Cancer cells avoid ferroptosis induced by immune cells via fatty acid binding proteins

Freitas-Cortez, Maria Angelica; Masrorpour, Fatemeh; Jiang, Hong; Mahmud, Iqbal; Lu, Yue; Huang, Ailing; Duong, Lisa K.; Wang, Qi; Voss, Tiffany A.; Kettlun Leyton, Claudia S.; Wei, Bo; Chan, Wai-Kin; Lin, Kevin; Zhang, Jie; Tsouko, Efrosini; Ganjoo, Shonik; Barsoumian, Hampartsoum B.; Riad, Thomas S.; Hu, Yun; Leuschner, Carola; Puebla-Osorio, Nahum; Wang, Jing; Hu, Jian; Davies, Michael A.; Puduvalli, Vinay K.; Billon, Cyrielle; Burris, Thomas P.; Lorenzi, Philip L.; Gan, Boyi; Welsh, James W.

doi:10.1186/s12943-024-02198-2

Research
Open access
Published: 03 February 2025

Cancer cells avoid ferroptosis induced by immune cells via fatty acid binding proteins

Maria Angelica Freitas-Cortez^1,2,15,
Fatemeh Masrorpour³,
Hong Jiang³,
Iqbal Mahmud⁴,
Yue Lu⁵,
Ailing Huang³,
Lisa K. Duong³,
Qi Wang⁶,
Tiffany A. Voss³,
Claudia S. Kettlun Leyton⁷,
Bo Wei⁴,
Wai-Kin Chan⁴,
Kevin Lin⁵,
Jie Zhang⁸,
Efrosini Tsouko⁹,
Shonik Ganjoo³,
Hampartsoum B. Barsoumian³,
Thomas S. Riad³,
Yun Hu³,
Carola Leuschner³,
Nahum Puebla-Osorio³,
Jing Wang⁶,
Jian Hu¹⁰,
Michael A. Davies¹¹,
Vinay K. Puduvalli¹²,
Cyrielle Billon¹³,
Thomas P. Burris¹⁴,
Philip L. Lorenzi⁴,
Boyi Gan⁸ &
…
James W. Welsh³

Molecular Cancer volume 24, Article number: 40 (2025) Cite this article

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Abstract

Background

Cancer creates an immunosuppressive environment that hampers immune responses, allowing tumors to grow and resist therapy. One way the immune system fights back is by inducing ferroptosis, a type of cell death, in tumor cells through CD8 ⁺ T cells. This involves lipid peroxidation and enzymes like lysophosphatidylcholine acyltransferase 3 (Lpcat3), which makes cells more prone to ferroptosis. However, the mechanisms by which cancer cells avoid immunotherapy-mediated ferroptosis are unclear. Our study reveals how cancer cells evade ferroptosis and anti-tumor immunity through the upregulation of fatty acid-binding protein 7 (Fabp7).

Methods

To explore how cancer cells resist immune cell-mediated ferroptosis, we used a comprehensive range of techniques. We worked with cell lines including PD1-sensitive, PD1-resistant, B16F10, and QPP7 glioblastoma cells, and conducted in vivo studies in syngeneic 129 Sv/Ev, C57BL/6, and conditional knockout mice with Rora deletion specifically in CD8⁺ T cells, Cd8 cre;Rora^fl mice. Methods included mass spectrometry-based lipidomics, targeted lipidomics, Oil Red O staining, Seahorse analysis, quantitative PCR, immunohistochemistry, PPARγ transcription factor assays, ChIP-seq, untargeted lipidomic analysis, ROS assay, ex vivo co-culture of CD8⁺ T cells with cancer cells, ATAC-seq, RNA-seq, Western blotting, co-immunoprecipitation assay, flow cytometry and Imaging Mass Cytometry.

Results

PD1-resistant tumors upregulate Fabp7, driving protective metabolic changes that shield cells from ferroptosis and evade anti-tumor immunity. Fabp7 decreases the transcription of ferroptosis-inducing genes like Lpcat3 and increases the transcription of ferroptosis-protective genes such as Bmal1 through epigenetic reprogramming. Lipidomic profiling revealed that Fabp7 increases triglycerides and monounsaturated fatty acids (MUFAs), which impede lipid peroxidation and ROS generation. Fabp7 also improves mitochondrial function and fatty acid oxidation (FAO), enhancing cancer cell survival. Furthermore, cancer cells increase Fabp7 expression in CD8⁺ T cells, disrupting circadian clock gene expression and triggering apoptosis through p53 stabilization. Clinical trial data revealed that higher FABP7 expression correlates with poorer overall survival and progression-free survival in patients undergoing immunotherapy.

Conclusions

Our study uncovers a novel mechanism by which cancer cells evade immune-mediated ferroptosis through Fabp7 upregulation. This protein reprograms lipid metabolism and disrupts circadian regulation in immune cells, promoting tumor survival and resistance to immunotherapy. Targeting Fabp7 could enhance immunotherapy effectiveness by re-sensitizing resistant tumors to ferroptosis.

Introduction

Cancer creates an immunosuppressive environment that hinders immune responses, facilitating tumor growth and therapy resistance [1]. One way the immune system counterattacks is by inducing ferroptosis in tumor cells. This process is mediated by CD8⁺ T cells, which promote lipid peroxidation via the interferon gamma (IFNγ) pathway [2, 3]. This involves acyl-CoA synthetase long-chain family member 4 (ACSL4) activation, influencing tumor lipid composition and correlating with better outcomes in immune checkpoint therapy [2, 3]. However, the mechanisms by which cancer avoids ferroptosis induced by immune cells remains unclear.

Ferroptosis, a form of regulated cell death, involves iron-dependent lipid peroxidation and reactive oxygen species (ROS) accumulation [4,5,6]. Key elements include iron-catalyzed peroxidation of polyunsaturated fatty acids (PUFAs), iron metabolism, and mitochondrial processes [5]. Ferroptosis is regulated by multiple pathways that converge on cellular antioxidant systems and iron homeostasis. One key pathway involves the System Xc- cystine/glutamate antiporter, which imports cystine for glutathione (GSH) synthesis. GSH is an essential substrate for glutathione peroxidase 4 (GPX4), which reduces lipid hydroperoxides to non-toxic lipid alcohols [5]. ACSL4 and lysophosphatidylcholine acyltransferase 3 (LPCAT3) are crucial for ferroptosis by incorporating PUFAs into phospholipids, making them prone to peroxidation [5]. LPCAT3 plays a role in the incorporation of PUFA-containing phospholipids into cellular membranes, which is essential for maintaining membrane fluidity and cellular signaling [5,6,7]. Inhibition of LPCAT3, ACSL4, or cytochrome P450 oxidoreductase (POR) significantly reduces ferroptosis, underscoring their critical roles in cellular lipid metabolism and their potential impact on cell viability under oxidative stress [4, 6, 7].

Beyond the canonical ferroptosis-controlling axis including xc- cystine–glutamate antiporter and glutathione systems [5, 7, 8], multiple signaling pathways including hypoxia-inducible factors (HIFs) [9,10,11], nuclear factor erythroid 2-related factor 2 (NRF2) [12,13,14], and the circadian master regulator brain and muscle ARNT-like 1 (BMAL1) also modulate ferroptosis [15,16,17]. The autophagy-mediated degradation of BMAL1 increases EGLN2 expression, destabilizing hypoxia-inducible factor 1-alpha (HIF1A), which reduces the expression of fatty acid-binding proteins (FABPs), particularly FABP7, and promotes lipid peroxidation and cell death [17]. While FABPs are known to protect cancer cells from ferroptosis by forming lipid droplets, the precise molecular mechanisms remain unclear.

Fatty acid-binding proteins (FABPs), such as FABP7, are crucial for fatty acid metabolism and transport by binding long-chain fatty [18]. FABP7, primarily found in the brain, regulates neurogenesis and neural stem cells and has a high affinity for PUFAs [19, 20]. In glioma, high FABP7 levels correlate with stemness and poor prognosis [21,22,23]. FABP7 also plays a key role in HER2⁺ breast cancer brain metastasis [24] and is upregulated in metastatic non-small cell lung cancer, indicating poor survival [25]. It activates WNT signaling and is highly expressed in melanomas, linking it to disease progression [26,27,28,29].

Here, we identified a novel mechanism by which cancer cells evade ferroptosis induced by immune cells during immunotherapy. This resistance is mediated by FABP7, which promotes the epigenetic reprogramming of genes involved in ferroptosis, specifically downregulating LPCAT3 and upregulating protective genes, such as BMAL1. Furthermore, our findings demonstrate that cancer cells induce the expression of FABP7 in CD8 T cells, which in turn disrupts the circadian-clock gene expression. This disruption leads to apoptosis in CD8 T cells via p53 activation. These findings elucidate a mechanism by which cancer cells evade immune-mediated ferroptosis, highlighting novel potential therapeutic targets to induce ferroptosis in tumors that are resistant to immunotherapy.

Methods

Cell lines

The 344SQ parental (anti-PD1-sensitive) cell line (Sen) was a generous gift from Dr. Jonathan Kurie (MD Anderson). We used the Sen cell line to generate an anti-PD1-resistant cell line (Res) [30]. B16F10 cells were obtained from the American Type Culture Collection (Manassas, VA, USA). Cells were cultured in complete medium (RPMI-1640 supplemented with 100 units/mL penicillin, 100 μg/mL streptomycin, and 10% heat-inactivated fetal bovine serum) and incubated at 37 °C in 5% CO₂. QPP7 glioblastoma cells were generated in the laboratory of Dr. Jian Hu and cultured in DMEM/F12 medium with B-27 supplement (Gibco), epidermal growth factor, and fibroblast growth factor (STEMCELL Technologies) [31]. Cell lines were validated by DDC Medical (http://ddcmedical.com; Fairfield, OH) by using short-tandem-repeat DNA fingerprinting.

In vivo studies

All mouse studies were approved by the Institutional Animal Care and Use Committee (IACUC) of The University of Texas MD Anderson Cancer Center before their initiation; animal care was provided according to IACUC standards, and all mice were bred and maintained in our own specific pathogen-free mouse colony. Primary tumors were established by subcutaneous injection of Sen, Res, Res-ctrl, or Res-shFabp7 cells (0.5 × 10⁶ in 100 μL of sterile phosphate-buffered saline [PBS]) into syngeneic 129 Sv/Ev mice (12–16 weeks old). B16F10-ctrl or B16F10-shFabp7 cells (0.5 × 10⁶ in 100 μL of sterile PBS) were injected into syngeneic C57BL/6 mice (12–16 weeks old). Mice were then given intraperitoneal injections of anti-PD1 or control IgG antibodies (10 mg/kg; BioXcell) (n = 5 mice/group) starting on day 4 after tumor cell inoculation and continuing twice per week for a total of four doses. Rora^fl mice were kindly provided by Dr. Cyrielle Billon and Thomas P. Burris and generated as described elsewhere [32]. To generate conditional knockout mice with Rora deletion specifically in CD8⁺ T cells, Rora^fl mice were crossed with CD8-Cre transgenic mice (Jackson Laboratory, Stock No. 008766, C57BL/6-Tg(Cd8a-cre)1Itan/J). This cross resulted in offspring with the genotype Cd8 cre; Rora^fl. Genomic DNA was extracted from tail biopsies of the offspring for genotyping. Polymerase chain reaction (PCR) was performed to confirm the presence of the floxed Rora alleles and the Cd8-Cre transgene. Primer sequences and PCR conditions are available upon request. The validation of the conditional knockout was performed by Transnetyx, Inc. (Cordova, TN). The Cd8 cre; Rora^fl mice (12–16 weeks old) were subcutaneously injected with B16F10, B16F10-ctrl, or B16F10-shFabp7 cells (0.5 × 10⁶ in 100 μL of sterile PBS), followed by intraperitoneal injections of anti-PD1 or control IgG antibodies (10 mg/kg; BioXcell) (n = 5 mice/group), starting on day 4 after tumor cell inoculation and continuing twice per week for a total of four doses. Tumors were measured with calipers three times per week and recorded as tumor volume (in mm³) = width² × length/2. Tumor growth curves were compared with two-way analysis of variance. Tumor tissues were collected either at 24 h after the final anti-PD1 treatment (for lipidomics, global gene expression, qPCR, and immunohistochemical analyses) or at a week after the final anti-PD1 treatment (for isolating tumor-infiltrating lymphocytes [TILs]).

