The ferroptosis–cuproptosis crosstalk in hematological malignancies: multi-omics insights into gene-regulated cell death and emerging therapeutic opportunities

Abstract

The broadening field of regulated cell death (RCD) has shown a complex, intertwined web of pathways that are not confined to apoptosis but also to ferroptosis, cuproptosis, necroptosis, and other non-canonical mechanisms, especially in haematological malignancies (HMs). Out of these, ferroptosis and cuproptosis stand out as metabolic-based forms of death that are regulated by iron-dependent and copper-dependent processes, respectively. This review presents the notion of a single ferroptosis-cuproptosis axis, a Metallo-redox network that encompasses lipid peroxidation, mitochondrial dysfunction, and proteotoxic stress to control cell fate decisions. We underscore the fact that haematological cancers use metal ion flux, redox homeostasis, and metabolic plasticity to bypass apoptosis, but are susceptible to other RCD pathways. This work, based on multi-omics methods, such as genomics, transcriptomics, proteomics, metabolomics, and metal omics, describes how integrative modelling can unravel RCD susceptibility landscapes and discover context-specific therapeutic targets. The single-cell and spatial analyses further demonstrate intra-tumoral heterogeneity and switching between death pathways in response to therapeutic pressure. New treatment options that are being developed to address this axis such as ferroptosis inducers, copper ionophores, and complex nanomedicine systems with the ability to deliver therapeutics and modify pathways. The review also covers such challenges of translation as the lack of standardization of biomarkers, clinical trial design, and dynamic stratification of patients. Lastly, suggest an outlook system combining artificial intelligence, synthetic biology, and organoid-based systems to permit precision cell death engineering. Together, this literature makes the ferroptosis-cuproptosis axis a novel change in the outlook of future therapies in haematological cancers.

Graphical abstract

The provided diagram illustrates the Ferroptosis-Cuproptosis Axis, showing how iron-driven and copper-driven cell death pathways intersect to influence patient outcomes. By integrating multi-omics data (genomics, transcriptomics, proteomics, and metabolomics) with AI and machine learning, researchers can build predictive models for patient stratification. This computational workflow ultimately helps identify whether a patient will show sensitivity to therapy (leading to cell death) or treatment resistance (leading to tumor survival and disease relapse).

Introduction

The classical paradigm of regulated cell death (RCD) has long been dominated by apoptosis and is now understood as a dynamic network of mechanistically distinct but functionally interconnected death programs. In hematological malignancies (HMs), malignant cells can occupy different states of death susceptibility that include ferroptosis, cuproptosis, necroptosis, pyroptosis, and autophagy-dependent cell death [1, 2]. This networked view emphasizes context-specific cell-fate decisions governed by metabolic state, intracellular metal-ion flux, redox homeostasis, and microenvironmental signals. Ferroptosis and cuproptosis are among these modalities and are mechanistically distinct: ferroptosis is driven by iron-dependent phospholipid peroxidation, whereas cuproptosis is associated with copper-dependent stress involving lipoylated mitochondrial tricarboxylic acid (TCA)-cycle proteins [3–5]. Evidence suggests that the two processes can share selected metabolic and redox determinants, including mitochondrial activity, ROS handling, glutathione and NADPH metabolism, and metal homeostasis. We therefore use the term “ferroptosis–cuproptosis crosstalk” as a conceptual framework rather than as an established molecular signaling pathway. This distinction is important because several proposed links remain incompletely tested. The framework may be particularly relevant to HMs, in which malignant cells exhibit metabolic plasticity, altered mitochondrial dependence, and dysregulated metal transport [4, 6]. Leukemias, lymphomas, and multiple myeloma occupy metabolically distinct niches in which oxygen availability, nutrient supply, stromal interactions, and immune signals can modify RCD susceptibility [7]. These observations support investigation of whether therapy-resistant clones that evade apoptosis may display context-dependent vulnerabilities to ferroptosis or cuproptosis, but the extent of such switching remains to be established experimentally. Cell-death plasticity therefore represents an important testable concept rather than a universal property of HMs [8, 9].

A major challenge is to determine whether apparently shared regulators represent true molecular crosstalk, parallel responses to a common metabolic state, or indirect associations. Integrative multi-omics can help resolve this distinction by combining genomic alterations with transcriptional states, protein abundance and activity, metabolic flux, lipid peroxidation, and intracellular iron and copper measurements. A practical workflow should proceed from harmonized multi-omics preprocessing and feature selection to cross-omics integration, network inference, identification of candidate convergence nodes, experimental perturbation, and prospective prediction. Temporal and spatial sampling can then be used to determine whether changes in metal trafficking, mitochondrial activity, redox balance, or lipid metabolism precede commitment to a specific RCD program. Integration with CRISPR perturbation, isotope-tracing experiments, and patient-derived organoid or ex vivo models can provide causal validation rather than relying solely on correlation [10, 11].

Molecular architecture of ferroptosis and cuproptosis

Ferroptosis core machinery

The core ferroptotic machinery coordinates iron handling, lipid remodeling, and antioxidant defense in a threshold-dependent manner rather than through a simple linear pathway [12]. The transferrin–transferrin receptor (TFRC) system is coupled to intracellular iron buffering through ferritin heavy chain (FTH1) and ferritin turnover mediated in part by nuclear receptor coactivator 4 (NCOA4)-dependent ferritinophagy [13]. These processes can increase the labile iron pool and thereby influence susceptibility to lipid peroxidation. Reports of spatially restricted iron handling at mitochondria–lysosome contact sites are emerging, but the extent to which such microdomains determine ferroptotic commitment in hematological malignancies remains to be established [14, 15]. Lipid remodeling is also highly regulated. The acyl-CoA synthetase long-chain family member 4 (ACSL4), lysophosphatidylcholine acyltransferase 3 (LPCAT3) axis enriches membranes in polyunsaturated fatty-acyl phospholipids that are susceptible to oxidation, while phospholipase activity and lipid-droplet metabolism can modify the availability of oxidizable substrates [16]. Throughout this analysis, mechanistic assertions are evaluated at three levels of evidence in order to preserve a clear separation between established mechanisms and suggested models. Mechanisms backed by direct experimental research and repeatable findings are referred to as established evidence. Findings that require additional validation but are backed by preliminary or context-dependent experimental data are referred to as emerging evidence, especially in haematological malignancies. Mechanistic interpretations, anticipated interactions, or author-suggested extensions that have not yet been proven are referred to as hypotheses or proposed models. Consequently, suggested connections involving subcellular metal redistribution, differential lipoylation, pathway switching, or propagation of protein-aggregation states are specifically identified as emerging or hypothetical, while canonical ferroptotic and cuproptotic execution mechanisms are discussed as established when supported by experimental evidence. Thus, the ferroptotic lipidome may extend beyond canonical phosphatidylethanolamine species, although the contribution of ether phospholipids and oxidized cardiolipins requires further validation [17].

