Pan-cancer convergence of tumour–immune microenvironment motifs revealed by CyTOF and imaging mass cytometry

Abstract

Mass cytometry (CyTOF) and Imaging Mass Cytometry (IMC) provide single-cell resolution for over 50 protein markers, enabling unprecedented exploration of tumour and immune heterogeneity. We conducted a scoping review of 61 original studies (inception–2025), spanning 17 cancer types, to map current applications, analytical strategies, and emerging biological insights. 46 studies used CyTOF alone, 12 employed IMC exclusively, and 3 combined both platforms. Median panel sizes were 33.5 markers for CyTOF and 33 for IMC. While lineage and immune checkpoint markers were universal, phospho-epitopes, metabolic enzymes, and stromal proteins appeared in more focused subsets. Most studies followed a three-step analytical workflow: (i) segmentation or gating, (ii) unsupervised clustering, and (iii) downstream spatial or functional analyses. CyTOF investigations frequently identified exhausted CD8⁺ T-cell subsets (e.g., PD-1⁺TIM-3⁺CD39⁺), suppressive myeloid populations (e.g., CD163⁺HLA-DR⁻ macrophages), and metabolically reprogrammed Tregs. IMC studies uncovered spatial patterns predictive of outcome, such as tertiary lymphoid structures (TLSs) and macrophage–T cell exclusion zones. Several studies proposed predictive immune signatures or integrated CyTOF with transcriptomic or spatial datasets. We identified five recurrent immunobiological motifs, CD8⁺ T-cell bifurcation, CD38⁺ TAM barriers, TLS maturity, CTLA-4⁺ NK-cell signatures and metabolically defined niches, highlighting convergent axes of resistance and response. Bioinformatic pipelines converged around FlowSOM or PhenoGraph clustering, CITRUS or elastic-net feature selection, and increasingly, machine learning and agent-based spatial modelling. Collectively, CyTOF and IMC are redefining biomarker discovery, therapeutic stratification, and virtual trial design in oncology, establishing high-dimensional CyTOF as a cornerstone of next-generation precision cancer medicine.

1 Introduction

Cancer is marked by remarkable cellular heterogeneity: malignant clones coexist with diverse immune, stromal and vascular elements that continually evolve under therapeutic pressure (1–3). Conventional bulk “omics” approaches obscure this complexity, whereas single-cell technologies now allow high-resolution dissection of the tumour microenvironment (TME) (4). Among these, mass cytometry, commercially known as Cytometry by Time-Of-Flight (CyTOF), has emerged as a leading platform for multidimensional profiling of individual cells in both suspension and tissue sections (5–7).

CyTOF couples inductively coupled-plasma ionisation with time-of-flight mass spectrometry to quantify antibodies conjugated to isotopically pure heavy-metal tags. By eliminating the spectral overlap inherent to fluorescence, a single assay can routinely measure > 50 protein markers per cell without the need for compensation, while maintaining flow-cytometry-level throughput and sensitivity (8, 9). This capacity enables simultaneous assessment of lineage, activation, signalling and metabolic markers, delivering a systems-level view of immune and tumour cell states.

Since its introduction, CyTOF has transformed immuno-oncology research. High-parameter phenotyping has revealed treatment-induced remodelling of peripheral and intratumoural immunity, uncovered rare suppressive or progenitor T-cell subsets linked to checkpoint-blockade outcomes, and identified circulating signatures that anticipate toxicities or relapse (5, 10). Furthermore, integration with transcriptomics and machine-learning pipelines is accelerating the translation of CyTOF-derived biomarkers toward personalised therapy and minimal-residual-disease monitoring in clinical trials (11–13).

An important derivative technology, Imaging Mass Cytometry (IMC), extends the CyTOF principle to formalin-fixed tissue sections via laser ablation and metal-tag detection, preserving spatial context at sub-cellular resolution (14, 15). IMC has already mapped immunological “hot” and “cold” niches in melanoma, quantified tertiary lymphoid structures in hepatobiliary tumours and generated spatial risk scores that outperform conventional histopathology (16).

Given the rapid expansion of CyTOF and IMC applications (17), a comprehensive synthesis is timely. This scoping review collates original studies spanning various malignancies, summarises panel design trends, computational pipelines and key biological insights, and highlights emerging opportunities for biomarker discovery and therapeutic stratification in oncology.

2 Methods search strategy

A comprehensive literature search was conducted across three databases, PubMed/MEDLINE, EMBASE, and Science Direct, to capture relevant studies published from inception up to April 30, 2025.

The search strategy combined keywords related to CyTOF with terms related to cancer. In each database, we used Boolean operators to broaden or restrict the search as appropriate. For example, in PubMed the following keywords were used: (“CyTOF” OR “mass cytometry”) AND (“cancer” OR “tumour” OR “neoplasm”) AND (“biomarker” OR “signature” OR “immune profile”) AND (“prediction” OR “predictive” OR “prognostic” OR “diagnostic” OR “response”).

This strategy was adjusted to each database’s syntax. In EMBASE and Science Direct, we searched within article Title/Abstract/Keywords for terms. No language filter was applied initially, but we later limited inclusion to English-language results.

2.1 Eligibility criteria

Studies were eligible for inclusion if they:

• were original research articles (including experimental studies, observational studies, computational modelling studies) published in peer-reviewed sources;

• focused on a CyTOF/IMC application in the context of cancer;

• were published in English; and

• were published between inception and April 2025 (inclusive).

We excluded review articles, meta-analyses, pre-prints, editorials/commentaries, and conference abstracts without full papers from the main analysis, since our focus was on original data. Where eligibility was unclear, we reached consensus among reviewers by carefully assessing whether the study’s methods aligned with the topic.

2.2 Study selection

All references retrieved from the database searches were imported into a reference manager, and duplicates were removed. Title and abstract screening was performed on the unique records to identify obviously irrelevant papers. Two reviewers independently screened each title/abstract against the eligibility criteria. We retained any article that either reviewer marked as potentially relevant. Next, we obtained the full-text of all remaining articles and conducted a detailed evaluation for final inclusion. Disagreements on inclusion were resolved through discussion or consultation with a third reviewer. The selection process was documented following PRISMA guidelines.

The initial search across databases yielded a total of 148 records. Of them, 51 were excluded based on title/abstract screening. We assessed full-text articles for eligibility. 36 were excluded for reasons such as not being original research (e.g., review), not actually involving the topic of the research, or being commentary/case report. 61 studies met all criteria and were included in the qualitative synthesis and data extraction for this review (Figure 1). Supplementary Table S1 presents the characteristics of all the 61 analysed studies.

Figure 1

2.3 Risk of bias assessment

To assess the methodological quality and potential risk of bias of the included studies, we applied a structured framework adapted from established tools depending on study type. For observational studies (retrospective or prospective cohorts), we used a simplified version of the NIH Quality Assessment Tool (18), focusing on five core criteria: clearly defined population, justification of sample size, blinding of outcome assessors, validity of outcome measurements, and presence of external validation. For interventional studies without randomisation, we referenced ROBINS-I (Risk Of Bias in Non-Randomized Studies – of Interventions) (19) to evaluate confounding and selection bias. For studies involving diagnostic marker evaluation (e.g., spatial IMC biomarkers), we used selected domains from QUADAS-2 (20). Preclinical studies were noted as not applicable for formal bias scoring but were qualitatively appraised. Each study received a global bias rating (low, moderate, or high) based on the number of criteria met and methodological transparency. The full bias matrix with per-criterion assessments and assigned method is provided in Supplementary Table S2.

