LILRB1 blockade enhances bispecific T cell engager antibody-induced tumor cell killing by effector CD8+ T cells

Elicitation of tumor cell killing by CD8+ T cells is an effective therapeutic approach for cancer. In addition to using immune checkpoint blockade to reinvigorate existing but unresponsive tumor-specific T cells, alternative therapeutic approaches have been developed, including stimulation of polyclonal T cell cytolytic activity against tumors using bispecific T cell engager (BiTE) molecules that simultaneously engage the TCR complex and a tumor-associated Ag. BiTE molecules are efficacious against hematologic tumors and are currently being explored as an immunotherapy for solid tumors. To understand mechanisms regulating BiTE molecule­–mediated CD8+ T cell activity against solid tumors, we sought to define human CD8+ T cell populations that efficiently respond to BiTE molecule stimulation and identify factors regulating their cytolytic activity. We find that human CD45RA+CCR7− CD8+ T cells are highly responsive to BiTE molecule stimulation, are enriched in genes associated with cytolytic effector function, and express multiple unique inhibitory receptors, including leukocyte Ig-like receptor B1 (LILRB1). LILRB1 and programmed cell death protein 1 (PD1) were found to be expressed by distinct CD8+ T cell populations, suggesting different roles in regulating the antitumor response. Engaging LILRB1 with its ligand HLA-G on tumor cells significantly inhibited BiTE molecule–induced CD8+ T cell activation. Blockades of LILRB1 and PD1 induced greater CD8+ T cell activation than either treatment alone. Together, our data suggest that LILRB1 functions as a negative regulator of human CD8+ effector T cells and that blocking LILRB1 represents a unique strategy to enhance BiTE molecule therapeutic activity against solid tumors

Toward developing a metastatic breast cancer treatment strategy that incorporates history of response to previous treatments.

BackgroundInformation regarding response to past treatments may provide clues concerning the classes of drugs most or least likely to work for a particular metastatic or neoadjuvant early stage breast cancer patient. However, currently there is no systematized knowledge base that would support clinical treatment decision-making that takes response history into account.MethodsTo model history-dependent response data we leveraged a published in vitro breast cancer viability dataset (84 cell lines, 90 therapeutic compounds) to calculate the odds ratios (log (OR)) of responding to each drug given knowledge of (intrinsic/prior) response to all other agents. This OR matrix assumes (1) response is based on intrinsic rather than acquired characteristics, and (2) intrinsic sensitivity remains unchanged at the time of the next decision point. Fisher's exact test is used to identify predictive pairs and groups of agents (BH p < 0.05). Recommendation systems are used to make further drug recommendations based on past 'history' of response.ResultsOf the 90 compounds, 57 have sensitivity profiles significantly associated with those of at least one other agent, mostly targeted drugs. Nearly all associations are positive, with (intrinsic/prior) sensitivity to one agent predicting sensitivity to others in the same or a related class (OR > 1). In vitro conditional response patterns clustered compounds into five predictive classes: (1) DNA damaging agents, (2) Aurora A kinase and cell cycle checkpoint inhibitors; (3) microtubule poisons; (4) HER2/EGFR inhibitors; and (5) PIK3C catalytic subunit inhibitors. The apriori algorithm implementation made further predictions including a directional association between resistance to HER2 inhibition and sensitivity to proteasome inhibitors.ConclusionsInvestigating drug sensitivity conditioned on observed sensitivity or resistance to prior drugs may be pivotal in informing clinicians deciding on the next line of breast cancer treatments for patients who have progressed on their current treatment. This study supports a strategy of treating patients with different agents in the same class where an associated sensitivity was observed, likely after one or more intervening treatments

Toward developing a metastatic breast cancer treatment strategy that incorporates history of response to previous treatments.

