Role of mass spectrometry-based serum proteomics signatures in predicting clinical outcomes and toxicity in patients with cancer treated with immunotherapy

Abstract

Immunotherapy has fundamentally changed the landscape of cancer treatment. However, only a subset of patients respond to immunotherapy, and a significant portion experience immune-related adverse events (irAEs). In addition, the predictive ability of current biomarkers such as programmed death-ligand 1 (PD-L1) remains unreliable and establishing better potential candidate markers is of great importance in selecting patients who would benefit from immunotherapy. Here, we focus on the role of serum-based proteomic tests in predicting the response and toxicity of immunotherapy. Serum proteomic signatures refer to unique patterns of proteins which are associated with immune response in patients with cancer. These protein signatures are derived from patient serum samples based on mass spectrometry and act as biomarkers to predict response to immunotherapy. Using machine learning algorithms, serum proteomic tests were developed through training data sets from advanced non-small cell lung cancer (Host Immune Classifier, Primary Immune Response) and malignant melanoma patients (PerspectIV test). The tests effectively stratified patients into groups with good and poor treatment outcomes independent of PD-L1 expression. Here, we review current evidence in the published literature on three liquid biopsy tests that use biomarkers derived from proteomics and machine learning for use in immuno-oncology. We discuss how these tests may inform patient prognosis as well as guide treatment decisions and predict irAE of immunotherapy. Thus, mass spectrometry-based serum proteomics signatures play an important role in predicting clinical outcomes and toxicity.

Exploring the Molecular Players behind the Potentiation of Chemotherapy Effects by Durvalumab in Lung Adenocarcinoma Cell Lines

“…We are not aware of other studies utilizing a similar experimental approach to explore the cancer-driven (i.e., as compared to the immune system-driven) aspects of ICI sensitivity and resistance. Multiple publications [ 39 , 40 , 41 , 42 , 43 ] have reported proteomic studies on NSCLC patients’ blood serum and plasma (often both before and within the course of the ICI-containing treatment schemes), attempting to correlate the pattern of the measured markers to the observed responsiveness of individual patients to therapy. Another study focused specifically on the blood-circulating endothelial-derived extracellular vesicles (EVs) isolated from NSLC patients’ samples [ 44 ].…”

Advances in artificial intelligence to predict cancer immunotherapy efficacy

“…In addition to genomics and imaging, proteomics technologies have been extensively explored to identify biomarkers for the effectiveness of tumor immunotherapy ( 46 ). An AI-based serum proteomics test model has been developed to predict response to ICIs in patients with metastatic melanoma ( 48 ).…”

The artificial intelligence and machine learning in lung cancer immunotherapy

“…Proteomics is an intuitive biomarker distinct from genomics for the reason that expressed proteins undergo post-translation modifications and interact directly with the host immune system and TME. Recently, mass spectrometry (MS) analysis of serum samples has been utilized to characterize independent proteome features and develop models for predicting clinical outcomes and side effects of patients [ 59 ].…”