Unbiased lipidomics

Lipid extraction, data acquisition by mass spectrometry, raw data processing, and data analysis were done by The Baylor College of Medicine Metabolomics Core and are all described elsewhere [33]. Briefly, samples were extracted with water / methanol / dichloromethane (2:2:2 v/v), with mouse liver samples as a quality control to monitor the extraction efficiency and instrument performance. Lipids were separated by reverse-phase chromatography (Acquity HSS UPLC T3 column [1.8 μm particle 50 × 2.1 mm], Waters, Milford, MA) on a Shimadzu CTO-20A Nexera X2 UHPLC system, with LC mobile phase solvent A acetonitrile/water (40:60, v/v) with 10 mM ammonium acetate and solvent B acetonitrile / water / isopropanol (10:5:85 v/v) with 10 mM ammonium acetate. The flow rate for these experiments was 0.4 mL/min. Mass spectrometry data were acquired by Data Independent Acquisition (DDA) methods in both positive and negative ionization modes by using a TripleTOF 5600. Identified peaks and retention time were carefully reviewed with MultiQuant software (ver. 1.1.0.26, AB Sciex, Concord, Canada). The relative peak area was log2 transformed followed by internal standard normalization for each method. Analysis of differentially expression between groups was done with the Benjamini–Hochberg method for false discovery rate (FDR) of < 0.25 correction to account for multiple comparisons.

Lipid (Oil Red O) staining

PD1-sensitive (Sen) and PD1-resistant (Res) cells were counted and seeded into 6-well plates at a density of 300,000 cells per well. After 20 h, neutral lipids within lipid droplets were quantified by using an Oil Red O Staining Kit (Catalog #MAK194, Sigma) specifically designed for cultured cells according to the manufacturer's instructions. For in vivo experiments, tumors were harvested from mice that had been subcutaneously injected with either Sen (n = 3) or Res (n = 3) cells at a concentration of 500,000 cells per mouse. Mice were then treated twice weekly for 2 weeks with either IgG or PD1 antibody after tumor implantation. Tumors were snap-frozen in optimal cutting temperature compound and preserved at −80 °C immediately after collection. Tissue sections (thickness 5 µm) were stained for lipid content by using an Oil Red O Stain Kit (Catalog #ab140678, Abcam) per the manufacturer's guidelines. Slides were examined with a Leica DMI6000B microscope (Leica, Buffalo Grove, IL), and images were captured with a charge-coupled device camera.

Targeted lipidomics mass spectrometry for eicosanoids/oxylipins

Twelve tumors were obtained from mice that had been injected subcutaneously with anti-PD1-sensitive (Sen) (n = 3) or PD1-resistant (Res) (n = 3) cells (500,000 cells/mouse), followed by twice-weekly treatment with either IgG or PD1 antibody after implantation for 2 weeks. Tumors were collected, snap-frozen immediately, and stored at –80 °C until analysis processing and analysis by Creative Proteomics, Shirley, NY). Sample extraction and cleanup was done according to a protocol outlined by Watrous [34]. Briefly, each tumor was individually weighed, and 10 ng of d8-arachidonic acid or d4-resolvin e1 were added to each. Samples were then processed by adding 250 μL of –20 °C chilled 75% ethanol and homogenized in a ThermoFisher bead mill for 2 min. The homogenates were transferred to 2.0 mL centrifuge tubes, to which an additional 750 μL of –20 °C chilled 75% ethanol was added. Samples were vortexed for 30 min, incubated at –20 °C for 1 h to precipitate proteins, and centrifuged at 15,000 × g for 20 min. The supernatants were collected, and the protein pellets were re-extracted with the supernatants pooled. The pooled supernatants were then diluted to 10 mL with HPLC water, applied to preconditioned solid-phase extraction (SPE) columns, washed, and eluted as per the established protocol. After the SPE process, samples were dried under vacuum, reconstituted in acetonitrile, and stored at –80 °C until analysis. For LC–MS analysis, we used a Shimadzu Prominence HPLC coupled to a Thermo LTQ-Orbitrap Velos mass spectrometer. The LC system included a specific HPLC column and gradient conditions adapted from Watrous [34], with the mass spectrometer operated in negative ion mode under specific settings. Peaks were identified and quantified with MAVEN software, with concentrations normalized to the weight of the individual tumor and compared against reference standards for confirmation. Data were analyzed with Thermo Scientific LipidSearch software (version 5.0) and R scripts written by the Metabolomics Core Facility at MD Anderson Cancer Center.

Long-chain fatty acid oxidation stress test

Oxygen consumption rates (OCR) of the mitochondria in PD1-sensitive (Sen) and PD1-resistant (Res) cells were measured by a Seahorse XFe24 Analyzer (Agilent Technologies) and an XF Long Chain Fatty Acid Oxidation Stress test kit (catalog #103,672–100, Agilent Technologies). Briefly, cells were seeded and cultured in XFe24 cell culture plates; for the experiments, the medium was replaced with pre-warmed assay medium (Seahorse base medium supplemented with 1 mM pyruvate, 10 mM glucose, 2 mM glutamine, and 4 uM Etomoxir, pH = 7.4) and the plate were incubated at 37 °C in a non-CO₂ incubator for 1 h. Next, plates were transferred to the Seahorse XFe24 to record the OCR of the cells at different times. The OCR readings were normalized to the cell numbers in each well. Mitochondrial respiration, including basal respiration, proton leak, and maximal respiration were calculated by the Seahorse Wave software.

Global mRNA expression profiling

PD1-sensitive (Sen) or PD1-resistant (Res) cells were inoculated into the right flank of 129 Sv/ev mice (female, 12–14 weeks old, 5 mice per group). Anti-PD1 or control IgG antibodies (10 mg/kg) were given on days 4 and 7 after tumor inoculation. On day 11, tumor tissues were collected, immediately frozen in liquid nitrogen, and homogenized with grinders in the presence of Trizol (Qiagen). Total RNA was isolated by phenol-based extraction and resolved in water treated with diethylpyrocarbonate. The purity and concentration of total RNAs were determined with a Nanodrop 1000 spectrophotometer. The total RNA samples were further analyzed with Agilent’s 2100 Bioanalyzer to assess sample quality and integrity. RNAs from three independent biological replicates per group were used for GeneChip Mouse genome 430 2.0 arrays (Affymetrix). Sample labeling and processing, GeneChip hybridization, and scanning were done according to Affymetrix protocols (3’ IVT plus reagent labeling kit). Each array was washed and stained with streptavidin–phycoerythrin (Invitrogen) and amplified with biotinylated anti-streptavidin antibody (Vector Laboratories) on a GeneChip Fluidics Station 450 (Affymetrix). Arrays were scanned with the GeneArray G7 scanner (Affymetrix) to obtain image and signal intensities. After scanning, the images were processed by using Affymetrix Expression Console Software Version 1.0 to generate gene expression intensity values. Differentially expressed genes were identified by using parametric tests with log2-transformed gene expression values. P values obtained were adjusted using the false discovery rate (Benjamini–Hochberg). Heatmaps were created by using the heatmap.2 function in the gplots R package. To identify pathways or functions that were overrepresented among the genes at the top or bottom of the ranked gene list, we used the pre-ranked gene set enrichment analysis method.

Quantitative polymerase chain reaction

Total RNA was isolated from cells and tumors with Triazol (Life Technologies) according to the manufacturer’s protocol. For studies of Fabp7, Pparg, Hif1a, Rora, Bmal1 (Arntl), Clock, Nrip1, Bcl2l11, CD45 and Rps18 expression, mRNA was retrotranscribed with an iScript gDNA Clear cDNA Synthesis Kit (BioRad, catalog# 1,708,890) or High-Capacity cDNA Reverse Transcription Kit (ThermoFisher Scientific, catalog# 4,368,814) and analyzed by quantitative PCR by using SYBR Green (Life Technologies) with specific primers (Supplementary Table S1) according to the manufacturer’s protocol. The comparative Ct method was used to calculate the relative abundance of mRNAs compared with Rps18 or CD45 expression.

Immunohistochemical analysis

Formalin-fixed mouse tissues were processed at the MD Anderson Research Histology Core Laboratory. Briefly, tissues were submitted on an automatic tissue processor, embedded in paraffin (Peloris, Leica), and cut into 4-μm sections. Immunohistochemical staining was done with an automated staining system (Leica Bond Max, Leica Microsystems, Vista, CA, USA) as follows. Briefly, slides were deparaffinized and hydrated, and antigen was retrieved by incubating in citrate buffer (pH 6.0) for 1 h with Fabp7 (ThermoFisher, Catalog #PA5-23,469) and 4-hydroxynonenal (4-HNE) antibody (Abcam, Catalog #ab48506) according to the manufacturer’s protocol. Slides were examined with a Leica DMI6000B microscope (Leica, Buffalo Grove, IL), and images were captured with a charge-coupled device camera, imported into the Advanced Spot Image analysis software package, and quantified with Fiji software (http://fiji.sc).

Stable Fabp7-knockdown and control cells

To establish stable Fabp7-knockdown cells, GIPZ Non-silencing Lentiviral shRNA Control (Catalog #RHS4348, Dharmacon) and specific mouse shRNA targeting Fabp7 (pGIPZ Clone ID V2LMM_50996, Dharmacon) viral supernatants were purchased from the shRNA and ORFeome Core at MD Anderson Cancer Center. Res cells were infected and incubated with the viral particles supplemented with polybrene (8 µg/mL, Sigma) overnight at 37 °C. Puromycin (1 µg/mL) was used to select and maintain Fabp7-knockdown in Res, B16F10, and QPP7 cells. Stable repression was verified by quantitative PCR and western blotting.

PPARγ transcription factor assay

PPARγ activation was evaluated by using a PARγ Transcription Factor Assay kit (catalog #ab133101) according to the manufacturer’s protocol. To initiate the assay, a specific double-stranded DNA sequence containing the peroxisome proliferator response element (PPRE) was immobilized onto the bottom wells of a 96-well plate. Subsequently, nuclear extracts from Sen, Res, ctrl, or shFabp7 cells containing PPARs were introduced into the wells; PPARγ specifically bound to the immobilized PPRE was detected by adding a specific primary antibody targeted to PPARγ. A secondary antibody conjugated to horseradish peroxidase was then applied to enable a sensitive colorimetric readout at 450 nm with a BioTek Sinergy plate reader (Agilent Technologies).

Enzyme-linked immunosorbent assay

Enzyme-linked immunosorbent assay (ELISA) was used to measure Fabp7 levels in the culture supernatants. Supernatants were freshly collected from four different cell types: Sen, Res, Res-ctrl, and Res-shFabp7, and were immediately processed for analysis. The Fabp7 concentration was quantified by using an ELISA kit according to the manufacturer's instructions (LSBio, Catalog # LS-F11416). The absorbance readings necessary for determining Fabp7 levels were obtained with a BioTek Sinergy plate reader (Agilent Technologies).

