Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5346_Библиотеки_им_академика_М_И_Перельмана

[1]. Patients that respond to immune checkpoint blockade therapy (ICB) often enjoy

long-term disease control [2]. Furthermore, remarkable responses can be achieved even in advanced cases of metastatic disease. However, while effective for some patients, ICB fails in more than half of cancer patients [3]. These patients lose valuable therapeutic win­dow while participating in ICB trials or receiving checkpoint blockade as a standard of care. Meanwhile, their disease progresses. They are also at risk for dangerous and poten­tially fatal side effects associated with ICB. This clinical dichotomy of the ICB outcome calls for a personalized approach where immunotherapy is ideally given only to those patients that will benefit. This personalized approach, as opposed to the “one-drug­fits-all” strategy, will not only improve immunotherapy response rates in the trial, but will spare immunotherapy-unresponsive patients the physical, emotional, and financial burden of a treatment failure. The key question is, how to determine if a patient will respond to checkpoint blockade therapy?

There is a tremendous ongoing effort to identify biomarkers that can predict if a patient will benefit from immunotherapy. Alas, we are yet to find immunotherapy response biomarkers that accurately identify immunotherapy candidates. Nevertheless, as a part of this effort, key molecular and genetic correlates of ICB outcome have been identified. This information, along with preclinical discoveries using functional genomics and animal modeling, continues to improve our understanding of tumor immune evasion and intrinsic ICB resistance. This ever-growing knowledge, in turn, fuels the develop­ment of combinatorial strategies to improve ICB outcomes. The promise of personalized immunotherapy is that ICB responders will be identified and treated, while nonre­sponders will receive a highly effective combination treatment specifically tailored to overcome their tumor’s unique immune evasion mechanisms. Below we describe cur­rently used and emerging approaches to predict the outcome of immune checkpoint inhibition.

Multiple ICB agents targeting the interaction of T cell inhibitory receptor PD-1 with its ligand PD-L1 have rapidly emerged and gained FDA approval within the past decade. The success of these therapies in only a subset of patients has prompted a search for response-predictive biomarkers. Malignant and nonmalignant cells within the tumor microenvironment can express PD-L1 to limit the activity of tumor-infiltrating T cells. Therefore, it seems reasonable to think that tumors that highly express PD-L1 will respond robustly to antibodies targeting PD-1/PD-L1 interaction because (a) they are dependent on PD-L1 for immune evasion; and (b) there are therapeutic tar­gets present in the tumor. Along with this logic, PD-L1 expression in the tumor was the first candidate biomarker for anti-PD-1/PD-L1 therapy response [4].