Hallmarks of Resistance to Immune-Checkpoint Inhibitors

Abstract

Immune-checkpoint inhibitors (ICI), although revolutionary in improving long-term survival outcomes, are mostly effective in patients with immune-responsive tumors. Most patients with cancer either do not respond to ICIs at all or experience disease progression after an initial period of response. Treatment resistance to ICIs remains a major challenge and defines the biggest unmet medical need in oncology worldwide. In a collaborative workshop, thought leaders from academic, biopharma, and nonprofit sectors convened to outline a resistance framework to support and guide future immune-resistance research. Here, we explore the initial part of our effort by collating seminal discoveries through the lens of known biological processes. We highlight eight biological processes and refer to them as immune resistance nodes. We examine the seminal discoveries that define each immune resistance node and pose critical questions, which, if answered, would greatly expand our notion of immune resistance. Ultimately, the expansion and application of this work calls for the integration of multiomic high-dimensional analyses from patient-level data to produce a map of resistance phenotypes that can be utilized to guide effective drug development and improved patient outcomes.

Figure 1.

The hallmarks of resistance to ICIs are supported by seminal data sets and organized into key biological processes referred to as resistance nodes.

Figure 2.

Efforts to better understand resistance continue to be of great interest to the oncology community. We postulate that resistance mechanisms to immune-checkpoint blockade converge on eight resistance nodes. A deep and systematic characterization of the eight resistance nodes, using integrated multiomic measurements, is likely to inform better clinical trial design, identify clinically actionable insights, discover composite biomarkers, and reveal new drug targets and treatment combinations. Future advancements will likely provide highly accurate prediction capabilities, making it possible to identify patients who might resist immunotherapy so that more meaningful treatments can be applied. CyTOF, cytometry by time of flight.