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28 February 2026

Phosphatidylcholine and CHPT1 as Central Drivers of Chemoresistance in Colorectal Cancer: Lipidomic and Functional Insights

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1
Université Bourgogne Europe, INSERM, CTM UMR 1231, TIRECS Team, Bioactive Molecules and Health Research Group, 21000 Dijon, France
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Université Bourgogne Europe, Centre de Lutte Contre le Cancer G.-F. Leclerc, Institut Agro, CHU Dijon Bourgogne, INSERM, BioSanD US 58 (Diviomics Platform), 21000 Dijon, France
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Université Bourgogne Europe, Centre de Lutte Contre le Cancer G.-F. Leclerc, INSERM, CTM UMR 1231, TIRECS Team, 21000 Dijon, France
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Université Bourgogne Europe, Centre de Lutte Contre le Cancer G.-F. Leclerc, Institut Agro, CHU Dijon Bourgogne, INSERM, BioSanD US 58 (PTBC Platform), 21000 Dijon, France
Cells2026, 15(5), 439;https://doi.org/10.3390/cells15050439
This article belongs to the Section Cell Signaling
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Highlights

What are the main findings?
  • CHPT1-driven phosphatidylcholine remodeling acts as a key metabolic determinant of chemoresistance in colorectal cancer.
  • CHPT1 overexpression promotes multidrug resistance and is associated with poor patient survival in colorectal cancer (Human Protein Atlas analysis).
  • Lipidomic profiling identifies altered PC and LPC levels as a metabolic signature of resistant cells.
  • Edelfosine sensitizes CHPT1-high resistant colorectal cancer cells to chemotherapy by targeting the Kennedy pathway.
What are the implication of the main findings?
  • CHPT1-mediated lipid remodeling represents a previously underexplored metabolic vulnerability in colorectal cancer.
  • Targeting the Kennedy pathway may provide a therapeutic strategy to overcome chemoresistance in CHPT1-overexpressing tumors.

Abstract

Chemoresistance remains a major barrier to effective colorectal cancer (CRC) therapy, yet its metabolic underpinnings are poorly defined. Here, we integrate lipidomic profiling, enzymatic analysis, and functional perturbation approaches to elucidate the contribution of phosphatidylcholine (PC) metabolism and its biosynthetic regulator Choline Phosphotransferase 1 (CHPT1) to drug response. Comparative analysis of chemosensitive and chemoresistant CRC cell lines revealed that resistant HT29 cells exhibited significantly higher PC content and altered PC/lysophosphatidylcholine (LPC)ratios relative to sensitive counterparts. Importantly, functional perturbation confirmed causality: CHPT1 overexpression in SW620 cells was sufficient to promote PC accumulation and confer a chemoresistant phenotype. These findings identify CHPT1 as a metabolic gatekeeper of chemoresistance. Consistently, Human Protein Atlas survival analyses further support its clinical relevance, as elevated CHPT1 expression correlates with poor patient outcomes in CRC. Mechanistically, CHPT1-driven PC enrichment may sustain pro-survival signaling, while reducing lysophospholipid-mediated stress pathways. To therapeutically target this vulnerability, we investigated edelfosine (Edel), an alkyl-lysophospholipid that disrupts lipid rafts and inhibits PC biosynthesis upstream of CHPT1. Notably, edelfosine-mediated disruption of the Kennedy pathway enhances chemosensitivity in the resistant CRC model. Collectively, our study identifies CHPT1 and PC metabolism as central determinants of CRC drug response and proposes edelfosine-based metabolic reprogramming as a promising strategy to overcome resistance.

1. Introduction

Lipid metabolism is increasingly recognized as a central driver of cancer progression and therapeutic resistance [1,2]. Beyond its role in sustaining membrane biogenesis and energy storage, dysregulated lipid metabolism actively supports tumor growth, survival, metastatic dissemination, and resistance to treatments and is now considered a hallmark of malignancy [3]. Key metabolic pathways, including fatty acid (FA) and cholesterol synthesis and phospholipid metabolism, are often hijacked by cancer cells to support their aggressive phenotypes [4].
Within this context, choline metabolism has emerged as a critical pathway in cancer biology [5,6]. Choline is an essential nutrient and a precursor for the synthesis of phosphatidylcholine (PC), the most abundant phospholipid in cellular membranes. Aberrations in choline metabolism, including elevated PC synthesis, are observed in multiple tumor types and are associated with increased aggressiveness, therapy resistance, and poor clinical outcomes [7,8,9,10]. Two main pathways are involved in PC biosynthesis: the Lands cycle and the Kennedy pathway [11]. The first pathway relies on lysophosphatidylcholine acyltransferases (LPCATs), enzymes with different isoforms, which catalyze the reacylation of lysophosphatidylcholine (LPC) into PC [12]. Among these isoforms, LPCAT2 seemed to be crucial in chemoresistance in colorectal cancer (CRC). Indeed, we previously demonstrated that LPCAT2 promotes lipid droplet (LD) formation, contributing to chemoresistance to 5-fluorouracil (5-Fu), oxaliplatin (OXA) and FOLFOX-based regimens [13,14]. Moreover, LPCAT2 impacts immune responses by regulating lipid-derived signaling molecules [15,16,17], enhancing tumor cell resilience to chemotherapy-induced stress and modulating tumor immune evasion. In parallel, the Kennedy pathway represents the major route of de novo PC biosynthesis, which involves a series of enzymatic steps, culminating in the activity of choline phosphotransferase 1 (CHPT1) [11]. This enzyme catalyzes the final step of the pathway by converting CDP-choline and diacylglycerol into PC, a process essential for membrane expansion, cell proliferation and survival. Recent evidence has drawn attention to CHPT1, similar to LPCAT2, as a potential driver of therapy resistance, particularly through its capacity to stabilize membrane architecture and support adaptive responses under therapeutic pressure [18]. Indeed, CHPT1 is overexpressed in several cancers, where it promotes membrane synthesis and sustains oncogenic signaling networks [19,20,21]. Importantly, its upregulation correlates with enhanced tumor growth and reduced sensitivity to anticancer treatments. This underscores the potential of CHPT1 as a therapeutic target, particularly in cancers characterized by dysregulated choline metabolism. Indeed, evidence from other malignancies highlights CHPT1 as a critical determinant of cancer cell survival and therapy resistance. In prostate cancer, aberrant super-enhancer activation drives CHPT1 overexpression, promoting PC synthesis and conferring resistance to anti-androgen therapy through enhanced membrane biogenesis and signaling stability [18,22]. Similarly, in breast cancer, CHPT1 acts as an estrogen receptor target gene, and its depletion markedly impairs proliferation and metastatic potential, underscoring its contribution to tumor progression [20]. Given that CRC frequently develops resistance to fluoropyrimidines, platinum-based chemotherapy, and targeted agents, it would be highly relevant to investigate whether CHPT1-dependent PC biosynthesis contributes to chemoresistant phenotypes and whether CHPT1 represents a viable therapeutic target in CRC.
Despite growing interest in lipid metabolism as a therapeutic axis, the specific role of CHPT1 in CRC remains largely unexplored. The current literature provides only fragmentary insights, leaving a major gap in understanding how PC biosynthesis influences drug response and patient outcomes in colorectal malignancies. Here, we address this question by providing comprehensive evidence that CHPT1-driven PC accumulation is a major determinant of chemoresistance in CRC. We show that modulation of CHPT1 expression profoundly alters sensitivity to standard chemotherapeutic agents, reshapes PC and LPC levels and membrane signaling. Moreover, survival analyses support the clinical relevance of CHPT1 as a potential prognostic marker. Finally, we explore edelfosine (Edel) as a proof-of-concept therapeutic strategy to perturb PC metabolism and potentiate chemotherapy efficacy in CRC models. Collectively, our findings uncover an underappreciated lipid metabolic vulnerability in CRC and provide a rationale for CHPT1-targeted interventions and membrane-active agents as innovative approaches to overcome drug resistance.