3 Results

3.1 Study characteristics

The 61 eligible papers span 17 malignant entity types and remain dominated by suspension CyTOF (46/61, 75%) (21–66), with IMC-only analyses (12/61, 20%) (67–78) and dual-platform studies (3/61, 5%) making up the remainder. Solid tumours account for 52 of 61 studies (85%) (79–81), while haematological malignancies (acute leukaemias and lymphomas) contribute 9 (15%). The most represented families were Skin/Melanoma (n=10), Lung/Thoracic (n=9), Leukaemia/Lymphoma (n=9), Liver/Hepatic (n=7), Bladder/Urothelial (n=7), and Colorectal/Colon/Rectal (n=6). Mid-frequency families included Breast (n=4) and Pancreatic (n=3), while Esophageal/Gastric (n=3) and Gynaecological (n=3) were also present. The remaining families, Head & Neck (n=2), General carcinoma/other (n=2), Mesothelioma (n=2), Renal/Kidney (n=2), Sarcoma (n=2), Brain/CNS (n=1), and Prostate (n=1), were less frequent. Counts are per family; multi-cancer studies contribute to multiple families (Supplementary Table S1).

3.2 CyTOF and IMC panel design and marker utilisation

Across the 61 eligible entries, we catalogued 46 CyTOF articles and 12 IMC articles, with 3 studies deploying both technologies. Although panel composition was highly study-specific, three clear trends emerged (Table 1).

3.2.1 Lineage-based CyTOF cores supplemented by context-specific functional markers

The median CyTOF panel contained 35 antibodies (std 9.6). CyTOF panels typically include broad immune lineages: 41% contain the full CD3/CD4/CD8/CD19/CD56/CD14 backbone, and 73% include ≥ 1 immune-checkpoint (PD-1/TIM-3/TIGIT/CTLA-4).

Functional markers were then tailored to the biological question:

• - DNA-damage sensors, for example p53 and γH2AX, were added to a glioblastoma drug-response assay to track chemo-induced stress (26).

• - For example, Transferrin receptor (TFRC) was incorporated to monitor iron metabolism in an anlotinib + PD-1 hepatocellular-carcinoma study (23).

• - For example, CD39 and CD103 were paired to discriminate Tex^stem from Tex^term CD8 subsets in malignant pleural effusions (54).

3.2.2 IMC panels combining tumour, immune and stromal features

IMC panels median 33.0 antibodies (std 8.0) and usually blended immune-lineage markers with stromal or extracellular-matrix components (e.g., α-SMA, collagen-I, FAP), enabling simultaneous cellular and spatial read-outs.

• - For example, an oesophageal-squamous-cell-carcinoma study deployed a 25-marker stromal-centric panel to map CAF–TAM co-localisation (68).

• - For example, the NeoTRIP trial in triple-negative breast cancer used a 43-marker pan-immune/tumour panel that included CD15 to highlight a chemoresistant tumour subset (67).

3.3 Marker reuse and cross-study convergence

25 antibodies appeared in more than 70% of 17 types of cancers, more than 35% of panels and both in the two technics (CyTOF and IMC) among the 61 studies (Table 2). Figure 2 shows the frequency distribution of the 25 core immune markers grouped by biological class (lineage, checkpoint, Treg, myeloid, proliferation, etc.). This treemap highlights the dominance of T-cell and checkpoint markers across panels, while also illustrating the recurrent inclusion of myeloid, B-cell, NK and APC/MHC-II markers.

Figure 2

Across the 61 studies analysed, we observed a convergent panel of immune markers that consistently map onto five major metabolic axes of the tumour microenvironment:

• - Glycolysis (Warburg metabolism),

• - Oxidative phosphorylation (OXPHOS),

• - Fatty acid oxidation (FAO)/lipid metabolism,

• - Amino acid metabolism (arginine, glutamine, tryptophan, NAD⁺/adenosine),

• - Nucleotide biosynthesis.

These axes capture the central modes of energy and biosynthetic adaptation that govern immune cell function, plasticity, and exhaustion in cancer. Glycolysis represents the classical “Warburg” program, fuelling rapid glucose uptake and lactate production to sustain proliferating and cytotoxic effector T cells as well as NK cell activity (82, 83). Oxidative phosphorylation (OXPHOS) reflects mitochondrial respiration via the tricarboxylic acid cycle, which is critical for memory T cells and long-lived plasma cells, ensuring durable immunity (84). Fatty acid oxidation (FAO) and broader lipid metabolism dominate in regulatory T cells and M2-like macrophages, linking β-oxidation to immune suppression and tissue repair (85). Amino acid metabolism, including arginine, glutamine, and tryptophan pathways, regulates effector responses and checkpoint-associated exhaustion, with enzymes such as Arg1 and IDO shaping the immune landscape through nutrient depletion and metabolite production (86). Finally, nucleotide biosynthesis underpins cell cycle progression, antibody production, and clonal expansion, as proliferating Ki-67⁺ lymphocytes rely on enhanced purine and pyrimidine synthesis (87, 88).

Figure 3 further illustrates the prevalence of these 25 core immune markers across cancer families. While T-cell lineage and checkpoint markers were universally represented, myeloid and stromal components showed more heterogeneous inclusion, highlighting disease-specific tailoring of CyTOF/IMC panels.

Figure 3

3.4 Tumour and immune cell populations identified

CyTOF and IMC profiling resolved five recurrent cellular modules:

• CD8/CD4 T-cell differentiation and exhaustion states,

• B-cell and tertiary-lymphoid-structure programmes,

• innate-lymphoid and NK-cell subsets,

• myeloid-lineage heterogeneity, and

• tumour–stromal phenotypes (Table 3).

The most frequently highlighted phenotype was the exhausted PD-1^hi TIM-3⁺ CD39⁺ CD8⁺ T-cell subset, detected in 9 of the 61 studies (14%) and typically enriched among non-responders, e.g. in peripheral blood of advanced NSCLC patients (47) and in cases of immune-checkpoint-inhibitor pneumonitis (59). Conversely, tissue Abbreviations: APC, A-resident memory CD8 T cells (CD103⁺ CD69⁺) and stem-like Tex^stem cells (PD-1^int CD39⁻ CD28⁺) tracked durable benefit, as shown in endometrial cancer treated with cabozantinib + nivolumab (39) and in malignant pleural effusions from thoracic tumours (54).

Myeloid diversity was equally striking. Urothelial-carcinoma specimens contained CD38⁺ tumour-associated macrophages (TAMs) with stronger immunosuppressive signatures than canonical PD-L1⁺ TAMs (49), whereas diffuse large B-cell lymphoma blood samples showed expansion of prognostically adverse S100A9^high non-classical monocytes (56).

Mapping of the innate-lymphoid compartment uncovered disease-specific NK-cell states: CD56^bright CD69⁺ activated NK cells were enriched in high-risk acute lymphoblastic leukaemia (48), while a conserved CTLA-4⁺ NK signature predicted ipilimumab responsiveness across mouse and human melanomas (52).

Finally, several studies focused on tumour-intrinsic and stromal programmes. In lung adenocarcinoma, a NOTCH3-driven network of FAP⁺ cancer-associated fibroblasts and ACTA2⁺ pericytes delineated perivascular immune-exclusion zones (35). Bladder tumours contained IGF2BP3^high ALDH⁺ cancer stem-like cells that shaped responsiveness to anti-PD-1 therapy (80), while prostate tumours harboured rare CD15⁺ epithelial subclones linked to high-grade pathology (29).

3.5 Bioinformatics tools and algorithms utilised

Analytical workflows varied substantially across the 61 studies, yet several core toolsets recurred consistently (Table 4). Dimensionality-reduction and clustering constituted the entry point for virtually every CyTOF or IMC dataset. Across the corpus, viSNE/t-SNE projections and UMAP embeddings were the most frequently adopted two-dimensional visualisations, while FlowSOM self-organising maps and PhenoGraph community detection dominated unsupervised clustering. These methods enabled, for example, construction of an immune atlas of urothelial carcinoma and classification of B-cell subtypes in lung adenocarcinoma.

