Flow Cytometry and Cell Marker Analysis in Cancer Diagnosis, Prognosis, and Treatment

 
Flow cytometry stands at the forefront of cancer diagnostics and therapeutics, transforming oncology practice through its capacity to dissect cellular and molecular complexity. By enabling the simultaneous measurement of multiple markers, it allows clinicians to determine tumor type, prognosis, response, and resistance with unprecedented speed and resolution. The integration of marker-based flow cytometry profiling guides precision medicine, informing risk stratification, choice of therapy, and early adjustment of treatment strategies in response to evolving tumor adaptation. As technologies advance and new markers are discovered, the role of flow cytometry in personalized cancer care will only expand, driving improved patient outcomes through biology-focused decision making. This review explores key topics in the clinical application of flow cytometry to cancer therapy, including its role in prognosis, differentiation between cancer types, prediction of treatment response, identification of immune correlates of therapy success, and molecular monitoring of emerging resistance.

Flow Cytometry for Cancer Prognosis

Flow cytometry allows for an in-depth analysis of tumor and hematopoietic cell populations, providing critical information on both the extent and biological nature of malignancies. In hematologic cancers, evaluating DNA content, cell ploidy, and proliferation rates helps stratify patient risk. For chronic lymphocytic leukemia (CLL), the immunophenotype defined by CD5 and CD23 expression is essential for both diagnosis and prognosis, helping distinguish CLL from other lymphoid malignancies like mantle cell lymphoma. Similarly, CD10 and BCL-2 serve as key markers for diagnosing and prognosticating follicular lymphoma.

Quantitative analysis of CD33 and CD34 expression helps classify AML subtypes by identifying the leukemic cell populations present and directing the most appropriate targeted treatments. For example, patients with CD33-positive AML may benefit from anti-CD33 immunotherapy, while the presence of CD34+ leukemic stem cells supports approaches targeting these drug-resistant progenitors. Thus, combined marker profiling is essential to personalized AML therapy strategies and improving the safety and efficacy of treatments. Outside hematologic malignancies, multiparametric flow cytometry, such as DNA content analysis with propidium iodide staining, is increasingly applied to solid tumors to assess tumor aggressiveness and prognosis objectively.

Differentiation of Specific Cancer Cell Types by Flow Cytometric Surface Markers

The identification and classification of solid tumors, especially epithelial-derived cancers such as breast and ovarian tumors, can be effectively performed by analyzing specific surface markers detectable by flow cytometry. This method allows precise discrimination between tumor types based on marker expression, which aids in diagnosis and informs treatment strategies.

In breast cancer, key flow cytometry-detectable markers include EpCAM (Epithelial Cell Adhesion Molecule), broadly expressed on epithelial tumor cells and used extensively for capturing circulating tumor cells (CTCs).  The EpCAM is notably overexpressed in breast cancer cells compared to normal tissue, making it a sensitive marker for tumor cell detection and isolation. Human Epidermal Growth Factor Receptor 2 (HER2/neu) is another critical marker, a receptor tyrosine kinase overexpressed in a subset of aggressive breast cancers. Its expression is quantitatively measured by flow cytometry to assess HER2 status and guide targeted therapies.

Ovarian cancer cells also express EpCAM, aiding their identification. Additionally, ovarian tumors often express CD44 and CD133, markers associated with cancer stem cells important for distinguishing ovarian tumors from other epithelial cancers. E-cadherin, involved in cell adhesion, is another valuable marker whose altered expression detected by flow cytometry provides context in tumor characterization, especially in ovarian cancers.

Flow cytometry’s utility extends beyond tissue analysis to the detection and quantification of circulating tumor cells (CTCs) in peripheral blood, offering a minimally invasive tool for diagnosis and monitoring. The CTCs are typically identified as EpCAM-positive and CD45-negative cells, where CD45 excludes hematopoietic cells. Inclusion of HER2 detection in CTCs increases breast cancer specificity. Elevated EpCAM+CD45- cells often signal ovarian cancer presence or progression. Multiparametric flow cytometry gives a comprehensive phenotypic profile of tumor cells, supporting diagnosis, prognosis, and therapeutic monitoring.

