Targeting TOMM40 unleashes immunogenic mitophagy to facilitate antigen presentation and immunotherapy sensitization

Zhiwei Zhang; Yu-An Xie; Chenglin Mu; Shiying Sheng; Jinyuan Zhang; Jie Han; Jiwen Li; Zhengfu He; Xia Liu

doi:10.1136/jitc-2026-015462

. 2026 Jul 27;14(7):e015462. doi: 10.1136/jitc-2026-015462

Targeting TOMM40 unleashes immunogenic mitophagy to facilitate antigen presentation and immunotherapy sensitization

Zhiwei Zhang ^1,², Yu-An Xie ^2,³, Chenglin Mu ², Shiying Sheng ⁴, Jinyuan Zhang ^2,³, Jie Han ¹, Jiwen Li ^1,^*, Zhengfu He ^5,^*, Xia Liu ^1,^2,^✉

PMCID: PMC13409098 PMID: 42508829

Abstract

Background

Mitophagy is a mitochondrial quality control process that maintains cellular homeostasis in cancer, yet whether its dysregulation can be exploited to induce tumor immunogenicity remains unclear.

Methods

We integrated pancancer single-cell transcriptomic analyses with genetic perturbation strategies in hepatocellular carcinoma models, including CRISPR/Cas9-mediated gene depletion, in vivo syngeneic tumor systems, and RNA-based lipid nanoparticle delivery. Mechanistic investigations combined mitochondrial functional assays, imaging-based mitophagy analysis, flow cytometry, and transcriptional profiling, together with evaluation of immune checkpoint blockade responses in preclinical and clinical cohorts.

Results

We identify translocase of the outer mitochondrial membrane 40 (TOMM40) as a mitochondrial import gatekeeper that restrains PINK1-Parkin-dependent mitophagy. Loss of TOMM40 induces catastrophic mitochondrial dysfunction and triggers a lethal form of hyperactivated mitophagy. This process is immunogenic and converts immune-cold tumors into immune-inflamed states characterized by enhanced CD8⁺ T-cell infiltration and activation. Mechanistically, TOMM40 deficiency leads to intracellular reactive oxygen species accumulation, which activates NF-κB signaling and drives upregulation of major histocompatibility complex class I antigen presentation machinery, thereby increasing tumor visibility to cytotoxic T cells. In parallel, TOMM40 loss induces programmed death-ligand 1 upregulation, establishing an adaptive immune resistance program. Functionally, TOMM40-deficient tumors exhibit markedly increased responsiveness to immune checkpoint blockade and generate systemic antitumor immune protection. Clinically, a TOMM40-loss transcriptional signature is associated with improved immunotherapy outcomes across multiple independent patient cohorts.

Conclusions

TOMM40 functions as a mitochondrial immune checkpoint that controls the threshold of immunogenic mitophagy. Its loss reprograms mitochondrial stress into antigen presentation and immune activation, providing a strategy to convert immune-cold tumors into immune-responsive states.

Keywords: Antigen Presentation, Mitochondria, Immunotherapy

WHAT IS ALREADY KNOWN ON THIS TOPIC

Mitophagy is a fundamental mitochondrial quality control pathway that supports tumor cell survival under stress conditions. While basal mitophagy is generally considered cytoprotective, its role in regulating tumor immunogenicity and antitumor immune responses remains incompletely understood.

WHAT THIS STUDY ADDS

We identify translocase of the outer mitochondrial membrane 40 (TOMM40) as a mitochondrial protein import gatekeeper that restrains PINK1-Parkin-mediated mitophagy in tumor cells. Loss of TOMM40 induces excessive mitochondrial dysfunction and activates a lethal form of hypermitophagy. This process is accompanied by reactive oxygen species accumulation and NF-κB activation, leading to enhanced major histocompatibility complex class I antigen presentation and increased CD8⁺ T cell–mediated antitumor immunity. Importantly, TOMM40 loss also induces programmed death-ligand 1 upregulation, revealing a coupled immune activation and adaptive resistance program. Functionally, TOMM40-deficient tumors show increased sensitivity to immune checkpoint blockade and systemic antitumor immune responses.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

This work establishes mitochondrial protein import as a previously unrecognized regulator of tumor immune visibility and identifies TOMM40 as a potential therapeutic target for reprogramming tumor immunogenicity. These findings provide a conceptual framework for targeting mitochondrial quality control to convert immune-cold tumors into immune-responsive states and may inform patient stratification strategies for immune checkpoint blockade therapy.

Introduction

Mitochondrial quality control, orchestrated primarily by mitophagy, has canonically been established as a cytoprotective mechanism that safeguards malignant cells against metabolic stress and therapeutic insults.^1–3 With the paradigm shift toward cancer immunotherapy, the intersection of mitochondrial dynamics and antitumor immunity has recently garnered intense scrutiny. Emerging evidence highlights a vital role for mitophagy in sustaining the effector function of immune cells; for instance, augmenting mitophagy via USP30 inhibition prevents T cell exhaustion,⁴ and the gut microbial metabolite DOPAC(3,4-dihydroxyphenylacetic acid) potentiates CD8⁺ T cell cytotoxicity through NRF2-mediated mitochondrial clearance.⁵ However, in contrast to these advances in immune cells, how tumor cells intrinsically manipulate autophagic flux to subvert immune surveillance remains a critical gap in our understanding. While basal mitophagy is known to support tumor survival,^6
7 the precise molecular mechanisms by which cancer cells use mitochondrial clearance to dampen immunogenicity and orchestrate immune evasion remain largely elusive.

The precise execution of mitochondrial quality control relies on the efficient import of nuclear-encoded proteins, a process strictly gated by the translocase of the outer mitochondrial membrane (TOM) complex. As the core channel-forming subunit, TOMM40 dictates the entry of preproteins and serves as a critical determinant of mitochondrial fitness.^8
9 Historically, TOMM40 has garnered extensive attention in the field of neurology; genetic variants in the TOMM40 locus are well-established risk factors for late-onset Alzheimer’s disease and other neurodegenerative disorders, where mitochondrial dysfunction is a hallmark pathology.^10
11 Furthermore, TOMM40 expression levels have been explored as clinical biomarkers for cognitive decline, linking import defects to neuronal survival.¹² However, beyond this neuro-centric view, the function of TOMM40 in oncology remains largely underappreciated. To identify a universal molecular determinant that intrinsically couples mitochondrial quality control to tumor immune evasion across diverse malignancies, we performed a comprehensive screening of single-cell transcriptomic landscapes. To identify a candidate mitochondrial quality-control regulator for mechanistic investigation, we combined pan-cancer single-cell analyses of mitophagy-associated inflammatory states with an independent HCC-focused candidate selection strategy based on curated mitophagy pathway genes and The Cancer Genome Atlas (TCGA)-LIHC(Liver Hepatocellular Carcinoma) tumor-upregulated transcripts. Our analysis unbiasedly pinpointed TOMM40 as a top-ranking candidate that inversely correlates with antitumor immune responses, positioning it as a core checkpoint linking mitochondrial dynamics to immune surveillance.

In this study, we provide a groundbreaking resolution to the mitochondrial-immune dialectic by identifying the TOMM40–Mitophagy axis as a major determinant of tumor antigenicity. We demonstrate that the targeted depletion of TOMM40 does not simply induce organelle turnover, but rather precipitates a hyperactivated PINK1-dependent form of mitophagic death that elicits immunogenicity. For the first time, we reveal that this hypermitophagic state mediated by TOMM40 loss directly couples hyperactivated mitochondrial stress to the transcriptional upregulation of major histocompatibility complex class I (MHC-I) antigen presentation machinery via an NF-κB-dependent signaling circuit. This mechanism effectively strips the tumor of its metabolic armor, restoring CD8⁺ T-cell recognition and transforming refractory “cold” tumors into “hot” inflamed phenotypes that are exquisitely sensitive to immune checkpoint blockade (ICB). Our findings establish the induction of hyperactivated mitophagy via TOMM40 targeting as a pioneering strategy to overcome immunotherapy resistance, offering a powerful clinical framework for coupling mitochondrial quality control to systemic antitumor immunity.

Results

Hyperactivated mitophagy correlated with activated immune inflammation in TME

To systematically explore the functional coupling between mitochondrial quality control and tumor immune activation, we first performed a pancancer single-cell RNA sequencing analysis encompassing hepatocellular carcinoma (HCC), breast cancer (BC), and colorectal cancer (CRC) cohorts. By using a curated mitophagy gene signature comprising core regulators of mitochondrial clearance and quality control (See online supplemental table 1), we quantified mitophagy pathway activity at the single-cell level in malignant cells across all three cancer types. Based on mitophagy activity scores, malignant cells were stratified into distinct subpopulations, with cells in the top decile defined as exhibiting hyperactivated mitophagy (here designated as “Mitophagy^hyper”) versus those in the bottom decile with low activity (“Mitophagy^low”) (figure 1a). This stratification revealed substantial transcriptional divergence between the two populations across all tumor types examined (figure 1b).

Strikingly, differential gene expression analysis demonstrated that Mitophagy^hyper malignant cells consistently displayed robust enrichment of immune inflammatory activation signatures, a pattern conserved across HCC, BC, and CRC datasets. Intersection analysis further identified a set of common core genes shared among Mitophagy^hyper populations across all three cancers, indicating the presence of a conserved transcriptional program linked to elevated mitochondrial turnover (figure 1c).

Detailed examination of this conserved signature revealed marked induction of the AP-1 transcription complex components (JUN, JUNB, JUND, FOS, FOSB), critical regulators of T cell activation and cytokine production. In parallel, we observed coordinated induction of core components of the canonical NF-κB signaling pathway, including RELA (p65) and its negative feedback regulator NFKBIA (IκBα), indicative of active but tightly regulated NF-κB signaling dynamics. This transcriptional pattern is consistent with a state of ongoing immune inflammatory activation rather than immune quiescence.

To spatially visualize the coupling between mitophagy activity and inflammatory signaling, we mapped the expression intensity of the immune inflammatory activation signature onto UMAP(Uniform Manifold Approximation and Projection) embeddings of malignant cells. Across HCC, BC, and CRC datasets, a direct comparison revealed that Mitophagy^hyper tumor cells consistently exhibited robust enrichment of the immune inflammatory activation signature, manifesting as high-intensity clusters on the UMAP projection. In sharp contrast, Mitophagy^low counterparts were largely quiescent with markedly attenuated expression of these inflammatory markers (figure 1d–f). This visualization confirms that the intrinsic immune-activated transcriptional state is preferentially enriched within the hypermitophagic tumor subpopulation.