Antioxidant systems provide multiple layers of protection against lipid peroxidation. The cystine/glutamate antiporter system Xc−, solute carrier family 7 member 11 (SLC7A11), and glutathione peroxidase 4 (GPX4) are major components, but parallel defenses can contribute depending on cellular context [18, 19]. Ferroptosis suppressor protein 1 (FSP1) functions as an NAD(P)H-dependent plasma-membrane-associated CoQ10 (coenzyme Q10) reductase and can suppress lipid peroxidation independently of GPX4 [20]. GPX4 and FSP1 therefore represent complementary antioxidant defenses rather than redundant pathways. Compartment-specific antioxidant capacity may influence the local threshold for ferroptotic damage, but the existence and therapeutic significance of discrete “ferroptosis-resistance zones” remain hypotheses requiring direct experimental confirmation. In HMs, metabolic rewiring of glutathione, NADPH, CoQ10 (coenzyme Q10), and lipid metabolism may consequently modify ferroptotic sensitivity, providing candidate mechanisms for combination therapy [21–23].

Cuproptosis core pathways

Cuproptosis is also becoming a type of mitochondrial-based, proteotoxic cell death that is characterized by dependence on intracellular copper flux and maintenance of the lipoylated tricarboxylic acid (TCA) cycle apparatus [24, 25]. It has been suggested that cuproptotic susceptibility may be determined by localised mitochondrial copper accumulation and redistribution. The precise role of mitochondria-associated membranes or specific protein-import interfaces in creating cuproptotic microdomains is still an emerging idea that needs direct spatial and kinetic validation in haematological malignancies, despite the biological significance of copper trafficking to mitochondria. Consequently, it is not appropriate to regard these suggested subcellular copper distributions as proven characteristics of cuproptotic execution. Instead of being controlled by the total copper levels alone, conceptual developments in recent years have turned to a highly controlled copper trafficking gradient where the concerted action of importers like solute carrier family 31 member 1 (SLC31A1/CTR1) and the copper-transporting ATPases ATP7A and ATP7B, which regulate intracellular copper availability and distribution to create subcellular copper pools with different functional implications. New transient copper accumulation in mitochondria may be dynamically rewired in haematological malignancies during metabolic stress or therapeutic pressure [26]. This localized enrichment has been postulated to take place at mitochondria-associated membranes, and protein import interfaces, forming microenvironments in which copper has a direct interaction with its protein targets. This spatiotemporal regulation of copper distribution indicates that the sensitivity to cuproptosis does not simply depend on copper abundance but its bioavailability and organelle-specific trafficking kinetics. The central feature of cuproptotic execution is a special reliance on lipoylated TCA cycle proteins, especially Dihydrolipoamide S-acetyltransferase (DLAT), which is oligomerized by copper through covalent lipoic acid addition to it, making it highly vulnerable to copper-contained lipoic acid [27, 28]. There is emerging evidence for a model where lipoic acid synthesis enzymes like LIAS and LIPT1, which also mediate transfer of lipoic acid, serve as upstream licensing factors that control the density and localization of lipoylated substrates in the mitochondrial matrix [29]. This forms an aggregation-prone proteins landscape that can be tuned, effectively establishing an aggregation-prone initiation threshold. Another hypothesis that has not been done before and is still relatively unexplored is to see how the specific pattern of differential lipoylation, which may depend on metabolic state, nutrient availability, or epigenetic regulation, produces different cuproptotic signatures in haematological malignancies. These signatures can not only determine susceptibility, but also the kinetics and irreversibility of cell death, placing lipoylation machinery as a crucial and underestimated regulatory pathway. The metabolism of mitochondria in its own right is centrally integrative, with cuproptosis seemingly being closely linked to an active and respiration-competent TCA cycle. Highly oxidatively phosphorylated (OXPHOS)-dependent cells, like some leukemic stem cell populations, can be especially vulnerable because they can have more flux on lipoylated enzyme complexes [30]. Here, the copper binding causes aberrant crosslinking of lipoylated proteins resulting in the appearance of high-molecular-weight aggregates that inhibit enzymatic activity and cause a cascade reaction of proteotoxic stress. Notably, this aggregation cannot be effectively addressed by canonical mitochondrial quality control mechanisms, such as chaperones and proteases, indicating that cuproptosis is a type of irreversible mitochondrial collapse [31]. One of the least investigated concepts is that the protein aggregation of cuproptosis can spread in a prion-like manner within the mitochondrial matrix and expand dysfunction beyond the initial copper-binding processes and form a feed-forward loop of metabolic collapse. Protein aggregation in cuproptosis can spread in a manner akin to prion proteins, allowing the spread of mitochondrial dysfunction beyond the confines of those copper binding sites. Such a prion-like spread within the mitochondrial matrix may worsen the initial damage from copper accumulation, increasing the failure of the cell’s metabolism. The above feed-forward loop in the expansion of the dysfunction implies that protein aggregation related to cuproptosis will continue and become more severe as the mitochondrial collapse progresses. This could explain the progression of mitochondrial dysfunction in copper dysregulation-related diseases and metabolic disorders. The prion-like propagation of the cuproptosis aggregates may provide new clues for disrupting metabolic collapse and mitochondrial damage cycle. It is a concept that suggests a possible mechanism by which localised copper toxicity may lead to generalised mitochondrial dysfunction, and thus lead to cell death. Differential protein lipoylation may influence cuproptotic susceptibility and molecular signatures. However, whether these differences produce reproducible cuproptotic signatures remains to be experimentally established; therefore, they should be considered emerging hypotheses rather than established features of cuproptosis [32–34].

Moreover, recent conceptualizations suggest that cuproptosis overlaps with other proteostasis systems, such as the mitochondrial unfolded protein response (UPRmt) and cytosolic stress signalling pathways [35]. In haematological malignancies, in which proteostasis is already overstretched by the high rates of protein synthesis and oncogenic stress, the extra burden caused by copper-induced aggregation can surpass the adaptive ability, pushing the balance towards cell death [36]. This combination of copper homeostasis, lipoylation-dependent availability of substrates, and mitochondrial metabolic condition promotes a systems-level viewpoint of cuproptosis as a highly vulnerable and yet underutilized vulnerability. The identification of regulatory nodes that orchestrate these processes, especially the copper trafficking dynamics and lipoylation machinery, could allow the selective induction of cuproptosis in metabolically prepared hematologic cancer cells to provide a novel therapeutic dimension in addition to traditional apoptosis-based approaches [37]. The Ferroptosis-Cuproptosis Axis Model is illustrated in Fig. 1.

Fig. 1.

Conceptual model of ferroptosis–cuproptosis metabolic crosstalk in hematological malignancies. Ferroptosis is driven primarily by iron-dependent phospholipid peroxidation and loss of antioxidant capacity, whereas cuproptosis involves copper-dependent stress associated with pre-existing lipoylated TCA-cycle proteins and mitochondrial proteotoxicity. Shared metabolic determinants including ROS, NADPH and glutathione homeostasis, mitochondrial function, and iron/copper trafficking may create convergence between the pathways. Solid arrows indicate mechanisms supported by established experimental evidence; dashed arrows indicate proposed or indirect relationships; and dotted arrows indicate hypotheses requiring experimental validation. Multi-omics and computational approaches may help identify context-specific susceptibility states, but the proposed crosstalk framework is not presented as a validated molecular signaling pathway

Ferroptosis–cuproptosis axis: a unified concept

The ferroptosis–cuproptosis axis is herein construed as a conceptual systems-level framework for the functional interplay between iron-dependent lipid peroxidation and copper-dependent mitochondrial proteotoxic stress via shared metabolic and redox regulatory networks, not an empirical molecular signaling pathway. The framework suggests that rather than depending primarily upon direct molecular cross-talk, both regulated cell death modalities converge in common determinants of cellular fate such as mitochondrial function, reactive oxygen species (ROS) generation, glutathione homeostasis, NADPH availability, tricarboxylic acid (TCA) cycle activity, iron–copper homeostasis and antioxidant defense systems. Whether perturbation of these common regulators shifts cellular sensitivity towards ferroptosis, cuproptosis or convergent cell death is a biological hypothesis to be tested, rather than an established mechanism. Here, we provide an operational definition that gives a mechanistic foundation to assess experimental evidence and separate molecular facts from nascent conceptual models [4, 38].

