Immunohistochemistry in the Era of Precision Medicine: Expanding Applications and Future Directions

Sergi Ovio^*
*Correspondence: Sergi Ovio, Department of Internal Medicine, University of Southern California, Los Angeles, CA 90007, USA, Email:

Keywords

Immunohistochemistry • Patient outcomes • Histopathology

Introduction

Immunohistochemistry plays a vital role in modern pathology by providing insights into the expression patterns and localization of specific proteins within tissue samples. In the era of precision medicine, the ability to characterize molecular markers using IHC has become increasingly important for guiding targeted therapies and personalized treatment strategies. IHC is widely used in the diagnosis and subclassification of cancers, allowing pathologists to identify specific biomarkers associated with tumor type, grade, and prognosis. In addition to traditional markers such as hormone receptors and HER2 in breast cancer, IHC is increasingly used to assess the expression of immune checkpoint proteins (e.g., PD-L1) and molecular alterations (e.g., EGFR mutations) that guide immunotherapy and targeted therapy decisions.

Cancer diagnosis and prognosis involve identifying the presence of cancer and predicting its likely course and outcome. Immunohistochemistry plays a crucial role in this process by detecting specific biomarkers within tumor tissue samples. Immunohistochemistry helps pathologists identify the type and origin of cancer cells by detecting the expression of specific proteins associated with different types of cancer. For example, markers such as cytokeratins, CD20, and CD3 help distinguish between various types of carcinoma, lymphoma, and sarcoma, respectively.

In autoimmune and inflammatory diseases, IHC can help identify immune cell infiltrates, assess tissue damage, and characterize the expression of cytokines, chemokines, and other inflammatory mediators [1-3]. This information is valuable for understanding disease pathogenesis and guiding treatment selection, such as targeted biologic therapies. Autoimmune and inflammatory disorders are conditions characterized by abnormal immune responses that result in inflammation and tissue damage. Immunohistochemistry plays a key role in understanding these disorders by identifying specific immune cells, cytokines, and other inflammatory mediators within tissue samples.

Literature Review

IHC can also aid in the differentiation between primary tumors and metastases by detecting markers indicative of the tumor's tissue of origin. In addition to diagnosing cancer, IHC can provide information about the tumor's aggressiveness, grade, and stage, helping guide treatment decisions. Immunohistochemistry markers can predict the likelihood of disease progression and patient survival. For instance, in breast cancer, the expression of hormone receptors (estrogen receptor, progesterone receptor) and HER2/ neu can help predict response to hormonal therapy and targeted therapies like Herceptin [4,5].

Other markers, such as Ki-67, can indicate the rate of tumor cell proliferation, which correlates with aggressiveness and prognosis. Immune checkpoint markers, such as PD-L1, are used to predict response to immunotherapy in various cancers, including lung cancer and melanoma. In summary, immunohistochemistry in cancer diagnosis and prognosis involves identifying specific protein markers within tumor tissue samples to determine the type, origin, aggressiveness, and likely course of the disease. This information is critical for tailoring treatment strategies and predicting patient outcomes.

In autoimmune disorders, the immune system mistakenly attacks healthy tissues, leading to inflammation and tissue damage. Immunohistochemistry can help identify immune cell infiltrates within affected tissues, such as lymphocytes, macrophages, and plasma cells. IHC can also detect autoantibodies or immune complexes deposited in tissues, which are characteristic of autoimmune diseases like rheumatoid arthritis, systemic lupus erythematosus, and autoimmune hepatitis.

Discussion

Inflammatory disorders involve inflammation in response to various triggers, such as infections, toxins, or tissue injury. Immunohistochemistry can identify inflammatory cells and markers associated with specific pathways or types of inflammation. For example, CD3 and CD20 markers can help identify T cells and B cells, respectively, in inflammatory infiltrates. IHC can detect cytokines, chemokines, and other inflammatory mediators within tissues, providing insights into the underlying mechanisms of inflammation and potential therapeutic targets. Overall, immunohistochemistry in autoimmune and inflammatory disorders helps characterize the immune response within affected tissues, providing valuable information for diagnosis, understanding disease pathogenesis, and guiding treatment decisions.