Supervised differential-abundance frameworks were less common but crucial when outcome-associated sub-populations were sought. CITRUS facilitated feature selection in metastatic pancreatic cancer, and CITRUS combined with elastic-net regularisation yielded blood-based predictive models in melanoma. Limma/edgeR-based diffcyt pipelines offered a parametric alternative.

Predictive modelling methods, a subset of machine-learning approaches, and cross-cohort transfer-learning are rapidly gaining traction. The random-forest-based hDirect-MAP algorithm successfully transferred T-cell phenotypes across five independent skin-cancer cohorts. CoGAPS factorisation coupled with projectR enabled cross-species learning between murine and human checkpoint-inhibitor studies.

Spatial IMC datasets exploited specialised graph analytics. Graph-based community detection informed a cabozantinib + nivolumab hepatocellular-carcinoma study, whereas nearest-neighbour distance scoring delineated stromal niches in oesophageal squamous-cell carcinoma. One study even integrated IMC with Visium spatial transcriptomics in an agent-based spQSP simulation to perform in silico virtual trials.

Panel-specific utilities also appeared. CyTOFmerge harmonised datasets with non-overlapping marker panels, and an automated batch-correction engine (e.g., Harmony or Seurat Integration) was reported in 29 studies.

A call for a unified analytical framework: The wide dispersion of tools, several studies relying on t-SNE while 31 omit any advanced batch integration, underscores a fragmentation that complicates cross-study synthesis. It argues for a unifying pipeline grounded in geometric scattering, entropic optimal-transport distances, and semi-supervised graph learning. Because the scattering representation is provably stable to affine batch perturbations and OT provides a principled geometry for whole-population comparison, this framework could replace the current patchwork of visualisation-first heuristics with an end-to-end, mathematically rigorous alternative. Adopting such a cohesive approach would directly address the two most frequent failure modes revealed by the audit, uncorrected batch effects and distortion-prone embeddings, while offering a common feature space that enables meta-analysis across future studies.

3.6 Spatial analysis by imaging mass cytometry

15 of the 61 unique studies (25%) generated IMC data, and many carried out explicit spatial statistics beyond single-cell quantification (Table 5).

Most groups applied a similar three-step workflow:

• Sub-cellular segmentation of 18- to 46-plex images (median 33);

• Phenotype assignment from multiplex marker intensities;

• Distance- or graph-based calculations to capture cell–cell interactions and tissue organisation.

15 of the 61 studies generated IMC data, and many went beyond simple single-cell counts to apply explicit spatial statistics. Most groups followed the same three-step workflow: first, they segmented 25- to 43-plex images into single-cell masks; next, they assigned phenotypes from multiplexed marker intensities; and finally, they calculated distance- or graph-based metrics to quantify cell–cell interactions and tissue organisation.

Nearest-neighbour and perimeter metrics were reported in seven studies. In the NeoTRIP trial of triple-negative breast cancer, the average distance between proliferating CD8 TCF1-positive T cells and MHC-II–positive tumour cells predicted pathological complete response (67). In oesophageal squamous-cell carcinoma, a perimeter score that combined α-SMA–positive cancer-associated fibroblasts with CD163-positive macrophages stratified overall survival (68).

The majority of the studies modelled tissues as spatial graphs. For example, in hepatocellular-carcinoma biopsies from a cabozantinib plus nivolumab trial, modularity-based community detection separated “immune-inflamed” from “immune-excluded” regions; enrichment of a CD8-negative, Arg1-high macrophage module was linked to treatment resistance (69).

Two investigations performed whole-section archetype or niche mapping. A melanoma study defined six tumour-microenvironment archetypes ranging from myeloid-dense “immune-cold” to lymphoid-rich “immune-hot” niches, with the latter correlating with durable PD-1 benefit (76). In pancreatic ductal adenocarcinoma, co-registration of adenosine mass-spectrometry imaging with IMC revealed hypoxic extracellular-adenosine niches co-localised with Arg1-high macrophages and regulatory T cells (78).

Finally, several studies focused on tertiary lymphoid structures or immune micro-aggregates. In combined hepatocellular–cholangiocarcinoma, an intra-tumour TLS score derived from a 32-marker panel was associated with lower regulatory-T-cell density and improved overall survival (71). In HPV-positive oropharyngeal squamous-cell carcinoma, CXCL13-driven aggregates of CD8, CD4 and dendritic cells defined an “immune-reactive” subtype with favourable ten-year survival (77).

Beyond individual studies, we synthesised the spatial findings into three conserved archetypes, myeloid/TAM barriers, TLS maturity, and CD8 distribution, captured across multiple cancers and platforms.

3.7 Clinical impact and translational integration of CyTOF/IMC signatures

While CyTOF and IMC offer high-dimensional resolution of the tumour–immune landscape, their ultimate value lies in shaping clinical decision-making. Several studies have begun to demonstrate how specific cellular or spatial signatures derived from these platforms inform patient stratification, treatment selection, and response monitoring (Table 6).

Expanding on these translational highlights, a recurrent theme is the bifurcation between progenitor and terminally exhausted CD8⁺ T cells. Progenitor Texstem subsets (TCF1⁺, PD-1 ^int, CD39⁻) consistently associate with durable ICI benefit across melanoma, NSCLC, and breast cancer, while Texterm (PD-1^hi TIM-3⁺ CD39⁺) predict therapeutic failure. Similarly, myeloid suppressive circuits emerge as critical determinants of immune escape. CD38⁺ and Arg1⁺ TAMs create metabolic and spatial barriers, observed in HCC, PDAC, and urothelial carcinoma, often correlating with non-response to ICI. These findings support ongoing trials of anti-CD38 antibodies and arginase inhibitors in solid tumours. Spatial biomarkers derived from IMC further underscore the importance of tissue architecture. TLS maturity and CAF–TAM exclusion perimeters are not only descriptive, but provide quantifiable, reproducible metrics that stratify patient prognosis and response. Collectively, these insights indicate that CyTOF/IMC biomarkers do not merely catalogue immune states but increasingly guide patient selection, risk stratification, and rational design of combination therapies.

3.7.1 Stratification in immunotherapy trials

In melanoma and NSCLC, CyTOF-derived immune signatures have been used to predict differential response to anti-PD-1 or anti-CTLA-4 therapies. For instance, the frequency of stem-like PD-1 ^int CD39⁻ CD28⁺ CD8⁺ T cells has emerged as a predictor of durable response to checkpoint blockade in both tumour tissue and effusion samples (53, 54, 67). Similarly, exhausted PD-1^hi TIM-3⁺ CD8⁺ T cells are enriched in non-responders and have been used to define immunotherapy-resistant profiles (53, 57).

3.7.2 Spatial risk scores improving histopathology

In oesophageal squamous cell carcinoma, an “CAF–macrophage perimeter score” stratified overall survival independently of tumour stage (68), while in Hodgkin lymphoma, a composite spatial score based on CXCL13⁺ macrophage–CXCR5⁺ tumour cell pairing predicted post-transplant relapse with higher precision than any single marker (74).

3.7.3 Guiding combinatorial treatments

In hepatocellular carcinoma, CyTOF–IMC integration was used to identify Arg1^hi macrophage-dense niches resistant to cabozantinib plus nivolumab, prompting spatial simulation (spQSP) of alternative regimens (70).

3.8 Emergent immunobiological motifs and hypothesis-generating convergence

Beyond cataloguing marker panels and cellular phenotypes, our synthesis identified several cross-study biological motifs that recur across distinct tumour types, suggesting deeper, conserved mechanisms of immune–tumour interaction. These motifs, summarised in Table 7, represent not just descriptive findings but potential hypotheses for future functional validation and clinical translation.