Consistent with this observation, gene set-based scoring revealed that Mitophagy^hyper malignant cells exhibited significantly higher expression of an immune inflammatory activation signature, whereas Mitophagy^low cells were largely devoid of this transcriptional state. Volcano plot analyses further demonstrated that Mitophagy^hyper populations were selectively enriched for immediate early genes and inflammatory regulators (JUN, FOS), while Mitophagy^low cells showed minimal induction of these pathways (figure 1g, i and k). Quantitative comparisons confirmed that the proportion of cells expressing the immune inflammatory activation signature (PCT) was significantly higher in the Mitophagy^hyper subpopulation compared with the Mitophagy^low group across all three cancer types (figure 1h, j and l). This indicates a more widespread activation of immune-related pathways within the mitophagy-hyperactivated malignant cells. To delineate the functional pathways underlying this immune inflammatory phenotype, we performed pathway enrichment analyses on differentially expressed genes between Mitophagy^hyper and Mitophagy^low malignant cells in each cancer type. Notably, Mitophagy^hyper cells across all three indications showed consistent enrichment of tumor necrosis factor-alpha (TNF-α) signaling via NF-κB, accompanied by a broad activation of interferon (IFN) responses (“Interferon Gamma” and “Interferon Alpha”). This data suggests that hyperactivated mitophagy is intrinsically coupled to a robust immune inflammatory program (figure 1m–o). These pathways collectively reflect a transcriptional state characterized by heightened immune inflammatory responsiveness and cellular stress adaptation rather than immune quiescence in Mitophagy^hyper cancer cells.

Prompted by the pancancer association between hyperactivated mitophagy and inflammatory transcriptional programs, we next sought to identify a specific cancer context for mechanistic investigation. A comprehensive pan-cancer screen identified LIHC as a tumor type characterized by pervasive transcriptional activation of the mitophagy machinery. The robust and widespread upregulation of these key genes (see online supplemental figure 1a) distinguished HCC from other cohorts, leading us to focus on this indication for subsequent mechanistic studies. To validate mitophagy activation in human HCC tissues, we analyzed an independent clinical cohort comprising matched tumor and adjacent normal liver samples using quantitative phosphoproteomics. Notably, activating phosphorylation of PRKN at S131 and S101 (hallmark during canonical mitophagy)—were significantly elevated in tumor tissues compared with matched normal controls (figure 1p). Consistent with these findings, immunohistochemical analysis of paired HCC specimens demonstrated marked accumulation of LC3B puncta within tumor regions, confirming a state of heightened basal autophagy/mitophagy flux specifically in malignant cells (figure 1q–r).

To identify the specific molecular drivers responsible for sustaining this hyperactivated mitochondrial state in HCC, we applied an unbiased integrative screening strategy. Specifically, we intersected a curated mitophagy “SuperPath” gene set (30 genes) with transcripts significantly overexpressed in HCC tumors from the TCGA-LIHC cohort (1471 genes), yielding ten candidate genes (figure 1s). Among these, TOMM40, a core channel-forming subunit of the outer mitochondrial membrane translocase (TOM) complex, emerged as a top candidate. Pancancer expression analyses further confirmed that TOMM40 messenger RNA (mRNA) was broadly elevated across multiple tumor types, with particularly pronounced upregulation in HCC relative to normal liver tissues (see online supplemental figure 1b,c). Importantly, we performed IHC assay to validate in independent patients with HCC and demonstrated significantly higher TOMM40 protein abundance in tumor tissues compared with adjacent normal liver (see online supplemental figure 1d,e). Collectively, these data identify TOMM40 as a clinically upregulated mitochondrial import component in HCC and nominate it as a plausible upstream regulator coupling mitochondrial quality control to immune inflammatory activation programs in cancer cells.

Loss of TOMM40 inhibits HCC growth and induces apoptosis

To further establish the tumor-intrinsic requirement of TOMM40 in HCC progression, we systematically evaluated its role in cancer cell survival, proliferative capacity, and in vivo tumor growth using complementary genetic perturbation strategies. In both human Hep3B and murine Hepa1-6 HCC cells, TOMM40 depletion markedly increased apoptotic cell death, as evidenced by elevated Annexin V–positive populations, whereas TOMM40 overexpression rescued the knockdown phenotype, confirming an on-target and dosage-dependent effect (figure 2a–d). Consistent with enhanced cell death, TOMM40 silencing significantly impaired cellular growth kinetics and clonogenic potential by approximately 70–80% at 72 hours postseeding compared with controls across a range of seeding densities in both models. Importantly, re-expression of TOMM40 in knockdown cells substantially rescued these growth defects (see online supplemental figure 2a–h).

We next assessed whether TOMM40 loss impacts tumor growth in vivo. In syngeneic C57BL/6J mice, CRISPR-mediated Tomm40 deletion in Hepa1-6 cells markedly suppressed subcutaneous tumor growth and significantly reduced tumor weight, whereas re-expression of TOMM40 in the knockout background substantially restored tumor expansion, confirming a TOMM40-dependent effect (figure 2e–h). Consistent with impaired tumor progression, mice bearing Tomm40-deficient Hepa1-6 tumors exhibited a pronounced survival advantage, with median survival extended approximately twofold compared with control animals (from ~15 days to ~25 days; see online supplemental figure 2i). Similar growth inhibition was observed in an orthotopic liver implantation model, in which Tomm40-deficient tumors displayed markedly reduced tumor burden and size (figure 2i–k).

To further validate the tumor-suppressive effect of TOMM40 deficiency in human HCC in vivo, we next employed xenograft models using human Hep3B cells. In immunodeficient Rag1⁻/⁻ mice, TOMM40-depleted Hep3B tumors exhibited significantly attenuated subcutaneous growth kinetics, resulting in an approximately 70% reduction in final tumor volume compared with control tumors (see online supplemental figure 2j,m). To define the cellular basis underlying impaired tumor growth, we performed immunohistochemical analyses on tumor tissues. Consistent with reduced tumor expansion, TOMM40-deficient tumors showed a significant decrease in the proliferation marker Ki-67 (figure 2l,m) alongside a greater than threefold increase in the apoptosis marker cleaved caspase-3 (figure 2n,o). Collectively, these multimodel in vivo analyses establish TOMM40 as a critical tumor-intrinsic regulator required for sustaining HCC cell proliferation, survival, and tumor progression.

TOMM40 deficiency triggers hyperactivated mitochondrial depolarization and lethal mitophagy

Given TOMM40’s essential role in mitochondrial protein import, we first investigated how its deficiency impacts mitochondrial integrity and quality-control dynamics in HCC. Transcriptomic profiling of TOMM40-deficient Hepa1-6 cells and subsequent pathway enrichment analysis revealed a robust activation of stress- and mitochondrial damage–associated programs, including mitophagy, autophagy, and apoptosis pathways (see online supplemental figure 3a,b). This transcriptional signature signifies a cellular state experiencing profound mitochondrial stress and compensatory activation of organelle quality-control mechanisms.

To functionally evaluate this mitochondrial crisis, we examined mitochondrial membrane potential (ΔΨm) and energy homeostasis. TOMM40-deficient HCC cells exhibited a severe collapse of ΔΨm, as evidenced by a marked shift from red to green JC-1 fluorescence, which was accompanied by a significant reduction in intracellular ATP levels (figure 3a–c; see online supplemental figure 3c,d). Seeking ultrastructural confirmation of mitochondrial damage, transmission electron microscopy (TEM) demonstrated extensive mitochondrial abnormalities in TOMM40-depleted cells, including swelling, cristae disorganization, and vacuolization (figure 3d and f; see online supplemental figure 3e,f). In parallel, we observed a massive accumulation of autolysosome structures containing mitochondrial remnants, providing gold-standard morphological evidence of activated mitophagy (figure 3e,f; see online supplemental figure 3g,h).

Consistent with mitochondrial depolarization serving as a canonical trigger for mitophagy, confocal microscopy revealed enhanced mitochondria–lysosome colocalization in TOMM40-depleted cells, characterized by diminished MitoTracker intensity and elevated LysoTracker signal (figure 3g–i; see online supplemental figure 3i,j). To precisely quantify mitophagic flux, we employed a tandem LC3B–GFP–mCherry reporter system (figure 3j). TOMM40 knockdown precipitated a substantial increase in mitophagy reporter-positive populations, closely phenocopying the response induced by the mitochondrial uncoupler CCCP (figure 3k,l). Fluorescence imaging further confirmed an increased mCherry/GFP ratio, indicative of accelerated autolysosome maturation (figure 3m,n; see online supplemental figure 3k,l). Biochemical analyses corroborated these imaging findings: TOMM40-deficient cells accumulated LC3B-II and exhibited enhanced p62 turnover. The LC3B-II levels were further augmented on bafilomycin A1 treatment, confirming an elevated autophagic flux rather than a blockade in autophagosome degradation (figure 3o; see online supplemental figure 3m).

Building on the finding that TOMM40 deficiency sensitizes HCC cells to lethal mitophagy, we hypothesized that pharmacological hyperactivation of autophagic flux could exacerbate this mitophagy crisis, thereby synergizing to suppress tumor progression in vivo. To test this therapeutic proof-of-concept, we established a syngeneic Hepa1-6 subcutaneous tumor model and administered rapamycin—a potent mTOR inhibitor and autophagy inducer¹³—to mice bearing Control or Tomm40-deficient tumors (see online supplemental figure 3p). Monitoring of tumor kinetics revealed that while genetic ablation of TOMM40 alone significantly attenuated tumor growth, confirming its requirement for tumorigenesis, its combination with rapamycin yielded a profound synergistic effect. Strikingly, the co-intervention resulted in near-complete tumor stasis, with endpoint tumor volumes and weights markedly reduced compared with either single-agent cohort (see online supplemental figure 3o–rfigure 3). Collectively, these data validate that targeting the mitochondrial import machinery creates a vulnerability to “mitophagic burnout,” offering a potent strategy to halt HCC progression when coupled with pharmacological autophagy induction.

TOMM40 loss-induced mitophagic cell death is strictly orchestrated by the PINK1 axis

We next sought to determine the specific cell death execution mechanisms induced by TOMM40 loss. Although TOMM40 depletion triggered pronounced apoptotic features, transcriptional profiling and Annexin V staining revealed a concurrent upregulation of markers associated with ferroptosis, apoptosis and necroptosis (see online supplemental figure 4a), indicating a mixed-lineage cell death response to severe mitochondrial stress. However, this lethal phenotype could not be rescued by specific pharmacological inhibitors of ferroptosis (Liproxstatin-1), apoptosis (Z-VAD-FMK), or necroptosis (Nec-1) (see online supplemental figure 4b–g). This functional uncoupling suggests that the activation of these canonical pathways is a secondary event, and that the primary driver is a distinct, mitochondria-centered execution program.

