Flow Cytometry in Cancer: From Tumor Profiling to Immunotherapy

One of the most important discoveries in cancer biology is that tumors are not biologically uniform.

Cancer develops through the accumulation of genetic and epigenetic alterations that disrupt normal cellular regulation. As malignant cells divide, additional changes may emerge, creating populations with different molecular characteristics and survival advantages.

This process, often described as clonal evolution, contributes to cancer progression, metastatic potential, and resistance to treatment.

For example, two populations within the same leukemia sample may express different levels of surface proteins, even though they originate from a related malignant lineage. Similarly, immune cells surrounding a solid tumor may exist in very different functional states.

Some T lymphocytes may be actively responding to tumor antigens, while others exhibit signs of chronic stimulation or impaired function.

Flow cytometry helps scientists investigate this diversity by identifying populations according to combinations of measurable characteristics.

Rather than treating a biological specimen as a homogeneous collection of cells, researchers can evaluate how its composition changes across experimental conditions or clinical time points.

Consider a hypothetical leukemia study in which a bone marrow sample contains several populations of immature cells. Some express CD34 and CD117, while others show altered expression of additional lineage-associated markers.

These differences may indicate developmental heterogeneity or phenotypically distinct populations that require further investigation.

However, differences in protein expression do not automatically establish the existence of genetically distinct cancer clones. Molecular studies are often required to determine whether the populations represent separate genetic lineages.

The key contribution of flow cytometry is therefore the ability to reveal phenotypic heterogeneity, which can then be connected to genetic, functional, and clinical findings.

This relationship between cellular identity and cancer behavior provides the foundation for many applications of cytometry in oncology.

Cancer cells frequently display changes in protein expression that reflect abnormal differentiation, disrupted signaling pathways, or altered cellular identity.

In hematological malignancies, these changes are particularly informative because blood-forming cells normally follow recognizable developmental pathways.

During hematopoiesis, immature precursor cells gradually acquire markers associated with specific cell lineages, including myeloid cells, B lymphocytes, and T lymphocytes.

Malignant transformation can interrupt this process.

As a result, leukemia or lymphoma cells may display unusual combinations of proteins, express markers at abnormal intensities, or retain characteristics associated with immature differentiation stages.

Immunophenotyping allows researchers and clinical laboratories to investigate these patterns.

Importantly, malignant cells are usually recognized through combinations of markers rather than a single cancer-specific protein.

For example, CD34 is frequently associated with immature hematopoietic cells, but normal hematopoietic progenitors can also express CD34. Its presence alone therefore does not establish malignancy.

Similarly, CD19 is a B-lineage marker expressed by normal B lymphocytes and many B-cell malignancies.

The interpretation becomes more informative when several markers are examined together and compared with expected normal differentiation patterns.

The biological significance of each marker depends on the disease, cellular context, expression intensity, and accompanying markers.

In solid tumors, immunophenotyping also supports research into tumor-cell characteristics, although tumor-associated antigens often lack complete specificity.

The ability to identify complex cellular phenotypes has made immunophenotyping an important bridge between fundamental cancer biology and clinical hematopathology.

Scientific figure 1: Leukemia immunophenotyping

View figures from Acute Myeloid Leukemia: Diagnosis and Evaluation by Flow Cytometry, Cancers, 2024.

The publication illustrates how combinations of antigen-expression patterns support the characterization of acute myeloid leukemia.

One of the most established clinical uses of flow cytometry is the investigation of hematological cancers, particularly leukemia and lymphoma.

These malignancies arise from cells involved in blood formation or immune function. Because their normal counterparts have relatively well-characterized immunophenotypes, deviations from expected maturation patterns can provide important diagnostic evidence.

In acute leukemia, the identification of lineage and differentiation is particularly important.

Acute myeloid leukemia (AML), for example, encompasses biologically diverse diseases characterized by abnormal proliferation and differentiation of myeloid precursor cells.

Flow cytometry helps characterize these populations, distinguish patterns of differentiation, and identify phenotypic features that may suggest particular disease subtypes.

However, contemporary AML classification also depends on morphology, cytogenetics, and molecular findings.

In acute lymphoblastic leukemia (ALL), immunophenotyping helps distinguish B-lineage from T-lineage disease and characterize the maturation stage of malignant lymphoblasts.

Flow cytometry also plays an important role in investigating mature lymphoid malignancies.

For example, chronic lymphocytic leukemia (CLL) commonly exhibits a characteristic B-cell immunophenotype involving CD19, CD5, and CD23, together with other relevant findings.