2. Materials and Methods

2.1. Cell Culture

Human colorectal cancer cell lines SW620 (CCL-227™) and HT29 (HTB-38™) were obtained from the American Type Culture Collection (ATCC, Molsheim, France). All cell lines have a microsatellite stable (MSS) phenotype. HT29 and SW620 cells were cultivated as previously described in [13].

2.2. Drug Treatments

Edelfosine (Edel, #6091, Cayman Chemical, Ann Arbor, MI, USA) stock solution was prepared in absolute ethanol and then diluted in the culture medium to the indicated concentrations. The chemotherapeutic agent OXA (Accord Healthcare Limited, Lille, France) and the antimetabolite 5-FU (Accord Healthcare Limited, Lille, France) were supplied as ready-to-use infusion solutions for clinical use and diluted in the culture medium to the indicated concentrations.

2.3. Cell Viability

Cell viability was determined using Alamar Blue reagent (DAL1100, Thermo Fisher Scientific, Strasbourg, France). After treatment, the Alamar Blue cell viability reagent was added directly to the culture medium (final concentration: 10% v/v) and incubated for 3 h at 37 °C. The fluorescence (excitation: 530–560 nm; emission: 590 nm) was read using a PerkinElmer® Multimode Plate Reader Envision (PerkinElmer, Villebon sur-Yvette, France). Data were normalized to untreated controls and expressed as a percentage of cell viability. Each condition was tested in technical replicates and across at least three independent biological replicates.

2.4. Target Quantitative and Qualitative Lipid Analyses by Mass Spectrometry (MS)

Lipids were extracted using the methodology previously described in [13] and analyzed by Liquid Chromatography–Tandem Mass Spectrometry (LC-MS/MS) as previously described in [13]. The analytical conditions for lipidomic analysis by LC-MS/MS and Gas Chromatography–Mass Spectrometry (GC/MS) are listed in Supplementary Table S1.

2.5. RNA Extraction and Quantitative PCR Analysis

Cells were seeded in 6- or 12-well plates and cultured for 24 h before exposure to the indicated treatments for the specified times. Total RNA was then extracted, and quantitative PCR analysis was performed as previously described in [13,23]. Relative mRNA levels were determined by the 2−ΔCt method and normalized to the expression levels of human ACTB. The primer sequences used are listed in Supplementary Table S2.

2.6. Western Blot Analysis

Western blot analysis was performed on protein extracts from cells (SW620, HT29, SW620-Ctl and SW620-CHPT1) as previously described in [13]. Briefly, primary antibodies (see Supplementary Table S3) were incubated overnight at 4 °C, followed by HRP-conjugated secondary antibodies (#115-035-146 and #111-035-144, Jackson ImmunoResearch, West Grove, PA, USA) for 1 h at room temperature. Protein bands were visualized using enhanced chemiluminescence (ECL, #1705061, #1705062, Bio-Rad, Hercules, CA, USA) and detected with the ChemiDoc™ MP imaging system (#17001402, Bio-Rad). Densitometric analysis was performed using Image Lab™ software version 6.0.1 (Bio-Rad). HSC-70 and β-Actin were used as loading controls.

2.7. Enzymatic Activity Assays

SW620 and HT29 cells were seeded and cultured for 48 h, then scraped in ice-cold PBS (1×), washed, centrifuged, and resuspended in lysis buffer (100 mM Tris, pH 7.4; 10 mM NaCl; protease inhibitors). Lysates underwent three freeze–thaw cycles (liquid nitrogen/37 °C water bath) followed by sonication. Protein concentrations were determined using the QuantiPro™ BCA assay kit (#QPBCA, Sigma-Aldrich, St. Quentin Fallavier, France), and equal amounts of protein were used for enzymatic activity assays.

2.7.1. Phospholipase C (PLC) and D (PLD) Activity

PLC and PLD activities were measured using the Amplex® Red Phospholipase Assay Kit (#A12218 and #A12219, Thermo Fisher Scientific, Illkirch-Graffenstaden, France). Fluorescence was measured every 5 min for 75 min at 37 °C (Ex: 530–560 nm; Em: 590 nm).

2.7.2. Phospholipase A2 (PLA2) Activity

PLA2 activity was assessed using the EnzChek® PLA2 Assay Kit (#E10217, Thermo Fisher Scientific). Fluorescence was recorded every 5 min for 60 min at 37 °C (Ex: 450–490 nm; Em: 515 nm and 575 nm).