Chromatin immunoprecipitation sequencing (ChIP-Seq)

Samples from Sen, Res, Res-ctrl and Res-shFabp7 cells were processed for H3K27ac and H3K9ac ChIP and ChIP-Seq libraries were prepared at the MD Anderson Epigenomics Profiling Core Facility. The ChIP-Seq libraries, along with the corresponding input libraries, were sequenced by using a 50-base single-read protocol on an Illumina NovaSeq 6000 instrument at the MD Anderson Advanced Technology Genomics Core. In total, 24 ChIP-Seq libraries (three biological replicates per histone mark and condition), as well as the corresponding input libraries, were sequenced, generating 53–158 million reads per sample. For mapping, the reads were mapped to the mouse genome (mm10) by using Bowtie (version 1.1.2) with the following parameters: "-v 2 -m 1 –best –strata". To avoid PCR bias, only one copy of multiple reads mapped to the same genomic position was retained for further analysis. For peak calling, peaks were identified on each ChIP-Seq sample by using MACS (version 1.4.2) with comparison against the corresponding input sample. A window size of 500 bp was used, and a p value cutoff of 1e-5 was applied. Peaks that overlapped with ENCODE blacklisted regions were removed. For each differential comparison, peaks from all the involved samples were merged, and the number of reads within these merged peaks was counted for each sample. Merged peaks with less than 10 reads in all samples were removed. The resulting count table was used to identify differential peaks by using the R/Bioconductor package edgeR. The numbers of reads within the common peaks of all samples were used as the library sizes in edgeR. Peaks with a false discovery rate (FDR) of ≤ 0.05 and a fold change of ≥ 1.5 or 2 were identified as differential peaks and presented in heatmap plots. For signal tracking, each read was extended by 150 bp to its 3’ end. The count of reads covering each genomic position was multiplied by 1 × 10⁷ divided by the library size used in edgeR. These values were then averaged over a 10 bp resolution. The resulting averaged values were displayed by using the Integrative Genomics Viewer. To generate heatmaps, the region spanning 10 kb upstream to 10 kb downstream from the center of each differential peak was divided into 250 bp bins. The number of reads within each bin was multiplied by 1 × 10^–6, divided by the library size used in edgeR, and then averaged across replicate samples. The resulting value tables were then visualized in heatmap by using the R function heatmap.2. Motif enrichment was anayzed with The MEME Suite 5.5.5 [35], and Giggle score was obtained with the genomic search engine GIGGLE (https://github.com/ryanlayer/giggle) [36].

Untargeted lipidomic analysis by LC–MS/MS

Samples were sent to Metabolomics Core Facility at the MD Anderson for processing and analysis. Around 10 million cells were harvested for each run as instructed by the core facility. Briefly, upon harvest, cells were washed with ice-cold 0.85% ammonium bicarbonate in deionized water. Cells were then scraped down and pelleted at 400 × g for 3 min. The supernatant was removed, and the cells were snap-frozen in liquid nitrogen and kept in a –80ºC freezer before being sent to the Metabolomics Core Facility. To determine the relative abundance of lipid in different prostate cancer cells, extracts were prepared and analyzed by high-resolution mass spectrometry-based lipidomics at the Metabolomics Core Facility. Briefly, to each cell sample, 200 µL of extraction solution containing 2% Avanti SPLASH® LIPIDOMIX® Mass Spec Standard, 1% 10 mM butylated hydroxytoluene in ethanol was added and the tubes were vortexed for 10 min. The tubes were placed in ice for 10 min and centrifuged at 13,300 rpm for 10 min at 4ºC. The supernatant was transferred to a glass autosampler vial, and the injection volume was 10 µL. Mobile phase A (MPA) was 40:60 acetonitrile: water with 0.1% formic acid and 10 mM ammonium formate. Mobile phase B (MPB) was 90:9:1 isopropanol:acetonitrile: water with 0.1% formic acid and 10 mM ammonium formate. The chromatographic method included a Thermo Fisher Scientific Accucore C30 column (2.6 µm, 150 × 2.1 mm) maintained at 40 °C, autosampler tray chilling at 8 °C, a mobile phase flow rate of 0.200 mL/min, and a gradient elution program as follows: 0–3 min, 30% MPB; 3–13 min, 30–43% MPB; 13.1–33 min, 50–70% MPB; 48–55 min, 99% MPB; 55.1–60 min, 30% MPB. A Thermo Fisher Scientific Orbitrap Fusion Lumos Tribrid mass spectrometer with heated electrospray ionization source was operated in data-dependent acquisition mode, in both positive and negative ionization modes, with scan ranges of 150–827 and 825–1500 m/z. An Orbitrap resolution of 120,000 (FWHM) was used for MS1 acquisition, and a spray voltage of 3,600 and –2900 V were used for positive and negative ionization modes. Vaporizer and ion transfer tube temperatures were set at 275 and 300 °C, respectively. The sheath, auxiliary and sweep gas pressures were 35, 10, and 0 (arbitrary units), respectively. For MS2 and MS3 fragmentation, a hybridized HCD/CID approach was used. Each sample was analyzed by using four injections, making use of the two aforementioned scan ranges, in both ionization modes. Data were analyzed with Thermo Scientific LipidSearch software (version 5.0) and R scripts written in-house.

Reactive oxygen species assay

Resistant control (Res-ctrl) and Resistant shFabp7 (Res-shFabp7) cells were cultured under standard conditions. For the assay, 10,000 cells from each line were counted and plated into individual wells of a 96-well plate. Cellular oxidative stress was analyzed with CellROX Deep Red Reagent (Thermo Fisher Scientific, Catalog #C10422), a cell-permeant dye that is non-fluorescent under reduced conditions and exhibits fluorescence when oxidized by ROS. The dye has absorption and emission maxima at approximately 644 / 665 nm. The Incucyte SX1 Live-Cell Analysis System (Sartorius) was used for monitoring the cells over a period of 40 h. Image acquisition was automated and set at regular intervals during this period. Images were analyzed with Incucyte software, which provided quantitative data on the levels of oxidative stress in the cells.

Ex vivo co-culture of CD8⁺ T cells with cancer cells

Viable cells were quantified with a hemocytometer with a 0.4% Trypan blue solution, and subsequently diluted to a density of 300,000 cells per well in 6-well plates. Res-ctrl or Res-shFabp7 cells were seeded in the upper inserts (24.5-mm Transwell with 0.4-µm pore polycarbonate membrane inserts, Fisher Scientific), whereas CD8⁺ T cells were placed in the lower chamber of the transwell system. CD8⁺ T cells were isolated from splenocytes before seeding by using the Dynabeads Untouched Mouse CD8 Cells Kit (Thermo Fisher Scientific–Life Technologies, Catalog #11417D). The cells were then activated with Ultra-LEAF Purified anti-mouse CD3ε Antibody (5 μg/mL) (Biolegend, Catalog # 100,301) and LEAF purified anti-mouse CD28 antibody (1 μg/mL) (Biolegend, Catalog # 102,101). Cells were cultured in complete medium (RPMI-1640 supplemented with 100 units/mL penicillin, 100 μg/mL streptomycin, and 10% heat-inactivated fetal bovine serum), and incubated at 37 °C in a 5% CO₂ for 24 or 48 h. Subsequently, RNA was isolated from the CD8⁺ T cells for gene expression analysis by quantitative PCR or Western blotting.

Assay for transposase-accessible chromatin with sequencing (ATAC-Seq)

ATAC-Seq libraries were prepared from T cells cocultured with Res-ctrl or Res-shFabp7 for 48 h at the MD Anderson Epigenomics Profiling Core Facility. The ATAC-Seq libraries were sequenced by using a 2 × 76 base paired-end protocol on an Illumina NovaSeq 6000 instrument at the MD Anderson Advanced Technology Genomics Core. In total, six ATAC-Seq libraries (three biological replicates per condition) were sequenced, generating 35–75 million pairs of reads per sample. Each pair of reads represents a DNA fragment from the library. For mapping, adapter sequences were removed from the 3' ends of reads by using Trim Galore! (version 0.6.5) and cutadapt (version 2.8). The reads were then mapped to the mouse genome (mm10) with Bowtie (version 1.1.2) with the following parameters: "–allow-contain –maxins 2000 -v 2 -m 1 –best –strata". To avoid PCR bias, only one copy of multiple fragments mapped to the same genomic position was retained for further analysis. After the removal of fragments from chrM, for each fragment, the 5’ end was offset by + 4 bp and the 3’ end was offset by −5 bp to adjust both ends to represent the center of a transposon binding event. For peak calling, peaks were identified for each sample by using MACS2 (version 2.2.7.1) without any control. Each binding event (i.e., the 5’ or 3’ end of a fragment) was smoothed by 73 bp (i.e., extended 36 bp upstream and 36 bp downstream from the event center). MACS2 was configured to call peaks from the pile-up of smoothed binding events, and a q-value cutoff of 0.05 was applied. The peaks that overlapped with ENCODE blacklisted regions were removed. For differential peak analysis, peaks from all the samples were merged, and the number of transposon binding events within these merged peaks was counted for each sample. Merged peaks with less than 10 binding events in all samples were removed. The resulting count table was used to identify differential peaks with the R/Bioconductor package edgeR. The numbers of binding events within the common peaks of all samples were used as the library sizes in edgeR. Peaks with a false discovery rate (FDR) of ≤ 0.05 and a fold change of ≥ 2 were identified as differential peaks and presented in heatmap plots. For signal tracking, each transposon binding event was smoothed to a length of 73 bp, spanning from −36 bp to + 36 bp around the center. The count of binding events covering each genomic position was multiplied by 1 × 10⁷ divided by the library size used in edgeR. These values were then averaged over a 10 bp resolution. The resulting averaged values were displayed by using the Integrative Genomics Viewer. To generate heatmaps, the region spanning 10 kb upstream to 10 kb downstream from the center of each differential peak was divided into 250 bp bins. The number of transposon binding events within each bin was multiplied by 1 × 10.⁶, divided by the library size used in edgeR, and then averaged across replicate samples. Subsequently, the resulting value tables were visualized in heatmap by using the R function heatmap.2. Motif enrichment was analyzed with The MEME Suite 5.5.5 [35], and the Giggle score was obtained with the genomic search engine GIGGLE (https://github.com/ryanlayer/giggle) [36].

RNA sequencing

T cells were co-cultured with cancer cells as previously described. After 48 h, T cells were collected, and RNA was isolated for RNA-seq analysis by using Trizol according to the manufacturer’s protocol. RNAseq sample quality control was done with FastQC. Sequencing reads were aligned to Genome Reference Consortium Human Build 38 (GRCh38.p13) (Genome Reference Consortium Mouse Build 39 [GRCm39]) by using STAR. The expression abundance and variations of mRNA were calculated as expected counts and transcripts per million (TPM) by using RSEM software. Differentially expressed genes were identified with parametric tests and log2-transformed TPM values. P values obtained from the tests were adjusted by using the false discovery rate (Benjamini–Hochberg). To identify pathways or functions that were overrepresented among the genes at the top or bottom of the ranked gene list, we used the pre-ranked Gene Set Enrichment Analysis method and Gene Ontology (https://geneontology.org/).

Protein extraction and western blot analysis

Total protein was extracted by using NP40 lysis buffer (0.5% NP40, 250 mmol/L NaCl, 50 mmol/L HEPES, 5 mmol/L ethylenediaminetetraacetic acid, and 0.5 mmol/L egtazic acid) supplemented with protease inhibitors cocktails (Sigma-Aldrich). Lysates were centrifuged at 10,000 × g for 10 min, and the supernatant was collected for experiments. Protein lysates (40 μg) were resolved on denaturing gels with 4%–20% sodium dodecyl sulfate–polyacrylamide and transferred to nitrocellulose membranes (BioRad Laboratories, Hercules, CA). Membranes were probed with primary antibodies directed against vinculin (Millipore Sigma, Catalog #05–386), p53 (Millipore Sigma, Catalog #OP43) and FABP7 (ThermoFisher, Catalog# PA5-31,864), (dilution 1:500), and a secondary antibody conjugated with horseradish peroxidase (dilution 1:2000) (Amersham GE Healthcare). The secondary antibody was visualized by using a chemiluminescent reagent (Pierce ECL kit, Thermo Fisher Scientific, Waltham, MA, USA).