These examples are illustrative rather than complete diagnostic criteria.

The clinical value of flow cytometry comes from the interpretation of the full phenotype within the context of the sample and suspected malignancy.

In certain cases, immunophenotypic features may also suggest underlying genetic abnormalities, helping laboratories prioritize additional molecular investigations.

This demonstrates how cellular protein expression can provide information that extends beyond cell identification and contributes to disease classification.

Clinical perspective: A flow cytometry test may strongly support a leukemia or lymphoma diagnosis, but it should not be interpreted as a universal or independent test for all types of cancer.

Cancer progression is influenced not only by malignant cells but also by the surrounding biological environment.

The tumor microenvironment (TME) consists of immune cells, stromal cells, extracellular matrix components, blood vessels, and soluble signaling molecules that interact with cancer cells.

These interactions can influence tumor growth, immune surveillance, metastasis, and treatment response.

For example, cytotoxic CD8-positive T cells can recognize and eliminate certain malignant cells when appropriate antigen recognition and activation conditions are present.

However, tumors may develop mechanisms that interfere with effective immune responses.

Regulatory T cells, suppressive myeloid populations, and some tumor-associated macrophage states can contribute to an environment in which antitumor immunity becomes less effective.

Flow cytometry allows researchers to investigate the composition and functional characteristics of these populations.

An experiment examining tumor-associated immune cells may evaluate the relative abundance of CD4-positive and CD8-positive T cells, natural killer cells, B cells, and selected myeloid populations.

Functional markers can provide further information about cellular activation, proliferation, and inhibitory signaling.

One important research question is whether a tumor contains immune cells capable of responding to malignant cells or whether inhibitory conditions limit their activity.

For example, the expression of PD-1 on T cells may be investigated alongside activation markers, cytokine production, and other functional measurements.

PD-1 expression alone, however, does not establish that a cell is functionally exhausted.

The interpretation requires additional biological evidence.

An important limitation of conventional flow cytometry is the loss of spatial information during preparation of tissue into a cell suspension.

Researchers therefore combine cytometry with histology, imaging, or spatial molecular methods when the physical relationship between tumor and immune cells is important.

Scientific figure 2: Characterizing the tumor immune microenvironment

View Figure 1 and the associated scientific review, Computational Approaches for Characterizing the Tumor Immune Microenvironment.

The figure provides an overview of complementary methods for examining immune populations and the biological organization of tumors.

Uncontrolled cell proliferation is a defining characteristic of many cancers.

Normal cells progress through regulated stages of growth, DNA replication, and division. Cancer-associated mutations can disrupt the mechanisms controlling these processes, allowing abnormal cells to proliferate under conditions that would normally restrict cell division.

Flow cytometry helps researchers investigate several aspects of this behavior.

DNA-content measurements can be used to estimate the proportions of cells in different phases of the cell cycle, including G0/G1, S, and G2/M.

These observations can reveal changes in cellular proliferation following experimental treatment.

For example, a compound that interferes with DNA replication may increase the proportion of cells accumulating in a particular cell-cycle phase.

However, DNA content alone does not distinguish every cell-cycle state or establish the precise molecular mechanism responsible for an observed change.

Other approaches, including nucleotide incorporation assays and proliferation-associated markers, may provide complementary information.

Flow cytometry is also widely used to examine apoptosis, a regulated form of cell death.

Cancer cells can evade apoptosis through alterations in proteins that control survival pathways, including members of the BCL-2 family and components of the p53 signaling network.

Researchers may use apoptosis-associated measurements, such as Annexin V binding and membrane-integrity dyes, to assess cellular changes following exposure to experimental treatments.

Each measurement has limitations.

For instance, Annexin V positivity is not entirely specific to apoptosis under every biological condition, and the interpretation of cell-death assays benefits from multiple independent measurements.

By combining cell-cycle analysis with viability and apoptosis assessments, researchers can distinguish changes in proliferation from changes in cell survival.

This is particularly useful when studying how cancer cells respond to experimental therapeutic compounds.

Scientific figure 3: Cancer cell proliferation and drug-induced cell death

View Figure 1 from Flow Cytometric Single Cell-Based Assay to Simultaneously Detect Cell Death, Cell Cycling, DNA Content and Cell Senescence.

The study illustrates how multiple cellular responses can be evaluated to investigate treatment-induced changes in cancer-derived cell lines.

Cancer treatment may eliminate the majority of malignant cells without completely removing every disease-associated population.

In selected hematological cancers, small numbers of abnormal cells can remain even when routine microscopic examination shows no obvious evidence of malignancy.

