotal role in transcriptionally regulating the expression of several ATP-driven drug efflux pumps [18,39]. Current studies have reported the regulatory role of SP1 in ferroptosis, especially in terms of diseases of the nervous system and local ischemia. It has been reported that neurons respond to ferroptotic stimuli by inducing selenoproteins, including the antioxidant glutathione peroxidase 4 (GPX4). Pharmacological selenium (Se) augments GPX4 and other genes in this transcriptional program, the selenome, via coordinated activation of the transcription factors TFAP2c and SP1 to protect neurons [22]. Li et al. demonstrated that SP1 induces ferroptosis by regulating ACSL4 in intestinal ischemia‒reperfusion diseases, which in turn induces tissue damage [23]. However, in the field of oncology, there are no definitive studies on the relationship between SP1 and ferroptosis. In our study, the preliminary analysis of luminal A breast cancer in TCGA and GEO data showed that SP1 was positively correlated with SLC7A11 expression levels. Moreover, several studies have shown that SP1 plays a role in the estradiol induction of the p21Waf1/Cip1 promoter in MCF-7 breast cancer cells, and correspondingly, an SP1 inhibitor prevented the estradiol-mediated increased expression of the p21Waf1/Cip1 gene, coding for the CDK inhibitor protein in cell cycle progression [47]. These results confirm the important function of SP1 in the cell cycle. The speculation that abemaciclib may regulate the level of SLC7A11 by affecting SP1 seemed acceptable. As was assumed, the knockdown of SP1 alone could decrease the protein level of SLC7A11 and induce mild ferroptosis, while the combination with abemaciclib significantly enhanced ferroptosis. As a selective SP1 inhibitor approved by the FDA, MIT has been reported to have antitumor activities in multiple cancers [[47], [48], [49], [50]]. However, strategies to exert antitumor effects on luminal A breast cancer have not been precisely elucidated. Our study found that MIT treatment could cause a series of changes, such as triggering ferroptosis. More importantly, its application combined with abemaciclib has achieved good synergistic therapeutic effects both in vivo and in vitro. However, future studies are also needed to fully clarify the physiological role of the SP1-SLC7A11 axis in luminal A breast cancer cells and its direct relevance to ferroptosis in vivo.

Although the combination of CDK4/6 inhibitors with endocrine therapy has led to a significant increase in the progression-free survival of patients with advanced HR+/HER2-breast cancer [3,4], the emergence of acquired resistance to CDK4/6 inhibitors represents a major clinical challenge [[51], [52], [53], [54]]. Moreover, ribociclib and palbociclib did not show significant clinical activity as single agents. In the present study, it was proposed that CDK4/6i treatment suppressed SLC7A11 by inhibiting SP1 binding to the promoter region of SLC7A11. Our results also established that SP1 inhibition or SLC7A11 inhibition could enhance cell sensitivity to abemaciclib and induce synergistic tumor cell inhibition effects in vitro in combination with abemaciclib. Collectively, these results together also provide evidence linking CDK4/6 inhibitors with lipid peroxidation and ferroptosis as well as a broad framework for further understanding and targeting ferroptosis in cancer therapy, which might generate opportunities for diagnostics and therapeutic interventions for luminal A breast cancer.

5 Ethics approval statement and consent to participate

All mouse experiments were approved by the Animal Ethical and Welfare Committee of the Chinese PLA General hospital carried out following their legal requirements (approval number: 2019-x15-68). All human tumor tissues were obtained with written informed consent from patients or their guardians prior to participation in the study and the collection and use of clinical samples were in accordance with research ethics board approval from the Seventh medical center of Chinese PLA General hospital Review Board (ethics code: LZEC2013-YW-004).

Permission to reproduce material from other sources

No. It is not applicable for the article.

Funding statement

This research was supported by National Key Research Development Program of China (2023YFA0914900 , 2018YFB0407202 ), the 10.13039/100014717 National Natural Science Foundation of China (61975239 , 82272762 ), the Beijing Nova Project(No. 20220484176 ) and 10.13039/501100004826 Beijing Natural Science Foundation (7222174 ).

Data availability statement

All the data supporting the findings of this study are available within the article and its supplemental files. Publicly available datasets used in this paper are from the GEO databases and The Cancer Genome Atlas.

CRediT authorship contribution statement

Yingshu Cui: Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis. Yi Li: Writing – original draft, Visualization, Validation, Supervision, Formal analysis. Yuanyuan Xu: Writing – original draft, Visualization, Supervision, Formal analysis, Data curation. Xinxin Liu: Supervision, Resources, Methodology, Investigation. Xiaofeng Kang: Validation, Software, Resources. Junwen Zhu: Validation, Investigation, Data curation. Shan Long: Validation, Supervision, Resources. Yuchen Han: Visualization, Validation, Supervision. Chunyuan Xue: Validation, Resources, Methodology, Investigation. Zhijia Sun: Validation, Supervision, Resources. Yimeng Du: Investigation, Formal analysis, Data curation. Jia Hu: Supervision, Project administration, Methodology. Lu Pan: Validation, Supervision, Resources, Project administration. Feifan Zhou: Supervision, Methodology, Investigation, Formal analysis, Conceptualization. Xiaojie Xu: Validation, Supervision, Funding acquisition, Formal analysis, Conceptualization. Xiaosong Li: Visualization, Supervision, Resources, Funding acquisition, Formal analysis, Data curation, Conceptualization.

Declaration of competing interest

The authors declare no conflict of interest.

Appendix A Supplementary data

The following is/are the supplementary data to this article:Multimedia component 1

Multimedia component 1

Multimedia component 2

Multimedia component 2

Data availability

The data that has been used is confidential.

Acknowledgments

We thank the Central Laboratory of Beijing Keystone Life Technology Co., Ltd. for providing us with relevant instruments and resources for experiments. We are grateful to Bingxuan Huangfu, Xixia Li and Tianmiao Li for helping with electron microscopy sample preparation and taking TEM images at the Center for Biological Imaging (CBI), Institute of Biophysics, Chinese Academy of Science.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.redox.2024.103304.
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