Beyond breast and ovarian cancers, flow cytometry is also widely used for several other solid and hematologic cancers:

• Leukemias and lymphomas: Flow cytometry is a standard tool in diagnosing and classifying leukemias and lymphomas by immunophenotyping cell populations using markers like CD19, CD20, CD3, CD4, CD8, CD34, and more. This helps differentiate subtypes and monitor minimal residual disease (MRD).
• Hodgkin lymphoma: Characterized by detection of neoplastic Reed-Sternberg cells with distinct markers such as CD30 and CD15.
• Multiple myeloma: Detected and monitored using markers like CD138, CD38, and light chain restriction, with multiparameter flow cytometry enhancing disease characterization and treatment monitoring.
• Pancreatic cancer, colorectal cancer, and hepatocellular carcinoma: These epithelial cancers express EpCAM, and their circulating tumor cells or tissue specimens can be assessed by flow cytometry for early detection and monitoring. Additional markers such as CK19 and CA19-9 may be analyzed in these contexts.
• Cancer stem cell markers: Beyond CD44 and CD133, other markers such as ALDH1, CD24, and Notch1 are used to identify cancer stem cell populations in various solid tumors, including breast, ovarian, and pancreatic cancers.
• Cell cycle and DNA content analysis: Flow cytometry can assess tumor cell ploidy (diploid vs. aneuploid) and proliferation rate (S-phase fraction), providing prognostic insights across many cancers.

Multiparametric flow cytometry permits simultaneous analysis of multiple markers, enabling comprehensive phenotyping of tumor cells. This capability underpins its growing use in cancer diagnostics, treatment selection, and monitoring therapeutic response, including immunotherapies.

Table 1: Flow Cytometric Markers as Prognostic Indicators

Predicting Treatment Responses with Flow Cytometry

The ability to profile tumor and immune cells before, during, and after therapy using flow cytometry makes it a powerful predictive tool in oncology. By quantitatively measuring cancer and immune cell populations, clinicians can better estimate the likelihood of therapeutic success or resistance across multiple cancer types. Flow cytometry not only enables disease monitoring but also guides therapy decisions by identifying key cellular markers associated with tumor progression, immune response, and minimal residual disease.

In melanoma, the enumeration of tumor-infiltrating lymphocytes (TILs), particularly activated CD8+ T cells, correlates strongly with immunotherapy response. This highlights the importance of immune profiling to predict outcomes in solid tumors that respond to checkpoint blockade therapies.

In hematologic malignancies such as leukemias and lymphomas, minimal residual disease (MRD) quantification is a routine practice that guides post-remission strategies. Established markers include CD19 for B-cell acute lymphoblastic leukemia (ALL) and CD33 for acute myeloid leukemia (AML). Flow cytometry detects and quantifies residual malignant cells with high sensitivity, providing critical prognostic information.

Cancer stem cells (CSCs), which are implicated in relapse and therapy resistance, can also be identified by flow cytometry in various cancers. For example, breast cancer CSCs are characterized by CD44+/CD24- profiles, while other markers such as ALDH1 are used in different tumor types to assess stem-like properties.

Beyond these, flow cytometry has been applied extensively across multiple cancer types for immune and tumor cell profiling, with panels designed to detect relevant surface and intracellular markers.

Table 2: Flow Cytometric Markers for Predicting Treatment Responses

Additionally, immune cell profiling in the tumor microenvironment often includes markers for T cells (CD3, CD4, CD8), B cells (CD19), natural killer cells (CD56), macrophages (CD68), and checkpoint molecules (PD-1, PD-L1, CTLA-4), enabling comprehensive assessment of immune status and potential mechanisms of immune evasion.

With advancements like full spectrum flow cytometry, up to 40 parameters can be analyzed simultaneously on single cells, enhancing the precision of these profiles in both solid tumors and hematologic malignancies. This technology supports not only diagnosis but also the development of personalized immunotherapies by monitoring immune cell subsets and activation states.

Changes in Flow Cytometry Markers Imply Emerging Resistance to Treatment

Tumor adaptation to therapy at the molecular and phenotypic levels is often reflected in changes to cell surface marker expression. Acquired secondary mutations in receptor tyrosine kinases, such as EGFR T790M in lung cancer or amplifications of HER2 in breast cancer, reactivate signaling and confer resistance even prior to clinical relapse. These changes are detected by shifts in flow cytometric marker profiles.