Previous studies in non-malignant contexts have established three principal mitophagy-initiating pathways—namely the PINK1–Parkin axis,¹⁴ the BNIP3/BNIP3L (NIX) pathway,¹⁵ and the FUNDC1-dependent pathway¹⁶—each operating under specific physiological or stress conditions. Genetic analyses revealed that PINK1 was uniquely and robustly induced at both the mRNA and protein levels on TOMM40 depletion in both Hep3B and Hepa1-6 cells (figure 4a–d). Importantly, activation of the PINK1–Parkin pathway was recapitulated in vivo. In Tomm40-deficient Hepa1-6 tumors, phosphorylation of Parkin at Ser65—a hallmark molecular event of PINK1–Parkin pathway engagement—was significantly elevated relative to control tumors, as detected by immunoblotting of tumor tissues (see online supplemental figure 4h,i). This in vivo biochemical evidence further substantiates activation of PINK1-dependent mitophagy signaling within the tumor context. Functionally, co-depletion of PINK1 in TOMM40-deficient cells fully restored mitochondrial mass and abrogated excessive lysosomal accumulation (figure 4e–l). Consequently, PINK1 knockdown efficiently rescued clonogenic growth capacity (figure 4m–p) and almost entirely reversed the apoptotic phenotype induced by TOMM40 loss (figure 4q–t). These data establish PINK1 as the indispensable upstream mediator of TOMM40 loss-induced mitophagic cell death.

Collectively, these data establish a mechanistic paradigm where TOMM40 deficiency precipitates catastrophic mitochondrial dysfunction and selectively hijacks the PINK1 mitophagy axis, ultimately driving HCC cells into mitophagic cell death.

Targeted depletion of TOMM40 elicits mitophagic death that elicits immunogenicity and systemic antitumor immunity in the TME

Reasoning that the catastrophic mitochondrial dysfunction and cell death triggered by TOMM40 depletion would inevitably release immunostimulatory signals, we hypothesized that TOMM40 loss might actively remodel the immunosuppressive tumor microenvironment. To test this, we first performed a pancancer immunogenomic analysis using TCGA datasets. Strikingly, TOMM40 expression displayed a broad negative correlation with gene signatures indicative of effector antitumor immunity—including CD8⁺ effector memory (Tem) cells, CD4⁺ Tem cells, and T helper cell 1 (Th1) responses—across diverse cancer types (figure 5a). Conversely, TOMM40 levels positively correlated with potentially suppressive or regulatory myeloid cells. Focusing specifically on the LIHC cohort, we confirmed robust inverse correlations between TOMM40 mRNA levels and signatures for naive CD8⁺, CD8⁺ Tem, CD4⁺ Tem, and Th1 cells (figure 5b). These bioinformatics data suggest that high TOMM40 expression is a critical maintainer of an immunosuppressive or “cold” TME in HCC.

To experimentally validate these findings in vivo, we systematically characterized the immune landscape of Hepa1-6 tumors following Tomm40 depletion. Transcriptional profiling demonstrated that Tomm40-deficient tumors exhibited marked upregulation of key T cell-recruiting chemokines, including Cxcl10 and Ccl5, together with increased expression of the cellular stress marker Ddit3 (figure 5c). In parallel, we observed a significant induction of Cd274 (programmed death-ligand 1 (PD-L1)) expression (figure 5c). Importantly, rather than reflecting a purely immunosuppressive state, PD-L1 upregulation is widely recognized as a hallmark of adaptive immune resistance, thereby providing strong evidence for an inflamed tumor phenotype characterized by active immune activation.

Consistent with the establishment of an inflamed tumor microenvironment, we performed flow cytometric profiling of tumor-infiltrating lymphocytes (TILs) and observed a pronounced increase in overall CD45⁺ leukocyte infiltration in sgTomm40 tumors compared with control tumors (figure 5d, see online supplemental figure 5a). We found that this increased immune infiltration was largely driven by a robust expansion of the CD8⁺ T cell population (figure 5e). Concordantly, immunohistochemical analysis confirmed a marked enrichment of CD8⁺ T cells within the tumor parenchyma following Tomm40 loss (figure 5f,g).

Beyond the numerical expansion of T cells, we next assessed whether TOMM40 deficiency enhances their functional quality and systemic alertness. Intracellular cytokine staining of tumor-infiltrating T cells revealed that sgTomm40 tumors harbored significantly higher proportions of CD4⁺ and CD8⁺ T cells capable of producing the effector cytokines IFN-γ and TNF-α, indicating a cytotoxic potential within the immunosuppressive niche (figure 5h,i). To determine if this invigoration extended beyond the local microenvironment, we analyzed secondary lymphoid organs. We observed a concurrent increase in the frequency of cytokine-producing T cells in both draining lymph nodes (DLNs) (figure 5j,k) and spleens of sgTomm40 mice (figure 5l,m). These data demonstrate that local mitochondrial perturbation triggers a systemic “alarm” state, licensing T cells throughout the host to acquire a robust effector phenotype.

We next sought to delineate the lineage specificity of this immune remodeling. Quantitative immunophenotyping of the myeloid compartment revealed that the absolute numbers (normalized per gram of tumor) of macrophages, dendritic cells, and myeloid-derived suppressor cells within the tumor remained largely unchanged following Tomm40 depletion (see online supplemental figure 5b), indicating that the observed immune activation was not driven by alterations in myeloid cell abundance. In contrast, analysis of T cell distribution across secondary lymphoid organs uncovered a striking spatial divergence. We observed a significant increase in the total numbers of both CD4⁺ and CD8⁺ T cells in the tumor-DLNs, accompanied by a concomitant expansion of the central memory T cell subsets within both lineages (see online supplemental figure 5c,e). Notably, this pattern was not mirrored in the spleen, where the overall T cell pool was reduced, rather than expanded (see online supplemental figure 5d,f).

To further determine whether TOMM40 depletion elicits antitumor immunity beyond the local tumor microenvironment, we performed a bilateral tumor challenge experiment. Control or TOMM40-depleted Hepa1-6 cells were first implanted subcutaneously into one flank of C57BL/6J mice (see online supplemental figure 5g). 11 days later, wild-type Hepa1-6 cells were implanted into the contralateral flank without further treatment (see online supplemental figure 5j). As expected, TOMM40-depleted primary ipsilateral tumors exhibited significantly reduced growth and tumor burden compared with control tumors (see online supplemental figure 5h,i). Notably, the growth of contralateral wild-type tumors was also markedly suppressed in mice bearing TOMM40-depleted primary tumors (see online supplemental figure 5k,l). Since the contralateral tumors retained intact TOMM40 expression, this distal tumor suppression indicates that TOMM40 depletion in the primary lesion can induce a systemic antitumor response capable of controlling untreated tumors at distant sites. These findings suggest that TOMM40-targeted intervention may contribute to systemic immune surveillance, although future rechallenge experiments will be required to formally define the durability of the memory response.

Taken together, these results demonstrate that Tomm40 depletion elicits a profound rewiring of antitumor immunity in HCC. Tumor-intrinsic mitochondrial dysfunction promotes the release of immunostimulatory cues that drive T cell-recruiting chemokine expression, leading to a marked increase in both the abundance and effector function of intratumoral T cells. This local immune activation is coupled with spatially coordinated T cell priming and expansion within tumor-DLNs, alongside a redistribution of the systemic T cell pool away from the spleen toward tumor-associated sites. Collectively, these processes converge to convert an immunosuppressive, immune-cold tumor into an immune-hot, inflamed microenvironment characterized by quantitatively and functionally enhanced T cell immunity.

TOMM40 deficiency promotes antigen presentation and tumor cell vulnerability via an ROS–NF-κB–MHC-I signaling axis

CD8⁺ T cells play a pivotal role in tumor immune surveillance, particularly in the tumor microenvironment.^17
18 In our earlier studies, we observed not only an increase in the number of CD8⁺ T cells but also a significant enhancement in the proportion of CD8⁺ T cells secreting cytotoxic cytokines, such as IFN-γ and TNF-α (figure 5h,i), suggesting an augmentation of CD8⁺ T cell cytotoxicity. Building on these findings, we sought to determine whether TOMM40 deficiency increases tumor-cell vulnerability under cytotoxic effector-cell killing. To explore this, we focused on the expression of MHC-I molecules, which are crucial for CD8⁺ T cell recognition.¹⁹ Flow cytometry analysis of non-immune CD45− cells, specifically Hepa1-6 tumor cells, from tumor tissues revealed a substantial upregulation of H-2Kᵇ (MHC-I) in TOMM40-deficient cells compared with control cells (figure 6a,b). To ascertain whether this MHC-I upregulation was a direct consequence of TOMM40 loss in tumor cells or a secondary effect induced by the cytokine-rich TME, we examined both basal and cytokine-induced MHC-I expression. Western blotting demonstrated that while TOMM40-deficient tumor cells exhibited modest basal MHC-I upregulation, IFN-γ stimulation significantly amplified MHC-I expression compared with controls (figure 6c). Flow cytometry confirmed these results, showing consistent MHC-I upregulation in TOMM40-deficient cells (figure 6d).

Given that MHC-I molecules are essential for antigen-specific T cell recognition,²⁰ we next sought to functionally assess whether the enhanced MHC-I expression in TOMM40-deficient cells translated into increased tumor cell vulnerability to cytotoxic T cell attack. To this end, we co-cultured GPC3-specific CAR-T cells with TOMM40-knockdown or control Hep3B cells (figure 6e). TOMM40-deficient tumor cells showed reduced baseline survival under PBMC control conditions and exhibited additional vulnerability under GPC3-CAR-T effector-cell killing by increased lactate dehydrogenase (LDH) release (figure 6f). Crucially, genetic ablation of β2-microglobulin (B2M), which eliminates MHC-I surface expression (figure 6g, see online supplemental figure 6b–d), as well as anti-MHC-I antibody treatment in coculture system partially attenuated the enhanced vulnerability of TOMM40-deficient cells, supporting a contribution of MHC-I antigen-presentation machinery to the immune-sensitization phenotype (see online supplemental figure 6 e-g). These data support a model in which TOMM40 loss induces cell-autonomous mitophagic vulnerability while also enhancing MHC-I-associated antigen presentation and cytotoxic lymphocyte recognition.