The Tex^stem-to-Tex^term bifurcation, captured in CD8⁺ T cells via CyTOF and IMC in melanoma, NSCLC, and colorectal cancer, emerged as a pan-cancer axis of T-cell exhaustion. Several studies (53, 59, 67) demonstrated that PD-1^int CD39⁻ CD28⁺ Tex^stem CD8⁺ cells were enriched in responders to immune checkpoint inhibitors (ICI), whereas PD-1^hi TIM-3⁺ CD39⁺ Tex^term cells predominated in non-responders.

A myeloid-barrier archetype characterised by Arg1^hi and CD38⁺ tumour-associated macrophages (TAMs), often co-localised with α-SMA⁺ CAFs, was described in urothelial, pancreatic, and hepatocellular carcinomas (68, 72, 80). These immunosuppressive cell clusters were spatially associated with T-cell exclusion zones and ICI resistance, suggesting potential utility of CD38/Arg1 as combinatorial therapeutic targets.

Tertiary lymphoid structures (TLSs) with mature CXCL13⁺CD21⁺BCL6⁺ cell aggregates were associated with favourable prognosis in HCC (69), HPV-positive oropharyngeal cancer (37), and Hodgkin lymphoma (41). TLS maturity scores derived from IMC were predictive of ICI benefit in some of these settings.

Taken together, these motifs underscore that CyTOF and IMC are not merely descriptive but hypothesis-generating technologies, revealing recurrent immune organisational structures that may inform biomarker development, patient stratification, and mechanistically guided therapeutic design.

4 Discussion

4.1 Mechanistic perspectives: immunometabolism, T-Cell plasticity, and cytokine dysregulation

Beyond descriptive phenotyping, high-dimensional cytometry offers a powerful window into the mechanistic underpinnings of tumour–immune interactions. Three emerging axes, immunometabolism, T-cell plasticity, and cytokine network dynamics, deserve greater integration into single-cell analyses to support functional interpretation and therapeutic targeting.

4.2 Immunometabolism and suppressive niches

Several CyTOF and IMC studies have uncovered metabolically defined immunosuppressive niches, particularly in pancreatic ductal adenocarcinoma and bladder cancer, where extracellular adenosine (eAdo) accumulates in hypoxic regions enriched with Arg1^hi macrophages and FOXP3⁺ regulatory T cells (62, 78). Other panels incorporating transferrin receptor (TFRC), CD71, and enzymes of the adenosine pathway (CD39/CD73) have demonstrated that metabolic reprogramming shapes T-cell exhaustion and myeloid immunosuppression (23, 80, 89). These findings underscore the utility of integrating metabolic markers into CyTOF workflows to map immunometabolic circuits and identify actionable targets such as CD73, IDO1, or MCT1.

4.3 T-cell plasticity and lineage bifurcation

Recent high-dimensional profiling has illuminated dynamic transitions between progenitor-like (Tex^stem) and terminally exhausted (Tex^term) CD8⁺ T-cell states, often coexisting within the same tumour (53, 54, 67). Moreover, cytokine-rich environments can drive unconventional phenotypes, such as CD4⁺ cytotoxic T lymphocytes or hybrid Treg–Th17 cells, which are now detectable via CyTOF when panels include granzyme/perforin and lineage transcription factors (e.g., FOXP3, RORγt, T-bet) (90). These data reinforce the concept that T-cell states represent a continuum modulated by antigenic stimulation, epigenetic imprinting, and cytokine exposure.

4.4 Cytokine and chemokine dysregulation

While surface phenotyping remains dominant, select studies have incorporated intracellular cytokine readouts (e.g., IL-10, TNF-α, IFN-γ), revealing differential cytokine bursts associated with response or resistance to immunotherapy (47, 59). Such profiles can be used to stratify immune “inflamed” versus “exhausted” microenvironments and may predict irAE risk. The integration of cytokine data into supervised learning models has already yielded predictive signatures in melanoma and NSCLC cohorts (53, 91).

Incorporating these mechanistic layers, metabolic profiling, plasticity trajectories, and cytokine output, into future CyTOF/IMC studies will deepen biological insight and enhance biomarker robustness across cancer types.

4.5 Clinical relevance and use in trial stratification

Although the review outlines the diversity of CyTOF and IMC applications, it is important to further emphasise their tangible impact on clinical practice and translational oncology. Several studies have gone beyond descriptive profiling and leveraged CyTOF- or IMC-derived signatures to guide patient stratification, therapeutic choices, or response monitoring in clinical trials.

In particular, CyTOF-defined T-cell states, such as progenitor exhausted (Tex^stem) CD8⁺ subsets, have been associated with durable responses to immune checkpoint blockade, influencing eligibility or stratification in melanoma, NSCLC, and colorectal cancer trials (53, 67, 70). Conversely, the enrichment of terminally exhausted PD-1^hi TIM-3⁺ CD8⁺ T cells in non-responders has helped identify patients less likely to benefit from anti-PD-1 monotherapy (53, 57).

In the spatial context, IMC-derived metrics such as the α-SMA⁺CAF–CD163⁺ macrophage perimeter score in oesophageal squamous cell carcinoma (68) and CXCR5⁺–CXCL13⁺ spatial pairings in Hodgkin lymphoma (74) have stratified survival risk independently of classical histopathological parameters. These spatial biomarkers are now being considered as decision tools for tailoring follow-up or selecting intensified regimens.

Additionally, longitudinal CyTOF profiling has supported early prediction of treatment efficacy in vaccine-based immunotherapy trials, as seen in metastatic pancreatic cancer (45), and has informed in silico modelling of drug combinations based on spatial resistance niches in hepatocellular carcinoma (70).

4.6 Conserved archetypes of tumour–immune spatial organization

Our synthesis reveals that certain immune organizational structures, previously reported as context-specific, actually recur across multiple tumour types and analytical platforms. For instance, CD38⁺/Arg1⁺ myeloid barriers, initially described as immunosuppressive niches in pancreatic cancer and hepatocellular carcinoma, now emerge as recurrent spatial motifs that may impede CD8⁺ T-cell infiltration (92, 93). Similarly, tertiary lymphoid structures (TLS) with high maturity, marked by CXCL13⁺ dendritic cells and germinal-center-like B-cell organization, appeared in independent studies across HCC, Hodgkin lymphoma, and HPV-positive head and neck cancer, consistently correlating with enhanced immunotherapy response.

Likewise, on the functional axis of CD8⁺ T-cell exhaustion, a bifurcation between stem−like (Tex^stem/Tpex) and terminally exhausted (Tex^term) states has emerged as a pan-cancer conserved phenomenon. Stem-like TCF−1⁺PD−1⁺ progenitor exhausted (Tpex) or Tex^stem cells are enriched in responders to checkpoint blockade and localize within TLS or tumour-draining lymph nodes (94–96). In contrast, PD−1^hi TIM−3⁺ CD39⁺ Tex^term cells, found in multiple tumour contexts, are associated with poor response and therapeutic resistance (94, 95, 97, 98). Taken together, our spatial meta-analysis delineates three conserved archetypes of tumour–immune organization, myeloid exclusionary barriers, TLS maturity, and CD8 T-cell bifurcation, that recur across cancer types and platforms. Their repeated observation suggests that they represent pan-cancer axes of immune–tumour interaction, whose shared regulatory mechanisms (e.g., metabolic constraints, stromal interactions, epigenetic programming) merit further investigation as potential universal biomarkers and therapeutic targets.

4.7 Methodological limitations and biases

Despite the richness of data extracted from CyTOF and IMC studies, several methodological limitations must be acknowledged when interpreting their findings collectively. A substantial proportion of the included studies were conducted in small cohorts, often ≤30 patients, limiting statistical power and the generalisability of identified immune signatures. For example, many exploratory studies reported associations between rare T-cell subsets or spatial arrangements and clinical outcomes without independent validation cohorts (54, 68, 75).