Isolation of tumor-infiltrating lymphocytes

Tumor-infiltrating leukocytes (TILs) were isolated from freshly extracted primary tumor tissues (obtained from three mice per group) by using a Tumor Dissociation Kit (Miltenyi, Catalog # 130–096–730) in conjunction with the gentleMACS Octo Dissociator with Heaters (Miltenyi, Catalog #130–096–427), according to the manufacturer's protocol. After dissociation, TILs were enriched by using a Dynabeads Untouched Mouse T Cells Kit (Thermo Fisher Scientific–Life Technologies, Catalog #11413D). The enriched TILs were subsequently used for both quantitative PCR and flow cytometry analysis.

Co-immunoprecipitation assay

Cell lysates from Res cells treated with dimethylsulfoxide or DHA (30 μg/mL) (Cayman Chemicals, catalog #90,310) were processed for immunoprecipitation analysis by Creative Proteomics (Shirley, NY). Briefly, cell lysates were first precleared by adding 1 µg of anti-Fabp7 antibody (Abcam, Catalog# ab279649) and 20 µL of Protein A/G PLUS-Agarose. This mixture was incubated at 4 °C for 30 min. After the incubation, the lysate-beads mixture was centrifuged at 3,000 rpm for 5 min at 4 °C, and the supernatant was transferred to a fresh 1.5 mL conical tube and kept on ice. Next, 20 µL of anti-Fabp7 antibody was added to 500 µg of the cell lysate in a 1.5 mL microcentrifuge tube, which was incubated for 1 h at 4 °C. Subsequently, 20 µL of Protein A/G PLUS-Agarose was added to the mixture, which was incubated overnight at 4 °C on a rotating device. The immunoprecipitated samples were collected by centrifugation at 3,000 rpm for 5 min at 4 °C, after which the supernatant was carefully aspirated and discarded. The beads were washed four times with PBS, with centrifugation repeated after each wash. After the final wash, the supernatant was aspirated and discarded, and the beads were resuspended in 40 µL of 1X electrophoresis sample buffer. Finally, the samples were boiled for 15 min and prepared for Western blotting by using Rora antibody (Abcam, Catalog #ab256799).

Flow cytometry

To block Fc receptor-mediating binding, cells were pre-incubated with anti-CD16/CD32 antibody prior to staining (1:200). For flow cytometry purposes, fluorochrome-conjugated anti-CD3 (Cat #100353), anti-CD45 (Cat #103126), and anti-CD8 (Cat #100734) antibodies were purchased from BioLegend. Apoptosis in CD8⁺ T cells was analyzed with an APC Annexin V Apoptosis Detection Kit with PI (Biolegend, Catalog # 640932). Samples were stained according to the manufacturer’s protocol and analyzed with an LSR II flow cytometer and FlowJo software (version 10.10).

Statistical analysis

Statistical analyses were done with R (version 4.0.1). An unpaired t test was used to compare the mean of two different groups when the distribution of the population was normal. One-way analysis of variance was used to compare the means of three or more groups under the assumption of normal distribution, followed by Tukey's HSD (honestly significant difference) test. The nonparametric Mann–Whitney U test and Kruskal–Wallis H test were used to compare the mean ranks between two groups (U test) or three groups (H test). Student’s t tests (two-tailed) were used to compare differences between individual groups, with error bars representing the standard deviation. P values were adjusted for multiple hypothesis testing by using Benjamini–Hochberg method when needed. Statistical significance was indicated by p < 0.05.

Results

Tumors resistant to immunotherapy have altered lipid metabolic profiling to avoid ferroptosis

In our previous work, we developed a mouse model exhibiting acquired resistance to PD1 blockade [30]. In this model, PD1-sensitive tumors (Sen) displayed necrotic regions when treated with PD1 inhibitors. Conversely, PD1-resistant tumors (Res) exhibited enhanced proliferation, mitotic activity, and an absence of necrosis [30]. Considering recent findings that link T cell–derived IFNγ and lipid metabolism to ferroptosis in PD1 inhibitor–treated tumors, we hypothesized that PD1-resistant tumors have a distinct lipid metabolism that may protect them from ferroptosis. To confirm our hypothesis, we first conducted a mass spectrometry-based lipidomics analysis of Sen and Res tumors under PD1 inhibitor treatment (Fig. 1a). We found that PD1-resistant tumors had elevated triglycerides (TGs) levels, suggestive of lipid accumulation, relative to their PD1-sensitive counterparts. Subsequent validation of this increased lipid accumulation in Res cells (relative to Sen cells) was confirmed through Oil Red O staining in vitro and in vivo settings (Fig. 1b,c). Global lipid and fatty acid profiles differed significantly between Sen and Res tumors, regardless of whether they were treated with an IgG control or a PD1 inhibitor (Fig. 1d,e). We then used targeted lipidomics with liquid chromatography-mass spectrometry to quantify eicosanoids derived from AA, docosahexaenoic acid (DHA), and other related PUFAs in Sen and Res tumors treated with PD1 inhibitors. Intriguingly, Sen tumors treated with PD1 inhibitor had higher total fatty acid levels than Sen tumors treated with IgG or Res tumors treated with PD1 inhibitor (Fig. 1f). However, Res tumors treated with PD1 inhibitor had increased levels of fatty metabolites relative to Sen tumors treated with either IgG or PD1 inhibitor or Res tumors treated with IgG (Fig. 1g). Moreover, Sen tumors treated with PD1 inhibitor showed higher PUFA levels than Res tumors (Fig. 1h,i), whereas Res tumors treated with PD1 exhibited higher levels of AA, DHA, and cytochrome P450-derived metabolites (Fig. 1h, i). Res tumors treated with PD1 further displayed increased levels of ferroptosis-protective lipids, particularly saturated fatty acids (SFAs) and monounsaturated fatty acids (MUFAs), compared with sensitive tumors (Fig. 1i). In evaluating mitochondrial fatty acid oxidation (FAO) in Sen versus Res cells with the Seahorse oxidative stress test, we found that Res cells experienced reduced mitochondrial function upon FAO inhibition with etomoxir, unlike Sen cells, in which mitochondrial respiration remained unaffected (Fig. 1j). Consistent with these observations, the ATP production rate was higher in Res cells than in Sen cells (Fig. 1k). These findings suggest that PD1-resistant tumors have developed adaptive metabolic strategies to evade ferroptosis, and that they have a distinct metabolic profile characterized by increased lipid accumulation, particularly SFAs and MUFAs and lower levels of PUFAs, that contribute to their resistance against PD1 inhibitors. The concept of metabolic reprogramming is further supported by the altered mitochondrial FAO dynamics observed in resistant cells. The increased ATP production in these cells underscores their enhanced metabolic efficiency. Collectively, our findings reveal that PD1-resistant tumors reprogram their metabolism by increasing levels of TGs, SFAs, and MUFAs, while decreasing levels of PUFAs. This reprogramming enhances mitochondrial FAO and ATP production, facilitating the evasion of ferroptosis and contributing to resistance against PD1 inhibitors.

Lipid chaperone Fabp7 upregulation in immunotherapy-resistant tumors modulates ferroptosis-related genes via epigenetic reprogramming

To elucidate the mechanism by which cancer cells resist ferroptosis induced by immunotherapy, we performed global gene expression profiling of Sen and Res tumors treated with PD1 inhibitor. We found that Fabp7 is among top upregulated genes in PD1-resistant tumors (Fig. 2a). Gene set enrichment analysis further supported this finding, highlighting the concurrent upregulation of pathways related to fatty acid metabolism, oxidative phosphorylation, and heme metabolism in these tumors (Fig. 2b). We validated the overexpression of Fabp7 at both the RNA and protein levels in Res relative to Sen tumors treated with PD1 inhibitor (Fig. 2c, e, f). We also assessed the expression of genes for several fatty acid binding proteins (FABPs), specifically Fabp1, Fabp2, Fabp3, Fabp4, and Fabp6 (Supplementary Figure S1) and found that only Fabp4 showed a notable decrease in expression in Res tumors compared with Sen tumors treated with PD1 inhibitors or IgG control. Other FABPs did not show significant changes in expression. Fabp5 was excluded from further analysis because of its minimal relevance in our initial global gene expression evaluation. To assess the functional relevance of Fabp7 in Res cells, we confirmed previous findings that activation of peroxisome proliferator-activated receptor γ (PPARγ) depends on Fabp7 [37, 38] by using Fabp7 knockdown (shFabp7) and control (ctrl) cells (Fig. 2d). The efficacy of Fabp7 knockdown with specific shRNA in Res-shFabp7 cells was confirmed by quantitative PCR (Supplementary Figure S2a) and western blot analyses (Supplementary Figure S2b). Given the established role of Fabp7 in modulating histone acetylation [39, 40], we next investigated its effects on the acetylation patterns of H3K27ac and H3K9ac in Res versus Sen cells and in Res-ctrl versus Res-shFabp7 cells via chromatin immunoprecipitation (ChIP) assays coupled with sequencing (ChIP-seq) (Fig. 2g). Our analysis revealed a preferential occurrence of H3K9ac and H3K27ac in promoter regions of Sen versus Res cells (70.80% vs 49.75%) and in Res-ctrl versus Res-shFabp7 cells (51.10% vs 33.40%) (Fig. 2h). We found significant changes in acetylation levels of genes related to ferroptosis, particularly for Lpcat3 and Bmal1. Fabp7 knockdown led to increased acetylation of Lpcat3 and decreased acetylation of Bmal1, indicating that Fabp7 modulates histone acetylation bidirectionally (Fig. 2i). By altering the acetylation landscape, Fabp7 appears to fine-tune the expression of key genes involved in cell survival and death pathways, highlighting its potential as a therapeutic target in cancer treatment. For visual confirmation, we used the Integrative Genomics Viewer to display differences in signal enrichment in the promoter region of Lpcat3 for H3K27ac and H3K9ac in Sen versus Res and Res-ctrl versus Res-shFabp7 (Fig. 2j). To identify transcription factors (TFs) regulated by Fabp7, we used motif enrichment analysis of significantly downregulated peaks (i.e., those with a false discovery rate [FDR] of < 0.05) in Sen versus Res, and Res-ctrl versus Res-shFabp7 cells. This analysis identified binding motifs of Pparg as being significantly enriched along with other TFs of both H3K27ac and H3K9ac (Fig. 2k). Finally, we undertook a comprehensive validation of our ChIP-seq results by performing global gene expression profiling in the Sen versus Res tumor models. This analysis confirmed downregulation of Lpcat3 and upregulation of Bmal1(Arntl) in Res tumors compared to Sen (Fig. 2m). The modulation of other genes previously identified on ChIp-seq analysis of both H3K27ac and H3K9ac, such as Lepr, Runx3, Rxra, Cyp1b1, Wnt10b and Bmp7 were also validated (Fig. 2m). To further corroborate these findings, we validated the differential expression of Lpcat3 and Bmal1 in Sen versus Res tumors and in Res-ctrl versus Res-shFabp7 tumors treated with either IgG control or PD1 inhibitor by using qPCR (Fig. 2n). Our study reveals that the upregulation of Fabp7 in PD1-resistant tumors plays a crucial role in modulating histone acetylation and gene expression, particularly influencing key genes involved in ferroptosis.