2.8. Analysis of the Involvement of Phospholipids in 5-FU Sensitivity

The following lipids were used: PC (#1535733; CHCl3; Sigma-Aldrich), PE (#1535744; CHCl3; Sigma-Aldrich, Saint Quentin-Fallavier, France), PA (#A84128; CHCl3; Avanti Polar Lipids, Alabaster, AL, USA), PS (#870336P; CHCl3:MeOH (95:5); Sigma-Aldrich), PI (#1535788; CHCl3:MeOH (9:1); Sigma-Aldrich), LPC (#1372050; CHCl3:MeOH (9:1); Sigma-Aldrich), LPE (#860081P; CHCl3:MeOH:H2O (70:27:3); Sigma-Aldrich), and LPS (#A85092; CHCl3:MeOH:H2O (70:27:3); Avanti Polar Lipids). Lipid combinations were prepared in Pyrex tubes, dried under nitrogen, and solubilized in culture medium. To generate homogeneous lipid dispersions, samples were subjected to three cycles of 5 min sonication followed by 5 min vortexing, a commonly used procedure to obtain micelle or liposome-like structures suitable for in vitro phospholipid delivery (Avanti Polar Lipids. Preparation of Liposomes and Lipid Dispersions by Sonication [24]). The 5-FU-sensitive SW620 cells were pre-incubated with the corresponding lipid dispersions for 4 h, after which the lipid-containing medium was removed and replaced with fresh medium supplemented with or without 250 nM 5-FU. After 72 h, cell viability was assessed using the viability assay described in Section 2.3. This approach was designed as a proof-of-concept functional assay to evaluate whether short-term modulation of membrane phospholipid availability impacts chemotherapy sensitivity, rather than to fully mimic physiological lipid transport in vivo.

2.9. Generation of a Stable CHPT1 (Choline Phosphotransferase 1) Overexpressing Cell Line

CHPT1 cDNA (Genewiz, Leipzig, Germany, pUC vector) was PCR-amplified with primers bearing overlaps for the FMC63EF1aGFP lentiviral backbone (see Supplementary Table S4). The backbone fragments and insert were assembled using In-Fusion® Snap Assembly (Takara, Kusatsu, Japan). Recombinant constructs were transformed into Stellar E. coli, selected on LB-ampicillin, and verified by restriction digest and Sanger sequencing. Confirmed clones were prepared by midiprep for lentiviral production. Lentiviral particles were generated by transient transfection of HEK293T-LentiX cells with CHPT1-GFP, psPAX2, and pCMV-VSV-G plasmids using jetPRIME® transfection reagent (Polyplus-transfection SA, Illkirch-Graffenstaden, France). Viral supernatants were collected at 72 h, filtered (0.45 µm), and concentrated by centrifugation. SW620 cells were transduced on retronectin-coated plates, and GFP+ cells were sorted by FACS (top 20%) and expanded. CHPT1 overexpression was validated by immunoblotting and RT-qPCR.

2.10. Combination Index Analysis

HT29 cells were seeded into 96-well plates and incubated overnight. Cells were pretreated for 24 h with increasing concentrations of Edel, followed by 48 h of treatment with increasing concentrations of FOX in fresh medium. Cell viability was assessed using the Alamar Blue assay as described in Section 2.3. Drug interactions were quantified as previously described in [25], according to the Chou–Talalay method [26].

2.11. Data Source and Cohorts

CHPT1 expression and survival data were obtained from The Human Protein Atlas (HPA; https://www.proteinatlas.org, accessed on 15 January 2026), integrating TCGA RNA-seq and IHC images. CRC cohort: TCGA colorectal adenocarcinoma (≈258 tumors, ≈9 normal). Survival curves from HPA Kaplan–Meier plots dichotomized patients by optimal CHPT1 cut-off; log-rank tests performed by HPA. CRC: p ≈ 0.14 (NS) (high CHPT1 = worse survival). Representative IHC images, boxplots, and survival curves were combined into composite figures.

2.12. Analysis of the NCI-60 Human Cancer Cell Line Dataset

Publicly available transcriptomic and drug sensitivity data from the NCI-60 panel were analyzed using the CellMinerCDB portal [27]. Gene expression levels of enzymes involved in phospholipid synthesis (CHKA, CHKB, PCYT1A, CHPT1, LPCAT1, and LPCAT2), as well as Ki67 (MKI67), were correlated with tumor cell sensitivity to 5-FU and oxaliplatin (CI50). Spearman’s correlation coefficients were calculated.

2.13. Statistical Analysis

Results are presented as mean ± standard error of the mean (s.e.m.) or standard deviation (SD) for at least three independent experiments, each including a minimum of three replicates per experimental condition. Classical statistical analyses were performed using GraphPad Prism (v10.6.0, GraphPad Software, Boston, MA, USA). Continuous variables were compared using an unpaired t-test or, when appropriate, one- or two-way ANOVA followed by multiple comparison tests (Tukey, Dunnett, Dunn, Bonferroni or Šidák), after confirming a normal distribution and variance homogeneity. All p-values are two-tailed; p-values less than 0.05 were considered significant (ns: non-significant; * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001).