To investigate the upstream mechanisms underlying MHC-I upregulation in TOMM40-deficient cells, we first examined whether this regulation occurred at the transcriptional level. Quantitative PCR (qPCR) analysis revealed significant induction of B2M and several MHC-I-encoding genes, including H2-Q5 (see online supplemental figure 6a), suggesting that TOMM40 deficiency promotes MHC-I expression via a transcriptional mechanism. To further elucidate this pathway, we analyzed patient-derived single-cell transcriptomic data across HCC, BC, and CRC. Our analysis revealed a conserved pattern: tumor cell subclusters with low TOMM40 expression exhibited a robust enrichment of NF-κB-associated gene signatures, whereas TOMM40-high clusters showed a functional quiescence with respect to this pathway (figure 6h–j). Pathway enrichment analysis identified “TNF-α signaling via NF-κB” as the most activated pathway in TOMM40-low populations (figure 6k–m), implicating NF-κB signaling in the regulation of MHC-I transcription. Differentially expressed genes confirmed this inflammatory state, showing specific upregulation of NF-κB downstream targets and stress-response mediators, such as TNF superfamily receptors (eg, TNFRSF12A, TNFSF10), negative feedback regulators (TNIP1, TNFAIP2), and AP-1 transcription factors (JUN, FOS), exclusively in the TOMM40-low subpopulation (see online supplemental figure 6h–j).

Consistently, global pathway enrichment analysis ranked “TNF-α signaling via NF-κB” as the top activated pathway in TOMM40-low populations, suggesting a potential mechanistic link to MHC-I transcriptional regulation. Importantly, previous studies have established that NF-κB family transcription factors can directly regulate MHC class I gene expression by binding HLA gene promoters to modulate their transcriptional output.^21
22 Hence, we experimentally validated this hypothesis in our context. Subcellular fractionation analysis revealed that TOMM40 deficiency fundamentally alters NF-κB signaling dynamics. Even in the absence of exogenous stimulation, TOMM40-depleted cells exhibited spontaneous degradation of overall IκBα and concomitant nuclear accumulation of p65 compared with controls, indicating a state of constitutive basal activation (figure 6n, left panel). Furthermore, this basal stress primed the cells for heightened inflammatory responses: on TNF-α stimulation, TOMM40-deficient cells displayed a significantly potentiated p65 nuclear translocation relative to the physiological response observed in control cells. This activation was strictly dependent on canonical IKK signaling, as treatment with the NF-κB inhibitor Bay 11–7082 completely abolished p65 nuclear entry in TOMM40-deficient conditions (figure 6n, right panel). Crucially, pharmacological inhibition of NF-κB with Bay 11–7082 significantly attenuated the IFN-γ-induced upregulation of surface MHC-I in TOMM40-deficient cells (figure 6o,p), confirming a causal relationship.

To dissect the upstream signaling nodes bridging TOMM40 deficiency-induced lethal mitophagy to NF-κB nuclear translocation, we first evaluated the cGAS-STING (cyclic GMP-AMP synthase – stimulator of interferon genes) cytosolic DNA sensing pathway, a canonical trigger of inflammation following mitochondrial leakage.^23
24 However, we observed no canonical activation of STING signaling in TOMM40-deficient cells (data not shown), indicating that this pathway is dispensable for the observed phenotype. We thus shifted our focus to oxidative stress, given that disrupted mitophagy inevitably precipitates the accumulation of dysfunctional mitochondria. Indeed, flow cytometric analysis demonstrated a conserved and significant accumulation of intracellular reactive oxygen species (ROS) in both TOMM40-depleted Hep3B and Hepa1-6 cells compared with controls (see online supplemental figure 6k-n), implicating oxidative stress as a potential initiating event.

To validate ROS as the bona fide driver of this signaling axis, we employed the antioxidant N-acetylcysteine (NAC) to scavenge excess ROS and assessed the downstream consequences. Strikingly, ROS scavenging completely uncoupled TOMM40 deficiency from NF-κB activation. In both human and murine models, NAC treatment restored the stability of the NF-κB inhibitor IκBα—which was otherwise degraded in TOMM40-deficient cells—and concomitantly blunted the activation of p65 (see online supplemental figure 6o, p). Crucially, this pharmacological blockade of the ROS-NF-κB axis effectively abrogated the IFN-γ-induced upregulation of surface MHC-I, as confirmed by both flow cytometric quantification of H-2K^b (see online supplemental figure 6q). Collectively, these findings establish ROS accumulation as the critical molecular link that translates TOMM40 loss-induced mitochondrial crisis into adaptive immune priming.

TOMM40 deficiency sensitizes HCC to immune checkpoint blockade

Building on our earlier observation that Cd274 (PD-L1) mRNA was upregulated in the inflamed microenvironment (figure 5c), we sought to determine whether this translated into functional protein expression at the tumor site. Immunohistochemical staining of Hepa1-6 tumors revealed a striking upregulation of PD-L1 protein, which was predominantly localized to the plasma membrane of tumor cells in the sgTomm40 group compared with controls (figure 7a,b). To precisely identify the cellular source of this upregulation, we performed flow cytometric analysis on single-cell suspensions derived from fresh tumor digests. Gating specifically on the malignant population, we confirmed a significant quantitative increase in surface PD-L1 intensity in TOMM40-deficient tumors (figure 7c).

We next investigated whether this phenotype was intrinsic or reactive. In vitro stimulation assays revealed a distinct regulatory pattern: while basal surface PD-L1 levels were minimal and comparable between genotypes, IFN-γ stimulation elicited a significantly potentiated hyper-induction of PD-L1 in TOMM40-knockdown cells compared with the modest upregulation seen in controls (figure 7d). This suggests that TOMM40 deficiency lowers the threshold for cytokine-induced checkpoint expression. To determine if this hyper-responsiveness shared the same mechanism as MHC-I, we treated cells with the NF-κB inhibitor Bay 11–7082. Crucially, NF-κB blockade effectively abolished this hyper-induction, restoring PD-L1 levels to baseline (figure 7e). Collectively, these data confirm that the TOMM40-NF-κB axis concurrently drives both antigen presentation (MHC-I) and adaptive immune checkpoint (PD-L1) expression, positioning the tumor for adaptive immune resistance.

To substantiate the clinical relevance of the TOMM40–PD-L1 axis, we examined the transcriptomic landscape across diverse human malignancies in TCGA. Consistent with our mechanistic findings, a robust inverse correlation between TOMM40 and CD274 (PD-L1) mRNA levels was observed in HCC and numerous other solid tumors (see online supplemental figure 7f). Given that PD-L1 abundance is a primary determinant of sensitivity to ICB,^25
26 we hypothesized that TOMM40 expression could serve as a predictive stratifier for immunotherapy efficacy. Indeed, survival analysis of independent cohorts treated with anti-programmed cell death protein-1 (anti-PD-1) or anti-cytotoxic T-lymphocyte-associated protein 4 (anti-CTLA-4) therapies revealed that patients with low tumorous TOMM40 expression experienced significantly prolonged overall survival compared with the TOMM40-high subgroup (figure 7f,g)

Reasoning that this adaptive PD-L1 upregulation—while potentially suppressive—creates a specific vulnerability to ICB, we sought to functionally translate these findings into a therapeutic strategy (figure 7h). In the clinically refractory Hepa1-6 syngeneic tumor model, anti-PD-1 monotherapy elicited only modest growth inhibition. In stark contrast, TOMM40 deficiency dramatically sensitized tumors to checkpoint blockade. The combination of TOMM40 depletion and anti-PD-1 therapy produced the strongest tumor growth inhibition, evidenced by significantly blunted tumor growth kinetics (figure 7i) and reduced terminal tumor burden (figure 7j,k) compared with either monotherapy alone. To determine the breadth of this sensitization, we extended our investigation to anti-CTLA-4 therapy. Kaplan-Meier survival analysis revealed that the combination of TOMM40 deficiency and anti-CTLA-4 treatment yielded the most profound survival benefit, with prolonged survival compared with the control or single-treatment groups (see online supplemental figure 7g). These data establish that the immunogenic vulnerability exposed by TOMM40 depletion is not limited to the PD-1/PD-L1 axis but represents a fundamental sensitization to broad-spectrum checkpoint blockade.

To further address the translational feasibility of TOMM40 targeting, we next tested whether a clinically more relevant RNA-based intervention could reproduce the antitumor and immune-activating effects observed in our genetic TOMM40-depletion models. Because highly selective small-molecule inhibitors specifically targeting TOMM40 are currently not available, we formulated Tomm40-targeting siRNA into lipid nanoparticles (LNP-siTomm40) and administered it locally to immunocompetent C57BL/6J mice bearing established Hepa1-6 subcutaneous tumors. Mice receiving lipid nanoparticle-formulated scrambled siRNA (LNP-siScr) served as vehicle-matched controls (see online supplemental figure 7a). Consistent with our sgTomm40-based findings, local LNP-siTomm40 treatment markedly suppressed Hepa1-6 tumor growth. Representative tumor images showed a clear reduction in tumor burden in the LNP-siTomm40-treated group compared with the LNP-siScr control group (see online supplemental figure 7a). Quantitatively, LNP-siTomm40 significantly reduced endpoint tumor weight and delayed tumor growth kinetics over time (see online supplemental figure 7b,c). Importantly, LNP-mediated Tomm40 silencing also reproduced the immune-activating phenotype induced by genetic TOMM40 depletion. Flow cytometric analysis revealed a higher proportion of CD45⁺ leukocytes among live cells (see online supplemental figure 7d) and an increased frequency of CD8⁺ T cells among CD45⁺ immune cells (see online supplemental figure 7e) in LNP-siTomm40-treated tumors. These data demonstrate that local RNA-based silencing of Tomm40 phenocopies the antitumor and immune-activating effects of genetic TOMM40 depletion, supporting the therapeutic feasibility of TOMM40 targeting through a clinically more translatable delivery strategy.

Given that TOMM40 depletion promotes inflammatory activation and antigen-presentation remodeling in tumor cells, we next asked whether this experimentally defined transcriptional state is associated with clinical benefit from ICB. We therefore derived a TOMM40-loss response signature (TLRS) from genes upregulated in TOMM40-deficient HCC cells by bulk RNA-seq. TOMM40 itself was not included in the scoring gene set, ensuring that the TLRS captures the downstream transcriptional response to TOMM40 loss rather than TOMM40 abundance. We evaluated the TLRS in four independent immunotherapy-treated patient cohorts, including two melanoma cohorts and two non-small cell lung cancer (NSCLC) cohorts. In melanoma, TLRS-high patients displayed significantly prolonged survival in GSE91061 and in the Melanoma-Nathanson 2017 cohort (figure 7l and o; p=0.0059 and p=0.026, respectively). In NSCLC, TLRS-high patients also showed improved survival in GSE126044 and a similar favorable trend in GSE135222 (figure 7m,n; p=0.01 and p=0.11, respectively). Consistently, TLRS-high tumors were associated with a higher proportion of responders in both melanoma cohorts, GSE91061 and Melanoma-Nathanson 2017 (figure 7p and s), and in both NSCLC cohorts, GSE126044 and GSE135222 (figure 7q,r).