BackgroundInformation regarding response to past treatments may provide clues concerning the classes of drugs most or least likely to work for a particular metastatic or neoadjuvant early stage breast cancer patient. However, currently there is no systematized knowledge base that would support clinical treatment decision-making that takes response history into account.MethodsTo model history-dependent response data we leveraged a published in vitro breast cancer viability dataset (84 cell lines, 90 therapeutic compounds) to calculate the odds ratios (log (OR)) of responding to each drug given knowledge of (intrinsic/prior) response to all other agents. This OR matrix assumes (1) response is based on intrinsic rather than acquired characteristics, and (2) intrinsic sensitivity remains unchanged at the time of the next decision point. Fisher's exact test is used to identify predictive pairs and groups of agents (BH p < 0.05). Recommendation systems are used to make further drug recommendations based on past 'history' of response.ResultsOf the 90 compounds, 57 have sensitivity profiles significantly associated with those of at least one other agent, mostly targeted drugs. Nearly all associations are positive, with (intrinsic/prior) sensitivity to one agent predicting sensitivity to others in the same or a related class (OR > 1). In vitro conditional response patterns clustered compounds into five predictive classes: (1) DNA damaging agents, (2) Aurora A kinase and cell cycle checkpoint inhibitors; (3) microtubule poisons; (4) HER2/EGFR inhibitors; and (5) PIK3C catalytic subunit inhibitors. The apriori algorithm implementation made further predictions including a directional association between resistance to HER2 inhibition and sensitivity to proteasome inhibitors.ConclusionsInvestigating drug sensitivity conditioned on observed sensitivity or resistance to prior drugs may be pivotal in informing clinicians deciding on the next line of breast cancer treatments for patients who have progressed on their current treatment. This study supports a strategy of treating patients with different agents in the same class where an associated sensitivity was observed, likely after one or more intervening treatments

Intravital imaging reveals distinct responses of depleting dynamic tumor-associated macrophage and dendritic cell subpopulations

Tumor-infiltrating inflammatory cells comprise a major part of the stromal microenvironment and support cancer progression by multiple mechanisms. High numbers of tumor myeloid cells correlate with poor prognosis in breast cancer and are coupled with the angiogenic switch and malignant progression. However, the specific roles and regulation of heterogeneous tumor myeloid populations are incompletely understood. CSF-1 is a major myeloid cell mitogen, and signaling through its receptor CSF-1R is also linked to poor outcomes. To characterize myeloid cell function in tumors, we combined confocal intravital microscopy with depletion of CSF-1R-dependent cells using a neutralizing CSF-1R antibody in the mouse mammary tumor virus long-terminal region-driven polyoma middle T antigen breast cancer model. The depleted cells shared markers of tumor-associated macrophages and dendritic cells (M-DCs), matching the phenotype of tumor dendritic cells that take up antigens and interact with T cells. We defined functional subgroups within the M-DC population by imaging endocytic and matrix metalloproteinase activity. Anti-CSF-1R treatment altered stromal dynamics and impaired both survival of M-DCs and accumulation of new M-DCs, but did not deplete Gr-1(+) neutrophils or block doxorubicin-induced myeloid cell recruitment, and had a minimal effect on lung myeloid cells. Nevertheless, prolonged treatment led to delayed tumor growth, reduced vascularity, and decreased lung metastasis. Because the myeloid infiltrate in metastatic lungs differed significantly from that in mammary tumors, the reduction in metastasis may result from the impact on primary tumors. The combination of functional analysis by intravital imaging with cellular characterization has refined our understanding of the effects of experimental targeted therapies on the tumor microenvironment

Targeted Activation in Localized Protein Environments via Deep Red Photoredox Catalysis

State-of-the art photoactivation strategies in chemical biology provide spatiotemporal control and visualization of biological processes. However, using high energy light (λ < 500 nm) for substrate or photocatalyst sensitization can lead to background activation of photoactive small molecule probes and reduce its efficacy in complex biological environments. Here we describe the development of targeted aryl azide activation via deep red light (λ = 660 nm) photoredox catalysis and its use in photocatalyzed proximity labeling. We demonstrate that aryl azides are converted to triplet nitrenes via a novel redox-centric mechanism and show that its spatially localized-formation requires both red light and a photocatalyst-targeting modality. This technology was applied in different colon cancer cell systems for targeted protein environment labeling of epithelial cell adhesion molecule (EpCAM). We identified a small subset of proteins with previously known and unknown association to EpCAM, including CDH3, a clinically relevant protein that shares high tumor selective expression with EpCAM

Electroaffinity Labeling: A New Platform for Chemoproteomic-based Target Identification

Target identification is a critical pillar within the drug discovery process that involves deconvoluting the protein target of a pharmacologically active small molecule ligand. While photoaffinity labeling strategies have become the benchmark for target deconvolution of small molecules owing to their reliance on external activation to induce covalent protein capture, the process of target identification remains one of the most technically challenging aspects of early drug discovery. Thus, there is a strong demand for new technologies that allow for controlled activation of chemical probes to covalently label their protein target. Here, we introduce an electroaffinity labeling (ECAL) platform which leverages the use of a small, redox-active diazetidinone (DZE) functional group to enable chemoproteomic-based target identification of pharmacophores within live cell environments