Optimizing cancer immunotherapy: Is it time for personalized predictive biomarkers?

Music M1, Prassas I2, Diamandis EP1,2,3,4.

Author information

Abstract

Cancer immunotherapy, a treatment that selectively augments a patient's anti-tumor immune response, is a breakthrough advancement in personalized medicine. A subset of cancer patients undergoing immunotherapy have displayed robust and long-lasting therapeutic responses. Currently, the spotlight is on the use of blocking antibodies against the T-cell checkpoint molecules, cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) and programed cell death-1 (PD-1)/programed death-ligand 1 (PD-L1), which have been effectively used to combat many cancers types. Despite the overall enthusiasm, immune checkpoint blockade inhibitors suffer from significant limitations such as high cost, serious toxicity in a substantial proportion of patients, and a response rate as low as 10%-40% in some clinical trials. Consequently, there is an urgent and unmet medical need for companion biomarkers that could both predict the response of individual patients to these therapies, and provide the means for precise monitoring of their therapeutic outcome. In this era of precision medicine, predictive biomarkers are a hot commodity because they can effectively separate responders from non-responders, and spare non-responders from serious therapy-related toxicity. Emerging predictive biomarkers for immune checkpoint blockade are: PD-L1 expression, increased amounts of tumor-infiltrating lymphocytes, increased mutational load and mismatch repair deficiency. Other well-studied biomarkers include inflammatory infiltrate, absolute lymphocyte count and lactate dehydrogenase levels. We review recent progress on predictive cancer biomarkers in immunotherapy, with a special emphasis on serum autoantibodies that have the potential to be personalized for optimal clinical outcomes.

KEYWORDS:

Proteomics; anti-tumor immune response; autoantibodies; immune checkpoint blockade; immunotherapy; personalized medicine; predictive biomarkers; tumor-associated antigens