These results suggest that the TOMM40-loss-induced transcriptional state is associated with improved immunotherapy responsiveness across distinct tumor types. Together with our mechanistic findings, this clinical analysis supports a model in which TOMM40 loss promotes mitochondrial stress, inflammatory activation and antigen-presentation remodeling, thereby linking TOMM40-dependent mitochondrial homeostasis to ICB sensitivity.

Discussion

In this study, we identify the TOMM40 as a critical orchestrator of tumor immunogenicity, resolving a long-standing paradox regarding how mitochondrial quality control influences immune surveillance. Recent years have witnessed a paradigm shift in our understanding of mitochondria, positioning them not merely as biosynthetic powerhouses but as central hubs for signal transduction and innate immunity.^27
28 By delineating a direct mechanistic link between TOMM40 deficiency, hyperactivated mitochondrial stress, and the activation of immune inflammatory pathways, our findings fundamentally reshape the current understanding of “mitophagic cell death.” We propose that TOMM40 does not merely function as a protein import channel²⁹ but serves as an intrinsic “immunological brake” that restrains the PINK1-Parkin axis from triggering a hyperactivated, immunogenic form of mitophagy. Loss of this brake precipitates a metabolic collapse that is sensed by the nuclear transcription machinery, thereby stripping the tumor of its immune-privileged status and restoring T cell-mediated cytotoxicity.

Mechanistically, our study fills a critical gap in understanding how intracellular organelle stress is transduced into extracellular “danger” signals that prime adaptive immunity. Loss of antigen presentation, particularly through the downregulation of MHC-I, represents a primary mechanism of immune evasion in solid tumors.^30
31 While basal mitophagy is traditionally viewed as a silent, housekeeping process that prevents inflammation by scavenging DAMPs (damage‑associated molecular patterns),³² we reveal that hyperactivated mitophagy—triggered specifically by TOMM40 blockade—paradoxically fuels inflammation. We map this to a novel signaling axis where mitochondrial proteotoxic stress activates the PINK1-Parkin pathway, which, instead of maintaining homeostasis, drives a lethal catabolic flux. Unlike the canonical cGAS-STING pathway triggered by mitochondrial DNA leakage,²³ we identify NF-κB as the essential transducer that couples this mitochondrial crisis to the transcriptional upregulation of MHC-I antigen presentation machinery. This finding is significant because it establishes that mitochondrial integrity and antigen presentation are not parallel processes but are mechanistically coupled via the NF-κB–MHC-I axis,³³ providing a molecular explanation for how metabolic interventions can “heat up” cold tumors.

Placing our findings within the evolving landscape of immunometabolism, our work engages in a timely dialog with recent breakthroughs linking mitophagy to antitumor immunity. Current literature presents a dichotomous view of autophagy/mitophagy in cancer: it acts as a prosurvival mechanism for tumor cells to resist chemotherapy,^34
35 while simultaneously functioning as a vital metabolic checkpoint for T-cell longevity.⁴ Indeed, emerging studies have predominantly focused on the immune compartment, demonstrating that physiological mitophagy is indispensable for sustaining CD8⁺ T cell metabolic fitness and preventing exhaustion.^3
5
36 Conversely, targeting mitochondrial metabolism in tumor cells has emerged as a strategy to overcome therapeutic resistance.^37
38 In the tumor compartment, a very recent 2025 study used nanoparticle-encapsulated CCCP to induce excessive mitophagy, showing potential synergy with checkpoint blockade.³⁹ However, while these pharmacological approaches establish a phenomenological link, the precise endogenous molecular switches governing this “mitophagy-immunity” interface have remained elusive. Our study advances this field by identifying TOMM40 as the bona fide genetic determinant that endogenously regulates this threshold. Unlike broad-spectrum mitochondrial uncouplers which may have off-target effects, targeting TOMM40 pinpointed the specific vulnerability of the mitochondrial import machinery. Thus, we provide the requisite molecular resolution to explain why excessive mitophagy becomes immunogenic: it forces a stress-adaptive upregulation of MHC-I and PD-L1, creating a unique window of therapeutic opportunity.

From a clinical perspective, our data offer a compelling rationale for stratifying patients and designing combination therapies. Resistance to ICB remains a major hurdle, with “cold” tumors lacking T cell infiltration being particularly refractory.^40
41 The robust correlation between low TOMM40 expression in pretreated tumor tissues and improved survival in ICB-treated cohorts suggests that mitochondrial metabolic status could serve as a powerful predictive biomarker. Patients with “TOMM40-high” tumors, which correspond to an immune-excluded or “cold” phenotype, may be inherently resistant to monotherapy. Our preclinical data suggest that targeting TOMM40 could serve as an “immunological sensitizer.” This aligns with recent strategies aiming to reprogram the tumor microenvironment through metabolic intervention.⁴² By inducing the TOMM40-deficiency phenotype, we can effectively force the tumor to upregulate MHC-I and PD-L1—an adaptive resistance mechanism that, paradoxically, renders them exquisitely sensitive to anti-PD-1/CTLA-4 blockade. This “priming” strategy transforms the metabolic vulnerability of the tumor into a therapeutic advantage.

While our study establishes the TOMM40–mitophagy–immunity axis as a potent immunotherapeutic target, intrinsic limitations warrant careful consideration for clinical translation. First, although we employed syngeneic mouse models to evaluate immune responses, these systems may not fully capture the profound heterogeneity and stromal complexity of the human HCC microenvironment. Second, given that TOMM40 serves as the essential gatekeeper for mitochondrial protein import in normal physiology, systemic inhibition theoretically poses risks of on-target toxicity in metabolically active healthy tissues. However, our multicohort clinical analysis provides a critical counter-narrative supporting a viable safety profile. As evidenced by the robust differential expression data, TOMM40 is disproportionately upregulated in tumors compared with adjacent normal liver (see online supplemental figure 1c,d). This suggests that cancer cells exhibit a distinct “mitochondrial addiction” to TOMM40-mediated quality control, thereby creating a specific therapeutic window that spares normal cells. To further widen this safety margin and mitigate potential off-target effects, future translational efforts should prioritize tumor-restricted delivery modalities to achieve precision targeting while preserving systemic homeostasis.

Although TOMM40 targeting induced robust antitumor and immune-activating effects in our models, the potential toxicity of systemic TOMM40 inhibition requires careful consideration. TOMM40 is the channel-forming component of the mitochondrial outer membrane translocase complex and is required for mitochondrial protein import; therefore, sustained pan-tissue inhibition of TOMM40 may disrupt mitochondrial homeostasis in normal tissues.⁴³ Previous clinical experience with mitochondrial-targeted therapies has highlighted this challenge. The complex I inhibitor IACS-010759 showed encouraging preclinical activity but displayed a narrow therapeutic index in phase I studies, with dose-limiting toxicities including elevated blood lactate and neurotoxicity.⁴⁴ These observations indicate that mitochondrial-targeted therapies require careful dose optimization, tumor-selective delivery and systematic toxicity monitoring.⁴⁵

In this regard, our LNP-siTomm40 experiment should be viewed as a translational proof-of-concept using a transient and titratable RNA-based modality rather than permanent genetic ablation. LNP-formulated siRNA has already been clinically validated by patisiran, which significantly improved outcomes in hereditary transthyretin amyloidosis in a phase III trial.⁴⁶ Thus, our LNP-siTomm40 data support TOMM40 targeting as a reversible RNA-based therapeutic strategy, while also underscoring that its future development should prioritize delivery-dependent therapeutic window optimization rather than unrestricted systemic mitochondrial inhibition.

In summary, we identify a previously unrecognized TOMM40-PINK1-NFκB signaling axis that directly couples mitochondrial integrity to immune surveillance (see online supplemental figure 7h). Our findings challenge the conventional view of mitochondrial protein import as a passive housekeeping function, revealing it as a critical node regulating tumor immunogenicity. Therapeutically, strategies aimed at inducing this specific state of “immunogenic hyperactivated mitophagy” by targeting TOMM40 represent a promising novel approach to overcome resistance to current immunotherapies in HCC and potentially other solid malignancies.

Methods

Single-cell RNA sequencing analysis

Raw count matrices for HCC, BC, and CRC were retrieved from the Gene Expression Omnibus database (accession numbers GSE151530, GSE176078, and GSE188711, respectively). These datasets were imported into R using the Seurat package (Butler et al, 2018) for subsequent analysis. Quality control was performed independently for each dataset using the following criteria:

Cell filtering: Cells with fewer than 500 genes or more than 5% of mitochondrial gene content were excluded.
Normalization: The LogNormalize method was used for global-scaling normalization. This method normalizes gene expression by dividing the gene count of each transcript by the total transcript count for the cell, multiplying by a scaling factor (default=10 000), and logarithmically transforming the result.
Detection of highly variable features: The FindVariableFeatures function was used to identify the 2,000 most variable genes for each dataset.
Scaling: Data were scaled using a linear transformation (“scaling”) prior to dimensionality reduction.
Principal component analysis (PCA): PCA was performed on the scaled data, with the first 30 principal components used for downstream analysis.
Clustering: A graph-based approach was applied to cluster the cells.
Dimensional reduction: t-distributed stochastic neighbor embedding/UMAP was used for non-linear dimensionality reduction and visualization.
Cluster markers: Differential expression analysis was performed using the FindAllMarkers function, with a log fold change threshold of 1, to identify markers that define cell clusters.

Mitophagy activity scoring and stratification

To evaluate the mitophagy activation status of individual cancer cells, we curated a specific mitophagy gene signature consisting of 103 genes (listed in online supplemental table 1). The mitochondrial autophagy activity score of each cell was calculated using the findermarker function in Seurat. Based on the distribution of mitophagy scores within the malignant cell population of each cohort, cells were ranked and stratified. We defined cells in the top 10th percentile of the score distribution as the “Mitophagy-High” (hyperactivated) subpopulation, while cells in the bottom 10th percentile were classified as the “Mitophagy-Low” (hypoactivated) subpopulation.

Bulk RNA sequencing and analysis

Total RNA was extracted using Trizol reagent (Invitrogen, Carlsbad, California, USA) and assessed for integrity on an Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, California, USA). mRNA was enriched using Oligo (dT) beads and fragmented for complementary DNA (cDNA) library construction using the NEBNext Ultra RNA Library Prep Kit (NEB, Cat# 7530, New England Biolabs, Ipswich, Massachusetts, USA). Libraries were sequenced on the Illumina NovaSeq X Plus platform (Astrocyte Technology, Hangzhou, China). Raw reads were quality-filtered using fastp (V.0.23.4) to remove adapters and low-quality bases (Q-value ≤20). Clean reads were mapped to the reference genome using HISAT2 (V.2.2.2.1), and transcripts were assembled with StringTie (V.2.2.1). Differential expression was analyzed using DESeq2, with significance defined as FDR (false discovery rate) <0.05 and |fold change|≥2.

