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Immuno-oncology is the study of immune responses in the context of cancer, with a focus on harnessing and modulating the immune system to recognize and eliminate malignant cells. Immuno-oncology strategies—including immune checkpoint blockade, cancer vaccines, and adoptive cell therapies—rely on complex and dynamic interactions between immune cells and the tumor. These interactions are highly heterogeneous across patients, tumor types, and treatment stages, creating a critical need for advanced analytical technologies, such as flow cytometry, that can resolve immune responses at single-cell resolution.
The tumor microenvironment (TME) is a highly complex ecosystem composed of tumor cells, infiltrating immune cells, stromal cells, and soluble mediators. The balance between immune activation and immune suppression within the TME is a major determinant of tumor progression and therapeutic response.
Fig. 1 Tumor microenvironment components.
Using antibody-based flow cytometry, immune cells within the TME can be precisely identified through lineage markers such as CD45, CD3, CD4, and CD8, enabling discrimination of tumor-infiltrating T cells, B cells, NK cells, and myeloid cell subsets. Beyond lineage definition, additional markers allow characterization of immune functional states, including activation, exhaustion, or immunosuppression. For example, quantitative analysis of PD-1 and CTLA-4 expression on CD8⁺ T cells directly reflects immune checkpoint engagement and provides insight into the immunosuppressive status of the TME, supporting both mechanistic studies and immunotherapy target assessment.
Key value
Immune checkpoints are regulatory pathways that modulate immune activation and maintain immune tolerance. In cancer, aberrant activation of checkpoint pathways such as PD-1/PD-L1 and CTLA-4 enables tumor cells to evade immune surveillance.
Flow cytometry allows quantitative, single-cell analysis of immune checkpoint molecules expressed on both tumor cells and immune cells. Using validated antibodies, markers such as PD-L1, CTLA-4, TIM-3, and other inhibitory receptors can be simultaneously assessed across defined cell subsets, providing detailed insight into immune regulatory networks within the tumor. Compared with bulk assays, flow cytometry offers superior resolution by distinguishing checkpoint expression patterns among specific immune populations, thereby supporting immunotherapy selection, biomarker discovery, and treatment stratification.
Key value
Minimal residual disease (MRD) refers to the small number of malignant cells that persist after treatment and are undetectable by conventional morphological methods, yet may lead to disease relapse, particularly in hematological malignancies.
Flow cytometry is a widely established approach for MRD detection due to its high sensitivity and quantitative capability. By employing multi-color antibody panels targeting markers such as CD19, CD20, CD5, and κ/λ light chains, rare residual tumor cells can be detected at sensitivities reaching 10-4. Flow cytometry–based MRD monitoring enables early assessment of treatment efficacy and informs therapeutic adjustment.
Key value
Immune cell function reflects the ability of immune cells to proliferate, secrete effector molecules, and induce tumor cell death, and is a key determinant of effective anti-tumor immunity.
Flow cytometry enables direct functional assessment of immune cells using antibody-based intracellular and surface staining. Cytokine production, such as IFN-γ and TNF-α, can be quantified at the single-cell level to evaluate T cell cytotoxic potential. In addition, cell proliferation and apoptosis provide important indicators of immune activation and treatment response. Ki-67 staining is commonly used to assess immune cell proliferation, while Annexin V/PI dual staining distinguishes early and late apoptotic stages, allowing comprehensive evaluation of immune and tumor cell responses to therapeutic interventions.
Key value
By integrating high-quality antibodies with flow cytometry, researchers can systematically analyze immune composition, immune regulation, residual disease, and immune function in cancer. These antibody-driven applications provide robust and clinically relevant data to support immuno-oncology research, antibody drug development, and therapeutic decision-making.
| Immune Cell Identity, Lineage, and Differentiation Markers | |||||
| Core Lineage Markers | |||||
| CD45 | CD3 | CD4 | CD8 | CD19 | CD20 |
| CD22 | CD79a | CD79b | CD56 | CD16 | NKp30 |
| NKp44 | NKp46 | CD11b | CD14 | CD15 | CD33 |
| CD64 | CD68 | CD11c | CD123 | ||
| Differentiation & Memory | |||||
| CD45RA | CD45RO | CCR7 | CD62L | CD27 | CD28 |
| CD57 | CD69 | CD103 | |||
| Immune Activation, Co-stimulation, and Effector Function Markers | |||||
| Activation & Early Response | |||||
| CD69 | CD25 | CD38 | HLA-DR | CD71 | |
| Co-stimulatory Molecules | |||||
| CD28 | ICOS | CD134 | CD137 | CD40 | CD27 |
| Effector and Cytotoxic Function | |||||
| GZMA | GZMB | GZMK | Perforin | CD107a | |
| Proliferation & Cell Cycle | |||||
| Ki-67 | BrdU | ||||
| Immune Checkpoint and Inhibitory Pathway Markers | |||||
| Canonical Immune Checkpoints | |||||
| PD-1 | CTLA-4 | TIM-3 | LAG-3 | TIGIT | PD-L1 |
| PD-L2 | CD80 | CD86 | |||
| Emerging / Co-inhibitory Receptors | |||||
| BTLA | VISTA | CD160 | CD244 | ||
| Immunosuppressive Cells and Tumor Immune Evasion Markers | |||||
| Regulatory T Cells (Tregs) | |||||
| CD4 | CD25 | FOXP3 | CD127 | CTLA-4 | TIGIT |
| Myeloid-Derived Suppressor Cells (MDSCs) | |||||
| CD11b | CD33 | CD14 | CD15 | ||
| Tumor-Associated Macrophages (TAMs) | |||||
| CD68 | CD163 | CD206 | |||
| Immunosuppressive Metabolism | |||||
| CD39 | CD73 | ||||
| Tumor-Associated Targets and Treatment Response Readouts | |||||
| Tumor Cell Identification | |||||
| EpCAM | EGFR | HER2 | |||
| Hematologic Malignancy & MRD | |||||
| CD19 | CD20 | CD5 | CD10 | CD22 | CD38 |
| Functional Cytokines (Intracellular FC) | |||||
| IFN-γ | TNF-α | IL-2 | IL-10 | TGF-β | |
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