通过理解髓系检查点作为T细胞检查点抑制剂耐药患者适应性耐药关键介导因素的作用而开发的癌症精准免疫疗法

Background: T-cell immune checkpoint inhibitor (ICI) therapies can deliver cures to cancer patients. However, about 80% of patients show only transient responses or outright resistance to these therapies. Thus, ICI resistance is a major unmet medical need. A key challenge is the lack of precise understanding of which specific resistance mechanism is operative in which specific subset of patients. Moreover, previous studies have demonstrated that molecular signals for clinical response or resistance to ICI therapies are evident only after initiation of treatment and that pre-treatment molecular profiles are poorly predictive. Hence, a precision strategy that accounts for the homeostatic nature of the immune response and the resulting adaptive nature of ICI resistance is key to successfully developing new ICI monotherapy or combination approaches. Methods: To elucidate adaptive mechanisms of resistance to ICI's and precisely determine which resistance mechanism was relevant in each patient we applied defined machine learning models to analyze serial longitudinal clinical and molecular data from over 400 cancer patients undergoing aPD1/aCTLA4 treatment. This led to the definition of a blood-based biomarker predictive of non-response and the identification of dynamically regulated drivers of resistance that represent targets for precision drug discovery. The newly established links between druggable adaptive resistance targets in ICI resistant patients and blood-based patient selection biomarkers can guide clinical development of agents directed to these targets. Results: Myeloid driven immune suppression was identified as the dominant mechanism of adaptive resistance in patients treated with ICI's. Specific myeloid checkpoints that drive resistance were indentified in subsets of patients that could be defined by protein signatures detectable in blood. These precision biomarkers identified patients across multiple indications in which ICI resistance is driven by a targetable myeloid checkpoint. Bectas has now developed monoclonal antibody therapies to a series of myeloid checkpoint targets identified through this approach. In each case, the antibody therapy is accompanied by a precision biomarker to enable patient selection. Conclusions: Our unique multi-dimensional blood-based biomarker strategy enables early detection of resistance to ICI treatment, and identifies specific myeloid checkpoints driving resistance in specific patients. This precision approach, coupled with first in class antibody therapies directed to these myeloid checkpoints has the potential to significantly accelerate the development of new therapeutic regimes to address the 80% of cancer patients who do not benefit currently from immune-based therapies.