Lastly, we evaluated Bmal1 expression in various tissues other than tumors, comparing mice without tumors to those bearing Res-ctrl and Res-shFabp7 tumors. For this purpose, we injected these cells into mice and collected tissues, including the liver, brain, spleen, and lungs, after the Res-ctrl tumors reached the endpoint. Subsequently, we isolated RNA from these tissues and used gene expression analysis with qPCR. Our results indicated a significant reduction in Bmal1 expression in the liver and brain of mice bearing Res-shFabp7 tumors, compared with the Res-ctrl group. These findings suggest that Fabp7 expression may influence Bmal1 expression in the liver and brain of these mice, implying that PD1-resistant tumors may affect the circadian clock in other tissues (Supplementary Figure S3). Our study demonstrates that Fabp7 upregulation in PD1-resistant tumors drives epigenetic reprogramming and alters gene expression patterns, particularly impacting genes associated with ferroptosis. This highlights Fabp7 as a potential therapeutic target to counteract resistance to immunotherapy and suggests its broader role in modulating circadian clock genes in various tissues.

Fabp7 modifies lipid composition and mitochondrial function to protect immunotherapy-resistant tumors from ferroptosis

To explore the changes in lipid composition after Fabp7 inhibition in PD1-resistant tumors, we used comprehensive lipidomic profiling via high-resolution mass spectrometry of cell lysates obtained from Res-ctrl and Res-shFabp7 tumors treated with IgG control or PD1 inhibitor. Distinct lipidomic signatures were observed between the Res-ctrl and Res-shFabp7 groups under each treatment condition (Fig. 3a-d). Notably, a global reduction in TGs was evident in Res-shFabp7 compared with Res-ctrl tumors (Fig. 3e). Further, Res-shFabp7 tumors had reduced levels of MUFAs, specifically FA 24:1 and FA 22:1 (Fig. 3f). These MUFAs are known to confer resistance to ferroptosis by impeding lipid peroxidation and ROS generation [41]. A decrease in adrenic acid (FA 22:4), a PUFA derivative of AA involved in mitigating endoplasmic reticulum stress through cytochrome P450 metabolism [42], was observed in Res-shFabp7 tumors (Fig. 3f). Lipid peroxidation was further investigated via immunohistochemical staining for 4-hydroxynonenal (4-HNE) in both Res-ctrl and Res-shFabp7 tumor groups treated with IgG or PD1 inhibitor. The findings indicate heightened lipid peroxidation in Res-shFabp7 tumors treated with PD1 inhibitor relative to the other groups (Fig. 3g, h). Lipidomic profiling shed light on the distinct organelle composition within Res-shFabp7 tumors, indicating a decreased enrichment score of mitochondria, glycerophosphoethanolamines, and endolysosomal structures when contrasted with those found in Res-ctrl tumors (Supplementary Figure S4a). A decreased abundance of phosphatidylethanolamine in Res-shFabp7 tumors treated with PD1 suggests a potential enhancement of autophagy mediated by Fabp7 (Supplementary Figure S4b). However, this needs to be validated through further studies. Because ChIp-seq and gene expression analysis showed that Fabp7 modulates genes involved in mitophagy, such as Bmal1, we further investigated the role of Fabp7 in mitophagy. To do so, we used a specific mitophagy dye that emits high fluorescence when damaged mitochondria fuse to lysosomes, which can be captured hourly by the Incucyte system. Our analysis demonstrated increased mitophagy in Res cells compared with Sen cells; further, Fabp7 knockdown attenuated mitophagy in a variety of cancer cell lines, including Res (lung cancer), B16F10 (melanoma), and QPP7 (glioblastoma) (Supplementary Figure S4c, d). ROS levels were found to be elevated in Res-shFabp7 cells compared with Res-ctrl (Fig. 3i). We also assessed mitochondrial function and FAO in Res-ctrl and Res-shFabp7 cells with the Seahorse assay (in which etomoxir is used for FAO analysis) and quantified ATP production in these cells as well. Fabp7 knockdown was found to influence FAO and to reduce ATP production compared with control (Fig. 3k, l). Our findings indicate that Fabp7 protects PD1-resistant tumors against ferroptosis by altering lipid composition and improving mitochondrial function, thereby decreasing sensitivity to ferroptosis. These changes, highlighted by increased TGs, MUFAs, and specific fatty acids, coupled with decreased ROS levels and lipid peroxidation, underscore Fabp7 role in cancer cell survival and suggest its potential as a target to enhance immunotherapy efficacy.

Cancer cells upregulate Fabp7 to disrupt circadian clock genes and promote apoptosis in CD8 + T cells

Next, we examined the role of Fabp7 in the tumor immune microenvironment, particularly its influence on T cells, which are affected by PD1 inhibitors. Previous research indicated that T cells express FABPs corresponding to the patterns found in the tissues or organs where they reside, optimizing local fatty acid availability [43]. Therefore, we hypothesized that T cells infiltrating PD1-resistant tumors that overexpress Fabp7 will also express Fabp7. To test this hypothesis, we analyzed the expression of Fabp7 in tissue-infiltrating lymphocytes (TILs) isolated from both Sen and Res tumors, as well as Res-ctrl and Res-shFabp7 tumors treated with a PD1 inhibitor, by using qPCR. Our findings confirmed that Fabp7 expression in TILs was higher in Res tumors than in Sen tumors, and that it was reduced in Res-shFabp7 versus Res-ctrl tumors (Fig. 4a). We next validated Fabp7 downregulation in CD8⁺ T cells that had been co-cultured with Fabp7-knockdown cells (Res and B16F10) compared with control cells (Fig. 4b, c). Previous studies have demonstrated that FABPs are secreted proteins [44,45,46]. Accordingly, we observed increased levels of Fabp7 in Res cells compared to Sen cells, as determined by ELISA. Additionally, we found reduced Fabp7 levels in the media of Res-shFabp7 cells compared to controls (Supplementary Figure S5). These findings suggest that Fabp7 expression in TILs may be influenced by Fabp7 secreted from tumor cells.

We then investigated the effect of Fabp7 expression on T cells, particularly its relationship to PD1 inhibitor resistance. For this purpose, we performed assay for transposase-accessible chromatin sequencing (ATAC-seq) in activated T cells co-cultured with Sen, Res, Res-ctrl, and Res-shFabp7 cells for 48 h (Fig. 4d-f). ATAC-seq analysis revealed global upregulation of differential peaks in gene promoters in T cells co-cultured with Res-shFabp7 versus those co-cultured with Res-ctrl, suggesting at a suppressive role of Fabp7 in activated T cells (Fig. 4g, h). Motif enrichment analysis of transcription factors revealed changes in T-cell activation and apoptotic processes in T cells co-cultured with Res-shFabp7 cells compared to the control. This analysis identified transcription factors related to apoptosis regulation, such as RORA and Tp63, among others, which were significantly decreased in T cells co-cultured with Res-shFabp7 cells compared to the control (Fig. 4i). Gene Ontology analysis of biological processes revealed that several pathways were significantly modulated in T cells co-cultured with Res-shFabp7 cells compared to the control. These pathways include the positive regulation of mitochondrial permeability in apoptosis, mitochondrial membrane permeability in cell death, and the regulation of lymphocyte activation and differentiation (Fig. 4j). Accordingly, GIGGLE score analysis, which integrates various genomic features to identify overlaps and similarities, showed that TP53 was the most significantly enriched transcription factor in T cells co-cultured with Res-shFabp7 cells (Fig. 4k). This suggests that TP53, along with these other factors, plays a crucial role in modulating the transcriptional landscape in T cells affected by Fabp7 inhibition. On the other hand, transcription factors that regulate T-cell activation, such as BATF, FOS, JUN, IRF4, and STATs, were enriched in T cells co-cultured with shFabp7 (Supplementary Figure S6). Our study reveals that Fabp7 plays a pivotal role in the tumor immune microenvironment by modulating T-cell activity. In PD1-resistant tumors, T cells infiltrate and express Fabp7, which contributes to their resistance. Fabp7 leads to significant changes in the transcriptional landscape of T cells, enhancing pathways and transcription factors associated with T-cell apoptosis, such as Rora and p53, while preventing T-cell activation. To validate ATAC-seq findings, we performed global gene expression analysis using RNA-seq in activated T cells co-cultured with Res-ctrl and Res-shFabp7 cells for 24 and 48 h. According to the ATAC-seq data, Rora and other clock-regulated genes including Clock, Bmal1 (Arntl) and Nrip1 in were downregulated in T cells co-cultured with Res-shFabp7 compared with Res-ctrl (Fig. 4l and Supplementary Figure S7). In addition, apoptotic genes regulated by Tp53, such as Fas, Bbc3, Gadd45a and Bcl2l11 (Bim), were also downregulated in T cells co-cultured with Res-shFabp7 compared with Res-ctrl for 48 h (Fig. 4l). In line with our previous results, Fabp7 was downregulated in T cells co-cultured with Res-shFabp7 compared with Res-ctrl for 48 h (Fig. 4l). On the other hand, genes promoting T-cell activation were upregulated, such as Irf4, Il2, Ifng, Gzmb, Fosl1 and Batf, along with Nfkb1/2 and its target Mdm2, which is known to facilitate p53 degradation in T cells co-cultured with Res-shFabp7 compared with Res-ctrl (Fig. 4l and Supplementary Figure S7). Accordingly, Gene Ontology analysis of genes significantly differentially expressed in RNA-seq analysis demonstrated that apoptosis signaling and p53 pathway are among the top pathways modulated in T cells co-cultured with Res-shFabp7 (Fig. 4m). Based on these findings and in previous studies demonstrating the role of Rora in p53-mediated apoptosis [47, 48], we hypothesized that Fabp7 promote apoptosis in CD8⁺ T cells via Rora/p53 axis. To test this hypothesis, we first validated Rora expression in vitro in CD8⁺ T cells co-cultured with Res (Fig. 4n) or B16F10-shFabp7 cells (Fig. 4o) compared to controls using qPCR. We further confirmed the downregulation of Rora expression in vivo in T cells infiltrating Res-shFabp7 tumors treated with either IgG or PD1, compared to Res-ctrl tumors treated with either IgG or PD1(Fig. 4p). We also validated other genes regulated by Rora, including Bmal1, Nrip1 and Bcl211, and Clock in vitro in CD8⁺ T cells co-cultured with Res (Supplementary Figure S8a) or B16F10-shFabp7 cells (Supplementary Figure S8b) compared to controls using qPCR. In further confirm our hypothesis that Fabp7 promote apoptosis in CD8⁺ T cells via p53 stabilization mediated by Rora, we isolated splenic CD8⁺ T cells from Rora^fl and Cd8 cre;Rora^fl mice [32] for ex vivo co-culture with Res-ctrl and Res-shFabp7 cells. Subsequent Western blot analysis showed reduced p53 expression in Cd8 cre;Rora^fl T cells co-cultured with Res-shFabp7, but not in Rora^fl T cells (Fig. 4q). This demonstrated that p53 stabilization in CD8⁺ T cells promoted by Fabp7 is Rora-dependent.

Since Fabp7 is known to interact with nuclear receptors such as Pparg [37, 38] and RXR [49], we investigated if Fabp7 also interacts with Rora in the protein level. As demonstrated by co-immunoprecipitation analysis, Fabp7 interacts with Rora independently of DHA treatment (Supplemental Figure S9). We then performed expression analysis of circadian clock genes Rora, Bmal1 and Nrip1 in activated CD8⁺ T cells alone or co-cultured with Res-shFabp7 compared with Res-ctrl (Fig. 4r). This analysis suggests that the co-culture with cancer cells induces oscillatory expression of circadian genes CD8⁺ T cells, and the specific knockdown of Fabp7 in Res cells alters this expression pattern by reducing the amplitude of these oscillations. These results suggest that interactions with cancer cells, influenced by Fabp7, significantly impact the circadian regulation of gene expression in CD8⁺ T cells, highlighting the role of the tumor microenvironment in modulating immune cells behavior.