The proposed model generates three experimentally testable predictions. First, perturbations that substantially alter mitochondrial respiration or TCA-cycle activity may change sensitivity to both death programs, but the direction and magnitude of the effect should be measured rather than assumed. Second, changes in glutathione metabolism, NADPH production, and ROS buffering may modify both pathways through shared redox constraints. Third, simultaneous perturbation of iron and copper homeostasis may produce additive, synergistic, or antagonistic effects that depend on lineage, metabolic state, and treatment sequence. CRISPR perturbation, isotope-resolved metabolomics, single-cell multi-omics, spatial profiling, and patient-derived models can be used to test these predictions [38]. Until such experiments establish direct mechanistic links, the axis should remain a descriptive and hypothesis-generating framework.

Testable mechanistic model of the ferroptosis–cuproptosis axis

It has to produce experimentally testable predictions according to a mechanistic framework. There are three main direct testable hypotheses within the proposed ferroptosis–cuproptosis axis. More formally, the following must be true for perturbations that alter mitochondrial respiration or TCA-cycle activity: First, they should impact susceptibility to both ferroptosis and cuproptosis in the same direction. The second way that death programs can be globally connected is how their regulators influence the balance of shared redox regulators, such as glutathione metabolism, NADPH production and ROS buffering systems; these shared redox pathways should coordinately affect both death programs should they differ in a mechanistic execution. Third, when iron and copper homeostasis are concurrently perturbed, they should generate predictable additive, synergistic or antagonistic effects on regulated cell death that depend upon the metabolic state of the cell. CRISPR-based genetic perturbation, isotope-resolved metabolomics, single-cell multi-omics, spatial transcriptomics and patient-derived organoid models are among the techniques that can be utilized to investigate these hypotheses. The axis is defined by testable predictions that may be experimentally determined and can only be regarded as a descriptive concept until it is converted into a mechanistic framework, which can always be refined or discarded based on future evidence [38].

Ferroptosis and cuproptosis share key metabolic bottleneck nodes, suggesting overlapping regulatory mechanisms that coordinate cell death pathways. These common metabolic nodes act as master regulators, integrating signals that determine the activation or suppression of ferroptosis and cuproptosis. Targeting these shared metabolic bottlenecks could provide a unified therapeutic strategy to modulate multiple regulated cell death pathways simultaneously. The hypothesis that ferroptosis and cuproptosis are controlled by a set of common metabolic nodes highlights the importance of metabolic state in dictating cell fate decisions. Identifying these master regulating nodes may reveal novel biomarkers for predicting susceptibility to ferroptosis and cuproptosis in pathological conditions. The regulation of death pathways through metabolic bottlenecks implies a central role for cellular metabolism in orchestrating diverse forms of regulated cell death. Common metabolic control points may coordinate cross-talk between ferroptosis and cuproptosis, influencing the balance between different cell death modalities under stress conditions [39–41]. These nodes can involve mitochondrial redox carriers (including NADH/FADH 2 pools), lipoic acid supply, glutathione recycling and the production of coenzyme Q - each of which can be a site where perturbation can both affect the capacity of lipid peroxidation and the stability of mitochondrial proteins [42]. As an example, NADPH depletion could impair GPX4-mediated detoxification during ferroptosis and reductive buffering to copper-induced oxidative stress during cuproptosis. Likewise, changes in the tricarboxylic acid (TCA) cycle would be able to regulate the production of lipoylated substrates (affecting cuproptosis) and the production of metabolic intermediates that affect lipid remodelling (affecting ferroptosis). Such overlapping requirements constitute a metabolic convergence layer that incorporates both of the pathways into a unified decision-making system [3, 43].

In haematological malignancies, this axis can be further determined by distinct metabolic plasticity and microenvironmental demands of the bone marrow and lymphoid niches. One of the most proposed aspects of this framework is the idea of dynamic switching, whereby cancer cells switch between ferroptotic and cuproptotic susceptibilities in response to treatment [44]. In conditions of induction of ferroptosis, including cystine uptake or GPX4 inhibition, the cell can rewire both lipid metabolism and antioxidant systems, unintentionally making mitochondria more dependent and lipoylation flux more active, making the cell susceptible to cuproptosis. On the other hand, mitochondrial metabolic or copper homeostasis disruption can change cellular reliance to iron-mediated lipid susceptibilities [45]. Two-way plasticity allows malignant cells to dynamically switch between different phenotypic states, enhancing their ability to survive therapeutic pressures that target a single pathway. - This metal-switch circuitry represents an adaptive mechanism that confers resistance to monotherapies by enabling cancer cells to bypass blocked signaling routes. The presence of such plasticity suggests that targeting multiple pathways simultaneously or in sequence could overcome resistance and improve treatment efficacy. Combinatorial or sequential therapeutic approaches exploit the vulnerabilities created by the plasticity, preventing malignant cells from effectively adapting to therapy. Understanding and targeting this adaptive circuitry could inform the design of more robust cancer treatments that minimize relapse and resistance [46–48]. The systems-level comparison of Ferroptosis and Cuproptosis is summarised in Table 1.

Table 1.

Systems-Level Comparison of Ferroptosis and Cuproptosis

Feature	Ferroptosis	Cuproptosis	Convergence layer	References
Metal Dependency	Iron (Fe²⁺)	Copper (Cu⁺/Cu²⁺)	Metal flux regulation	[38, 49]
Core Mechanism	Lipid peroxidation	Lipoylated protein aggregation	ROS amplification	[50–52]
Organelle Focus	Plasma membrane, ER	Mitochondria	Mitochondrial stress hub	[52, 53]
Key Regulators	GPX4, SLC7A11, ACSL4	DLAT, LIAS, LIPT1	NADPH, CoQ10	[24, 50, 54]
Metabolic Link	PUFA metabolism	TCA cycle	Redox metabolism	[27, 55]
Execution Mode	Membrane rupture	Proteotoxic collapse	Bioenergetic failure	[56, 57]
Therapeutic Targeting	Erastin, RSL3	Elesclomol	Combination therapy	[58, 59]

Gene-regulated cell death networks in haematological malignancies

Cross-talk with other RCD modalities

The ferroptosis-cuproptosis axis is not a stand-alone event but a highly integrated part of a more complex, highly interconnected network of regulated cell death (RCD) modalities with widespread cross-talk with apoptosis, necroptosis, pyroptosis, and autophagy-dependent death ultimately modulating cell fate choices in a context-dependent fashion [60]. One of the emerging ideas is that these pathways consist of a collection of integration hubs of stress, as opposed to parallel silos. An example is that mitochondrial integrity and outer membrane permeabilization, which are traditionally regulated by the BCL-2 family during apoptosis, can serve as an intersection point where ferroptotic lipid peroxidation and cuproptotic proteotoxic stress indirectly regulate apoptotic priming [61]. According to recent theoretical models, sublethal ferroptotic damage can change the composition of the mitochondrial membrane, reducing the activation threshold of BAX/BAK, and copper-induced aggregation of mitochondrial proteins can disrupt cristae structure, allowing cytochrome c to be released [62, 63]. This places apoptosis not only as its own pathway, but as a downstream amplifier or execution checkpoint, which can be activated either by metabolic and redox perturbations triggered by metal-dependent death programs.