Functional enrichment and network analysis

Functional enrichment analysis of the identified core genes and cell type markers was conducted using the clusterProfiler package (Yu, Wang et al, 2012). Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways were considered significantly enriched if the Benjamini-Hochberg adjusted p value was <0.05.

RNA extraction, library construction and sequencing

Total RNA was extracted using Trizol reagent kit (Invitrogen) according to the manufacturer’s protocol. RNA quality was assessed on an Agilent 2100 Bioanalyzer (Agilent Technologies) and checked using RNase-free agarose gel electrophoresis. After total RNA was extracted, eukaryotic mRNA was enriched by Oligo(dT) beads. Then the enriched mRNA was fragmented into short fragments using fragmentation buffer and reversely transcribed into cDNA by using NEBNext Ultra RNA Library Prep Kit for Illumina (NEB, Cat# 7530, New England Biolabs). The purified double-stranded cDNA fragments were end repaired, A base added, and ligated to Illumina sequencing adapters. The ligation reaction was purified with the AMPure XP Beads (1.0×) and PCR amplified. The resulting cDNA library was sequenced using Illumina NovaSeq X Plus (Astrocyte Technology).

Cell lines and cell culture

Human HCC cell line Hep3B and murine HCC cell line Hepa1-6 were purchased from American Type Culture Collection (Cat# HB-8064, CRL-1830). Cells were cultured in supplemented with 10% fetal bovine serum (ExCell Bio, Cat# FSP500) and 1% penicillin-streptomycin (Life, Cat# 15140163) at 37°C in a 5% CO₂ humidified incubator. Cellular MHC-I upregulation was stimulated using IFN-γ (Novoprotein, Shanghai, China, Cat# C746). All cell lines were authenticated by short tandem repeat profiling and routinely tested negative for mycoplasma contamination.

Lentiviral transduction and genetic manipulation

The lentiviral particles were produced in HEK293T cells using a three-plasmid packaging system consisting of psPAX2 (Addgene, Cat# 12260) and pMD2.G (Addgene, Cat# 12259). The plasmids were combined at optimized ratios to a total of 10 µg in 500 µL Opti-MEM medium (Sigma), referred to as Solution A. Separately, 30 µg of polyethyleneimine (YEASEN, Cat# 40 816ES02) was diluted in 500 µL Opti-MEM to form Solution B. Solution B was added dropwise to Solution A under gentle pipette mixing, followed by incubation at room temperature for 15–20 min to allow complex formation. Viral supernatants were harvested at 48 hours and 72 hours post-transfection, filtered through a 0.45 µm syringe filter (Millipore, Cat# HAWP04700), and used to infect target cells in the presence of polybrene (Sigma, Cat# H9268, 8 µg/mL). Stable cell lines were selected using 2 µg/mL puromycin (Yeasen, Cat# 60 210ES25) for days or sorted by flow cytometry based on fluorescent markers. The efficiency of knockdown or knockout was subsequently verified by Western blotting and/or qPCR. Gene knockdown and knockout cell lines were generated via lentiviral delivery of shRNA or CRISPR-Cas9 systems, respectively. For CRISPR-Cas9-mediated knockout, single-guide RNA (sgRNA) sequences targeting murine Tomm40 were designed using the CRISPick tool (Broad Institute). The specific sgRNA sequences used were: Mouse control sgRNA (GGCAAGCCGTTGCTGATTCG), Mouse Tomm40 sgRNA-1 (TGGTTCGGAACGGACTCCCG), and Mouse Tomm40 sgRNA-2 (GCCCAACCCGGGGACGTTCG). These sgRNAs were cloned into the LentiCRISPR-V2-puro vector (Addgene, # 52961). For stable knockdown, short hairpin RNAs (shRNAs) targeting human TOMM40, human PINK1, or murine Tomm40 were cloned into the tet-PLKO.1-puro vector (Addgene, Cat# 110421). The specific shRNA sequences were as follows: Mouse control shRNA (CCTAAGGTTAAGTCGCCCTCG), Mouse Tomm40 shRNA-1 (GGAAATACACACTGAACAACT), Mouse Tomm40 shRNA-2 (GCATGCATGCGACGTATTACC); Human control shRNA (CCTAAGGTTAAGTCGCCCTCG), Human TOMM40 shRNA-1 (GCATGCACGCAACATACTACC), and Human TOMM40 shRNA-2 (GCACGCAACATACTACCACAA). Human PINK1 shRNA (CGGCTGGAGGAGTATCTGATA). For overexpression rescue experiments, the coding sequence of TOMM40 was cloned into the pCDH-CMV-MCS-EF1-copGFP vector (Addgene, Cat# 99731).

Cell proliferation and clonogenic assays

For proliferation assays, cells were seeded in 48-well plates at a density of (Corning, Cat# 3548) cells per well. For clonogenic assays, cells were seeded at a low density (200, 400, 800, 1,600 cells/well) in 48-well plates and cultured for 5 days. Colonies were fixed with methanol (SCRC, Cat# 10014128) and stained with 0.1% crystal violet (GPC, Cat# AR062).

Mitochondrial function and mitophagy assays

Mitochondrial membrane potential (ΔΨm): Cells were incubated with the JC-1 probe (Beyotime, Cat# C2006) for 20 min at 37°C. The shift from red aggregates (healthy) to green monomers (depolarized) was analyzed by flow cytometry (Beijing Challen Biotechnology) or fluorescence microscopy.
Mitochondrial-lysosomal colocalization: Cells seeded on confocal dishes (NEST, Biotechnology, Cat# 801002) were stained with 50 nM MitoTracker Deep Red (Invitrogen, Cat# M22426) and 75 nM LysoTracker Green (Beyotime, Cat# C1047S) for 20 min. Live-cell imaging was performed using a confocal laser scanning microscope (Nikon, AX-SHR). Pearson’s correlation coefficient was calculated using Prism9 software.
Mitophagy flux reporter: Cells were stably transduced with the tandem LC3B–GFP–mCherry (MIAOLING PLASMID, Cat# p50847) reporter construct. Autophagosomes (GFP⁺mCherry⁺) and autolysosomes (GFP⁻mCherry⁺, red) were quantified by confocal microscopy (Nikon, AX-SHR) or flow cytometry (BD Biosciences, FACSAria III).
TEM: Cells were fixed in 2.5% glutaraldehyde (BR, Cat# 111–30-8), post-fixed in 1% osmium tetroxide, dehydrated, and embedded in resin. Ultrathin sections were stained with uranyl acetate and lead citrate and imaged on a transmission electron microscope (Hitachi HT7700).

Western blotting and subcellular fractionation

Total protein was extracted using RIPA lysis buffer supplemented with protease and phosphatase inhibitor cocktails (SEVEN, Beijing, China, Cat# SW107; Bioss, Cat# D50428). For nuclear/cytoplasmic fractionation, the Nuclear and Cytoplasmic Protein Extraction Kit (Beyotime, Cat# P0027) was used. Protein concentration was determined by the BCA assay (Meilunbio, Cat# MA0082). Equal amounts of protein were separated by SDS-PAGE (sodium dodecyl sulfate–polyacrylamide gel electrophoresis) and transferred to PVDF (polyvinylidene fluoride) membranes. Membranes were blocked and incubated overnight at 4°C with primary antibodies against TOMM40 (1:1000, ABclonal, Cat# a3213), PINK1 (1:1000, PGT, Cat# 23 247–1 AP), LC3B (1:1000, ABclonal, Cat# A19665), p65 (1:1000, Proteintech, Cat# 80979), IκBα (1:1000, Zenbio, Cat# 380682), MHC Class I (1:1000, CST, Cat# 35923), Lamin A/C (1:10000, ABclonal, Cat# A19524), and β-actin (1:20000, HUABIO, Cat# HA721186). Color prestained protein marker (4A Biotech.Cat# MPM9207-10). Immunoreactive bands were visualized using HRP (horseradish peroxidase)-conjugated secondary antibodies and an ECL (enhanced chemiluminescence) detection system (Melilunbio, Cat# MA0186-2).

Tumor challenge and syngeneic mouse models

All mouse studies were conducted in accordance with protocols approved by the Institutional Animal Care and Use Committee of Zhejiang University (ZJU20240253). All animals were housed at a suitable temperature (22℃) and humidity (40–60%) under a 12-hours light/dark cycle with unrestricted access to food and water for the duration of the experiment.

Subcutaneous model: 7–8 weeks old female C57BL/6 or Rag1^−/− mice (Shanghai Model Organisms Centre) were injected subcutaneously into the flank with, 1×10⁶ Hepa1-6 or Hep3B cells resuspended in phosphate-buffered saline (PBS) (Servicebio, Cat# G0002). Tumor volume was monitored every 2 days using calipers (Volume=0.5 × length×width²).
Orthotopic liver implantation: Mice were anesthetized, and a midline incision was made to expose the liver. 1×10⁶ Hepa1-6 cells suspended in 20 µL PBS were injected directly into the left liver lobe. The incision was sutured, and mice were monitored for survival or sacrificed at a defined endpoint for analysis.
In vivo treatments: Wild-type mice bearing established tumors (approximately 250 mm³) were randomized into four groups using a random number generator. Mice bearing control (sgScr) or Tomm40-deficient (sgTomm40) tumors were treated intraperitoneally with anti-PD-1 antibody (BioXCell, Cat# BE0273; clone RMP1-14, 200 µg/mouse) or isotype control IgG on days 8, 10, and 12 postinoculations. Tumor volume and body weight were monitored daily. At the endpoint, tumors were excised and weighed. Mice carrying control (sgScr) or Tomm40-deficient (sgTomm40) tumors were intraperitoneally injected with rapamycin (MCE, Cat# HY-1021; 2.0 mg/kg) on days 7, 9, 11 and 13 after inoculation. Tumor volume and body weight were monitored daily. At the endpoint, tumors were excised and weighed. Investigators were blinded to group allocation.
Procedure for local transfection with SDR8003 LNP-siRNA: For in vivo silencing of Tomm40, Tomm40-targeting siRNA or scrambled control siRNA was formulated into lipid nanoparticles according to the manufacturer’s instructions. C57BL/6J mice were subcutaneously inoculated with 1×10⁶ Hepa1-6 cells. Once tumors were established, mice were randomly assigned to receive lipid nanoparticle-formulated scrambled siRNA (LNP-siScr) or Tomm40-targeting siRNA (LNP-siTomm40: AUGACCUGUGCAUUGAGGCTT) as indicated in the experimental scheme (NeoLNP RNA, CAT#SDR8003). Tumor growth was monitored at the indicated time points, and tumors were harvested at the endpoint for tumor weight measurement, representative imaging, and flow cytometric analysis of tumor-infiltrating immune cells. For immune profiling, single-cell suspensions were prepared from fresh tumor tissues, and CD45⁺ immune cells and CD8⁺ T cells were quantified by flow cytometry and normalized to tumor weight.”
Contralateral tumor challenge assay: C57BL/6J mice were subcutaneously inoculated in the right flank with control or Tomm40-knockout Hepa1-6 cells (1×10⁶ cells in PBS) to establish ipsilateral primary tumors. On day 11 after the first implantation, wild-type Hepa1-6 cells (1×10⁶ cells in PBS) were subcutaneously inoculated into the contralateral left flank of the same mice. Tumor size on both flanks was measured at the indicated time points using digital calipers, and tumor volume was calculated as length×width²/2. At the experimental endpoint, ipsilateral and contralateral tumors were harvested separately, photographed and weighed. Control or TOMM40-knockout Hepa1-6 cells (1×10⁶) were injected subcutaneously into the right flank of C57BL/6J mice. 11 days later, wild-type Hepa1-6 cells (1×10⁶) were injected subcutaneously into the contralateral flank. Ipsilateral subcutaneous tumor volumes were measured periodically; at endpoint, tumors were excised and weighed to determine burden.