To further confirm whether Fabp7 promotes apoptosis in CD8⁺ T cells, we harvested spleens from mice and isolated CD8⁺ T cells by using specific magnetic beads. These cells were then activated with CD3/CD28 antibodies and co-cultured with Res-ctrl, Res-shFabp7, B16F10-ctrl, or B16F10-shFabp7 for 72 h. Apoptosis was assessed by flow cytometry with an Annexin-specific antibody. Our results revealed that Fabp7 knockdown in Res (Fig. 5a) and B16F10 (Fig. 5b) reduced apoptosis in co-cultured CD8⁺ T cells. This suggests that Fabp7 positively regulates apoptosis in CD8⁺ T cells.

Subsequently, to determine if the regulation of apoptosis in CD8⁺ T cells is Rora-dependent, we isolated CD8⁺ T cells from the spleens of Rora^fl and Cd8 cre;Rora^fl mice by using specific magnetic beads, activated them with CD3/CD28 antibodies, and co-cultured them under ex vivo contactless conditions with Res-ctrl or Res-shFabp7 cells for 72 h, after which apoptosis was analyzed by flow cytometry with an Annexin V-specific antibody. Fabp7 knockdown significantly reduced apoptosis relative to control in CD8⁺ T cells from Cd8 cre;Rora^fl mice (Fig. 5c). Conversely, no significant differences in apoptosis were observed between Fabp7 knockdown and control cells in CD8⁺ T cells from Rora^fl mice (Fig. 5d). These results suggest that the pro-apoptotic effect of Fabp7 is Rora-dependent. These findings underscore the critical role of Fabp7 in mediating Rora-dependent apoptosis in CD8⁺ T cells, highlighting its potential as a target for therapeutic interventions.

Knockdown of FABP7 restores sensitivity to immunotherapy in resistant tumors

Next, with the goal of corroborating our in vitro observations in an in vivo context, we injected Res-ctrl and Res-shFabp7 cells into 129 Ev mice and treated the resulting tumors with either an IgG control or a PD1 inhibitor. Subsequent isolation and flow cytometry analysis of CD8⁺ T cells from these tumors revealed that those with Fabp7 knockdown, when treated with PD1, showed increased CD8⁺ T-cell infiltration relative to other groups (Fig. 5e). Further, Fabp7-knockdown tumors treated with either IgG or PD1 had a reduced percentage of apoptotic CD8⁺ T cells compared with control tumors treated with both IgG and PD1 (Fig. 5f). To confirm these findings, we used an independent melanoma model B16F10 transplanted in C57BL/6 mice. Consistent with the results from the Res mouse model, we observed that B16F10 tumors with Fabp7 knockdown treated with PD1 exhibited higher CD8⁺ T-cell infiltration than the other groups (Fig. 5g). Moreover, these Fabp7-knockdown tumors treated with either IgG or PD1 showed fewer apoptotic CD8⁺ T cells than control tumors treated with IgG and PD1 (Fig. 5h).

To determine if Fabp7 knockdown sensitize resistant tumors to immunotherapy, we used two independent mice models. We first injected Res-ctrl or Res-shFabp7 cells into 129 Ev mice and treated the tumors with either IgG control or a PD1 inhibitor. In line with our previous findings, Fabp7 knockdown re-sensitized PD1-resistant tumors to immunotherapy (Fig. 5i). Similarly, Fabp7 knockdown enhanced the sensitivity of B16F10 cells to the PD1 inhibitor compared to control groups (Fig. 5j). To determine if Rora alone could influence the response to PD1 inhibition or if it required concurrent Fabp7 knockdown, we conducted the following experiments. First, we injected B16F10 cells into Cd8 cre;Rora^fl mice and treated them with either an IgG control or a PD1 inhibitor. There was no significant difference in tumor growth between the two treatment groups (Fig. 5k). Next, we injected Res-ctrl and Res-shFabp7 cells into Cd8 cre;Rora^fl mice, followed by treatment with either an IgG control or a PD1 inhibitor. Notably, only the tumors with Fabp7 knockdown showed a significant response to PD1 inhibition (Fig. 5l). Our study demonstrates that Fabp7 knockdown re-sensitizes resistant tumors to immunotherapy. In vivo experiments showed increased CD8⁺ T-cell infiltration and reduced apoptosis in Fabp7-knockdown tumors treated with PD1 inhibitors. These effects were confirmed in two independent mouse models (129 Ev and B16F10), where Fabp7 knockdown enhanced sensitivity to PD1 inhibition. Additionally, Fabp7 knockdown, not Rora alone, was necessary for the observed tumor response to PD1 treatment.

FABP7 expression correlates with CD8 T cell infiltration and patient outcomes in immunotherapy-treated patients

We conducted Imaging Mass Cytometry (IMC) analysis on melanoma patients categorized by high and low expression levels of Fabp7 (n = 4 for each group). The results demonstrated a clear distinction in CD8 T cell infiltration, with tumors exhibiting high Fabp7 expression having significantly fewer CD8 T cells, whereas those with lower Fabp7 expression showed markedly higher infiltration of these immune cells (Fig. 6a). This observation led us to further investigate the relationship between FABP7 and LPCAT3 expression and CD8 T cell infiltration in a broader cohort of patients undergoing PD1 (pembrolizumab) or CTLA4 (ipilimumab) inhibitor therapy using the Tumor Immune Dysfunction and Exclusion (TIDE) module (Fig. 6b, c). Consistent with our findings in melanoma patients, an inverse correlation was found between FABP7 expression and CD8 T cell infiltration in these clinical trial participants. Notably, LPCAT3 expression displayed a positive correlation with CD8 T cell infiltration in the same patient cohorts (Fig. 6c).

We then analyzed the association between FABP7 expression and patient survival in clinical trials involving immunotherapy using TIDE. Patients with higher FABP7 expression exhibited reduced overall survival and progression-free survival compared to those with lower FABP7 expression (Fig. 6d). Additionally, we used TIDE to predict if FABP7 could serve as a biomarker of response in these clinical trials. The analysis revealed that FABP7 expression is a significant predictor of response to immunotherapy, comparable to established biomarkers such as IFNG, mutation status, PDL1 expression, CD8 presence, Merck18, and B.clonality (Fig. 6e).

Collectively, these results demonstrated that high FABP7 expression is associated with reduced CD8 T cell infiltration and poorer survival outcomes, indicating its potential as a biomarker for predicting immunotherapy response. These studies validated our findings in preclinical models, demonstrating that targeting FABP7 may represent a viable approach to induce ferroptosis in tumors resistant to immunotherapy and improve response in non-responsive patients.

Discussion

Immunotherapy through CD8⁺ T cells can induce ferroptosis in cancer cells. However, the mechanisms by which cancer cells evade this immune-mediated cell death are not well understood. Our study demonstrates that immunotherapy-resistant tumors upregulate Fabp7, leading to metabolic adaptations that enhance tumor survival and immune evasion. Fabp7 modulates lipid composition, which enhances mitochondrial function and ATP production, providing protection against ferroptosis. It also decreases the transcription of ferroptosis-inducing genes like Lpcat3 and increases the transcription of ferroptosis-protective genes such as Bmal1 through epigenetic reprogramming. Furthermore, cancer cells increase Fabp7 expression in CD8 ⁺ T cells, which disrupts circadian clock gene expression and triggers apoptosis through p53 stabilization, thereby contributing to an immunosuppressive tumor microenvironment (Fig. 7). This study highlights Fabp7 as a key player in the adaptive resistance of PD1-resistant tumors, suggesting that targeting Fabp7 could be a novel therapeutic strategy to enhance the efficacy of immunotherapy.

Previous studies have shown that HIF1A enhances FABP7 expression, which in turn promotes lipid storage protecting cells against ROS and supports their survival during hypoxia-reoxygenation in GBM cells [50]. FABP7 was shown to inhibit ferroptosis in human fibrosarcoma or lung cancer cells through increasing fatty acid uptake and lipid droplet formation [17]. It was also demonstrated that Fabp7 protects astrocytes from ROS-mediated lipid peroxidation by increasing lipid droplet formation, which can serve as fuel for mitochondrial β-oxidation [51, 52]. Nonetheless, the molecular mechanisms by which Fabp7 protects cells from ferroptosis were not understood prior to our studies. In this study, we found that Fabp7 increases histone acetylation of genes that protect cells from ferroptosis, such as Bmal1, and decreases histone acetylation of genes that promote ferroptosis, such as Lpcat3. Lpcat3 is involved in incorporating AA into membrane phospholipids, which is essential for ferroptosis [53]. ChIP-seq analysis revealed that Fabp7 also enhances histone acetylation of genes related to iron metabolism (e.g., Runx3 and Bmp7 [54, 55]), mitochondrial function (e.g., Ppargc1a/b [PGC-1α and PGC-1β]), Cav1, P450 enzymes (e.g., Cyp1b1), and mitophagy (e.g., Atg4a [56]). Supporting these observations, as shown in Supplementary Figure S4b, our studies indicate that Fabp7 significantly influences lipid composition and mitophagy in cancer cells, highlighting its crucial role in regulating both lipid metabolism and mitochondrial quality control in tumors. The regulation of Cav1 by Fabp7 via histone acetylation in our study aligns with findings from others [39]. Previously, we reported that Bmp7, a TGFβ superfamily member, is upregulated in PD1-resistant tumors [57], which is consistent with our current findings.

Previous studies have demonstrated that BMAL1 promotes FABP7 expression through HIF1A stabilization in human cancer cells [17]. Our research extends these findings by showing that Fabp7 regulates Bmal1 expression via histone acetylation. This establishes a positive regulatory loop between Fabp7 and Bmal1, which is crucial for protecting PD1-resistant tumors from ferroptosis. Consistent with these findings, we found that Hif1a is upregulated in tumors resistant to immunotherapy compared to sensitive tumors (Fig. 2m).

Our findings underscore the critical role of Fabp7 in regulating lipid metabolism and mitochondrial integrity in PD1-resistant tumors. The significant reduction in TGs and MUFAs, following Fabp7 inhibition, highlights the protective function of these fatty acids against ferroptosis by inhibiting lipid peroxidation and ROS generation. The increased lipid peroxidation in Res-shFabp7 tumors treated with PD1 inhibitors further supports this notion (Fig. 3g). The reduced enrichment score for mitochondria and specific lipid types in Res-shFabp7 tumors, as shown in Supplemental Figure S4, suggests that Fabp7 affects mitochondrial integrity and function, reducing FAO and ATP production. This metabolic shift likely increases the vulnerability of these cells to ferroptosis when Fabp7 is inhibited. Collectively, these results reveal Fabp7 multifaceted role in maintaining cellular homeostasis and underscore its importance for the survival and resistance of PD1-resistant tumors to ferroptosis.

Our next line of investigation focused on Fabp7 in the tumor immune microenvironment, particularly on T cells. Previous studies have shown that T cells express the same FABPs as the tissues or organs in which they reside, optimizing local fatty acid availability [43]. Notably, this study also showed that T cells either do not express or exhibit very low expression of Fabp7. Consistent with these findings, we observed that Fabp7 is overexpressed in TILs from PD1-resistant tumors compared to sensitive tumors. Moreover, Fabp7 expression is significantly downregulated in TILs from Fabp7-knockdown tumors compared to control tumors. These findings agree with another study showing that FABP7 protein expression was lower in melanoma TILs of anti-PD1-responders relative to non-responders [58]. These results suggest that Fabp7 expression in TILs is influenced by its expression in the tumor cells, highlighting the interconnected role of Fabp7 in both the tumor and its immune microenvironment.