3. Results

3.1. Lipidomic and Enzymatic Landscape of Chemosensitive Versus Chemoresistant CRC Cells: From PC Remodeling to Phospholipase Dynamics

We first characterized the basal phospholipid composition of two CRC cell lines, HT29 and SW620, for which broad and convergent evidence, including our own previous work [13] and multiple independent studies [28,29,30,31], demonstrates that HT29 cells consistently exhibit a more chemoresistant phenotype than SW620 across distinct classes of cytotoxic agents. This reproducible and well-documented divergence makes the HT29-SW620 pair an informative and biologically robust system for dissecting the molecular determinants of chemoresistance. Indeed, dose–response analyses to 5-fluorouracil (5-FU), oxaliplatin (OXA) and FOLFOX (FOX) confirmed that HT29 cells exhibit a more chemoresistant phenotype than SW620 cells, as reflected by right-shifted curves and increased inhibitory concentration 50% (IC50) values (Figure 1A). Basal lipidomic profiling by LC-MS/MS analysis revealed that HT29 cells contained elevated levels of complex phospholipids and sphingolipids. In particular, HT29 cells exhibited significantly higher levels of PC, phosphatidylserine (PS), lysophosphatidylcholine (LPC), lysophosphatidylethanolamine (LPE), lysophosphatidylserine (LPS), and ceramides and cholesterol (Chol) (Figure 1B). Importantly, diacylglycerol (DG) species were also quantified and were found to be significantly reduced in HT29 cells compared to SW620 (Figure 1B), indicating an imbalance in phospholipid turnover. These observations prompted us to further investigate the enzymatic pathways governing PC biosynthesis and hydrolysis (Figure 2).
Figure 1. Differential lipid composition in chemosensitive and chemoresistant CRC cells. (A) Growth inhibition of SW620 (chemosensitive) and HT29 (chemoresistant) CRC cell lines was assessed using the Alamar Blue assay after 48 h exposure to increasing concentrations of 5-FU (0–1000 µM), OXA (0–100 µM), or FOX (0–500 μM). Data are expressed as mean percentage of control growth ± s.e.m. of three independent experiments (n = 6 per condition). IC50 values were calculated using non-linear regression (four-parameter logistic model). The dashed blue line represents 50% cell viability, corresponding to the IC50 threshold. (B) Total lipids were extracted from cell pellets of SW620 and HT29 cells under basal conditions. Phospholipids, sphingolipids and diacylglycerols (DGs) were quantified by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS), whereas sterols and fatty acids were analyzed by gas chromatography–mass spectrometry (GC-MS). Quantification included total content of PC, LPC, PE, LPE, PS, LPS, PI, plasmalogens (PLGNs), DG, ceramides (Cer), sphingomyelins (SMs), cholesterol (Chol) and total fatty acids (TFAs). Bars represent mean ± s.e.m. of three independent experiments. p-values were determined using an unpaired t-test. p-values ≤ 0.05 were considered significant; ns: not significant, * p  <  0.05, ** p  <  0.01, *** p  <  0.001.
Figure 2. Chemoresistant HT29 CRC cells exhibit enhanced expression of key regulators to overproduce PC. (A) Schematic overview of PC metabolic pathways, including de novo synthesis and remodeling. Solid arrows represent direct enzymatic reactions, whereas dashed arrows indicate phospholipase-mediated hydrolysis and alternative remodeling pathways. (B) Relative mRNA expression levels of PC-metabolizing enzymes were assessed by RT-qPCR in SW620 and HT29 cells under basal conditions. (C) Protein expression of PC biosynthetic enzymes (CKα, CCTα, and CHPT1) and remodeling enzymes (LPCAT1, LPCAT2, and LPCAT4) was assessed in SW620 and HT29 cells at a basal state. A representative immunoblot from three independent experiments is shown. HSC-70 was used as a loading control. The right panel displays densitometric quantification of protein levels obtained in (B). (D) Enzymatic activities of phospholipases C (PLC), D (PLD), and A2 (PLA2) were measured using fluorescence-based kinetic assays in cell lysates from SW620 and HT29 cells. RFUs: relative fluorescence units. Data are expressed as mean ± SD of three independent experiments (n = 3). p-values were determined using an unpaired t-test in (B,C) and using a two-way ANOVA with Bonferroni correction in (D). p-values ≤ 0.05 were considered significant; ns: not significant, * p  <  0.05, ** p  <  0.01, *** p  <  0.001, **** p  <  0.0001.
HT29 cells are characterized by a high lipid droplet (LD) content and elevated expression of enzymes involved in PC metabolism, notably LPCAT2, whereas SW620 cells display a low-LD phenotype and reduced LPCAT2 levels [13]. Because de novo PC synthesis is predominantly controlled by the Kennedy pathway, we next assessed the transcriptional and protein expression of its key enzymes, choline kinase α (CKα), CTP:phosphocholine cytidylyltransferase α (CCTα), and choline phosphotransferase 1 (CHPT1), to determine whether differential pathway activity could account for the phenotypic heterogeneity observed between these models (Figure 2A). Consistent with our previous work, chemoresistant HT29 cells exhibited marked upregulation of LPCAT2, as described by Cotte et al. [13], which demonstrated that LPCAT2-driven LD biogenesis supports resistance to 5-FU and OXA (Figure 2B,C). Notably, beyond this established mechanism, we identify CHPT1 as a prominently overexpressed factor in the resistant HT29 lineage, with significantly increased expression at both the mRNA and protein levels (Figure 2B,C).
To further investigate phospholipid turnover, we next assessed the activity of major phospholipases involved in PC hydrolysis. PC is synthesized through the Kennedy pathway, in which choline is phosphorylated by CKα, converted into CDP-choline by CCTα, and subsequently condensed with DG by CHPT1 to form PC. PC can then be remodeled through the Lands cycle, where phospholipase A2 (PLA2) produces LPC and a free FA, and LPCAT enzymes reacylate LPC to regenerate PC (Figure 2A). Interestingly, we observed a significant reduction in PLC activity in chemoresistant HT29 cells, whereas PLA2 and PLD activities remained unchanged between sensitive and resistant models (Figure 2D). Because PLC-mediated phospholipid hydrolysis represents a major source of intracellular DG, reduced PLC activity is expected to limit DG generation. Consistently, lipidomic profiling revealed decreased DG levels in HT29 cells (Figure 1B). Together with the marked upregulation of CHPT1, which consumes DG as a substrate for PC biosynthesis, these findings support a coordinated shift in PC turnover characterized by reduced hydrolysis and enhanced DG utilization toward PC production. Such PC enrichment may contribute to membrane remodeling and pro-survival signaling under chemotherapeutic stress, thereby reinforcing the resistant phenotype.

