Immunotherapy: Open Access
Open Access

Functions of Various Immunotherapy Related Biomarkers in Treating Cancerous Cells

Michael Graham^{* *}Correspondence: Michael Graham, Department of Biomolecular Medicine, Ghent University, Ghent, Belgium, Email:

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Description

Immunotherapy encompasses various techniques designed to enhance or restore the body's immune response against cancer. These include immune checkpoint inhibitors, adoptive cell transfer, cancer vaccines and cytokines. Each method operates on the principle of manipulating the immune system's inherent ability to recognize and eliminate abnormal cells, such as cancerous tumors. This has yielded remarkable results to patients with previously untreatable cancers. However, the immunotherapy hinges on a critical factor i.e., biomarkers [1].

Role of biomarkers

Biomarkers plays a pivotal role in cancer immunotherapy by serving as indicators that help in predicting treatment response, monitor disease progression and guide therapeutic decisions. In the context of immunotherapy, biomarkers can broadly be categorized into several types such as predictive biomarkers, prognostic biomarkers, monitoring biomarkers [2-4].

Predictive biomarkers: These biomarkers provide whether a patient is likely to respond to a specific immunotherapy treatment. They help identify individuals who are most likely to benefit from a particular therapy, thereby optimizing treatment selection and improving patient outcomes.

Prognostic biomarkers: Prognostic biomarkers gives information about the likely course of the disease in the absence of treatment. They help clinicians the overall outlook for a patient and to treat using adaptive methods.

Monitoring biomarkers: These biomarkers are used to assess treatment response and monitor disease progression during therapy. They provide real-time feedback on the effectiveness of treatment, enabling timely adjustments if necessary.

Key biomarkers in cancer immunotherapy

These include Programmed Death-Ligand 1 (PD-L1) expression, Tumor Mutational Burden (TMB), Microsatellite Instability (MSI) and Tumor-Infiltrating Lymphocytes (TILs).

PD-L1 expression: Programmed Death-Ligand 1 (PD-L1) is a protein expressed by some cancer cells that inhibits the immune response. Checkpoint inhibitors targeting PD-L1 or its receptor PD-1 have shown significant efficacy in various cancers, including melanoma, lung cancer and bladder cancer. PD-L1 expression levels in tumor tissues serve as a predictive biomarker for response to anti-PD-1/PD-L1 therapies [5].

Tumor Mutational Burden (TMB): Tumor Mutational Burden (TMB) quantifies the total mutations present in the Deoxyribonucleic Acid (DNA) of a tumor cell, usually expressed as mutations per Megabase (Mb) of examined DNA. It reflects the genetic mutation load within the tumor genome. Higher TMB is associated with enhanced production of neo-antigen. Abnormal proteins that can be recognized by the immune system by potentially enhancing response to immunotherapy. This has positioned TMB as a predictive biomarker in cancers like melanoma and lung cancer, especially in the context of treatment with immune checkpoint inhibitors.

Microsatellite Instability (MSI): MSI is a condition characterized by errors in DNA replication, resulting in the accumulation of mutations within microsatellite sequences. Tumors with high MSI exhibit a high mutational load and are more likely to respond to immune checkpoint inhibitors. MSI status serves as a predictive biomarker, particularly in colorectal cancer and other solid tumors [6-8].

Tumor-Infiltrating Lymphocytes (TILs): TILs are immune cells that have migrated into a tumor. High levels of TILs, particularly cytotoxic T lymphocytes, indicate an active immune response against the tumor and are associated with improved outcomes in various cancers, including melanoma and ovarian cancer. TILs serve as both prognostic and predictive biomarkers in the context of immunotherapy.

Challenges

Variability in biomarker assessment methods, tumor heterogeneity and evolving resistance mechanisms can complicate their utility in clinical practice. Additionally, identifying biomarkers applicable across different cancer types and treatment modalities remains a priority for ongoing study [9]. Biomarker discovery and validation include the integration of genomic and proteomic profiling, advanced imaging techniques and artificial intelligence-driven algorithms to refine biomarker identification and interpretation [10]. Collaborative efforts among researchers, clinicians and pharmaceutical companies is necessary to accelerate the translation of biomarker discoveries into clinical applications.

Conclusion

Biomarkers are indispensable tools in the era of cancer immunotherapy, guiding treatment decisions and improving patient outcomes. As per the complex exchange between the immune system and cancer, biomarkers help in treating the individual patients. The novel biomarkers expand the reach of immunotherapy and transforms the landscape for cancer treatment.

In summary, biomarkers serve as a basic for personalized cancer care, which is providing an approach for treatment optimization and patient-centric outcomes in the field of cancer immunotherapy.

References

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