These remaining populations are investigated through measurable residual disease (MRD) testing.

MRD assessment is particularly important in acute leukemias, where residual malignant cells may be associated with an increased risk of disease recurrence.

Flow cytometry-based MRD analysis uses detailed immunophenotypic information to identify abnormal populations that differ from expected normal hematopoietic cells.

Two complementary approaches are commonly discussed in AML research.

The leukemia-associated immunophenotype approach examines abnormal marker combinations identified in the original malignant population.

The different-from-normal approach investigates populations that deviate from established patterns of normal hematopoietic differentiation.

Using these approaches together can improve the ability to recognize residual abnormal cells, although performance depends on the disease, assay design, and laboratory validation.

The sensitivity of flow cytometry MRD assays varies. In appropriate settings, validated methods may identify abnormal populations at frequencies around one in 1,000 to one in 10,000 cells, but these figures should not be assumed for every assay or specimen.

These methods can be complementary rather than interchangeable.

For example, an abnormal immunophenotypic population may provide cellular evidence of persistent disease, while a molecular assay may track a specific genetic alteration.

MRD measurements are increasingly important for treatment-response assessment and risk stratification in selected hematological malignancies.

Nevertheless, an MRD-negative result does not prove the complete absence of malignant cells. It indicates that residual disease was not identified above the validated detection limit of the method.

Scientific figure 4: Detection and evolution of residual leukemia

View the figures in Pre-emptive Detection and Evolution of Relapse in Acute Myeloid Leukemia by Flow Cytometric Measurable Residual Disease Surveillance.

This publication illustrates the relevance of longitudinal residual disease monitoring in AML.

One of the greatest challenges in oncology is the development of resistance to anticancer therapy.

Cancer cells may become less responsive to treatment through multiple mechanisms, including genetic alterations, changes in drug transport, altered signaling pathways, and adaptations in cellular metabolism.

Resistance can also emerge through the selection of pre-existing populations that survive treatment.

Flow cytometry helps researchers investigate these processes by comparing cellular characteristics before and after exposure to experimental drugs.

For example, a laboratory may observe that a drug produces substantial apoptosis in one cancer-cell population while another population remains viable.

This result suggests a difference in treatment response, but it does not automatically establish the molecular mechanism responsible.

Further investigations may examine differences in signaling proteins, cell-cycle behavior, drug-target expression, and gene-expression profiles.

Flow cytometry can also be used to assess intracellular pathway activation through appropriately validated phospho-protein measurements.

These measurements may help researchers understand how cellular signaling changes following treatment.

In some cancer models, treatment-resistant populations exhibit altered expression of proteins associated with stem-like characteristics or survival pathways.

However, the identification of cancer stem cells requires careful functional validation. Marker expression alone is generally insufficient to prove that a population possesses tumor-initiating capacity.

Combining cytometry with molecular and functional studies allows researchers to move from an observation, such as increased cell survival, toward a mechanistic explanation.

This approach is valuable in preclinical drug development, where understanding why cancer cells survive treatment may guide the investigation of alternative therapeutic targets.

Cancer immunotherapy seeks to enhance the ability of the immune system to recognize and eliminate malignant cells.

Its development has created new research questions concerning immune cell identity, activation, persistence, and functional behavior.

Flow cytometry is widely used to investigate these features.

One important application involves chimeric antigen receptor T-cell therapy, commonly known as CAR-T therapy.

In this approach, T lymphocytes are genetically engineered to express receptors designed to recognize selected antigens on target cells.

CAR-T therapies have established clinical applications in several hematological malignancies, while research continues into other diseases and therapeutic designs.

Flow cytometry contributes to CAR-T research by helping characterize engineered T-cell populations and investigate changes during manufacturing, expansion, and functional testing.

Researchers may evaluate T-cell subset composition, cellular viability, CAR expression, differentiation-associated markers, and other relevant characteristics.

Monitoring studies can also investigate CAR-T cell populations in patient specimens following treatment.

Importantly, no single cellular measurement reliably predicts therapeutic efficacy across all CAR-T products or clinical settings.

In immune checkpoint research, flow cytometry can help characterize immune-cell populations expressing receptors such as PD-1 or CTLA-4.

These measurements contribute to investigations of immune regulation, but expression of a checkpoint receptor alone does not demonstrate that an individual patient will respond to a checkpoint inhibitor.

The biological insights provided by flow cytometry are most informative when integrated with functional assays, clinical observations, and complementary molecular measurements.

This integrated approach helps researchers understand not only which immune cells are present but also how their characteristics relate to antitumor activity.