Necroptosis adds a further degree of complexity in that it depends on the RIPK1–RIPK3 -MLKL signalling cascade and is a secondary form of death in case of caspase inhibition [64]. The new and mostly untested hypothesis is that the products of lipid peroxidation produced during ferroptosis can directly contribute to membrane vulnerability to MLKL-induced pore formation and in turn predispose cells to necroptotic rupture [65]. On the other hand, bioenergetic collapse of cuproptosis could reduce ATP-dependent checkpoints, which would suppress necroptosis, and reduce the threshold of RIPK signalling complexes. This implies that there is a continuum of membrane vulnerability, with ferroptosis priming lipid architecture and necroptosis performing membrane disruption with the formation of a sequence of events, rather than isolated results [66].

This cross-talk is supplemented by pyroptosis, which is caused by gasdermin pore formation in the downstream of the inflammasome activation. New paradigms suggest that ferroptotic and cuproptotic stress can be upstream stimulants of inflammasome assembly by the release of damage-associated molecular patterns (DAMPs), oxidized lipids, and mitochondrial DNA [67]. With strongly regulated immune surveillance in haematological malignancies, this may provide a situation where low-level ferroptosis or cuproptosis may shift to pyroptotic cell death, and thus turning a metabolically-controlled process into an immunogenic process. Interestingly, it is increasingly being speculated that copper build-up and mitochondrial dysfunction might directly contribute to NLRP3 inflammasome activation, which would make cuproptosis a potential mediator of inflammatory signalling in an as-yet-unsystematic way [5].

Transcriptional and epigenetic regulation

The ferroptosis cuproptosis axis exists as a multilayered regulation system that is regulated by transcriptional and epigenetics programs dynamically tuning cellular vulnerability to metal-dependent death [68, 69]. At the center of this regulation lies stress-reactive transcription factors, including p53, NRF2, and HIF-1, which do not act as individual regulators, but as part of an interdependent and orchestrated tri-axis regulatory system of redox homeostasis, metabolic flux, and metal homeostasis [70, 71]. In addition to its canonical tumour suppressor functions, p53 is also beginning to be viewed as a context-dependent modulator of bias toward death pathways, and can induce cells towards ferroptosis by inhibiting cystine transport and also regulate mitochondrial metabolism and may predispose cells to cuproptosis in conditions of increased oxidative phosphorylation [72, 73]. Conversely, NRF2 itself is a master antioxidant gatekeeper, although new hypotheses propose that its activation can be paradoxical: on the one hand, NRF2-mediated transcription increases glutathione production and ferroptosis resistance, but on the other hand, NRF2-mediated transcription increases mitochondrial metabolic throughput and access to cofactors, inadvertently predis HIF-1 adds an extra dimension reprogramming metabolic conditions under hypoxia, which inhibits mitochondrial respiration and, therefore, is potentially protective of cuproptotic, but also changes lipid metabolism in a manner that either facilitates or limits ferroptotic vulnerability depending on the cellular context [74, 75].

Tumour microenvironment (TME) influence

The haematological malignancy tumour microenvironment (TME) is a dynamic controller of the ferroptosis-cuproptosis axis, and the bone marrow niche environment is a distinctively protective and metabolically limiting ecosystem [76]. Limited vascularization causes hypoxia in this niche that reorganizes cellular metabolism via HIF-dependent signalling, inhibiting mitochondrial respiration limiting cuproptotic vulnerability and variably regulating ferroptotic lipid metabolism [77, 78]. This condition is further strengthened by the stromal cells that provide cysteine, lipids and metal-chelating factors, which effectively buffer against oxidative and metal-induced stress. Immune cells provide yet another control mechanism whereby macrophages and T cells can take part in the regulation of iron and copper by cytokine signalling and phagocytosis redefining intracellular metal pools [79]. Furthermore, spatial heterogeneity in redox balance and mitochondrial activity is produced by cytokine gradients and local metabolic fluxes, e.g. lactate build-up and amino acid catabolism. The TME, in aggregate, forms a dynamic Metallo-metabolic landscape, which dynamically adjusts ferroptosis-cuproptosis sensitivity and therapeutic response [80].

Multi-omics dissection of the ferroptosis–cuproptosis axis

Genomics and CRISPR screens

The ferroptosis-cuproptosis landscape is being reshaped by genomics and CRISPR-based functional screens, which can be used to systematically identify synthetic lethal interactions that cannot be isolated by individual gene analyses [3, 41]. In addition to canonical regulators, new high-resolution screens indicate that there are so-called dual-sensitivity nodes, perturbing a single gene can destabilize iron handling and mitochondrial proteostasis, destabilizing both ferroptotic and cuproptotic defences [3, 5]. This vulnerability map is further influenced by mutation landscapes in haematological malignancies; e.g., metabolic enzyme modifications, chromatin modifiers, or mitochondrial regulators can re-organize intracellular redox and metal use, establishing lineage- and clone-specific dependencies. One of the more proposed ideas is that some oncogenic mutations induce the existence of latent death liabilities, in which cells seem to be insensitive to basal conditions, but become extremely sensitive to metal-dependent stress when perturbed [81, 82]. A combination of CRISPR screens and patient genomics, therefore, provides a potent model to reveal context-dependent RCD vulnerabilities and construct targeted treatment plans.

Transcriptomics and single-cell RNA-seq

By revealing cell-type-specific vulnerability-signatures in heterogeneous haematological malignancies, transcriptomics and single-cell RNA-seq are revealing a previously unappreciated level of ferroptosis-cuproptosis control. Instead of homogenous sensitivity, there is growing evidence of a specific set of subpopulations that encode the states of RCD priming that is coordinated expression of metal transporters, lipid remodelling enzymes and mitochondrial regulators [83]. One of the new findings is the intra-clonal RCD heterogeneity, or genetic similar cells which evolve differently in death sensitivity because of transcriptional noise and microenvironmental imprinting. These results support a spectrum of death preparedness, and can be used to selectively target vulnerable subclones with a predictable response of adaptive changes in resistant populations in response to therapeutic pressure.

Proteomics and metalloproteomics

Proteomics and Metallo proteomics are also providing an extra layer of regulation to the ferroptosis-cuproptosis axis by identifying dynamic post-translational alterations and metal-protein interactions that cannot be predicted based on genomics or transcriptomics alone. In addition to classical lipoylation, there is newer evidence indicating that there are context-dependent hybrid modifications, where co-regulation of protein conformation, stability, and aggregation propensity is by oxidative stress and metal binding. The copper- and iron-binding proteomics studied have revealed hitherto unidentified metal-sensitive protein networks, such as enzymes and chaperones, which can serve as buffering hubs. The new idea is the establishment of transient metalloprotein clusters, which spatially clustering redox reactions enhances local ferroptotic or cuproptotic signalling [84, 85].