3.2. PC Accumulation Promotes Chemoresistant in CRC

Building on our observation that chemoresistant HT29 cells exhibit both LPCAT2 upregulation and marked CHPT1 overexpression, we next asked whether PC itself represents a critical lipid determinant of drug resistance. To address this, we performed exogenous phospholipid supplementation experiments in chemosensitive SW620 cells, introducing defined amounts of major phospholipids (PC, PE, PS, and PI) and their corresponding lysophospholipids (LPC, LPE, and LPS), followed by assessment of the cellular response to the anticancer agent 5-FU (Figure 3A). Importantly, to ensure effective delivery and comparable exposure across conditions, we quantified the incorporation of each supplemented lipid into cellular membranes by LC-MS/MS lipidomic analysis (Figure 3B). Strikingly, only PC supplementation was sufficient to reproduce a resistant-like phenotype, significantly enhancing cell viability upon 5-FU treatment. In contrast, supplementation with other phospholipids (PE, PS, or PI) or lysophospholipids (LPC, LPE, or LPS) did not confer protection under identical conditions (Figure 3A).
Figure 3. Impact of exogenous phospholipid supplementation on 5-FU response. (A) SW620 cells were pre-incubated with increasing concentrations (0–150 µM) of the indicated phospholipids (PC, PE, PS, PI, LPS, LPC or LPE) for 4 h, followed by 5-FU treatment (250 nM, 72 h) prior to cell viability assessment. Data represent mean ± s.e.m. of three independent experiments performed in duplicate (n = 6). p-values were determined by a one-way ANOVA followed by Dunnett’s multiple comparison test; p-values ≤ 0.05 were considered significant; ns: not significant, ** p  <  0.01. (B) Incorporation rate of supplemented phospholipids into SW620 cells was quantified by LC–MS/MS and is shown as incorporation rate relative to control conditions. Incorporation values correspond to measurements obtained from a standardized labeling-pulse assay performed under fixed experimental conditions. Signals were normalized to the internal control and sample load, reflecting phospholipid incorporation efficiency during a single pulse rather than independently replicated quantitative measurements. In accordance with this assay format, inferential statistical testing is not applied, and interpretation focuses on class-level incorporation of phospholipid patterns.
These functional data support the hypothesis that PC enrichment, rather than a general increase in phospholipid availability, underlies the survival advantage of resistant cells. Mechanistically, this finding is consistent with our earlier finding of reduced PLC activity in HT29 cells, which may limit PC hydrolysis and thereby favor intracellular PC accumulation. Collectively, our results identify PC as a key effector lipid in CRC chemoresistance and highlight CHPT1-driven PC biosynthesis as a targetable metabolic vulnerability for therapeutic intervention.

3.3. CHPT1 Is a Functional Regulator of Chemotherapy Response

To determine whether CHPT1 directly contributes to chemoresistance, we performed gain-of-function experiments in CRC models. CHPT1 was stably overexpressed in chemosensitive SW620 cells, and efficient induction was confirmed at both the transcript (Figure 4A) and protein (Figure 4B) levels, showing a clear and significant increase compared to control cells transfected with empty vectors. Notably, CHPT1 overexpression did not affect the expression of other Kennedy pathway enzymes, including CKα and CCTα, supporting the specificity of this manipulation (Figure 4A,B). Lipidomic profiling provided mechanistic insight, revealing a marked increase in PC and LPC levels in CHPT1-overexpressing cells, consistent with enhanced PC biosynthesis and altered phospholipid turnover (Figure 4C). We next assessed the functional consequences of CHPT1 upregulation on drug response. Using cytotoxicity assays, CHPT1-overexpressing SW620 cells displayed significantly increased IC50 values in response to 5-FU, OXA and the combined FOX regimen compared with control cells, demonstrating a robust acquisition of chemoresistance (Figure 4D).
Figure 4. CHPT1 promotes PC metabolism and chemoresistance in CRC cells. (A) SW620 cells were transduced with a lentiviral vector enabling CHPT1 overexpression (SW620-CHPT1) or with an empty lentiviral vector (SW620-Ctl). Relative mRNA expression of enzymes involved in PC metabolism (Kennedy pathway: CKα, CCTα, and CHPT1; Lands cycle: LPCAT2) in SW620-CHPT1 and SW620-Ctl cells was performed by RT-qPCR. Bars represent mean ± SD from three independent experiments. Statistical significance was determined using an unpaired Student’s t-test, and p-values were determined using multiple Student’s t-tests with Holm–Šidák correction. p-values ≤ 0.05 were considered significant; ns: not significant, *** p  <  0.001. (B) Protein expression of PC metabolic enzymes was assessed by immunoblotting. Densitometric quantification of the immunoblot is shown. Data are expressed as arbitrary units (a.u.s) relative to control cells (mean ± SD of four independent experiments). Statistical significance was determined using an unpaired Student’s t-test, and p-values were determined using multiple Student’s t-tests with Holm–Šidák correction. p-values ≤ 0.05 were considered significant; ns: not significant, * p  <  0.05. (C) Lipidomic profiling of SW620-CHPT1 and SW620-Ctl cells. After total lipid extraction, phospholipids, sphingolipids and diacylglycerols (DGs) were quantified by LC–MS/MS. Chol and TFA were analyzed by GC–MS. Total lipid contents (PC, LPC, PE, LPE, PS, LPS, PI, PLGN, Cer, SM, Chol, TFA and DG) were expressed as pmoles or nmoles/mg of protein. Lipid ratios (PC/LPC, PE/LPE, and PS/LPS) were also calculated. Bars represent mean ± SD from three independent experiments. Statistical significance was determined using an unpaired Student’s t-test, and p-values were determined using multiple Student’s t-tests with Holm–Šidák correction. p-values ≤ 0.05 were considered significant; ns: not significant, * p < 0.05. (D) Cells were treated with increasing concentrations of chemotherapy (OXA: 0–400 µM; 5-FU: 0–1000 µM; FOX: 0–500 µM) for 48 and 72 h, and cell viability was determined using the Alamar Blue assay. Cell viability is expressed as the percentage of viable cells relative to solvent-treated controls. IC50 values were calculated by four-parameter nonlinear regression. Comparative histograms of IC50 values for both cell lines as a function of time and treatment are shown (mean ± SD from six replicates per concentration, for at least three independent experiments). p-values were determined using a two-way ANOVA followed by Šidák’s multiple comparisons test; * p < 0.05, ** p < 0.01. (E) Spearman correlation heatmap showing associations between phospholipid metabolism-related gene expression and drug sensitivity across the NCI-60 human cancer cell line dataset (CellMinerCDB, https://discover.nci.nih.gov/cellminercdb/, accessed on 15 January 2026); * p < 0.05. (F) In silico analysis reveals that CHPT1 expression positively correlates with 5-FU resistance (r = 0.327, p = 0.018). The solid line represents the fitted linear regression trend line, and the dotted lines indicate 95% confidence interval of the regression. Correlation analysis was performed using Spearman’s rank correlation test across test across cell lines common to both datasets in CellMinerCDB, with two-sided statistical testing.
To extend these observations beyond our in vitro CRC models, we leveraged the publicly available NCI-60 pharmacogenomic dataset. In this independent resource, CHPT1 expression positively correlated with resistance to 5-FU across cancer cell lines (Spearman r = 0.327, p-value = 0.018) (Figure 4E,F), further supporting the clinical relevance of CHPT1 as a determinant of drug response.
5-FU and OXA, the two components of the FOLFOX (FOX) regimen, are known to induce DNA damage: 5-FU acts primarily by inhibiting thymidylate synthase and aberrantly incorporating into DNA and RNA, while OXA generates intra- and interstrand adducts, triggering a DNA damage response (DDR) [32,33]. These lesions classically activate sensors such as γ-H2AX, and their persistence leads to the activation of apoptotic pathways. We therefore assessed key apoptotic effectors, including PARP and caspase-3 cleavage. Upon treatment with the different chemotherapies at their respective IC50 for 24, 48, and 72 h, SW620-CHPT1 cells displayed reduced γ-H2AX induction together with reduced PARP and caspase-3 cleavage compared with control cells (Figure 5A). These results indicate an attenuated apoptotic response, consistent with the chemoresistance phenotype conferred by CHPT1 overexpression. To further explore adaptative mechanisms underlying this resistance, we next analyzed the Unfolded Protein Response (UPR) stress response, a major stress-adaptation pathway enabling tumor cells to tolerate therapeutic pressure by reshaping endoplasmic reticulum (ER) homeostasis and proteostasis [34]. Notably, SW620-CHPT1 cells exhibited increased expression of the ER chaperone BIP, indicative of an enhanced engagement of adaptive UPR signaling (Figure 5B). In parallel, the phosphorylation of eIF2α protein was reduced while total eIF2α levels remained unchanged, resulting in a lower p-eIF2α/eIF2α ratio. This pattern suggests a pro-survival orientation of the UPR, limiting stress-induced translational arrest and apoptosis. Altogether, these findings indicate that CHPT1-driven PC enrichment not only remodels membrane lipid composition but also promotes resistance to chemotherapy by dampening DNA damage-associated apoptosis and reinforcing ER stress adaptation.
Figure 5. CHPT1 overexpression attenuates apoptosis and modulates endoplasmic reticulum stress signaling in response to chemotherapy. SW620-Ctl and SW620-CHPT1 cells were treated with the different chemotherapies (OXA, 5-FU, and FOX) at their respective IC50 for 24, 48, and 72 h. (A) DNA damage and apoptotic signaling were assessed by immunoblot analysis of γ-H2AX, PARP, and caspase-3 (total and cleaved forms). (B) Endoplasmic reticulum (ER) stress and unfolded protein response (UPR) markers were analyzed by immunoblotting, including BIP, total eIF2α, and its phosphorylated form (p-eIF2α). Representative immunoblots from three independent experiments are shown. HSC-70 was used as a loading control.

