채영광 (Northwestern University Feinberg School of Medicine)

한빛사논문

조회199

Northwestern University Feinberg School of Medicine

CV File 408 kb

J. Immunother. Cancer, Mar 2022, 10 (3) e003566 | https://doi.org/10.1136/jitc-2021-003566 Role of mass spectrometry-based serum proteomics signatures in predicting clinical outcomes and toxicity in patients with cancer treated with immunotherapy

Yeonggyeong Park¹, Min Jeong Kim¹, Yoonhee Choi², Na Hyun Kim³, Leeseul Kim⁴, Seung Pyo Daniel Hong¹, Hyung-Gyo Cho¹, Emma Yu⁵ and Young Kwang Chae⁶

¹Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA

²Department of Internal Medicine, NewYork-Presbyterian Queens, Flushing, New York, USA

³Department of Internal Medicine, AMITA Health Saint Joseph Hospital Chicago, Chicago, Illinois, USA

⁴Department of Internal Medicine, AMITA Health Saint Francis Hospital Evanston, Evanston, Illinois, USA

⁵Department of Medicine, Northwestern University, Chicago, Illinois, USA

⁶Department of Hematology and Oncology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA

Correspondence to Dr Young Kwang Chae

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.

논문정보

형식Review Paper
게재일2022년 03월 (BRIC 등록일 2024-04-08)
연구진국외 연구진
분야 의약학 > 종양의학

댓글 작성 로그인

CV 열람하기

연락처 보기