Metabolomics and lipidomics

Metabolomics and lipidomics are re-inventorying the ferroptosis-cuproptosis interface by taking dynamic metabolic states that determine vulnerability to death. Instead of fixed biomarkers, lipid peroxidation signatures are becoming temporally dynamic oxidative fingerprints, which are waves of polyunsaturated lipid oxidation and membrane remodelling [86]. At the same time, the rewiring of the TCA cycle seems to produce unique redox conditions that organize the lipid vulnerability and mitochondrial proteotoxic stress. Another new idea is that there are metabolic phase transitions, with changes in NADH/NAD + and glutathione pools inducing a sudden switch between ferroptotic and cuproptotic sensitivity, exposing therapeutically relevant actionable metabolic checkpoints [41].

Cross-omics integration and computational workflow

Multi-omics integration can combine genomic, transcriptomic, proteomic, metabolomic, lipidomic, and metallomic data to identify molecular determinants of ferroptosis–cuproptosis susceptibility. After quality control, normalization, and feature selection, these datasets can be integrated using pathway, network, and multi-omics approaches to identify common molecular modules and convergence nodes linking iron, copper, lipid metabolism, and cellular stress [87]. Candidate nodes can then be prioritized based on cross-omics evidence and validated using CRISPR perturbation, pharmacological intervention, rescue experiments, isotope tracing, and measurements of lipid peroxidation and intracellular iron/copper levels. Thus, the workflow follows multi-omics profiling → preprocessing → cross-omics integration → network analysis → convergence-node identification → experimental validation → susceptibility prediction A practical workflow can integrate genomic alterations with transcriptomic and proteomic changes, followed by metabolomic, lipidomic, and metallomic profiling to capture redox metabolism, lipid peroxidation, and iron–copper homeostasis. These datasets can be integrated through pathway and network-based analyses to identify cross-omics convergence nodes associated with ferroptosis–cuproptosis susceptibility, which can subsequently be prioritized for experimental validation [88]. Ferroptosis–cuproptosis susceptibility can be further modelled using a quantitative systems-biology paradigm as a dynamic interaction between iron and copper flux, ROS production, glutathione availability, lipid peroxidation, and mitochondrial activity. In theory, increasing Fe/Cu flux may increase ROS while glutathione-dependent antioxidant capacity prevents oxidative damage; cells may move from a stressed state to ferroptotic, cuproptotic, or dual-cell-death states when oxidative stress and lipid peroxidation surpass predetermined thresholds. By altering iron or copper availability, antioxidant capacity, or mitochondrial activity and monitoring ROS, glutathione, lipid peroxidation, and cell survival, this approach can produce predictions that can be tested experimentally.

The multi-omics integration framework is illustrated in Fig. 2. and Multi-Omics Layers Defining RCD Susceptibility is summarized in Table 2.

Fig. 2.

Integrative multi-omics framework for characterizing regulated cell death susceptibility in hematological malignancies. Genomics, transcriptomics, proteomics, metabolomics, and metallomics datasets are integrated through computational and artificial intelligence-based analytical platforms to construct patient-specific digital twins. These models enable identification of molecular vulnerabilities, prediction of ferroptosis–cuproptosis sensitivity, biomarker discovery, patient stratification, and optimization of personalized therapeutic strategies

Table 2.

Multi-Omics Layers Defining RCD Susceptibility

Omics layer	Key insights	Technologies	Clinical utility	References
Genomics	Mutation-driven vulnerabilities	CRISPR screens	Target discovery	[89]
Transcriptomics	RCD priming states	scRNA-seq	Patient stratification	[90]
Proteomics	Metal-binding networks	Mass spectrometry	Drug targeting	[91]
Metallomics	Iron/copper flux	ICP-MS	Biomarkers	[92]
Metabolomics	Redox & TCA rewiring	LC-MS	Therapy response	[93]
Lipidomics	Lipid peroxidation signatures	Lipid profiling	Ferroptosis prediction	[94, 95]

Network inference, causal validation and predictive modelling

Network inference provides a mechanistic link between multi-omics alterations and regulated cell-death susceptibility. Integrated gene, protein, metabolite, lipid, and metal-associated networks can identify molecular modules that jointly regulate ferroptosis and cuproptosis. Experimentally validated nodes can then be incorporated into machine-learning models to classify samples as ferroptosis-sensitive, cuproptosis-sensitive, dual-sensitive, or resistant. Cross-validation and independent datasets should be used to evaluate model robustness and reduce overfitting. This integrated strategy can support the identification of mechanistic biomarkers and potential therapeutic targets in hematological malignancies. Important regulatory nodes between ferroptosis and cuproptosis can be found using integrated gene–protein–metabolite–lipid–metal networks. In order to classify ferroptosis-sensitive, cuproptosis-sensitive, dual-sensitive, or resistant phenotypes, candidate nodes can be verified through genetic or pharmacological perturbation and integrated into machine-learning models. The robustness of the model is evaluated using independent datasets and cross-validation.

Limitations of current multi-omics integration

Although individual omics technologies have made considerable progress, covering the biological complexity of regulated cell death requires an integrated approach instead of isolated datasets. One of the greatest challenges is building integrative frameworks to relate genomic variation with transcriptional dynamics, protein activity and subsequently metabolic flux and metal homeostasis. Instead of considering each omics layer independently as different ‘sources’ of information, future studies should focus on network-based integration underpinned by temporal sampling, spatially resolved molecular profiling and functional perturbation assays. These kinds of multi-level strategies can not only help identify causal regulatory circuits from correlative signatures but also enable improvement in mechanistic understanding of ferroptosis–cuproptosis interactions and facilitate robust predictive biomarker as well as precision therapeutic intervention development in hematologic malignancies [96, 97].

Ferroptosis–cuproptosis axis in specific haematological malignancies

The biological significance of ferroptosis–cuproptosis crosstalk is likely to vary substantially across haematological malignancies because individual tumour types differ in iron metabolism, copper homeostasis, mitochondrial dependence, lipid composition, antioxidant capacity, and interactions with the tumour microenvironment (TME). Although direct experimental evidence simultaneously linking ferroptosis and cuproptosis in individual haematological cancers remains limited, accumulating studies indicate that these two metal-dependent forms of regulated cell death converge on several metabolic processes, including mitochondrial respiration, redox homeostasis, tricarboxylic acid (TCA) cycle activity, glutathione metabolism, and lipid oxidation. This provides a rationale for investigating malignancy-specific vulnerabilities that could be exploited therapeutically.

