Multiplexed Immunohistochemistry (mIHC): Accelerating Clinical Development of Anti-Tumor Drugs

Multiplex immunohistochemistry (mIHC) has recently gained traction as an advanced technique utilizing multiple fluorescent markers in immunohistochemical staining. Widely adopted by pharmaceutical companies, mIHC has become an invaluable tool in the preclinical and clinical development of oncology drugs. It plays a critical role in probing the tumor immune microenvironment, identifying novel therapeutic targets, and advancing the discovery of innovative immunotherapy strategies.

Part 01 - Introduction to the Principles and Technology of mIHC

Various approaches exist for multicolor immunofluorescence, with the most established method being tyramide signal amplification (TSA). In this method, horseradish peroxidase (HRP) conjugated to a secondary antibody catalyzes hydrogen peroxide, leading to the oxidation of fluorescently labeled tyramide within the reaction system. This activated tyramide then covalently binds to tyrosine residues abundant in tissue samples, creating a localized fluorescent signal at specific antigen sites. Notably, this signal remains stable under microwave treatment, allowing antibody removal via microwave heating for successive rounds of primary and secondary antibody incubation (Figures 1 and 2) [1].

TSA enables the incubation of multiple distinct antibodies on tissue sections, which, when combined with high-resolution multichannel fluorescence scanning and analysis software, allows for the detection and analysis of multiple biomarkers in the tumor microenvironment. This approach is instrumental in revealing a range of physiological and pathological processes, such as tumorigenesis, the dynamics of the tumor immune microenvironment, and drug efficacy.

Part 02 - Advantages and Limitations of the mIHC

Conventional IHC widely used as a diagnostic technique in histopathology. However, it has several limitations, such as high inter-observer variability and the restriction of marking only a single target per tissue section. In contrast, mIHC provides a wealth of information, including spatial positioning of multiple targets, quantitative analysis, and cell type distribution, making it an increasingly valuable tool in clinical research.

The mIHC technique offers richer information and higher precision while requiring less tissue samples.

Traditional IHC requires multiple slides to stain for different targets, with results then compared across slides—a process susceptible to inaccuracies due to non-continuous sections. In contrast, mIHC enables simultaneous acquisition of various types of information on a single slide, including cell quantification, cell subset classification, and spatial arrangement, leading to more accurate results and significantly reducing the sample requirement. Compared to brightfield multiplex staining, which requires primary antibodies from different species to avoid cross-reactivity of secondary antibodies, mIHC offers greater flexibility. Each labeling cycle involves dye marking, followed by microwave heating to remove the bound antibodies, allowing for unrestricted antibody selection regardless of species. Additionally, when combined with intelligent image analysis software, mIHC enables precise quantitative assessment of cell density, phenotype, and localization, surpassing the accuracy of traditional pathologist interpretation.

The mIHC technique offers greater predictive value.

A meta-analysis based on literature covering over 8,000 patients demonstrated that, when predicting the efficacy of immune checkpoint inhibitors, tumor immune microenvironment analysis using mIHC offers the highest predictive value. This approach even surpasses the combined predictive power of traditional immunohistochemistry, tumor mutational burden (TMB), gene expression profiling (GEP), or tumor microenvironment analysis alone (Figure 3), highlighting the visualization, quantification, and high-throughput advantages of mIHC [3].

The mIHC technique is complex to perform and requires significant investment.

Although mIHC technology has made significant strides, it still presents certain limitations. The need for multiple rounds of antibody incubation and fluorescent labeling makes the experimental process time-consuming, and as the number of fluorescent markers increases, signal crosstalk between similar wavelengths becomes more likely. Furthermore, mIHC results require scanning and analysis using advanced software, producing large datasets that impose high demands on laboratory hardware for storage and computational power, which increases the overall experimental costs. To obtain high-quality mIHC results, fully automated staining instruments should be used to reduce variability associated with manual handling. Additionally, sophisticated analytical software and stringent control over the analysis process are necessary to achieve standardized image data analysis.

Part 03 - mIHC and it’s application

mIHC technology has become instrumental across numerous fields, including the study of changes in the tumor immune microenvironment, differential expression of relevant targets pre- and post-treatment, immune cell infiltration patterns, the structure of tertiary lymphoid tissues, and spatial analysis of interconnected targets within signaling pathways. The following examples illustrate these applications in greater detail.

