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Optimizing cancer immunotherapy: Is it time for personalized predictive biomarkers?
Milena Music1, Ioannis Prassas2, Eleftherios P Diamandis1,2,3,4
1a Department of Laboratory Medicine and Pathobiology , University of Toronto , Toronto , Canada.
Critical Reviews in Clinical Laboratory Sciences
|October 3, 2018
Summary
Cancer immunotherapy shows promise but has limitations. New biomarkers, including serum autoantibodies, are needed to predict patient response and monitor treatment effectiveness for better outcomes.
Area of Science:
- Oncology
- Immunology
- Personalized Medicine
Background:
- Cancer immunotherapy leverages the patient's immune system to fight tumors.
- Immune checkpoint inhibitors (ICIs) targeting CTLA-4 and PD-1/PD-L1 are effective but have limitations.
- High costs, toxicity, and low response rates (10-40%) necessitate better predictive tools.
Purpose of the Study:
- To review recent advancements in predictive biomarkers for cancer immunotherapy.
- To highlight the potential of serum autoantibodies as personalized biomarkers.
Main Methods:
- Review of current literature on cancer immunotherapy biomarkers.
- Emphasis on emerging predictive markers for immune checkpoint blockade.
Main Results:
- Established biomarkers include PD-L1 expression, tumor-infiltrating lymphocytes, and mismatch repair deficiency.
- Other biomarkers include inflammatory infiltrate, lymphocyte count, and lactate dehydrogenase.
- Serum autoantibodies show potential for personalized immunotherapy monitoring.
Conclusions:
- Predictive biomarkers are crucial for separating responders from non-responders in immunotherapy.
- Personalized biomarkers can mitigate toxicity and improve therapeutic outcomes.
- Serum autoantibodies represent a promising avenue for personalized cancer immunotherapy.
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