Epithelial-to-mesenchymal transition (EMT), marked by loss of epithelial markers (e.g., E-cadherin) and upregulation of mesenchymal markers like N-cadherin and fibronectin, is a common adaptation associated with chemoresistance in solid tumors. Expansion of cancer stem cell (CSC) populations bearing CD133 or CD44+/CD24- phenotypes indicates the onset of resistance mechanisms including drug efflux and enhanced survival signaling. Early identification of these changes enables clinicians to preemptively modify therapeutic regimens.

Additional flow cytometry markers implicated in tumor resistance include:

• Drug Efflux Transporters: Overexpression of ATP-binding cassette (ABC) transporter proteins such as P-glycoprotein (P-gp/MDR1), MRP1, and BCRP leads to active efflux of chemotherapeutic agents, reducing intracellular drug accumulation and conferring multidrug resistance. Antibodies detecting these efflux pumps can be used in flow cytometry to identify resistant tumor cell subsets.
• Immune Checkpoint Molecules: Upregulation of PD-L1 on tumor cells and associated immune cells in the tumor microenvironment (TME) contributes to immune evasion and resistance to immunotherapies. Flow cytometry markers including PD-1, CTLA-4, TIM-3, and LAG-3 on T cells can assess T cell exhaustion and tumor immune escape.
• Cancer Stemness and Survival Signals: Markers like CD90, CD117 (c-kit), and CD44 variant isoforms denote CSCs that are often resistant to therapy due to quiescence, efficient DNA repair, and niche interactions.
• Metabolic and Stress Adaptation Markers: Tumors undergoing metabolic reprogramming may express altered levels of glucose transporters (e.g., GLUT1), hexokinase II, or markers related to oxidative stress response. These changes support survival under therapeutic pressure.
• Tumor Microenvironment and Immune Cell Phenotypes: Expression of markers identifying tumor-associated macrophages (TAMs: CD163, CD206), myeloid-derived suppressor cells (MDSCs), regulatory T cells (Tregs: CD25, FOXP3), and other immunosuppressive populations can inform on resistance mechanisms mediated by the microenvironment.
• Proliferation and Apoptosis Markers: Changes in Ki-67 (proliferation) and anti-apoptotic proteins such as Bcl-2 family members can be monitored by flow cytometry to assess cell cycle dynamics and resistance to apoptosis.

Flow cytometry offers a powerful platform to detect and quantify shifts in tumor cell surface markers and immune phenotypes associated with therapeutic resistance. By integrating markers related to EMT, CSC populations, drug efflux pumps, immune checkpoint expression, and microenvironment components, clinicians and researchers can obtain a detailed profile of tumor adaptation. This multi-parametric approach enables early detection of resistance mechanisms and facilitates the design of personalized therapeutic strategies to overcome or prevent treatment failure.

Conclusion

Cancer is a highly heterogeneous disease with complexities that span from the molecular to the cellular level. Accurate diagnosis, risk stratification, and prediction of treatment response are critical to optimizing patient outcomes. Flow cytometry has emerged as an indispensable technique in clinical oncology, enabling rapid, multiparametric analysis of both tumor and immune cell populations. By leveraging specific cellular markers, flow cytometry contributes not only to the initial diagnosis and classification of malignancies, but also to the nuances of prognosis, monitoring of therapeutic efficacy, and early detection of resistance.

Related blog:  Flow cytometry in Developing Cell-Based Potency Assays for AAV Gene Therapies

Disclaimer: This blog post is intended solely for educational and scientific informational purposes. Any mention of therapeutic drug names, including FDA-approved medications, is for the purpose of accurate reporting and discussion of biomedical research and does not constitute medical advice, endorsement, or promotion. Readers should not interpret the content as a recommendation for any specific treatment. Always consult a qualified healthcare professional for medical advice or treatment decisions.

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2. Cell-Based Potency Assay for Anti-CD3-Anti-CD19 Diabody. bioRxiv 2025.04.15.648836v1 https://www.biorxiv.org/content/10.1101/2025.04.15.648836v1
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