Changes in the tumor immune microenvironment before and after treatment

The tumor microenvironment (TME) comprises a dynamic network of various interacting cell types, including tumor-associated macrophages, fibroblasts, tumor-infiltrating lymphocytes, myeloid-derived suppressor cells, and mast cells. This complex cellular network is particularly well-suited for analysis using mIHC technology, which is adept at examining intricate samples. For example, in HER2-positive breast cancer, quantitative analysis of immune cells within the tumor stroma and core—such as CD8+ cytotoxic T cells, CD4+/FoxP3- helper T cells, FoxP3+ regulatory T cells, CD20+ B cells, and CD68+ macrophages—can reveal the degree of responsiveness among different cell subtypes to immunotherapy and assist in identifying biomarkers predictive of treatment efficacy. Combined with genomic analysis, studies on melanoma patients treated with anti-PD-1 agents have shown that PTEN mutations are often associated with poor outcomes, a correlation linked to an immunosuppressive TME [2].

Furthermore, mIHC technology allows for tracking immune cell dynamics at various stages of targeted therapy, providing a clearer framework for treatment strategies. In metastatic lung cancer patients, mIHC staining with a DAPI/CK/CD14/CD8/CD3 panel has revealed that activated T cells increase while macrophages decrease during the response phase to therapy, with the opposite pattern observed in the resistance phase [2].

In a Phase II clinical trial on neoadjuvant therapy for resectable non-small cell lung cancer (with nivolumab alone or in combination with ipilimumab), mIHC analysis of tumor samples before and after treatment indicated a significant increase in CD3+ and CD3+CD8+ tumor-infiltrating lymphocytes (TILs), with a marked trend toward higher densities of CD3+CD8+CD45RO+ memory TILs following combination therapy. This combined therapy also promoted the infiltration of diverse immune cell subtypes, such as CD3+CD8+PD-1+ T cells, CD3+CD8+GZB+ T cells, CD3+CD8-FoxP3+ T cells, CD68+ macrophages, and CD68+PD-L1+ cells. In contrast, treatment with nivolumab alone produced minimal changes in immune cell subtypes. These findings underscore the utility of mIHC in evaluating immune responses following therapy, facilitating prognostic assessments, and guiding individualized treatment strategies [4].

Spatial positioning of tumor-associated immune cells and its prognostic value

Research on the spatial distribution of interactions between malignant cells and tumor-associated immune cells (TAICs) is essential for determining the likelihood of tumor progression, recurrence, or patient survival. The distribution patterns of immune cells have been shown to impact cancer prognosis across multiple tumor types, including breast, lung, and colorectal cancers.

In a study [5], a set of tumor tissue microarrays (TMAs) was analyzed using five mIHC panels (Figure 5) encompassing 23 markers, including T cells, B cells, immune checkpoints, and myeloid cell markers, to evaluate samples from a large cohort of patients with stage I–III non-small cell lung cancer (NSCLC). Analyzing these 23 markers on relevant tumor and immune cells, four cellular immune patterns were identified based on cell distribution and proximity to malignant cells:

Pattern 1: Mixed pattern with a median close proximity to malignant cells.

Pattern 2: Mixed pattern with a median farther distance from malignant cells.

Pattern 3: Non-mixed pattern with a median close proximity to malignant cells.

Pattern 4: Non-mixed pattern with a median farther distance from malignant cells.

Further findings revealed that Pattern 2 was the most prevalent, observed in 36.8% of patients, characterized by high T cell density with low immune checkpoint expression by malignant cells, suggesting an inflammatory effect within this tumor type. Pattern 1, featuring a mixed pattern with close proximity to malignant cells, was observed in 23.6% of samples, while Pattern 3, with a non-mixed pattern and close proximity, was observed in 9.0% of samples. In contrast, Pattern 4 exhibited the lowest T cell density alongside the highest immune checkpoint expression by malignant cells, indicating characteristics of "cold" tumors.

Survival analysis showed that, in adenocarcinoma (ADC), close proximity of CD3+CD8+ cytotoxic T cells, CD3+CD8+GZB+ activated cytotoxic T cells, and macrophages to malignant cells was associated with improved overall survival (OS), suggesting that proximity to these cells may mitigate the inhibitory effects of suppressive cells. Conversely, shorter distances between malignant cells and B7-H3+ T cells correlated with poorer OS (Figure 5). This study demonstrates that spatial phenotyping provides valuable insights into the TME and patient prognosis, laying a foundation for the development of new therapeutic strategies.

Tertiary Lymphoid Structures and Prognosis

Tertiary lymphoid structures (TLSs) are a prominent topic in immuno-oncology, playing a crucial role in shaping the tumor microenvironment. TLSs have been identified in various solid tumor types, where their presence within the tumor microenvironment is associated with improved survival. The mIHC technique can be employed to assess TLSs within tumor tissue, providing valuable insights for TLS research [6].