Moreover, external validation remains the exception rather than the rule. Only a few studies employed prospective replication or applied discovered signatures across multiple datasets, reducing the robustness of proposed biomarkers (45, 61). The field also remains vulnerable to publication bias, as studies demonstrating predictive or prognostic significance are more likely to be published, potentially inflating perceived reproducibility.

Finally, heterogeneity in panel composition, staining protocols, and analytical pipelines further complicates cross-study comparison. While recent efforts toward standardised reference panels and batch-correction frameworks are promising (99, 100), broader adoption is needed to support multi-centre reproducibility and meta-analytic synthesis.

Most solid-tumour CyTOF studies applied standard enzymatic dissociation protocols combining collagenase IV and DNase I, occasionally supplemented by hyaluronidase, to generate single-cell suspensions from fresh tumour specimens. These approaches, while widely used and reproducible, may introduce biases by preferentially preserving lymphoid and myeloid subsets over more fragile populations such as granulocytes or stromal cells.

5 Conclusion

Our pan-cancer synthesis of CyTOF/IMC studies reveals a consistent convergence of immune markers onto five core metabolic axes of the tumour microenvironment: (i) glycolysis (Warburg-like fuelling of activation and cytotoxicity), (ii) oxidative phosphorylation (OXPHOS) (mitochondrial programs underpinning memory and cellular longevity), (iii) fatty-acid oxidation/lipid metabolism (FAO) (predominant in Tregs and tissue-repair macrophages), (iv) amino-acid metabolism (arginine/tryptophan/glutamine and NAD⁺/adenosine pathways shaping exhaustion and myeloid polarization), and (v) nucleotide biosynthesis (proliferative demand in Ki-67⁺ lymphocytes). This unified framework clarifies the bioenergetic dependencies behind observed immune phenotypes and points to shared therapeutic vulnerabilities. Beyond this metabolic map, three recurrent spatial–immunological motifs emerge (1): the CD8⁺ Tex bifurcation between a TCF1⁺ PD-1^int progenitor reservoir (Tex^stem) and a PD-1^hi TIM-3⁺ terminal state (Tex^term) that governs the durability of checkpoint responses (2); a CD38⁺/Arg1^hi myeloid barrier that couples arginine depletion with NAD⁺ consumption to enforce T-cell exclusion and therapy resistance; and (3) the TLS maturity axis (CXCL13⁺ aggregates) in which germinal-center–like, mature TLS function as intratumoural priming hubs and associate with superior prognosis and immunotherapy benefit. Together, these metabolic axes and spatial motifs provide a common language to design composite spatial–metabolic signatures, standardize measurement panels, and guide rational interventions (e.g., CD38/Arg1 targeting, TLS maturation, exhaustion reprogramming) to be prospectively validated in harmonized, multicentre cohorts.

References

• 1

  MalekiEHBahramiARMatinMM. Cancer cell cycle heterogeneity as a critical determinant of therapeutic resistance. Genes Dis. (2023) 11:189–204. doi: 10.1016/j.gendis.2022.11.025

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 2

  McGranahanNSwantonC. Clonal heterogeneity and tumour evolution: past, present, and the future. Cell. (2017) 168:613–28. doi: 10.1016/j.cell.2017.01.018

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 3

  de VisserKEJoyceJA. The evolving tumour microenvironment: From cancer initiation to metastatic outgrowth. Cancer Cell. (2023) 41:374–403. doi: 10.1016/j.ccell.2023.02.016

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 4

  GohilSHIorgulescuJBBraunDAKeskinDBLivakKJ. Applying high-dimensional single-cell technologies to the analysis of cancer immunotherapy. Nat Rev Clin Oncol. (2021) 18:244–56. doi: 10.1038/s41571-020-00449-x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 5

  SpitzerMHNolanGP. Mass cytometry: single cells, many features. Cell. (2016) 165:780–91. doi: 10.1016/j.cell.2016.04.019

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 6

  GonzalezVDHuangY-WFantlWJ. Mass cytometry for the characterization of individual cell types in ovarian solid tumours. Methods Mol Biol Clifton NJ. (2022) 2424:59–94. doi: 10.1007/978-1-0716-1956-8_4

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 7

  AtkuriKRStevensJCNeubertH. Mass cytometry: a highly multiplexed single-cell technology for advancing drug development. Drug Metab Dispos Biol Fate Chem. (2015) 43:227–33. doi: 10.1124/dmd.114.060798

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 8

  BanduraDRBaranovVIOrnatskyOIAntonovAKinachRLouXet al. Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry. Anal Chem. (2009) 81:6813–22. doi: 10.1021/ac901049w

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 9

  TannerSDBaranovVIOrnatskyOIBanduraDRGeorgeTC. An introduction to mass cytometry: fundamentals and applications. Cancer Immunol Immunother CII. (2013) 62:955–65. doi: 10.1007/s00262-013-1416-8

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 10

  de Castro CGdel Hierro AGH-VázquezJCuesta-SanchoSBernardoD. State-of-the-art cytometry in the search of novel biomarkers in digestive cancers. Front Oncol. (2024) 14:1407580. doi: 10.3389/fonc.2024.1407580

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 11

  HuZTangASinghJBhattacharyaSButteAJ. A robust and interpretable end-to-end deep learning model for cytometry data. Proc Natl Acad Sci. (2020) 117:21373–80. doi: 10.1073/pnas.2003026117

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 12

  HuZBhattacharyaSButteAJ. Application of machine learning for cytometry data. Front Immunol. (2022) 12:787574. doi: 10.3389/fimmu.2021.787574

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 13

  FudaFChenMChenWCoxA. Artificial intelligence in clinical multiparameter flow cytometry and mass cytometry–key tools and progress. Semin Diagn Pathol. (2023) 40:120–8. doi: 10.1053/j.semdp.2023.02.004

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 14

  SchlechtABonevaSSalieHKillmerSWolfJHajduRIet al. Imaging mass cytometry for high-dimensional tissue profiling in the eye. BMC Ophthalmol. (2021) 21:338. doi: 10.1186/s12886-021-02099-8

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 15

  ChangQOrnatskyOISiddiquiILobodaABaranovVIHedleyDW. Imaging mass cytometry. Cytometry A. (2017) 91:160–9. doi: 10.1002/cyto.a.23053

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 16

  GiesenCWangHAOSchapiroDZivanovicNJacobsAHattendorfBet al. Highly multiplexed imaging of tumour tissues with subcellular resolution by mass cytometry. Nat Methods. (2014) 11:417–22. doi: 10.1038/nmeth.2869

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 17

  SeligerBMassaC. CyTOF as a suitable tool for stratification and monitoring of cancer patients. J Transl Med. (2025) 23:724. doi: 10.1186/s12967-025-06764-0

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 18

  MaL-LWangY-YYangZ-HHuangDWengHZengX-T. Methodological quality (risk of bias) assessment tools for primary and secondary medical studies: what are they and which is better? Mil Med Res. (2020) 7:7. doi: 10.1186/s40779-020-00238-8

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 19

  SterneJAHernánMAReevesBCSavovićJBerkmanNDViswanathanMet al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ. (2016) 355:i4919. doi: 10.1136/bmj.i4919

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 20

  WhitingPFRutjesAWSWestwoodMEMallettSDeeksJJReitsmaJBet al. QUADAS-2 Group. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. (2011) 155:529–36. doi: 10.7326/0003-4819-155-8-201110180-00009

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 21

  JiangXWangJWangMXuanMHanSLiCet al. ITGB4 as a novel serum diagnosis biomarker and potential therapeutic target for colorectal cancer. Cancer Med. (2021) 10:6823–34. doi: 10.1002/cam4.4216