Our findings reveal that Fabp7 plays a pivotal role in regulating CD8⁺ T cell function within the tumor microenvironment, particularly in modulating apoptosis and T cell activation through the Rora-p53 axis. We discovered that Fabp7 knockdown leads to increased activity of transcription factors associated with T cell activation, such as IRF4, BATF, STATs, NFKB1/2, while reducing the activity of factors involved in apoptosis, notably Rora (Fig. 4i and Supplementary Fig. 6). We validated Rora downregulation in activated CD8⁺ T cells co-cultured with Fabp7-knockdown and in TILs from Fabp7-knockdown tumors compared to control, treated with IgG or PD1 (Fig. 4n-p). We also validated the downregulation of Rora-target genes, such as Bmal1 and Nrip1, in activated CD8⁺ T cells co-cultured with Fabp7-knockdown cells compared to control cells (Supplementary Figure S8). We also tested if Fabp7 interacts with Rora, which is also a member of the nuclear receptor superfamily, because Fabp7 is known to interact with other nuclear receptors, (e.g., PPARγ, RXRs) [37, 38, 49]. Co-immunoprecipitation experiments confirmed an interaction between Fabp7 and Rora that is independent of DHA. The Giggle Score analysis, which ranks the significance of genomic loci shared between query features and genomic interval files, identified TP53 as the top transcription factor in T cells co-cultured with Fabp7-knockdown cells. In alignment with this findings, previous studies have shown that Rora, a key component of the mammalian circadian clock, promotes apoptotic genes via p53 stabilization [47, 48]. To further substantiate the role of Fabp7 and Rora in p53 regulation, we performed Western blotting, which confirmed that the downregulation of p53 in CD8⁺ T cells co-cultured with Res-shFabp7 cells occurs in a Rora-dependent manner. This was demonstrated using splenic CD8⁺ T cells isolated from Cd8 cre;Rora^fl and Rora^fl mice (Fig. 4q). These findings underscore that Fabp7 promotes p53 stabilization via Rora, highlighting its crucial role in modulating apoptosis in CD8⁺ T cells.

We then performed expression analysis of circadian clock genes Rora, Bmal1, and Nrip1 in activated CD8⁺ T cells alone or co-cultured with Res-shFabp7 compared with Res-ctrl (Fig. 4r). This analysis suggests that co-culture with cancer cells induces oscillatory expression of circadian genes in CD8⁺ T cells, and the specific knockdown of Fabp7 in Res cells alters this expression pattern by reducing the amplitude of these oscillations. These findings indicate that interactions with cancer cells, influenced by Fabp7, significantly impact the circadian regulation of gene expression in CD8⁺ T cells, underscoring the role of the tumor microenvironment in modulating immune cell behavior. Since the CD8⁺ T cells were activated but not synchronized, further studies are needed to fully understand the impact of cancer on the circadian rhythm of CD8⁺ T cells. Nonetheless, our study reveal that cancer cells can disrupt the expression of circadian clock genes in CD8⁺ T cells, opening new avenues for research on the interplay between the tumor microenvironment and circadian regulation of immune cells.

To corroborate our in vitro findings within an in vivo context, we injected Res-ctrl and Res-shFabp7 cells into 129 Ev mice and treated the resulting tumors with either an IgG control or a PD1 inhibitor. Our observations revealed that tumors with Fabp7 knockdown, when treated with PD1, exhibited increased CD8⁺ T-cell infiltration compared to other groups (Fig. 5e). Furthermore, these Fabp7-knockdown tumors had a reduced percentage of apoptotic CD8⁺ T cells regardless of the treatment with either IgG or PD1(Fig. 5e). These results were consistent when validated using an independent melanoma model, B16F10, in C57BL/6 mice, where we observed similar increases in CD8⁺ T-cell infiltration and decreased apoptosis in Fabp7-knockdown tumors (Fig. 5g,h). These in vivo findings emphasize the significant role of Fabp7 in modulating the tumor microenvironment, promoting CD8⁺ T-cell infiltration, and reducing apoptosis.

Fabp7 inhibition in both the Res and B16F10 models markedly sensitized these PD1-resistant tumors to immunotherapy, identifying an important role of Fabp7 in modulating the immune response. Our experiments with Cd8 cre;Rora^fl mice, where the Rora gene was deleted in CD8 T cells, further demonstrated that the enhanced response to PD1 inhibition was specifically dependent on Fabp7 knockdown, rather than PD1 inhibition alone. The results were consistent with the role of Rora in regulating the apoptotic response and p53 expression in CD8⁺ T cells. These results collectively suggest that targeting Fabp7 could be a promising strategy to improve the efficacy of immunotherapy in PD1-resistant tumors. Although there are small molecules targeting Fabp7 [59, 60], these compounds also target Fabp5, which is very important for T cells differentiation in memory T cells and possibly will prevent an adequate anti-tumor immunity. Therefore, the development of new small molecules targeting this protein specifically could represent a new therapeutic approach to promote ferroptosis in immunotherapy resistant tumors.

Our findings reveal a link between FABP7 expression and the immune landscape in tumors, as well as patient outcomes in immunotherapy-treated melanoma patients. IMC analysis showed that high FABP7 expression in tumors correlates reduced CD8⁺ T cell infiltration, while tumors with lower FABP7 expression had markedly higher infiltration of these immune cells as observed in mice. Although further analysis of additional patient samples is needed to validate these findings, this pattern was corroborated using the Tumor Immune Dysfunction and Exclusion (TIDE) module across a larger cohort of patients undergoing PD1 or CTLA4 inhibitor therapy. In this broader dataset, an inverse correlation between FABP7 expression and CD8⁺ T cell infiltration was consistently observed. Additionally, we identified a positive correlation between LPCAT3 expression and CD8⁺ T cell infiltration. Importantly, higher FABP7 expression was associated with reduced overall survival and progression-free survival in patients receiving immunotherapy, suggesting its potential as a predictive biomarker. TIDE analysis confirmed that FABP7 expression is a significant predictor of response to immunotherapy, on par with established biomarkers like IFNG, mutation status, PDL1 expression, CD8 presence, Merck18, and B.clonality. These comprehensive results underscore the impact of FABP7 on immune cell dynamics and patient outcomes, highlighting its promise as a target for enhancing immunotherapy efficacy. By validating our preclinical findings, this study suggests that targeting FABP7 could induce ferroptosis in immunotherapy-resistant tumors, potentially improving treatment responses in non-responsive patients.

Data availability

Availability of data and materials All data supporting the findings of this study are provided within the article and its supplementary materials. Raw sequencing data for Microarray, RNA-seq, ATAC-seq, and ChIP-seq are deposited in public repositories with the following accession numbers: GSE285215, GSE285216, GSE285287, and GSE285288. Validation datasets used in Fig. 6 include data from TIDE, accessible at http://tide.dfci.harvard.edu/login/. Further details and analytical methodologies related to TIDE are described in "Tumor Immune Dysfunction and Exclusion" (Jiang et al., 2019) available at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6487502/. Resources and materials generated in this study are available from the lead author upon reasonable request, subject to a material transfer agreement (MTA).

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Acknowledgements

We thank Christine F. Wogan, MS, ELS, of MD Anderson’s Division of Radiation Oncology, for editorial contributions. We also thank Joe Cozzarin at Standard Biotools for his assistance with the Imaging Mass Cytometry analysis.

Funding

M.A.F.C. was supported by the US Department of Defense (W81XWH2110336 [PI]) and National Cancer Institute (NCI) Melanoma SPORE Award (5P50 CA221703). The Baylor College of Medicine metabolomics core was supported by the Cancer Prevention and Research Institute of Texas (CPRIT) Core Facility Support (RP210227), NCI Cancer Center Support Grant P30CA125123, NCI/NIH R01CA220297, NIH/NCI R01CA216426, and intramural funds from the Dan L. Duncan Cancer Center. J.H. is supported by CPRIT RP220592, NIH 1R01NS132944, and NCI/NIH P50CA127001. M.A.D. is supported by the Dr. Miriam and Sheldon G. Adelson Medical Research Foundation, the AIM at Melanoma Foundation, NCI/NIH P50CA221703, the American Cancer Society, and the Melanoma Research Alliance, Cancer Fighters of Houston, the Anne and John Mendelsohn Chair for Cancer Research, and philanthropic contributions to the Melanoma Moon Shots Program of MD Anderson. J.W.W. is supported by Bristol-Myers Squibb, Astra Zeneca, Alkermes, Varian Medical Systems, Artidis, Nanobiotix, Kiromic Biopharma, Gilead, Hotspot Therapeutics, Bayer Health, DOD W81XWH2110336 and CPRIT AWD00007370.

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Authors and Affiliations

Children’s Research Institute, University of Texas Southwestern Medical Center, Dallas, TX, 75235, USA
Maria Angelica Freitas-Cortez
Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX, USA
Maria Angelica Freitas-Cortez
Department of Radiation Oncology, UT MD Anderson Cancer Center, 6565 MD Anderson Boulevard, Houston, TX, 77030, USA
Fatemeh Masrorpour, Hong Jiang, Ailing Huang, Lisa K. Duong, Tiffany A. Voss, Shonik Ganjoo, Hampartsoum B. Barsoumian, Thomas S. Riad, Yun Hu, Carola Leuschner, Nahum Puebla-Osorio & James W. Welsh
Department of Bioinformatics and Computational Biology, Metabolomics Core Facility, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
Iqbal Mahmud, Bo Wei, Wai-Kin Chan & Philip L. Lorenzi
Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
Yue Lu & Kevin Lin
Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
Qi Wang & Jing Wang
Department of Hematology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
Claudia S. Kettlun Leyton
Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
Jie Zhang & Boyi Gan
Department of Orthopedic Surgery, Baylor College of Medicine, Houston, TX, 77030, USA
Efrosini Tsouko
Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
Jian Hu
Department of Melanoma Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
Michael A. Davies
Department of Neuro-Oncology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
Vinay K. Puduvalli
Center for Clinical Pharmacology, Washington University School of Medicine and St. Louis College of Pharmacy at University of Health and Sciences, St. Louis, MO, 63110, USA
Cyrielle Billon
University of Florida Genetics Institute, Gainesville, FL, 32610, USA
Thomas P. Burris
Children’s Research Institute at UT Southwestern, Pediatrics 5323 Harry Hines Blvd., Dallas, TX, 75390, USA
Maria Angelica Freitas-Cortez

Authors

Maria Angelica Freitas-Cortez
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Fatemeh Masrorpour
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Hong Jiang
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Iqbal Mahmud
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Yue Lu
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Ailing Huang
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Lisa K. Duong
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Qi Wang
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Tiffany A. Voss
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Claudia S. Kettlun Leyton
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Bo Wei
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Wai-Kin Chan
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Kevin Lin
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Jie Zhang
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Efrosini Tsouko
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Shonik Ganjoo
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Hampartsoum B. Barsoumian
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Thomas S. Riad
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Yun Hu
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Carola Leuschner
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Nahum Puebla-Osorio
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Jing Wang
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Jian Hu
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Michael A. Davies
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Vinay K. Puduvalli
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Cyrielle Billon
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Thomas P. Burris
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Philip L. Lorenzi
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Boyi Gan
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James W. Welsh
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Contributions

M.A.F.C. designed the study, interpreted the data, contributed with key reagents and wrote the manuscript. F.M., L.K.D, H.J., T.V., and C.S.K. gave technical assistance with the molecular biology, flow cytometry and functional analyses in vitro. F.M., H.J. and M.A.C. conducted most the experiments. A.H., L.K.D., M.A.C., H.B., Y.H. and T.S.R. assisted with mouse model studies and tissue collection. J.Z., M.A.C. and E.T. conducted immunohistochemical and pathological analysis. I.M, B.W. and P.L.L. performed untargeted lipidomics experiments and analysis. W.K.C. performed Seahorse analysis. Y.L., K.L., Q.W. and J.W. performed statistical analysis for RNA-sequencing, ChIp-sequencing, and ATAC-sequencing. S.G., C.L., N.P.O. assisted with data analysis. C.B. and T.B. provided the Rora floxed mice. J.H., M.A.D., V.K.P., B.G. contributed with key reagents and critical revision of the manuscript. W.K.C. assisted with Seahorse analysis. J.W.W. helped with the design of experiments, coordinated the study, and contributed with key reagents. All authors edited and approved the manuscript.