3.4. CHPT1 Expression Is Associated with Poor Outcomes in CRC

To assess the clinical relevance of CHPT1 upregulation, we analyzed publicly available CRC datasets from The Human Protein Atlas (HPA). In CRC tissues, CHPT1 mRNA expression is reported at medium-to-high levels, and protein-level validation indicates enhanced expression in tumor cells compared with adjacent normal tissues. Immunohistochemistry consistently revealed strong cytoplasmic CHPT1 staining in CRC samples, whereas adjacent normal colon epithelium showed low or undetectable expression (Figure 6A), in agreement with the role of CHPT1 in PC biosynthesis and our in vitro observations in chemoresistant CRC cells.
Figure 6. CHPT1 expression and patient outcome in CRC. (A) Representative immunohistochemistry images from The Human Protein Atlas showing strong cytoplasmic CHPT1 staining in CRC tissue and weak staining in adjacent normal colon epithelium. (B) Kaplan–Meier survival curves stratifying colorectal adenocarcinoma patients into low- (blue) and high-CHPT1 (pink) expression groups (Human Protein Atlas). The y-axis indicates survival probability, and the x-axis represents time (years); dashed marks indicate censoring events. CRC: patients with low (n = 156) and high (n = 98) CHPT1 expression, based on the optimal threshold (cut-off = 46.25; cohort median = 40.42). High CHPT1 expression shows a trend toward poorer survival (5-year survival: 54% vs. 70% for “Low”), although the difference does not reach statistical significance (log-rank p = 0.14). Median follow-up: 1.71 years.
We next examined the association between CHPT1 expression and patient survival in colorectal adenocarcinoma. Kaplan–Meier analysis showed a trend toward reduced overall survival in patients with high CHPT1 expression compared with those with low expression (Figure 6B). Although this difference did not reach statistical significance in this cohort (log-rank p = 0.14), these data suggest that elevated CHPT1 levels may be linked to poorer clinical outcomes and support the potential relevance of CHPT1 as a candidate biomarker and therapeutic vulnerability in CRC.