A study [7] analyzed archived pre-treatment tumor samples from 328 patients treated with anti-PD-1 or anti-PD-L1 antibodies. TLSs were identified in 105 patients (32%), with 84 cases displaying mature TLSs (25.6%). In the mature TLS group, 31 of 84 patients (36.9%; 95% CI, 26.6%–48.1%) achieved an objective response, compared to 4 of 21 patients (19.3%; 95% CI, 5.4%–41.9%) with immature TLSs and 43 of 223 TLS-negative patients (19%; 95% CI, 14.3%–25.1%). The differences among the three groups were statistically significant (p=0.015), indicating a clear association between TLS presence and prognosis in immunotherapy (Figure 6).

In summary, multiplex immunohistochemistry enables simultaneous, in situ detection of multiple markers on a single FFPE slide. This technique identifies various cell subtypes by assessing the expression of multiple markers on individual cells. When paired with advanced analysis software, it provides detailed information on cell types, densities, and spatial relationships within different tissue regions—data that single-marker staining cannot capture. This additional depth of information elevates researchers’ understanding of target regions to a new dimension, supporting its extensive application in both clinical and preclinical drug studies.

Part 04: Introduction to Accurant mIHC Platform

The mIHC platform at Accurant Biotech has the most advanced equipment, including the Leica Bond Rx fully automated immunohistochemistry stainer, the Akoya PhenoImager full-spectrum imaging system, and the Halo digital pathology analysis platform. This setup provides a fully automated, high-quality mIHC research workflow, offering comprehensive support from panel design and validation to sample testing. We offer premium mIHC testing services for tissue samples in preclinical and clinical trial stages to support pharmaceutical companies. For more information, please reach out to Accurant Biotech BD team at [email protected]

Note: Article credit to Accurant Biotech China Team

Reference:

1. Sun, W., Zhou, J., & Zhou, J. Advances in Multicolor Immunohistochemistry and Immunofluorescence Staining in Lung Cancer Immunotherapy. Chinese Journal of Lung Cancer, 2021, 24(1):7.
2. Li, W., Yuan, X., Xu, B., et al. Applications of Multiplexed Immunohistochemistry/Immunofluorescence and Multispectral Imaging Technology in Tumor Immunotherapy. Journal of Chinese Pharmaceutical Sciences, 2020, 29(10).
3. Lu, S., et al. Comparison of Biomarker Modalities for Predicting Response to PD-1/PD-L1 Checkpoint Blockade: A Systematic Review and Meta-analysis. JAMA Oncology, 2019 Aug 1;5(8):1195-1204.
4. Cascone, T., et al. Neoadjuvant Nivolumab or Nivolumab Plus Ipilimumab in Operable Non-Small Cell Lung Cancer: The Phase 2 Randomized NEOSTAR Trial. Nature Medicine, 2021 Mar;27(3):504-514.
5. Parra, E.R., Zhang, J., Jiang, M., Tamegnon, A., Pandurengan, R.K., Behrens, C., Solis, L., Haymaker, C., Heymach, J.V., Moran, C., Lee, J.J., Gibbons, D., & Wistuba, I.I. Immune Cellular Patterns of Distribution Affect Outcomes of Patients with Non-Small Cell Lung Cancer. Nature Communications, 2023 Apr 25;14(1):2364.
6. Sautès-Fridman, C., Petitprez, F., Calderaro, J., & Fridman, W.H. Tertiary Lymphoid Structures in the Era of Cancer Immunotherapy. Nature Reviews Cancer, 2019 Jun;19(6):307-325.
7. Vanhersecke, L., Brunet, M., Guégan, J.P., Rey, C., Bougouin, A., Cousin, S., Moulec, S.L., Besse, B., Loriot, Y., Larroquette, M., Soubeyran, I., Toulmonde, M., Roubaud, G., Pernot, S., Cabart, M., Chomy, F., Lefevre, C., Bourcier, K., Kind, M., Giglioli, I., Sautès-Fridman, C., Velasco, V., Courgeon, F., Oflazoglu, E., Savina, A., Marabelle, A., Soria, J.C., Bellera, C., Sofeu, C., Bessede, A., Fridman, W.H., Loarer, F.L., & Italiano, A. Mature Tertiary Lymphoid Structures Predict Immune Checkpoint Inhibitor Efficacy in Solid Tumors Independently of PD-L1 Expression. Nature Cancer, 2021 Aug;2(8):794-802.