Corresponding authors

Correspondence to Maria Angelica Freitas-Cortez or James W. Welsh.

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Competing interests

M.A.F.C. and J.W.W. has pending patent on a small molecule targeting Fabp7. J.W.W. is supported by Alkermes (Research grant), Nanobiotix (Research grant, Travel expenses, SAB), GSK (Research grant), BMS (Research grant), Merck (Research grant), Varian (Research grant, Travel expenses, clinical sponsored research), Checkmate Pharmaceuticals (Research grant, SAB), Reflexion (Research grant, Travel expenses, Stock options, SAB), Artidis (research grants, clinical sponsored research), Takeda (Research grant), Hotspot Therapeutics (Research grant), Gilead (Research grant), Kiromic (Research grant), Bayer Health (Research grant), Agenus (SAB), Novocure (SAB), Alpine Immune Science (SAB, stock options), Oncoresponse (SAB, stock options), Astra Zeneca (consultant), Nanorobotix (stock options), GI innovation (consultant), Molecular Match (Stock options, consultant), Kezar Life Science (SAB). M.A.D. has been a consultant to Roche/Genentech, Array, Pfizer, Novartis, BMS, GSK, Sanofi-Aventis, Vaccinex, Apexigen, Eisai, Iovance, Merck, and ABM Therapeutics, and he has been the PI of research grants to MD Anderson by Roche/Genentech, GSK, Sanofi-Aventis, Merck, Myriad, Oncothyreon, Pfizer, ABM Therapeutics, and LEAD Pharma. All other authors have no conflict of interest to declare.

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Supplementary Information

12943_2024_2198_MOESM1_ESM.docx

Supplementary Material 1. Supplementary Figure S1. Comparative Analysis of FABP Isoforms in PD1-Sensitive and PD1-Resistant Tumors Treated with Anti-PD1 Therapy. Quantitative polymerase chain reaction analysis of fatty acid binding protein (Fabp) isoforms in PD1-sensitive (Sen) tumors (n = 4 mice per group) and PD1-resistant (res) tumors (n = 4 mice per group) treated with anti-PD1 (10 mg/kg) twice weekly. Rps18 expression was used as a housekeeping gene for qPCR. The comparative Ct method was used to calculate the relative abundance of mRNAs compared with Rps18 expression. Box-and-whisker plots show the minimum and maximum log2 expression. *p < 0.05, two-sided Mann–Whitney test. Supplementary Figure S2. Validation of Fabp7 knockdown in Res and B16F10 cells. (a) Quantitative polymerase chain reaction analysis of fatty acid binding protein 7 (Fabp7) in PD1-sensitive (Sen; lung), PD1-resistant (Res; lung), Res-ctrl (lung), Res-shFabp7 (lung with Fabp7 knockdown), B16F10-ctrl (melanoma), and B16F10-shFabp7 (melanoma with Fabp7 knockdown) tumors. Rps18 expression was used as a housekeeping gene for qPCR. The comparative Ct method was used to calculate the relative abundance of mRNAs compared with Rps18 expression. Box-and-whisker plots show the minimum and maximum. *p < 0.05, two-sided Mann–Whitney test. (b) Western blot analysis of Fabp7 in Sen, Res, Res-ctrl and Res-shFabp7 cells. Antibodies specific to Fabp7 and actin (normalization control) were used in the blotting process. Each lane represents a distinct sample group. Supplementary Figure S3. Bmal1 Expression in Mouse Liver, Brain, Spleen, and Lung Tissues from Control vs. Res-ctrl and Res-shFabp7 Tumors with Anti-PD1 Treatment. Quantitative polymerase chain reaction analysis of Bmal1 expression in liver, brain spleen and lung tissues collected from mice with no tumors (controls) and mice bearing either Res-ctrl or Res-shFabp7 tumors treated with anti-PD1 (10 mg/kg) twice weekly (n = 5 mice per group). Rps18 expression was used as a housekeeping gene for quantitative PCR analysis. The comparative Ct method was used to calculate the relative abundance of mRNAs compared with Rps18 expression. Heatmaps were generated with GraphPad Prism version 9.0.0. Supplementary Figure S4. Lipidomic Profiling and mitophagy studies in Fabp7-knockdown tumor cells. (a,b) Comparative lipidomic analysis of mitochondria, endolysosomal structures, and phosphatidylethanolamine based on untargeted lipidomics. High-resolution mass spectrometry was used to analyze lipid compositions in Res-ctrl and Res-shFabp7 tumors, treated with either IgG control or PD1 inhibitor. Each group consisted of n = 3 mice. The lipidomic signatures highlight distinct patterns between Res-ctrl and Res-shFabp7 groups under each treatment condition. Data were analyzed with Thermo Scientific LipidSearch software (version 5.0) and R scripts. (c, d) Mitophagy was measured in Res (lung cancer), B16F10 (melanoma), and QPP7 (glioblastoma) cell lines by using a Mtphagy Dye (Japan, Dojindo Molecular Technologies). induction of mitophagy leads to fusion of damaged mitochondria to lysosomes and emission of a highly fluorescent signal. Cultures were continuously monitored over 40 or 100 h with the Incucyte SX1 Live-Cell Analysis System, with automated image capture at set intervals. Quantitative analysis of mitophagy was done with Incucyte software. Data shown represent two reproducible independent experiments. Supplementary Figure S5. Fabp7 Secretion in Sen, Res, Res-ctrl, and Res-shFabp7 Cells Measured by ELISA. Fabp7 levels in culture supernatants from Sen, Res, Res-ctrl, and Res-shFabp7 cells were measured using an enzyme-linked immunosorbent assay (ELISA). Supernatants were freshly collected after 3 days of culture (performed in triplicate) and immediately processed for analysis using the Fabp7 ELISA kit (LSBio, Catalog #LS-F11416) according to the manufacturer’s instructions. Absorbance readings were obtained using a BioTek Synergy plate reader (Agilent Technologies). Statistical significance was determined using an unpaired t-test (P < 0.05). Supplementary Figure S6. ATAC-Seq analysis in T cells co-cultured with cancer cells. (a) Gene Ontology (GO) analysis revealed modulation of T-cell activation pathways and downregulation of apoptotic processes in T cells co-cultured with Res-shFabp7. Data were analyzed by GO enrichment scores. (b) Motif enrichment analysis of ATA-seq data showed that key transcriptional regulators (BATF, FOS, and others) were more active in T cells co-cultured with Res-shFabp7 than Res-ctrl. (c) Giggle score analysis indicated enrichment and positive modulation of transcription factors related to T-cell activation and effector function (e.g., IRF4, BATF, STATs) in Res-shFabp7 versus Res-ctrl. Motif enrichment analysis was done with The MEME Suite 5.5.589, and Giggle score was obtained with the genomic search engine GIGGLE (https://github.com/ryanlayer/giggle). Supplementary Figure S7. RNA-seq analysis in T-cells cocultured with Res-ctrl compared to Res-shFabp7. T cells were co-cultured with cancer cells for 24 h, after which T cells were collected and RNA isolated for RNA-seq analysis with Trizol according to the manufacturer’s protocol. RNAseq sample quality control was done with FastQC. Sequencing reads were aligned to Genome Reference Consortium Human Build 38 (GRCh38.p13) (Genome Reference Consortium Mouse Build 39 (GRCm39)) by using STAR. The expression abundance and variations of mRNA were calculated as expected counts and transcripts per million (TPM) by using RSEM software. Differentially expressed genes were identified with parametric tests with log2-transformed TPM values. P values obtained from the tests were adjusted with the false discovery rate (Benjamini–Hochberg). Supplementary Figure S8. Validation of Rora-regulated genes in CD8 ⁺ T cells co-cultured with Res-ctrl or Res-shFabp7 cells. Quantitative polymerase chain reaction analysis of Arntl, Clock, Nrip1 and Bcl2l11 in CD8 + T cells co-cultured with Res-ctrl or Res-shFabp7 cells (a) and B16F10-shFabp7 (melanoma with Fabp7 knockdown) cells (b). CD45 expression was used as a housekeeping gene for qPCR. The comparative Ct method was used to calculate the relative abundance of mRNAs compared with CD45 expression. Box-and-whisker plots show the minimum and maximum. *p < 0.05, two-sided T test. Supplementary Figure S9. Res cells were treated with either dimethylsulfoxide (DMSO) or DHA (30 μg/mL) for 48 h. Cell lysates were precleared with 1 µg of anti-Fabp7 antibody (Abcam, Catalog #ab279649) and Protein A/G PLUS-Agarose. After preclearing, 20 µL of anti-Fabp7 antibody was added to 500 µg of cell lysate, incubated for 1 h at 4 °C, followed by the addition of 20 µL of Protein A/G PLUS-Agarose and overnight incubation at 4 °C. The immunoprecipitated samples were collected, washed, resuspended in electrophoresis sample buffer, boiled, and analyzed by Western blotting with Rora antibody (Abcam, Catalog #ab256799). The lanes represent the following conditions: Marker, 1% FBS + DMSO, 10% FBS + DMSO, 1% FBS + DHA, and 10% FBS + DHA. The Western blot analysis shows the presence of Rora in the immunoprecipitated samples, indicating the interaction between Fabp7 and Rora under the given treatment conditions. Supplementary Figure S10. Uncropped images from Western blotting analysis.

Supplementary Material 2.

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Freitas-Cortez, M.A., Masrorpour, F., Jiang, H. et al. Cancer cells avoid ferroptosis induced by immune cells via fatty acid binding proteins. Mol Cancer 24, 40 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12943-024-02198-2

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Received: 04 June 2024
Accepted: 11 December 2024
Published: 03 February 2025
DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12943-024-02198-2

Cancer cells avoid ferroptosis induced by immune cells via fatty acid binding proteins

Abstract

Background

Methods

Results

Conclusions

Introduction

Methods

Cell lines

In vivo studies

Unbiased lipidomics

Lipid (Oil Red O) staining

Targeted lipidomics mass spectrometry for eicosanoids/oxylipins

Long-chain fatty acid oxidation stress test

Global mRNA expression profiling

Quantitative polymerase chain reaction

Immunohistochemical analysis

Stable Fabp7-knockdown and control cells

PPARγ transcription factor assay

Enzyme-linked immunosorbent assay

Chromatin immunoprecipitation sequencing (ChIP-Seq)

Untargeted lipidomic analysis by LC–MS/MS

Reactive oxygen species assay

Ex vivo co-culture of CD8+ T cells with cancer cells

Assay for transposase-accessible chromatin with sequencing (ATAC-Seq)

RNA sequencing

Protein extraction and western blot analysis

Isolation of tumor-infiltrating lymphocytes

Co-immunoprecipitation assay

Flow cytometry

Statistical analysis

Results

Tumors resistant to immunotherapy have altered lipid metabolic profiling to avoid ferroptosis

Lipid chaperone Fabp7 upregulation in immunotherapy-resistant tumors modulates ferroptosis-related genes via epigenetic reprogramming

Fabp7 modifies lipid composition and mitochondrial function to protect immunotherapy-resistant tumors from ferroptosis

Cancer cells upregulate Fabp7 to disrupt circadian clock genes and promote apoptosis in CD8 + T cells

Knockdown of FABP7 restores sensitivity to immunotherapy in resistant tumors

FABP7 expression correlates with CD8 T cell infiltration and patient outcomes in immunotherapy-treated patients

Discussion

Data availability

References

Acknowledgements

Funding

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Competing interests

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Publisher's Note

Supplementary Information

12943_2024_2198_MOESM1_ESM.docx

Supplementary Material 2.

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Keywords

Molecular Cancer

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Ex vivo co-culture of CD8⁺ T cells with cancer cells