3.5. Edelfosine Disrupts PC Homeostasis and Potentiates Chemotherapy Efficacy

These translational observations prompted us to explore pharmacological strategies capable of perturbing CHPT1-driven PC remodeling in chemoresistant CRC. We therefore investigated edelfosine (Edel), an alkyl-lysophospholipid with membrane-active properties. Due to its ether linkage, Edel is resistant to phospholipase hydrolysis and persists within cellular membranes, where it interferes with PC homeostasis and signaling. In yeast and mammalian cells, Edel decreases PC synthesis and selectively remodels cholesterol-/sphingolipid-rich lipid rafts, with cytostasis/apoptosis correlating to raft composition changes; this effect is attributable to its ether linkage and choline headgroup, both necessary for activity [35].
Edel reduced cell viability in both SW620 and HT29 cells, with resistant HT29 displaying increased sensitivity (Figure 7A). At its IC50 (2 µM), Edel decreased the protein levels of PC biosynthetic enzymes CKα and CCTα, while increasing LPCAT2 expression, consistent with the disruption of PC homeostasis (Figure 7B,C). Notably, combination studies revealed synergistic interactions between Edel and the FOX regimen in HT29 cells across multiple dose ranges (combination index CI < 1; Figure 7D,E). Dose-reduction analyses further indicated that Edel enables favorable reductions in FOX doses while maintaining comparable cytotoxic effects (Figure 7F). Mechanistically, by downregulating CKα and CCTα, enzymes required for CDP-choline production, Edel is expected to limit substrate availability for CHPT1-mediated PC synthesis. Thus, even in the absence of changes in CHPT1 expression, its functional output may be indirectly constrained through upstream metabolic restriction.
Figure 7. Edelfosine reduces viability and enhances FOX chemotherapy-induced cytotoxicity in chemoresistant HT29 cells. (A) Growth inhibition of SW620 (chemosensitive) and HT29 (chemoresistant) CRC cells was assessed using the Alamar Blue assay after 48 and 72 h of exposure to increasing Edelfosine (Edel) concentrations (0–50 μM). Data are expressed as mean percentage of control growth ± SD of three independent experiments (n = 6). IC50 values were calculated using non-linear regression (four-parameter logistic model). The dashed blue line represents 50% cell viability, corresponding to the IC50 threshold. (B) Cells were treated for 24 h with Edel at its IC50 concentration (2 µM) or vehicle control. Protein expression of PC biosynthetic (CKα, CCTα, and CHPT1) and remodeling enzymes (LPCAT2) after Edel treatment. A representative blot from three independent experiments is shown. β -actin was used as a loading control. (C) Densitometric quantification of immunoblots shown in (B). Data are expressed as mean ± SD of three independent experiments; p-values were determined using an unpaired t-test. p-values ≤ 0.05 were considered significant, ns: not significant, * p  <  0.05, *** p  <  0.001. (D,E) Synergy analysis of FOX (5-FU + OXA) in combination with Edel. Cells were pretreated with Edel for 24 h (0–20 µM) followed by FOX treatment for 48 h (0–500 µM). Heatmaps show percent viability in (D) determined by using Alamar Blue assay and corresponding combination index (CI) values calculated using CompuSyn software (v.1.0) in (E). Data are represented as means ± SD of four independent experiments. CI < 1 indicates synergism, CI = 1 indicates additivity, and CI > 1 indicates antagonism. (F) FA (fraction affected)-dose-reduction index (DRI) plot for FOX (left) or Edel (right), indicating the fold reduction in drug doses required to achieve comparable effects in combination. DRI > 1 (purple triangles) and DRI < 1 (orange triangles) indicate favorable and not favorable dose reduction; DRI = 1 (black triangles) indicates no dose reduction. The horizontal dash line at DRI = 1 was drawn. The data are representative of four independent experiments.
Collectively, our findings identify CHPT1-driven PC remodeling as a key determinant of chemoresistance in CRC, a marker of poor prognosis in patients with high expression scores, and support the therapeutic potential of membrane-targeting metabolic interventions, such as edelfosine-based strategies to potentiate chemotherapy efficacy in resistant tumors.