Flow cytometric analysis of tumor-infiltrating lymphocytes

Tumor tissues were harvested from euthanized mice, minced into small fragments, and digested with 1 mg/mL collagenase IV (Sigma-Aldrich, Cat# C5138) and 0.15 mg/mL DNase I (Roche, Cat# 11284932001) at 37°C for 45 min to generate single-cell suspensions. The digestion process was monitored to ensure optimal enzymatic activity. Single-cell suspensions were then passed through a 70 µm cell strainer (BD Falcon, Cat# 352350) to remove clumps and debris, ensuring a homogeneous cell population. Cells were washed with PBS and counted using a hemocytometer or automated cell counter (Thermo Fisher Scientific, Cat# AMQAX1000) before proceeding to staining.

For viability assessment, cells were stained with a viability dye (Zombie Aqua, BioLegend, Cat# 423101) for 10–15 min at room temperature to exclude dead cells from the analysis. After washing with PBS, surface markers were stained using fluorescently conjugated antibodies (listed in online supplemental table 2) for 30 min at 4°C in the dark. Cells were then washed twice with PBS and analyzed using a flow cytometer (BD Biosciences, FACSAria III; CytoFLUX Pro). The gating strategy for TIL subsets was carefully defined based on forward and side scatter profiles, followed by sequential gating on specific markers to identify distinct T lymphocyte populations.

For intracellular cytokine staining, cells were first stimulated with phorbol 12-myristate 13-acetate (Enzo Life, Cat# BML-PE160-0001), ionomycin (Sigma-Aldrich, Cat# I0634), and Brefeldin A (BioLegend, Cat# 423303) for 6 hours to induce cytokine production. After stimulation, cells were fixed and permeabilized using the Foxp3/Transcription Factor Staining Buffer Set (eBioscience, Cat# 00–5523-00), following the manufacturer’s protocol. Intracellular cytokine staining was performed using antibodies against IFN-γ (1:100, BioLegend, Cat# 505805), TNF-α (1:100, BioLegend, Cat# 506321), and Granzyme B (1:100, BioLegend, Cat# 515403) for 30 min at 4°C. After washing, cells were resuspended in PBS and analyzed by flow cytometry. Data analysis was performed using FlowJo software to assess the frequency of cytokine-producing T cells and the distribution of TIL subsets.

In vitro T cell cytotoxicity assay (CAR-T killing)

T cells were activated using anti-human CD3/CD28 antibodies (BioLegend, Cat# 317302, Cat# 302902) and 100 IU/mL rhIL-2 (Peprotech, Cat# 200–02). Recombinant (Takara, Cat# T100B) retroviruses were generated using a three-plasmid system (SFG-GPC3-41BB or control SFG-CD19-41BB, alongside RDF-Maxi and PEG-PAM-Maxi). T cell transduction was performed via spinoculation; briefly, viral supernatants were centrifuged (2000×g, 90 min) in RetroNectin-coated 24-well plates. Activated PBMCs were then seeded, centrifuged (1000×g, 30 min), and incubated for 3.5 hours at 37°C before medium replacement. CAR (chimeric antigen receptor) transduction efficiency was determined 48 hours later by flow cytometry using a FITC-conjugated human GPC3 protein (AcroBiosystems, Cat# GP3-HF2H1).

GPC3-targeting CAR-T cells were generated as previously described. Hep3B with or without TOMM40 knockdown were co-cultured with CAR-T cells at various effector:target ratios (1:2, 1:1, 2:1) for 24 hours. Cytotoxicity was assessed by measuring LDH release using the CytoTox 96 Non-Radioactive Cytotoxicity Assay (Meilunbio, Cat# MA0649) or by bioluminescence imaging.

Immunohistochemistry

Paraffin-embedded tumor sections (4 µm thick) were deparaffinized by incubating in xylene, followed by rehydration through a graded ethanol series (100%, 95%, 70%, and 50%) for 5 min each. Antigen retrieval was performed by heating the sections in citrate buffer (pH 6.0) at 95°C for 20 min, followed by cooling at room temperature for 10 min. Sections were then blocked with 5% normal goat serum (PINUOFEI, Cat# PN0038) for 1 hour at room temperature to reduce non-specific binding. Primary antibodies (listed in online supplemental table 3) were applied and incubated overnight at 4°C. After washing with PBS, the sections were incubated with HRP-conjugated secondary antibodies for 1 hour at room temperature. Antibody binding was visualized by 3,3’-diaminobenzidine staining (Sigma-Aldrich, Cat# D4293), and the reaction was stopped by rinsing in distilled water. Images were captured using a microscope (Olympus, Cat# BX53) and quantified using ImageJ software (National Institutes of Health). The intensity of staining was analyzed by calculating the integrated optical density for each image.

Quantitative real-time PCR

Total RNA was extracted from cells or tumor tissues using the RNA-easy Isolation Reagent (Vazyme, Cat# R701-01) according to the manufacturer’s instructions. For reverse transcription, equal amounts of total RNA 1 µg were reverse-transcribed into cDNA using the ABScript Neo RT Master Mix with gDNA Remover (ABclonal, Cat# RK20433). Quantitative real-time PCR was subsequently performed in technical triplicates using the BrightCycle Universal SYBR Green qPCR Mix (ABclonal, Cat# RK21219) on a Celemetor Real-Time PCR System (YEASEN, Model 80 520ES03). The thermal cycling conditions were: initial denaturation at 95°C for 2 min, followed by 40 cycles of 95°C for 10 s and 60°C for 30 s. Melt-curve analysis was performed to verify amplification specificity. All primer sequences used in this study are listed in Supplementary (listed in online supplemental table 4).

ROS detection

Intracellular ROS were detected using a commercial ROS assay kit (Beyotime, Cat# S0033S). Briefly, cells were loaded with 10 µM 2′,7′-dichlorodihydrofluorescein diacetate and incubated at 37°C for 20 min in a cell culture incubator, according to the manufacturer’s instructions. After washing, the cells were analyzed by flow cytometry to measure the fluorescence intensity of the oxidized product, 2′,7′-dichlorofluorescein, which reflects intracellular ROS levels.

Statistical analysis

Statistical analyses were performed using GraphPad Prism V.9.0 (GraphPad Software). All experimental data are expressed as the mean±SEM unless otherwise specified. Comparisons between two groups were analyzed using an unpaired, two-tailed Student’s t-test. For comparisons involving multiple groups, one-way analysis of variance (ANOVA) followed by Tukey’s post hoc test was employed. Tumor growth kinetics were analyzed using two-way ANOVA with repeated measures. Survival curves were generated using the Kaplan-Meier method and compared using the Log-rank (Mantel-Cox) test. For bioinformatics analyses, correlations between gene expression (eg, TOMM40) and immune infiltration were assessed using the TIMER2.0 platform. Survival analyses based on gene expression were performed using GEPIA 2.0. Gene set enrichment analysis and visualization were conducted using gene set cancer analysis, Enrichr, and other bioinformatics platforms. Schematic illustrations were generated using BioRender.com. A p value <0.05 was considered statistically significant.

Supplementary material

online supplemental file 1

jitc-14-7-s001.pdf^{(4MB, pdf)}

DOI: 10.1136/jitc-2026-015462

Acknowledgements

This work was supported by grants from Zhejiang Provincial Natural Science Foundation of China (LZ24C080001 to XL, LQ24H020002 to JL), the 2023 Hangzhou West Lake Pearl Project Leading Innovative Youth Team Project (TD2023017 to XL), National Natural Science Foundation of China (82471867 to XL, 82400293 to JL).

Footnotes

Funding: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Provenance and peer review: Not commissioned; externally peer reviewed.

Patient consent for publication: Not applicable.

Ethics approval: This study involves human participants and was approved by 上海闸新中西医结合医院伦理委员会，XF-WBC-230119. Participants gave informed consent to participate in the study before taking part.

Data availability free text: All data generated or analysed during this study are included in this published article and its supplementary information files. No separate datasets were deposited in public repositories for this study.

Data availability statement

Data are available upon reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

online supplemental file 1

jitc-14-7-s001.pdf^{(4MB, pdf)}

DOI: 10.1136/jitc-2026-015462

Data Availability Statement

Data are available upon reasonable request.