IHC plays a crucial role in the diagnosis of infectious diseases by detecting specific pathogens or their antigens within tissue specimens. In addition to identifying the causative agent, IHC can provide insights into the host response and tissue damage, aiding in the differential diagnosis and treatment of infectious diseases. Advances in multiplex staining techniques allow for the simultaneous detection of multiple biomarkers within a single tissue section. Multiplex IHC enables more comprehensive characterization of the tumor microenvironment, immune cell subsets, and signaling pathways, providing valuable insights for precision medicine approaches.

Digital pathology platforms coupled with AI-assisted image analysis offer new opportunities to standardize and automate IHC interpretation, improve diagnostic accuracy, and extract quantitative data from histological images. These technologies facilitate large-scale data analysis and integration with other molecular profiling techniques. Digital pathology involves the digitization of glass slides containing tissue samples, allowing for the viewing, analysis, and management of histopathological images using computer-based systems. Image analysis refers to the process of extracting quantitative data and insights from these digital images [6].

Digital pathology involves scanning glass slides to create high-resolution digital images. These digital images can be stored, managed, and viewed using computer-based systems, eliminating the need for physical slide handling and facilitating remote access to images. Digital pathology enables pathologists to review and analyze slides more efficiently, collaborate with colleagues remotely, and share images for consultation or education purposes. Image analysis techniques extract quantitative data and insights from digital histopathological images. These techniques can automate tasks such as cell counting, tissue segmentation, and biomarker quantification, improving efficiency and accuracy in histopathological analysis.

Image analysis can also be used to characterize tissue morphology, assess staining intensity, and identify specific cellular or subcellular features of interest. Advanced image analysis methods, including machine learning and artificial intelligence, can assist pathologists in interpreting complex patterns and identifying subtle changes in tissue architecture or biomarker expression. Integrating IHC data with genomic, transcriptomic, and proteomic data allows for a holistic understanding of disease biology and treatment response. By combining information from multiple platforms, clinicians can identify predictive biomarkers and develop personalized treatment strategies tailored to individual patients.

Conclusion

Immunohistochemistry remains a powerful tool in the era of precision medicine, enabling the characterization of molecular markers and guiding personalized treatment decisions across various disease contexts. As technology continues to advance, the expanding applications of IHC, coupled with innovations in multiplex staining, digital pathology, and integration with omics data, hold promise for improving patient outcomes and advancing our understanding of disease biology. Continued research and collaboration will be essential to harness the full potential of IHC in precision medicine.

Acknowledgement

None.

Conflict of Interest

There are no conflicts of interest by author.

References

1. Sorenmo, K. U., V. M. Kristiansen, M. A. Cofone and F. S. Shofer, et al. "Canine mammary gland tumours; A histological continuum from benign to malignant; Clinical and histopathological evidence." Vet Comp Oncol 7 (2009): 162-172.

   Google Scholar, Crossref, Indexed at

2. Zink, M. Christine, Parvene Farhoody, Samra E. Elser and Lynda D. Ruffini, et al. "Evaluation of the risk and age of onset of cancer and behavioral disorders in gonadectomized Vizslas." J Am Vet Med Assoc 244 (2014): 309-319.

   Google Scholar, Crossref, Indexed at

3. Burrai, Giovanni P., Andrea Gabrieli, Valentina Moccia and Valentina Zappulli, et al. "A statistical analysis of risk factors and biological behavior in canine mammary tumors: A multicenter study." Animals 10 (2020): 1687.

   Google Scholar, Crossref, Indexed at

4. Egenvall, Agneta, Brenda N. Bonnett, Patrik Öhagen and Pekka Olson, et al. "Incidence of and survival after mammary tumors in a population of over 80,000 insured female dogs in Sweden from 1995 to 2002." Prev Vet Med 69 (2005): 109-127.

   Google Scholar, Crossref, Indexed at

5. Merlo, D. F., La Rossi, C. Pellegrino and M. Ceppi, et al. "Cancer incidence in pet dogs: Findings of the Animal Tumor Registry of Genoa, Italy." J Vet Intern Med 22 (2008): 976-984.

   Google Scholar, Crossref, Indexed at

6. Salas, Yaritza, Adelys Márquez, Daniel Diaz and Laura Romero. "Epidemiological study of mammary tumors in female dogs diagnosed during the period 2002-2012: A growing animal health problem." PloS One 10 (2015): e0127381.

   Google Scholar, Crossref, Indexed at