4. Discussion

Although lipid metabolism reprogramming is increasingly recognized as a hallmark of CRC [3], most studies have primarily focused on global lipidomic alterations or FA pathways. In contrast, the contribution of PC biosynthesis, and particularly its terminal enzyme CHPT1, has remained largely unexplored in the context of chemotherapy resistance. Here, we provide evidence that CHPT1-driven PC accumulation represents a key determinant of chemoresistance in CRC. Across CRC models, chemoresistant HT29 cells consistently displayed elevated PC abundance compared with chemosensitive counterparts. Importantly, forced CHPT1 overexpression in sensitive SW620 cells was sufficient to promote resistance to 5-FU, OXA, and the combined FOX regimen, accompanied by shifts in PC/LPC balance toward a pro-survival lipid profile. As the major structural phospholipid of mammalian membranes, de novo synthesis of PC through the Kennedy pathway supports membrane expansion during proliferation and contributes to lipid-derived mediators (including LPC) that feed into signaling axes that shape tumor stress adaptation, immune communication and therapeutic response [15,16,17]. In addition, emerging work implicates CHPT1 in broader aspects of cellular metabolism, including mitochondrial lipid homeostasis and energetic balance, linking PC biosynthesis to tumor progression and metastatic potential [19]. Moreover, CHPT1 could be included in predictive models, underscoring its clinical relevance. In breast cancer, CHPT1 is found to be overexpressed and is integrated in a clinical prediction model for breast cancer distant metastasis using six mitochondrial-related genes, including CHPT1 [19,36]. This could be associated with the early metastasis of tamoxifen-resistant breast cancer cells [20].
Publicly available datasets further support the clinical relevance of CHPT1 in CRC. The Human Protein Atlas (HPA) provides TCGA-based survival analyses in CRC, and they indicate that elevated CHPT1 expression is associated with a trend toward poorer overall survival in CRC when patients are stratified by expression cut-offs, suggesting that CHPT1 may represent an unfavorable prognostic factor in this disease. In other cancers, for example, in brain tumors, the HPA’s glioma data report high CHPT1 expression in glioblastoma and a lower three-year survival in the high-expression subset versus the low-expression subset, though without reaching the HPA’s stringent significance threshold, a pattern that nevertheless points to biological relevance in the glioma and invites more granular cohort analyses beyond glioblastoma. These atlas-level analyses fit our functional data: when CHPT1 is downregulated, PC output falls and drug sensitivity returns; when it is upregulated, PC rises, PC/LPC balance tilts, and multidrug resistance emerges. Nevertheless, it seems it is not the same in function across cancer types. Indeed, in squamous-cell lung carcinoma, an analysis was conducted with the dataset from the Genomic Data Commons (GDC) and the Cancer Genome Atlas (TCGA), which showed that patients with a low level of CHPT1 had a lower survival probability than patients with a high level of CHPT1 [37]. In another example of cancer, such as esophageal carcinoma, Kaplan–Meier survival curves confirmed that lower expression of CHPT1 contributed to better survival [38]. Beyond solid tumors, CHPT1-dependent phospholipid metabolism has also been implicated in hematological malignancies, including leukemia, where it may contribute to tumor cell fitness and treatment resistance [39,40]. In this way, some authors have recently proposed an effective covariate-adjusted tensor classification in high dimensions (CATCH) model for predicting microsatellite instability (MSI) cancer status, where they identified cancer metabolic biomarkers, especially CHPT1 [41].
From a therapeutic perspective, our findings highlight PC metabolism as a targetable vulnerability in chemoresistant CRC. In this context, edelfosine (Edel) represents a relevant pharmacological probe, as this alkyl-lysophospholipid accumulates in membranes and perturbs lipid raft organization and survival signaling [42,43]. Mechanistically, Edel accumulates in cholesterol-rich lipid rafts, clusters Fas/CD95 and DISC components, and displaces PI3K/PDK1/mTOR/Akt from rafts, shifting the membrane’s signaling stoichiometry toward apoptosis; in many solid tumors, it also loads the ER, provoking ER stress that commits mitochondria to cell death [44,45,46,47]. At the level of phospholipid synthesis, Edel has also been shown to interfere with the CDP-choline branch of the Kennedy pathway by inhibiting CCT, the rate-limiting enzyme upstream of CHPT1, thereby reducing PC output even when CHPT1 transcription is elevated, and functionally counteracting CHPT1 dependence in leukemia cells [48]. Consistent with this mechanism, we demonstrated for the first time in CRC models that Edel reduces the expression of upstream PC biosynthetic enzymes and synergizes with FOX to enhance cytotoxicity in resistant cells. These pleiotropic effects provide an indirect yet effective strategy to constrain CHPT1-dependent PC output without requiring direct CHPT1 inhibition and underscore the therapeutic potential of targeting multiple nodes within the PC biosynthetic pathway. Edel has also been evaluated in a phase II clinical trial involving patients with brain tumors that were either inoperable or refractory to previous treatments. Administered orally, the compound demonstrated the ability to halt tumor progression and improve quality of life. Although systemic application of Edel has been limited by toxicity [49,50,51], recent advances in nanotechnology-based delivery systems may improve its therapeutic index by enhancing tumor targeting while reducing off-target effects. Indeed, lipid-based nanoformulations of Edel have shown encouraging activity across multiple malignancies, including lymphoma, leukemia, osteosarcoma, and metastatic disease [52,53,54,55,56,57]. Moreover, preclinical work suggests that Edel may impair metastatic organ colonization and angiogenesis, further supporting its translational potential [58]. Together, these observations provide a rationale for exploring PC-targeting strategies, including CHPT1-centered interventions and membrane-active agents, as innovative approaches to overcome chemoresistance in CRC. Future work will include CHPT1 loss-of-function studies in resistant CRC models (e.g., HT29) to complement our gain-of-function and pharmacological approaches and further validate CHPT1 as a therapeutic target.

5. Conclusions

In summary, our study identifies phosphatidylcholine (PC) metabolism as an actionable determinant of chemotherapy response in CRC. We demonstrate that CHPT1 upregulation promotes PC accumulation, drives resistance, reshapes PC/LPC homeostasis, and drives multidrug resistance to 5-FU, OXA, and FOX-based regimens. Furthermore, patient-derived datasets support the potential clinical relevance of CHPT1 as a candidate biomarker associated with adverse outcomes in CRC. Importantly, we provide proof-of-concept evidence that pharmacological perturbation of PC biosynthesis using edelfosine (Edel) can indirectly constrain CHPT1-dependent metabolic output and potentiate chemotherapy efficacy in resistant CRC models. Collectively, these findings establish CHPT1-driven PC remodeling as a central metabolic axis in CRC chemoresistance and highlight lipid metabolic reprogramming as a promising therapeutic avenue to restore drug sensitivity.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cells15050439/s1, Table S1: Analytical conditions for lipidomic analysis by LC-MS/MS and GC-MS; Table S2: Human primers used for RT-qPCR analysis; Table S3: Antibodies; Table S4: Forward and reverse oligonucleotide sequences used for cloning of CHPT1 into the fmc63ef1agfp vector.

Author Contributions

Conceptualization, V.A. and D.D.; Methodology, A.M., J.-P.P.d.B., E.L. and F.H.; Validation, V.A., F.G. and D.D.; Format analysis, A.M., J.-P.P.d.B., E.L. and V.A.; Investigation, A.M., J.-P.P.d.B. and E.L.; Writing—original draft preparation, V.A., F.G. and D.D.; Supervision, V.A. and D.D.; Project administration, D.D.; Funding acquisition, V.A. and D.D. All authors have read and agreed to the published version of the manuscript.

Funding

A.M. is supported by a grant from the Ligue Nationale contre le Cancer. V.A. is supported by grants from the Ligue Interrégionale contre le Cancer. F.H. is supported by the Ligue Interrégionale contre le Cancer and by the Ligue contre le Cancer Comité de Côte d’Or. D.D. is supported by the Ligue Interrégionale contre le Cancer, by the Ligue contre le Cancer Comité de Côte d’Or and by the Fondation de l’Avenir. This work was supported by a French Government grant managed by the French National Research Agency under the program “Investissements d’Avenir,” reference ANR-11-LABX-0021.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The authors declare that all data supporting the findings of this study are available within the article and the Supplementary Materials.

Acknowledgments

Flow cytometry experiments were performed at the ImaFlow core facility (US58 BioSanD, Dijon, France), supported by the Burgundy Regional Council. Lipidomic analyses were performed at the Diviomics core facility (US58 BioSanD, Dijon, France).

Conflicts of Interest

The authors declare no conflicts of interest.

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