Acute myeloid leukaemia (AML)

Acute myeloid leukaemia (AML) provides a particularly relevant model for investigating the intersection between iron metabolism, mitochondrial function, and therapy resistance. Leukemic stem cells (LSCs) exhibit substantial metabolic plasticity and can exploit iron availability to support mitochondrial biogenesis, respiratory activity, and redox adaptation. Increased dependence on mitochondrial metabolism may become particularly important in leukemic populations that survive therapeutic pressure, including cells that develop resistance to BCL-2 inhibition [98]. Venetoclax-resistant AML cells undergo metabolic rewiring involving oxidative phosphorylation, amino-acid utilization, iron metabolism, and TCA-cycle activity, allowing preservation of mitochondrial fitness despite BCL-2 inhibition [99]. Such metabolic adaptation potentially creates a vulnerability to ferroptosis because increased mitochondrial activity and altered iron handling can enhance the generation of reactive oxygen species and increase susceptibility to phospholipid peroxidation when antioxidant defences become insufficient. At the same time, increased mitochondrial dependence may provide a theoretical context in which cuproptosis-related mechanisms become relevant, particularly because copper-dependent toxicity is closely associated with mitochondrial respiration and lipoylated TCA-cycle proteins. Therefore, disruption of iron trafficking, mitochondrial iron utilization, glutathione-dependent antioxidant defence, or copper homeostasis may represent complementary strategies for targeting metabolically adapted AML cells. However, direct evidence demonstrating synergistic ferroptosis–cuproptosis induction in AML remains an important research gap. Future studies should determine whether iron and copper perturbation can selectively eliminate venetoclax-resistant or LSC-enriched populations while defining the contribution of mitochondrial lipoylation, Fe–S cluster metabolism, lipid peroxidation, and proteotoxic stress to treatment response.

Acute lymphoblastic leukaemia (ALL)

Acute lymphoblastic leukaemia (ALL) exhibits lineage-specific metabolic characteristics that may generate a pronounced dependence on redox homeostasis. Rapidly proliferating leukemic blasts require substantial biosynthetic and antioxidant capacity, while alterations in cysteine availability, glutathione synthesis, and transsulfuration pathways can constrain their ability to neutralize lipid-derived reactive oxygen species [100, 101]. Consequently, disruption of glutathione-dependent antioxidant systems may push ALL cells beyond a critical oxidative threshold and promote ferroptotic cell death. The vulnerability may be particularly important in ALL subtypes in which transcriptional or epigenetic programmes reduce the capacity of canonical antioxidant pathways. Increased availability of polyunsaturated fatty acids within membrane phospholipids, together with inadequate detoxification of lipid hydroperoxides, could further increase susceptibility to ferroptosis. Thus, inhibition of system x_c−, glutathione synthesis, glutathione peroxidase 4 (GPX4), or related lipid-protection mechanisms represents a potential strategy for exploiting this latent redox vulnerability. The relationship between this ferroptotic phenotype and cuproptosis remains less well defined. Nevertheless, perturbation of copper-dependent mitochondrial metabolism could theoretically amplify oxidative stress in metabolically constrained ALL cells. This possibility warrants investigation because mitochondrial dysfunction, impaired antioxidant buffering, and altered metal-ion homeostasis may create convergent stress signals rather than acting as independent pathways. Future studies should therefore determine whether copper perturbation enhances ferroptosis sensitivity in genetically or metabolically defined ALL subgroups and whether such interactions are dependent on mitochondrial respiration or TCA-cycle activity.

Multiple myeloma (MM)

Multiple myeloma (MM) represents another malignancy in which metabolic stress and metal-ion homeostasis may provide exploitable vulnerabilities. Malignant plasma cells continuously synthesize and secrete large quantities of immunoglobulins, generating substantial endoplasmic-reticulum and proteotoxic stress and increasing their dependence on proteostasis, mitochondrial metabolism, and antioxidant systems [102, 103]. These characteristics make MM cells particularly sensitive to perturbations that further compromise metabolic and proteostatic capacity. Copper metabolism may be especially relevant because excessive intracellular copper can interfere with mitochondrial function and promote copper-dependent toxicity involving lipoylated mitochondrial enzymes. The resulting destabilization of lipoylated TCA-cycle components and associated proteotoxic stress provides a mechanistic framework for cuproptosis in metabolically active MM cells. This vulnerability may be reinforced by the dependence of MM cells on oxidative phosphorylation and glutamine-supported anaplerosis to sustain energy production and biosynthetic requirements. Ferroptosis may provide a complementary vulnerability because MM cells also experience high oxidative pressure and depend on antioxidant systems to maintain redox balance. Perturbation of iron metabolism, glutathione synthesis, GPX4 activity, or lipid-remodelling pathways could therefore increase susceptibility to lipid peroxidation. From this perspective, combined manipulation of copper-dependent mitochondrial stress and iron-dependent lipid peroxidation represents a potentially attractive dual-axis strategy. However, whether such treatment produces genuine mechanistic synergy rather than additive cellular stress remains to be established experimentally. Future studies should integrate copper and iron measurements with mitochondrial respiration, lipoylated-protein abundance, lipid peroxidation, glutathione status, and cell-death rescue experiments to establish the mechanistic relationship.

Lymphomas

Lymphomas display substantial metabolic heterogeneity arising from differences in genetic background, cellular lineage, disease stage, and TME composition. Stromal cells, tumour-associated macrophages, cytokines, hypoxic gradients, and extracellular metabolites can remodel the metabolic state of lymphoma cells and promote adaptation to oxidative and therapeutic stress [104]. Spatially distinct lymphoma populations may preferentially utilize glycolysis, fatty-acid oxidation, or oxidative phosphorylation according to local nutrient and oxygen availability, generating metabolically complementary tumour niches [105]. This metabolic plasticity has important implications for ferroptosis–cuproptosis susceptibility. Lymphoma subclones relying predominantly on oxidative phosphorylation may exhibit greater dependence on mitochondrial integrity and therefore could be more susceptible to perturbations of copper-dependent mitochondrial metabolism. Conversely, populations characterized by high iron utilization, increased polyunsaturated lipid content, or weakened glutathione/GPX4 protection may display greater ferroptotic sensitivity. Hypoxia and TME-derived metabolites may further modify these responses by altering ROS generation, lipid metabolism, iron trafficking, and antioxidant capacity. The TME may therefore function not simply as a source of treatment resistance but as a determinant of metal-dependent cell-death sensitivity. Mapping spatially resolved iron and copper metabolism together with mitochondrial activity, lipid peroxidation, and antioxidant capacity could identify lymphoma subpopulations that are selectively vulnerable to ferroptotic or cuproptotic interventions [47]. Importantly, future studies should move beyond bulk tumour measurements and incorporate spatial transcriptomics, metabolomics, single-cell profiling, and functional perturbation to determine whether ferroptosis–cuproptosis vulnerabilities are encoded by specific lymphoma subclones or imposed dynamically by the surrounding microenvironment.

Therapeutic targeting of the ferroptosis–cuproptosis framework

Ferroptosis inducers

Ferroptosis-inducing compounds such as erastin, RSL3, and FIN56 are widely used mechanistic tools to interrogate ferroptosis [106]. Erastin inhibits system Xc− activity and can deplete intracellular cysteine and glutathione, whereas RSL3 directly inhibits GPX4. FIN56 has been reported to reduce GPX4 abundance and CoQ10 (coenzyme Q10)-dependent antioxidant capacity. These agents are valuable for proof-of-concept studies, but their experimental use should not be equated with clinical readiness. Translation requires demonstration of selective activity in disease-relevant hematological models, pharmacodynamic target engagement, tolerability, and an adequate therapeutic window [107, 108].