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 22

  WangMHuangY-KKongJCSunYTantaloDGYeangHXAet al. High-dimensional analyses reveal a distinct role of T-cell subsets in the immune microenvironment of gastric cancer. Clin Transl Immunol. (2020) 9:e1127. doi: 10.1002/cti2.1127

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 23

  SongFHuBLiangX-LChengJ-WWangC-GWangP-Xet al. Anlotinib potentiates anti-PD1 immunotherapy via transferrin receptor-dependent CD8+ T-cell infiltration in hepatocellular carcinoma. Clin Transl Med. (2024) 14:e1738. doi: 10.1002/ctm2.1738

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 24

  NissenMDKusakabeMWangXSimkinGGraciasDTyshchenkoKet al. Single cell phenotypic profiling of 27 DLBCL cases reveals marked intertumoural and intratumoural heterogeneity. Cytom Part J Int Soc Anal Cytol. (2020) 97:620–9. doi: 10.1002/cyto.a.23919

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 25

  FigueiredoCRKaliraiHSaccoJJAzevedoRADuckworthASlupskyJRet al. Loss of BAP1 expression is associated with an immunosuppressive microenvironment in uveal melanoma, with implications for immunotherapy development. J Pathol. (2020) 250:420–39. doi: 10.1002/path.5384

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 26

  PanovskaDNazariPColeBCreemersP-JDerweduweMSolieLet al. Single-cell molecular profiling using ex vivo functional readouts fuels precision oncology in glioblastoma. Cell Mol Life Sci CMLS. (2023) 80:147. doi: 10.1007/s00018-023-04772-1

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 27

  QingMZhouTPerovaTAbrahamYSweeneyCKrevvataMet al. Immune profiling of patients with extranodal natural killer/T cell lymphoma treated with daratumumab. Ann Hematol. (2024) 103:1989–2001. doi: 10.1007/s00277-023-05603-w

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 28

  GideTNQuekCMenziesAMTaskerATShangPHolstJet al. Distinct immune cell populations define response to anti-PD-1 monotherapy and anti-PD-1/anti-CTLA-4 combined therapy. Cancer Cell. (2019) 35:238–255.e6. doi: 10.1016/j.ccell.2019.01.003

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 29

  De Vargas RoditiLJacobsARueschoffJHBankheadPChevrierSJacksonHWet al. Single-cell proteomics defines the cellular heterogeneity of localized prostate cancer. Cell Rep Med. (2022) 3:100604. doi: 10.1016/j.xcrm.2022.100604

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 30

  MerASHeathEMMadani TonekaboniSADogan-ArtunNNairSKMurisonAet al. Biological and therapeutic implications of a unique subtype of NPM1 mutated AML. Nat Commun. (2021) 12:1054. doi: 10.1038/s41467-021-21233-0

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 31

  ChauhanSKDunnCAndresenNKRøssevoldAHSkorstadGSikeAet al. Peripheral immune cells in metastatic breast cancer patients display a systemic immunosuppressed signature consistent with chronic inflammation. NPJ Breast Cancer. (2024) 10:30. doi: 10.1038/s41523-024-00638-2

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 32

  SunWQiuFZhengJFangLQuJZhangSet al. CD57-positive CD8 + T cells define the response to anti-programmed cell death protein-1 immunotherapy in patients with advanced non-small cell lung cancer. NPJ Precis Oncol. (2024) 8:25. doi: 10.1038/s41698-024-00513-0

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 33

  GoldbergLHaasERVyasVUrakRFormanSJWangX. Single-cell analysis by mass cytometry reveals CD19 CAR T cell spatiotemporal plasticity in patients. Oncoimmunology. (2022) 11:2040772. doi: 10.1080/2162402X.2022.2040772

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 34

  LuYXueGZhengNHanKYangWWangR-Set al. hDirect-MAP: projection-free single-cell modeling of response to checkpoint immunotherapy. Brief Bioinform. (2022) 23:bbab575. doi: 10.1093/bib/bbab575

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 35

  Raju PaulSValievIKorekSEZyrinVShamsutdinovaDGancharovaOet al. B cell-dependent subtypes and treatment-based immune correlates to survival in stage 3 and 4 lung adenocarcinomas. FASEB BioAdv. (2023) 5:156–70. doi: 10.1096/fba.2023-00009

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 36

  SmelserWWWangJOgdenKMChangSSKirschnerAN. Intravesical oncolytic virotherapy and immunotherapy for non-muscle-invasive bladder cancer mouse model. BJU Int. (2023) 132:298–306. doi: 10.1111/bju.16012

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 37

  RodriguezEZwartESAffandiAAVerhoeffJde KokMBoydLNCet al. In-depth immune profiling of peripheral blood mononuclear cells in patients with pancreatic ductal adenocarcinoma reveals discriminative immune subpopulations. Cancer Sci. (2024) 115:2170–83. doi: 10.1111/cas.16147

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 38

  ZhouLZengZEgloffAMZhangFGuoFCampbellKMet al. Checkpoint blockade-induced CD8+ T cell differentiation in head and neck cancer responders. J Immunother Cancer. (2022) 10:e004034. doi: 10.1136/jitc-2021-004034

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 39

  LheureuxSMateiDEKonstantinopoulosPAWangBXGadallaRBlockMSet al. Translational randomized phase II trial of cabozantinib in combination with nivolumab in advanced, recurrent, or metastatic endometrial cancer. J Immunother Cancer. (2022) 10:e004233. doi: 10.1136/jitc-2021-004233

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 40

  KriegCWeberLMFossoBMarzanoMHardimanGOlcinaMMet al. Complement downregulation promotes an inflammatory signature that renders colorectal cancer susceptible to immunotherapy. J Immunother Cancer. (2022) 10:e004717. doi: 10.1136/jitc-2022-004717

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 41

  FehlingsMKimLGuanXYuenKTafazzolASanjabiSet al. Single-cell analysis reveals clonally expanded tumour-associated CD57+ CD8 T cells are enriched in the periphery of patients with metastatic urothelial cancer responding to PD-L1 blockade. J Immunother Cancer. (2022) 10:e004759. doi: 10.1136/jitc-2022-004759

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 42

  EdwardsJTaskerAPires da SilvaIQuekCBattenMFergusonAet al. Prevalence and cellular distribution of novel immune checkpoint targets across longitudinal specimens in treatment-naïve melanoma patients: implications for clinical trials. Clin Cancer Res. (2019) 25:3247–58. doi: 10.1158/1078-0432.CCR-18-4011

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 43

  LiBLiGYanXZhuDLinPPWangZet al. Fresh tissue multi-omics profiling reveals immune classification and suggests immunotherapy candidates for conventional chondrosarcoma. Clin Cancer Res. (2021) 27:6543–58. doi: 10.1158/1078-0432.CCR-21-1893

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 44

  OkumaHSWatanabeKTsuchihashiKMachidaRSadachiRHirakawaAet al. Phase II trial of nivolumab in metastatic rare cancer with dMMR or MSI-H and relation with immune phenotypic analysis (the ROCK trial). Clin Cancer Res. (2023) 29:5079–86. doi: 10.1158/1078-0432.CCR-23-1807

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 45

  NairNChenS-YLemmensEChangSLeDTJaffeeEMet al. Single-cell immune competency signatures associate with survival in phase II GVAX and CRS-207 randomized studies in patients with metastatic pancreatic cancer. Cancer Immunol Res. (2020) 8:609–17. doi: 10.1158/2326-6066.CIR-19-0650

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 46

  HoWJYarchoanMCharmsazSMundayRMDanilovaLSzteinMBet al. Multipanel mass cytometry reveals anti-PD-1 therapy-mediated B and T cell compartment remodeling in tumour-draining lymph nodes. JCI Insight. (2020) 5:e132286. doi: 10.1172/jci.insight.132286