[R1] 1.Du H, Xu T, Yu S, et al. Mitochondrial metabolism and cancer therapeutic innovation. Signal Transduct Target Ther. 2025;10:245. doi: 10.1038/s41392-025-02311-x. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R2] 2.Plata-Gómez AB, Chen W, Ho P-C, et al. Mitochondrial lipid metabolism in tumor immunosurveillance and evasion. Trends Immunol. 2025;46:766–78. doi: 10.1016/j.it.2025.08.005. [DOI] [PubMed] [Google Scholar]

[R3] 3.Ikeda H, Kawase K, Nishi T, et al. Immune evasion through mitochondrial transfer in the tumour microenvironment. Nature. 2025;638:225–36. doi: 10.1038/s41586-024-08439-0. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R4] 4.Zhang R, Gao F, Li J, et al. USP30 inhibition augments mitophagy to prevent T cell exhaustion. Sci Adv. 2025;11:eadv6902. doi: 10.1126/sciadv.adv6902. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R5] 5.Han P, Chu S, Shen J, et al. Quercetin-derived microbial metabolite DOPAC potentiates CD8+ T cell anti-tumor immunity via NRF2-mediated mitophagy. Cell Metab. 2025;37:2438–54. doi: 10.1016/j.cmet.2025.09.010. [DOI] [PubMed] [Google Scholar]

[R6] 6.Huang J, Pham VT, Fu S, et al. Mitophagy’s impacts on cancer and neurodegenerative diseases: implications for future therapies. J Hematol Oncol. 2025;18:78. doi: 10.1186/s13045-025-01727-w. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R7] 7.Xia J, Jin J, Dai S, et al. Mitophagy: A key regulator of radiotherapy resistance in the tumor immune microenvironment. Mol Aspects Med. 2025;105:101385. doi: 10.1016/j.mam.2025.101385. [DOI] [PubMed] [Google Scholar]

[R8] 8.Weng W, He Z, Ma Z, et al. Tufm lactylation regulates neuronal apoptosis by modulating mitophagy in traumatic brain injury. Cell Death Differ. 2025;32:530–45. doi: 10.1038/s41418-024-01408-0. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R9] 9.Li D, Li J, Guo Y, et al. PHB2 protects against pressure overload-induced myocardial remodeling in mice via stabilizing TOMM40 and regulating mitochondrial morphofunctional homeostasis. Acta Pharmacol Sin. 2025;46:3217–29. doi: 10.1038/s41401-025-01613-8. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R10] 10.Kulminski AM, Jain-Washburn E, Nazarian A, et al. Association of APOE alleles and polygenic profiles comprising APOE-TOMM40-APOC1 variants with Alzheimer’s disease neuroimaging markers. Alzheimers Dement. 2025;21:e14445. doi: 10.1002/alz.14445. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R11] 11.Ren Y, Chen Q. Alzheimer’s Imaging Consortium. Alzheimers Dement. 2025;21 Suppl 8:e109928 [Google Scholar]

[R12] 12.Deters KD, Mormino EC, Yu L, et al. TOMM40-APOE haplotypes are associated with cognitive decline in non-demented Blacks. Alzheimers Dement. 2021;17:1287–96. doi: 10.1002/alz.12295. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R13] 13.Panwar V, Singh A, Bhatt M, et al. Multifaceted role of mTOR (mammalian target of rapamycin) signaling pathway in human health and disease. Signal Transduct Target Ther. 2023;8:375. doi: 10.1038/s41392-023-01608-z. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R14] 14.Quarato G, Mari L, Barrows NJ, et al. Mitophagy restricts BAX/BAK-independent, Parkin-mediated apoptosis. Sci Adv. 2023;9:eadg8156. doi: 10.1126/sciadv.adg8156. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R15] 15.Li Y, Zheng W, Lu Y, et al. BNIP3L/NIX-mediated mitophagy: molecular mechanisms and implications for human disease. Cell Death Dis. 2021;13:14. doi: 10.1038/s41419-021-04469-y. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R16] 16.Chen D, Zhou L, Chen G, et al. FUNDC1-induced mitophagy protects spinal cord neurons against ischemic injury. Cell Death Discov. 2024;10:4. doi: 10.1038/s41420-023-01780-9. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R17] 17.Reina-Campos M, Scharping NE, Goldrath AW. CD8+ T cell metabolism in infection and cancer. Nat Rev Immunol. 2021;21:718–38. doi: 10.1038/s41577-021-00537-8. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R18] 18.Sun Q, Dong C. Regulators of CD8+ T cell exhaustion. Nat Rev Immunol. 2026;26:129–51. doi: 10.1038/s41577-025-01221-x. [DOI] [PubMed] [Google Scholar]

[R19] 19.Liu B, Greenwood NF, Bonzanini JE, et al. Design of high-specificity binders for peptide-MHC-I complexes. Science. 2025;389:386–91. doi: 10.1126/science.adv0185. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R20] 20.Chen X, Lu Q, Zhou H, et al. A membrane-associated MHC-I inhibitory axis for cancer immune evasion. Cell. 2023;186:3903–20. doi: 10.1016/j.cell.2023.07.016. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R21] 21.Jiang R, Fang Z, Wang Y, et al. LZTR1 regulates epithelial MHC-I expression via NF-κB1 to modulate CD8+ T cells activation. Cell Discov. 2025;11:84. doi: 10.1038/s41421-025-00837-6. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R22] 22.Yu H, Lin L, Zhang Z, et al. Targeting NF-κB pathway for the therapy of diseases: mechanism and clinical study. Signal Transduct Target Ther. 2020;5:209. doi: 10.1038/s41392-020-00312-6. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R23] 23.Zhao H, Wu L, Yan G, et al. Inflammation and tumor progression: signaling pathways and targeted intervention. Signal Transduct Target Ther. 2021;6:263. doi: 10.1038/s41392-021-00658-5. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R24] 24.Wang F, Zeng Y, Yan M, et al. Self-transforming hydrogel mimicking tertiary lymph nodes to activate cGAS-STING pathway for enhanced antitumor immunotherapy. Sci Adv. 2026;12:eadz5078. doi: 10.1126/sciadv.adz5078. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R25] 25.Doroshow DB, Bhalla S, Beasley MB, et al. PD-L1 as a biomarker of response to immune-checkpoint inhibitors. Nat Rev Clin Oncol. 2021;18:345–62. doi: 10.1038/s41571-021-00473-5. [DOI] [PubMed] [Google Scholar]

[R26] 26.Shen W, Moon I, Nguyen TH, et al. Generalizable ai predicts immunotherapy outcomes across cancers and treatments. Pharmacology and Therapeutics. 2025 doi: 10.1101/2025.05.01.25326820. Preprint. [DOI] [PubMed] [Google Scholar]

[R27] 27.Zong Y, Li H, Liao P, et al. Mitochondrial dysfunction: mechanisms and advances in therapy. Signal Transduct Target Ther. 2024;9:124. doi: 10.1038/s41392-024-01839-8. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R28] 28.Chen W, Zhao H, Li Y. Mitochondrial dynamics in health and disease: mechanisms and potential targets. Signal Transduct Target Ther. 2023;8:333. doi: 10.1038/s41392-023-01547-9. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R29] 29.Callegari S, Kirk NS, Gan ZY, et al. Structure of human PINK1 at a mitochondrial TOM-VDAC array. Science. 2025;388:303–10. doi: 10.1126/science.adu6445. [DOI] [PubMed] [Google Scholar]

[R30] 30.Lee JH, Shklovskaya E, Lim SY, et al. Transcriptional downregulation of MHC class I and melanoma de- differentiation in resistance to PD-1 inhibition. Nat Commun. 2020;11:1897. doi: 10.1038/s41467-020-15726-7. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R31] 31.Paulson KG, Voillet V, McAfee MS, et al. Acquired cancer resistance to combination immunotherapy from transcriptional loss of class I HLA. Nat Commun. 2018;9:3868. doi: 10.1038/s41467-018-06300-3. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R32] 32.Deretic V, Saitoh T, Akira S. Autophagy in infection, inflammation and immunity. Nat Rev Immunol. 2013;13:722–37. doi: 10.1038/nri3532. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R33] 33.Guo Q, Jin Y, Chen X, et al. NF-κB in biology and targeted therapy: new insights and translational implications. Signal Transduct Target Ther. 2024;9:53. doi: 10.1038/s41392-024-01757-9. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R34] 34.Yamamoto K, Venida A, Yano J, et al. Autophagy promotes immune evasion of pancreatic cancer by degrading MHC-I. Nature. 2020;581:100–5. doi: 10.1038/s41586-020-2229-5. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R35] 35.Bhatia D, Chung K-P, Nakahira K, et al. Mitophagy-dependent macrophage reprogramming protects against kidney fibrosis. JCI Insight. 2019;4:e132826. doi: 10.1172/jci.insight.132826. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R36] 36.Yu Y-R, Imrichova H, Wang H, et al. Disturbed mitochondrial dynamics in CD8+ TILs reinforce T cell exhaustion. Nat Immunol. 2020;21:1540–51. doi: 10.1038/s41590-020-0793-3. [DOI] [PubMed] [Google Scholar]

[R37] 37.Vasan K, Werner M, Chandel NS. Mitochondrial Metabolism as a Target for Cancer Therapy. Cell Metab. 2020;32:341–52. doi: 10.1016/j.cmet.2020.06.019. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R38] 38.Sainero-Alcolado L, Liaño-Pons J, Ruiz-Pérez MV, et al. Targeting mitochondrial metabolism for precision medicine in cancer. Cell Death Differ. 2022;29:1304–17. doi: 10.1038/s41418-022-01022-y. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R39] 39.Cao Y, Xu R, Fang J, et al. Nanoparticle-induced excessive mitophagy combined with immune checkpoint blockade for enhanced cancer immunotherapy. Acta Biomater. 2025;204:534–46. doi: 10.1016/j.actbio.2025.08.001. [DOI] [PubMed] [Google Scholar]

[R40] 40.Alsaafeen BH, Ali BR, Elkord E. Resistance mechanisms to immune checkpoint inhibitors: updated insights. Mol Cancer. 2025;24:20. doi: 10.1186/s12943-024-02212-7. [DOI] [PMC free article] [PubMed] [Google Scholar]

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PERMALINK

Targeting TOMM40 unleashes immunogenic mitophagy to facilitate antigen presentation and immunotherapy sensitization

Zhiwei Zhang

Yu-An Xie

Chenglin Mu

Shiying Sheng

Jinyuan Zhang

Jie Han

Jiwen Li

Zhengfu He

Xia Liu

Abstract

Background

Methods

Results

Conclusions

WHAT IS ALREADY KNOWN ON THIS TOPIC

WHAT THIS STUDY ADDS

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

Introduction

Results

Hyperactivated mitophagy correlated with activated immune inflammation in TME

Loss of TOMM40 inhibits HCC growth and induces apoptosis

TOMM40 deficiency triggers hyperactivated mitochondrial depolarization and lethal mitophagy

TOMM40 loss-induced mitophagic cell death is strictly orchestrated by the PINK1 axis

Targeted depletion of TOMM40 elicits mitophagic death that elicits immunogenicity and systemic antitumor immunity in the TME

TOMM40 deficiency promotes antigen presentation and tumor cell vulnerability via an ROS–NF-κB–MHC-I signaling axis

TOMM40 deficiency sensitizes HCC to immune checkpoint blockade

Discussion

Methods

Single-cell RNA sequencing analysis

Mitophagy activity scoring and stratification

Bulk RNA sequencing and analysis

Functional enrichment and network analysis

RNA extraction, library construction and sequencing

Cell lines and cell culture

Lentiviral transduction and genetic manipulation

Cell proliferation and clonogenic assays

Mitochondrial function and mitophagy assays

Western blotting and subcellular fractionation

Tumor challenge and syngeneic mouse models

Flow cytometric analysis of tumor-infiltrating lymphocytes

In vitro T cell cytotoxicity assay (CAR-T killing)

Immunohistochemistry

Quantitative real-time PCR

ROS detection

Statistical analysis

Supplementary material

Acknowledgements

Footnotes

Data availability statement

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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