Cuproptosis modulators

Copper-modulating strategies include copper ionophores, copper chelators, and approaches that alter copper transport. Elesclomol and disulfiram have been investigated as copper-dependent cytotoxic agents, while chelators can reduce copper availability [58, 109]. Their effects should be interpreted in relation to copper speciation, intracellular trafficking, mitochondrial respiration, and the abundance of lipoylated TCA-cycle proteins. Importantly, mechanistic activity in experimental systems does not by itself establish cuproptosis as the sole mode of cell death. Orthogonal assays and rescue experiments are required to distinguish cuproptosis from other forms of oxidative or mitochondrial injury.

Copper and the TCA cycle (the foundation of cuproptosis)

Copper is a double-edged sword for tumors. While cancer cells rely on copper for angiogenesis and growth, too much of it forces cuprous ions into the mitochondria, which binds to lipoylated proteins in the TCA cycle (like DLAT). This binding triggers protein aggregation and the loss of essential iron-sulfur (Fe-S) cluster proteins. The resulting proteotoxic stress and mitochondrial collapse trigger cell death. Research on this cell-death pathway validates that tuning copper availability and mitochondrial respiration can be utilized to kill tumor cells selectively [110].

Collapsing metabolic robustness

Highly adaptive cancers survive chemotherapies and targeted agents by rewiring their metabolism to upregulate (ATP7A/ATP7B) efflux pumps and scavenge glutathione. strategy of “alternating cycles” mirrors nanomedicine approaches that deliver copper in spatiotemporally controlled bursts. By oscillating from copper depletion (starving the cancer) to copper-mediated overload (causing fatal toxicity), tumors cannot adapt their metallothionein defenses and glutathione buffering in time [111].

Redox stress and combination strategies

Copper-dependent redox stress may interact with glutathione and NADPH buffering, but the precise contribution of ROS to cuproptotic execution remains context-dependent. Combination strategies that increase oxidative stress while weakening antioxidant defenses are therefore best considered experimental approaches requiring mechanistic confirmation and careful toxicity assessment [112].

Dynamic copper modulation as a future hypothesis

Rather from being a proven treatment approach, the suggested oscillation of intracellular copper between depletion and overload should be regarded as a future, unpublished theory. Systemic copper toxicity and a limited therapeutic window are possible drawbacks, and its biological viability and ideal dosage are still unclear. Therefore, before its therapeutic potential can be evaluated, future research should establish copper kinetics, dosing schedules, cancer selectivity, normal-tissue exposure, and reversibility.

Nanomedicine and targeted delivery

Nanomedicine may improve the therapeutic index of RCD-modulating agents by controlling tissue distribution, intracellular delivery, and release kinetics. Current approaches include metal–organic frameworks (MOFs), lipid nanoparticles (LNPs), polymeric nanoparticles, and stimuli-responsive systems [113–115]. For the ferroptosis–cuproptosis framework, rational nanoplatform design should be driven by a defined biological bottleneck: (i) target-cell recognition or tumour-niche accumulation; (ii) controlled delivery of a ferroptosis or copper-modulating payload; (iii) release triggered by disease-relevant cues such as pH, ROS, enzymes, or redox conditions; and (iv) simultaneous measurement of target engagement and toxicity. Co-delivery strategies should also consider whether combining payloads produces true pharmacological synergy or merely additive oxidative injury. Key translational parameters include particle stability, protein-corona effects, biodistribution, clearance, immunogenicity, scale-up, batch reproducibility, and off-target metal exposure. Thus, nanomedicine should be evaluated not only by loading capacity but by whether its physicochemical properties are mechanistically matched to the intended RCD vulnerability. The therapeutic targeting strategy is illustrated in Fig. 3. Therefore, it is important to consider if the physicochemical characteristics of nanomedicine are mechanistically compatible with the targeted RCD vulnerability in addition to loading capacity. Figure 3 depicts the therapeutic targeting approach. Significantly, the translational maturity of various methods varies. While elesclomol, disulfiram, and copper chelation have more pharmacological experience but are not clinically proven cuproptosis-specific treatments, erastin, RSL3, and FIN56 are primarily mechanistic tools. Nanomedicine is still mostly in the experimental stage. Disease-relevant models, target engagement and PK/PD studies, biomarker-guided selection, toxicity evaluation, and patient-derived validation will all be necessary for translation. Therapeutic safety and efficacy may be enhanced by thoroughly validated combination tactics, longitudinal monitoring, and selective delivery.

Fig. 3.

Nanomedicine-enabled strategy for experimentally testing ferroptosis–cuproptosis modulation. Candidate ferroptosis or copper-modulating agents can be incorporated into MOFs, LNPs, polymeric nanoparticles, or other delivery systems. Design variables include tumour targeting, intracellular trafficking, stimulus-responsive release, payload ratio, and exposure duration. The platform should be evaluated using pharmacodynamic measurements of lipid peroxidation, GPX4 activity, glutathione/NADPH status, copper and iron redistribution, mitochondrial function, and cell-death rescue assays, alongside systemic toxicity and biodistribution. Solid elements represent established delivery principles; dashed elements represent proposed applications to ferroptosis–cuproptosis research

Biomarkers, pharmacodynamic monitoring, and clinical translation

Biomarker development is central to translating RCD-directed therapies into the clinic. Static gene signatures may identify baseline susceptibility, but pharmacodynamic biomarkers are needed to demonstrate target engagement and distinguish ferroptosis, cuproptosis, and nonspecific oxidative injury. A practical biomarker panel could combine: (i) lipid-peroxidation products and oxidized phospholipid species for ferroptotic activity; (ii) GPX4/SLC7A11 activity and glutathione/NADPH status as measures of antioxidant-system engagement; (iii) labile iron and copper measurements or imaging-based metal redistribution; (iv) mitochondrial respiration, membrane potential, and proteotoxic-stress markers for cuproptotic involvement; and (v) rescue by pathway-selective inhibitors or genetic perturbation as orthogonal evidence of death mechanism. Longitudinal sampling is particularly important because target engagement, metal redistribution, lipid peroxidation, mitochondrial dysfunction, and cell death may occur on different time scales. Biomarker development should therefore incorporate baseline, early pharmacodynamic, and post-treatment measurements and should be analytically validated before use in patient selection. Multi-omics models may ultimately combine these dynamic measurements with clinical variables to define RCD susceptibility states, but prospective validation is required before clinical implementation. Ferroptosis can be further distinguished from cuproptosis and nonspecific oxidative damage using a time-resolved biomarker approach. Prior to downstream lipid peroxidation or mitochondrial malfunction, early target engagement and metal redistribution should be evaluated. Cell death and pathway-specific rescue should then be measured. This temporal sequence can be used to identify whether cuproptosis is more consistent with mitochondrial/proteotoxic stress or ferroptosis is more consistent with lipid peroxidation. Mechanistic specificity may be increased by combining these dynamic measurements with genetic validation or pathway-selective inhibition [116, 117].