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 47

  LuoY-HShenC-IChiangC-LChenY-M. Immune signatures of patients with advanced non-small-cell lung cancer for efficacy prediction after immunotherapy. Ther Adv Med Oncol. (2024) 16:17588359241284946. doi: 10.1177/17588359241284946

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 48

  DuaultCKumarATaghi KhaniALeeSJYangLHuangMet al. Activated natural killer cells predict poor clinical prognosis in high-risk B- and T-cell acute lymphoblastic leukaemia. Blood. (2021) 138:1465–80. doi: 10.1182/blood.2020009871

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 49

  ZhangQZhangWLinTLuWHeXDingYet al. Mass cytometry reveals immune atlas of urothelial carcinoma. BMC Cancer. (2022) 22:677. doi: 10.1186/s12885-022-09788-7

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 50

  DongYWangZXieG-FLiCZuoW-WMengGet al. Pregnane X receptor is associated with unfavorable survival and induces chemotherapeutic resistance by transcriptional activating multidrug resistance-related protein 3 in colorectal cancer. Mol Cancer. (2017) 16:71. doi: 10.1186/s12943-017-0641-8

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 51

  LiuZLiXGaoYLiuJFengYLiuYet al. Epigenetic reprogramming of Runx3 reinforces CD8 + T-cell function and improves the clinical response to immunotherapy. Mol Cancer. (2023) 22:84. doi: 10.1186/s12943-023-01768-0

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 52

  Davis-MarcisakEFFitzgeraldAAKesslerMDDanilovaLJaffeeEMZaidiNet al. Transfer learning between preclinical models and human tumours identifies a conserved NK cell activation signature in anti-CTLA-4 responsive tumours. Genome Med. (2021) 13:129. doi: 10.1186/s13073-021-00944-5

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 53

  SubrahmanyamPBDongZGusenleitnerDGiobbie-HurderASevergniniMZhouJet al. Distinct predictive biomarker candidates for response to anti-CTLA-4 and anti-PD-1 immunotherapy in melanoma patients. J Immunother Cancer. (2018) 6:18. doi: 10.1186/s40425-018-0328-8

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 54

  YeLRyuHGranadierDNguyenLTSimoniYDickIet al. Stem-like exhausted CD8 T cells in pleural effusions predict improved survival in non-small cell lung cancer (NSCLC) and mesothelioma. Transl Lung Cancer Res. (2024) 13:2352–72. doi: 10.21037/tlcr-24-284

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 55

  FerrantJLhommeFLe GallouSIrishJMRousselM. Circulating myeloid regulatory cells: promising biomarkers in B-cell lymphomas. Front Immunol. (2020) 11:623993. doi: 10.3389/fimmu.2020.623993

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 56

  Le GallouSLhommeFIrishJMMingamAPangaultCMonvoisinCet al. Nonclassical monocytes are prone to migrate into tumour in diffuse large B-cell lymphoma. Front Immunol. (2021) 12:755623. doi: 10.3389/fimmu.2021.755623

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 57

  LuoYZongYHuaHGongMPengQLiCet al. Immune-infiltrating signature-based classification reveals CD103+CD39+ T cells associate with colorectal cancer prognosis and response to immunotherapy. Front Immunol. (2022) 13:1011590. doi: 10.3389/fimmu.2022.1011590

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 58

  GovindarajanASalgiaNJLiHCastroDVMirzapoiazovaTArmstrongBet al. Characterization of papillary and clear cell renal cell carcinoma through imaging mass cytometry reveals distinct immunologic profiles. Front Immunol. (2023) 14:1182581. doi: 10.3389/fimmu.2023.1182581

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 59

  LuoY-HShenC-IChiangC-LHuangH-CChenY-M. Dynamic immune signatures of patients with advanced non-small-cell lung cancer for infection prediction after immunotherapy. Front Immunol. (2024) 15:1269253. doi: 10.3389/fimmu.2024.1269253

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 60

  Goshen-LagoTSzwarcwort-CohenMBenguiguiMAlmogRTurgemanIZaltzmanNet al. The potential role of immune alteration in the cancer-COVID19 equation-A prospective longitudinal study. Cancers. (2020) 12:2421. doi: 10.3390/cancers12092421

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 61

  ThomsenLCVKleinmannsKAnandanSGullaksenS-EAbdelaalTIversenGAet al. Combining mass cytometry data by CyTOFmerge reveals additional cell phenotypes in the heterogeneous ovarian cancer tumour microenvironment: A pilot study. Cancers. (2023) 15:5106. doi: 10.3390/cancers15205106

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 62

  LuQWangJTaoYZhongJZhangZFengCet al. Small cajal body-specific RNA12 promotes carcinogenesis through modulating extracellular matrix signaling in bladder cancer. Cancers. (2024) 16:483. doi: 10.3390/cancers16030483

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 63

  YoshidaYYoshioSYamazoeTMoriTTsustuiYKawaiHet al. Phenotypic characterization by single-cell mass cytometry of human intrahepatic and peripheral NK cells in patients with hepatocellular carcinoma. Cells. (2021) 10:1495. doi: 10.3390/cells10061495

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 64

  LeeH-SJangH-JChoiJMZhangJde RosenVLWheelerTMet al. Comprehensive immunoproteogenomic analyses of Malignant pleural mesothelioma. JCI Insight. (2018) 3:e98575. doi: 10.1172/jci.insight.98575

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 65

  PizzollaAKeamSPVergaraIACaramiaFThioNWangMet al. Tissue-resident memory T cells from a metastatic vaginal melanoma patient are tumour-responsive T cells and increase after anti-PD-1 treatment. J Immunother Cancer. (2022) 10:e004574. doi: 10.1136/jitc-2022-004574

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 66

  LuQGaoJTangSLiZWangXDengCet al. Integrated RNA sequencing and single-cell mass cytometry reveal a novel role of lncRNA HOXA-AS2 in tumourigenesis and stemness of hepatocellular carcinoma. OncoTargets Ther. (2020) 13:10901–16. doi: 10.2147/OTT.S272717

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 67

  WangXQDanenbergEHuangC-SEgleDCallariMBermejoBet al. Spatial predictors of immunotherapy response in triple-negative breast cancer. Nature. (2023) 621:868–76. doi: 10.1038/s41586-023-06498-3

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 68

  HeJ-ZChenYZengF-MHuangQ-FZhangH-FWangS-Het al. Spatial analysis of stromal signatures identifies invasive front carcinoma-associated fibroblasts as suppressors of anti-tumour immune response in esophageal cancer. J Exp Clin Cancer Res CR. (2023) 42:136. doi: 10.1186/s13046-023-02697-y

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 69

  MiHHoWJYarchoanMPopelAS. Multi-scale spatial analysis of the tumour microenvironment reveals features of cabozantinib and nivolumab efficacy in hepatocellular carcinoma. Front Immunol. (2022) 13:892250. doi: 10.3389/fimmu.2022.892250

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 70

  ZhangSDeshpandeAVermaBKWangHMiHYuanLet al. Integration of clinical trial spatial multiomics analysis and virtual clinical trials enables immunotherapy response prediction and biomarker discovery. Cancer Res. (2024) 84:2734–48. doi: 10.1158/0008-5472.CAN-24-0943

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 71

  GanXDongWYouWDingDYangYSunDet al. Spatial multimodal analysis revealed tertiary lymphoid structures as a risk stratification indicator in combined hepatocellular-cholangiocarcinoma. Cancer Lett. (2024) 581:216513. doi: 10.1016/j.canlet.2023.216513

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 72

  XiangHPanYSzeMAWlodarskaMLiLvan de MarkKAet al. Single-cell analysis identifies NOTCH3-mediated interactions between stromal cells that promote microenvironment remodeling and invasion in lung adenocarcinoma. Cancer Res. (2024) 84:1410–25. doi: 10.1158/0008-5472.CAN-23-1183

