{
  "abstract": "Background T-cell immune checkpoint inhibitor (ICI) therapies represent true breakthroughs in cancer care, delivering cures to cancer patients. However, 75-85% patients show only transient responses or outright resistance to these therapies. Thus, ICI resistance represents a major unmet medical need. A key challenge is the lack of precise understanding of which specific resistance mechanism of action (MOA) 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. 1 Without a precision strategy that accounts for the homeostatic nature of the immune response and the resulting adaptive nature of ICI resistance, the likelihood of successfully developing a new ICI monotherapy or combination will continue to be extremely low.Methods To elucidate adaptive mechanisms of resistance to ICI’s and precisely determine which resistance MOA was relevant in each patient we employed a dynamic approach to precision. Using defined machine learning models we analyzed serial longitudinal clinical and molecular data from 289 patients undergoing aPD1/aCTLA4 treatment, leading to the definition of a blood-based dynamic signature predictive of non-response and the identification of dynamically regulated drivers of resistance that represent targets for precision drug discovery. These results link druggable adaptive resistance targets in ICI resistant patients to blood-based patient selection biomarkers that 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. In addition, we identified specific myeloid checkpoints that drive resistance 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 checkpoint therapies.Reference Chen PL, et al. Analysis of immune signatures in longitudinal tumor samples yields insight into biomarkers of response and mechanisms of resistance to immune checkpoint blockade. Cancer Discov 2016 Aug;6(8):827–37. doi: 10.1158/2159-8290.CD-15-1545. Epub 2016 Jun 14. PMID: 27301722; PMCID: PMC5082984.",
  "authors": [
    {
      "affiliations": [
        "Bectas Therapeutics Inc., Arlington, MA, USA"
      ],
      "name": "Ronan O’Hagan"
    },
    {
      "affiliations": [
        "Bectas Therapeutics Inc., Houston, TX, USA"
      ],
      "name": "Magali Pederzoli-Ribeil"
    }
  ],
  "title": "26 Precision immune therapies for cancer developed through understanding the role of myeloid checkpoints as key mediators of adaptive resistance in T-cell checkpoint inhibitor resistant patients",
  "uid": "0605f991-a84b-5527-8733-905be0d1ec34"
}