Critical appraisal and translational challenges

Strategies to target ferroptosis or cuproptosis have shown preclinical efficacy; translation into clinical practice will be limited by multiple biological and technological hurdles. A fundamental shortcoming is that cellular susceptibility is an exceedingly plastic trait: instead of being an immutable characteristic of malignant cells, it varies with metabolic state, disease stage, clonal evolution and microenvironmental remodeling. In turn, the predictive value of static biomarkers is decreased since therapeutic responses may vary in the course of treatment. The lack of standardized functional assays that can quantify real-time ferroptotic or cuproptotic competence in patient-derived samples is another unresolved issue. Future translational studies should thus combine dynamic metabolic phenotyping with longitudinal molecular monitoring and integrate functional perturbation platforms to delineate transient adaptive responses from true therapeutic vulnerabilities. Instead of taking a purely pathway-centric perspective, clinical development should focus on the systems-level characterization of cancer cell-state transitions that would facilitate adaptive therapeutic approaches as tumor biology evolves to make regulated cell death–based interventions more precise [41, 118, 119].

Future perspectives

Future work should prioritize experimentally grounded precision RCD strategies. Artificial intelligence (AI) may support feature selection, multi-omics integration, image analysis, and prediction of treatment response, but current models should not be described as real-time clinical digital twins unless they have longitudinal patient data, calibrated mechanistic or statistical models, continuous or repeated measurements, and prospective validation. A set number of time points cannot be recommended generally because the necessary sample density will vary on the biological and clinical timeline being simulated. However, baseline status, early treatment response, intermediate adaptation, and post-treatment result should all be captured by repeated measures with enough data to calibrate temporal changes in RCD susceptibility. To evaluate reproducibility, lessen overfitting, and facilitate future validation, larger longitudinal cohorts with repeated molecular, pharmacodynamic, imaging, and clinical assessments will be needed. In this context, a digital twin should be defined as a computational representation updated using patient-specific longitudinal data to estimate disease state or treatment response [120, 121]. Development requires sufficiently dense and high-quality data on genomics, treatment exposure, pharmacokinetics, biomarkers, imaging, and clinical outcomes, as well as independent validation. A specific number of time points cannot be recommended universally because the necessary sample density will depend on the biological and clinical timeline being simulated. However, baseline status, early treatment response, intermediate adaption, and post-treatment outcome should all be captured by repeated measures, with enough data to calibrate temporal changes in RCD susceptibility. To evaluate repeatability, avoid overfitting, and facilitate prospective validation, larger longitudinal cohorts with repeated multi-omics and clinical assessments will be needed. Synthetic biology, organoids, organ-on-chip models, and patient-derived xenografts may provide intermediate experimental systems for testing whether predicted RCD vulnerabilities are biologically reproducible. Hybrid gene–metabolite switches and adaptive therapeutic schedules should remain future hypotheses until supported by controlled experiments. The most realistic near-term objective is therefore not autonomous precision cell-death engineering, but improved prediction, monitoring, and experimental validation of disease-specific RCD vulnerabilities [122, 123].

Key limitations

• Evidence hierarchy and lack of in vivo validation: Much of the proposed ferroptosis–cuproptosis convergence remains conceptual, and evidence is uneven across hematological malignancies. Findings from solid tumours should be clearly distinguished from evidence generated directly in leukemia, lymphoma, or multiple myeloma. In vivo validation and disease-specific models are needed before therapeutic conclusions can be generalized [124].

• Toxicity and Delivery Hurdles: Precision cell death engineering often relies on nanoparticle delivery systems. The clinical translation of these agents is hindered by rapid reticuloendothelial system clearance, potential off-target systemic toxicity (particularly from unregulated iron and copper fluxes), and poor tumor penetration [125].

• Intratumour and interpatient heterogeneity: Genomic, metabolic, and microenvironmental variability can produce distinct RCD susceptibility states and acquired resistance. A generalized precision model may therefore be insufficient without longitudinal monitoring [126].

• Computational limitations: Multi-omics, AI, and digital-twin approaches are powerful but remain dependent on data quality, model calibration, external validation, interpretability, and prospective testing. They should currently be framed as research and decision-support tools rather than established clinical substitutes for experimental or clinical evidence [127].

Conclusion

The emerging ferroptosis–cuproptosis axis represents a potentially important metabolic framework for understanding therapeutic vulnerability across haematological malignancies. Although ferroptosis and cuproptosis are mechanistically distinct forms of regulated cell death, both are closely connected to cellular metal homeostasis, mitochondrial metabolism, redox balance, and metabolic adaptation. The evidence discussed across AML, ALL, multiple myeloma, and lymphomas indicates that disease-specific dependencies on iron metabolism, copper homeostasis, oxidative phosphorylation, glutathione metabolism, lipid remodeling, and mitochondrial function may determine the relative susceptibility of malignant cells to these death pathways.

In AML, the coupling between iron utilization, mitochondrial fitness, and therapy-resistant metabolic states suggests that iron-dependent vulnerabilities may be particularly relevant in leukemic stem cells and venetoclax-resistant populations. In ALL, impaired antioxidant capacity and dependence on cysteine–glutathione metabolism may create a metabolic threshold that can be exploited through ferroptosis induction. Multiple myeloma may be especially susceptible to disruption of copper-dependent mitochondrial metabolism because of its high proteostatic burden and dependence on oxidative phosphorylation. In lymphomas, spatial and microenvironmental metabolic heterogeneity may determine whether individual tumour subclones preferentially depend on ferroptotic or cuproptotic pathways. Collectively, these observations support the concept that metal-dependent cell death should be investigated in a context-dependent rather than universally applicable manner. Importantly, direct experimental evidence establishing functional ferroptosis–cuproptosis crosstalk in haematological malignancies remains comparatively limited. Consequently, the field requires rigorous mechanistic studies that distinguish genuine pathway interaction from coincidental metabolic stress. Integration of iron and copper profiling with lipid peroxidation measurements, mitochondrial respiration, TCA-cycle activity, lipoylated-protein status, glutathione metabolism, and genetic or pharmacological rescue experiments will be essential for defining causal relationships. Single-cell and spatial multiomics may further reveal tumour subpopulations and microenvironmental niches with distinct metal-dependent vulnerabilities. From a therapeutic perspective, simultaneous targeting of iron and copper metabolism could provide a rational strategy for overcoming metabolic plasticity and treatment resistance, particularly when conventional apoptosis-based therapies become ineffective. However, therapeutic development must carefully consider systemic toxicity, normal haematopoietic cells, metal homeostasis, and the potential for adaptive metabolic compensation. Future studies should therefore prioritize biomarker-driven approaches capable of identifying patients whose malignant cells exhibit a ferroptosis- or cuproptosis-permissive metabolic state. Ultimately, translating the ferroptosis–cuproptosis axis into precision therapy will require integration of mechanistic cell-death biology with tumour metabolism, TME characterization, and patient-specific molecular profiling. Such an approach could establish metal-dependent regulated cell death as a new therapeutic dimension in the management of otherwise treatment-resistant haematological malignancies.

Author contributions

Pawan Kumar Goswami: Conceptualization, Methodology, and Data Curation. Sonakshi Antal, Tushar Anshu : Writing—Original Draft Preparation and Editing. Dinesh Kumar, Neeraj Choudhary: Software, Visualization, and Resources. Anita D. Kadam: Project Administration, Writing—Review & Editing, and Correspondence.

Funding

No source of funding.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Competing interests

The authors declare no competing interests.

Ethical approval and consent to participate

Not Applicable.

Consent for publication

Not Applicable.

Clinical trial number

Not Applicable.