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 73

  RayfordAGärtnerFRamnefjellMPLorensJBMicklemDRAanerudMet al. AXL expression reflects tumour-immune cell dynamics impacting outcome in non-small cell lung cancer patients treated with immune checkpoint inhibitor monotherapy. Front Immunol. (2024) 15:1444007. doi: 10.3389/fimmu.2024.1444007

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 74

  AokiTJiangAXuAYinYGamboaAMilneKet al. Spatially resolved tumour microenvironment predicts treatment outcomes in relapsed/refractory hodgkin lymphoma. J Clin Oncol. (2024) 42:1077–87. doi: 10.1200/JCO.23.01115

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 75

  Martinez-MorillaSVillarroel-EspindolaFWongPFTokiMIAungTNPelekanouVet al. Biomarker discovery in patients with immunotherapy-treated melanoma with imaging mass cytometry. Clin Cancer Res. (2021) 27:1987–96. doi: 10.1158/1078-0432.CCR-20-3340

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 76

  XiaoXGuoQCuiCLinYZhangLDingXet al. Multiplexed imaging mass cytometry reveals distinct tumour-immune microenvironments linked to immunotherapy responses in melanoma. Commun Med. (2022) 2:131. doi: 10.1038/s43856-022-00197-2

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 77

  AbdulrahmanZSantegoetsSJSturmGCharoentongPIjsselsteijnMESomarakisAet al. Tumour-specific T cells support chemokine-driven spatial organization of intratumoural immune microaggregates needed for long survival. J Immunother Cancer. (2022) 10:e004346. doi: 10.1136/jitc-2021-004346

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 78

  GrazianoVDannhornAHulmeHWilliamsonKBuckleyHKarimSAet al. Defining the spatial distribution of extracellular adenosine revealed a myeloid-dependent immunosuppressive microenvironment in pancreatic ductal adenocarcinoma. J Immunother Cancer. (2023) 11:e006457. doi: 10.1136/jitc-2022-006457

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 79

  TaoYLiXZhangYHeLLuQWangYet al. TP53-related signature for predicting prognosis and tumour microenvironment characteristics in bladder cancer: A multi-omics study. Front Genet. (2022) 13:1057302. doi: 10.3389/fgene.2022.1057302

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 80

  WangYSongWFengCWuSQinZLiuTet al. Multi-omics analysis unveils the predictive value of IGF2BP3/SPHK1 signaling in cancer stem cells for prognosis and immunotherapeutic response in muscle-invasive bladder cancer. J Transl Med. (2024) 22:900. doi: 10.1186/s12967-024-05685-8

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 81

  BarschMSaliéHSchlaakAEZhangZHessMMayerLSet al. T-cell exhaustion and residency dynamics inform clinical outcomes in hepatocellular carcinoma. J Hepatol. (2022) 77:397–409. doi: 10.1016/j.jhep.2022.02.032

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 82

  LiuSLiaoSLiangLDengJZhouY. The relationship between CD4+ T cell glycolysis and their functions. Trends Endocrinol Metab TEM. (2023) 34:345–60. doi: 10.1016/j.tem.2023.03.006

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 83

  ValléeALecarpentierY. Crosstalk between peroxisome proliferator-activated receptor gamma and the canonical WNT/β-catenin pathway in chronic inflammation and oxidative stress during carcinogenesis. Front Immunol. (2018) 9:745. doi: 10.3389/fimmu.2018.00745

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 84

  YanYHuangLLiuYYiMChuQJiaoDet al. Metabolic profiles of regulatory T cells and their adaptations to the tumour microenvironment: implications for antitumour immunity. J Hematol OncolJ Hematol Oncol. (2022) 15:104. doi: 10.1186/s13045-022-01322-3

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 85

  ZhangSLvKLiuZZhaoRLiF. Fatty acid metabolism of immune cells: a new target of tumour immunotherapy. Cell Death Discov. (2024) 10:39. doi: 10.1038/s41420-024-01807-9

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 86

  BaiDZhouYJingLGuoCYangQ. Arginine metabolism in cancer biology and immunotherapy. Immune Netw. (2025) 25:e30. doi: 10.4110/in.2025.25.e30

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 87

  O’SullivanDPearceEL. Targeting T cell metabolism for therapy. Trends Immunol. (2015) 36:71–80. doi: 10.1016/j.it.2014.12.004

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 88

  SunXKaufmanPD. Ki-67: more than a proliferation marker. Chromosoma. (2018) 127:175–86. doi: 10.1007/s00412-018-0659-8

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 89

  NunesJBIjsselsteijnMEAbdelaalTUrsemRvan der PloegMGieraMet al. Integration of mass cytometry and mass spectrometry imaging for spatially resolved single-cell metabolic profiling. Nat Methods. (2024) 21:1796–800. doi: 10.1038/s41592-024-02392-6

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 90

  WlosikJGranjeaudSGorvelLOliveDChretienA-S. A beginner’s guide to supervised analysis for mass cytometry data in cancer biology. Cytom Part J Int Soc Anal Cytol. (2024) 105:853–69. doi: 10.1002/cyto.a.24901

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 91

  BollhagenABodenmillerB. Highly multiplexed tissue imaging in precision oncology and translational cancer research. Cancer Discov. (2024) 14:2071–88. doi: 10.1158/2159-8290.CD-23-1165

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 92

  FerkelSAMHolmanEASojwalRSRubinSJSRogallaS. Tumour-infiltrating immune cells in colorectal cancer. Neoplasia N Y N. (2024) 59:101091. doi: 10.1016/j.neo.2024.101091

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 93

  ZanderRCuiW. Exhausted CD8+ T cells face a developmental fork in the road. Trends Immunol. (2023) 44:276–86. doi: 10.1016/j.it.2023.02.006

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 94

  NiL. Potential mechanisms of cancer stem-like progenitor T-cell bio-behaviours. Clin Transl Med. (2024) 14:e1817. doi: 10.1002/ctm2.1817

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 95

  WangDFangJWenSLiQWangJYangLet al. A comprehensive profile of TCF1+ progenitor and TCF1– terminally exhausted PD-1+CD8+ T cells in head and neck squamous cell carcinoma: implications for prognosis and immunotherapy. Int J Oral Sci. (2022) 14:8. doi: 10.1038/s41368-022-00160-w

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 96

  ZehnDThimmeRLugliEde AlmeidaGPOxeniusA. ‘Stem-like’ precursors are the fount to sustain persistent CD8+ T cell responses. Nat Immunol. (2022) 23:836–47. doi: 10.1038/s41590-022-01219-w

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 97

  WenSLuHWangDGuoJDaiWWangZ. TCF-1 maintains CD8+ T cell stemness in tumour microenvironment. J Leukoc Biol. (2021) 110:585–90. doi: 10.1002/JLB.5MR1120-778R

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 98

  SeyhanDAllaireMFuYContiFWangXWGaoBet al. Immune microenvironment in hepatocellular carcinoma: from pathogenesis to immunotherapy. Cell Mol Immunol. (2025) 22:1132–58. doi: 10.1038/s41423-025-01308-4

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 99

  HartmannFJBabdorJGherardiniPFAmirE-ADJonesKSahafBet al. Comprehensive immune monitoring of clinical trials to advance human immunotherapy. Cell Rep. (2019) 28:819–831.e4. doi: 10.1016/j.celrep.2019.06.049

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 100

  CasanovaRXuSBostPSivapathamSJacobsAEnglerSet al. Standardization of suspension and imaging mass cytometry single-cell readouts for clinical decision making. Cytometry Part A. (2025) 107:390–403. doi: 10.1002/cyto.a.24940

  • Pubmed Abstract
  • CrossRef
  • Google Scholar