Targeting immunosuppressive Ly6C+ classical monocytes reverses anti-PD-1/CTLA-4 immunotherapy resistance

IntroductionDespite significant clinical advancement with the use of immune checkpoint blockade (ICB) in non-small cell lung cancer (NSCLC) there are still a major subset of patients that develop adaptive/acquired resistance. Understanding resistance mechanisms to ICB is critical to developing new therapeutic strategies and improving patient survival. The dynamic nature of the tumor microenvironment and the mutational load driving tumor immunogenicity limit the efficacy to ICB. Recent studies indicate that myeloid cells are drivers of ICB resistance. In this study we sought to understand which immune cells were contributing to resistance and if we could modify them in a way to improve response to ICB therapy.ResultsOur results show that combination anti-PD-1/CTLA-4 produces an initial antitumor effect with evidence of an activated immune response. Upon extended treatment with anti-PD-1/CTLA-4 acquired resistance developed with an increase of the immunosuppressive populations, including T-regulatory cells, neutrophils and monocytes. Addition of anti-Ly6C blocking antibody to anti-PD-1/CTLA-4 was capable of completely reversing treatment resistance and restoring CD8 T cell activity in multiple KP lung cancer models and in the autochthonous lung cancer KrasLSL-G12D/p53fl/fl model. We found that there were higher classical Ly6C+ monocytes in anti-PD-1/CTLA-4 combination resistant tumors. B7 blockade illustrated the importance of dendritic cells for treatment efficacy of anti-Ly6C/PD-1/CTLA-4. We further determined that classical Ly6C+ monocytes in anti-PD-1/CTLA-4 resistant tumors are trafficked into the tumor via IFN-γ and the CCL2-CCR2 axis. Mechanistically we found that classical monocytes from ICB resistant tumors were unable to differentiate into antigen presenting cells and instead differentiated into immunosuppressive M2 macrophages or myeloid-derived suppressor cells (MDSC). Classical Ly6C+ monocytes from ICB resistant tumors had a decrease in both Flt3 and PU.1 expression that prevented differentiation into dendritic cells/macrophages.ConclusionsTherapeutically we found that addition of anti-Ly6C to the combination of anti-PD-1/CTLA-4 was capable of complete tumor eradication. Classical Ly6C+ monocytes differentiate into immunosuppressive cells, while blockade of classical monocytes drives dendritic cell differentiation/maturation to reinvigorate the anti-tumor T cell response. These findings support that immunotherapy resistance is associated with infiltrating monocytes and that controlling the differentiation process of monocytes can enhance the therapeutic potential of ICB

The Good, the Bad and the Unknown of CD38 in the Metabolic Microenvironment and Immune Cell Functionality of Solid Tumors

The regulation of the immune microenvironment within solid tumors has received increasing attention with the development and clinical success of immune checkpoint blockade therapies, such as those that target the PD-1/PD-L1 axis. The metabolic microenvironment within solid tumors has proven to be an important regulator of both the natural suppression of immune cell functionality and the de novo or acquired resistance to immunotherapy. Enzymatic proteins that generate immunosuppressive metabolites like adenosine are thus attractive targets to couple with immunotherapies to improve clinical efficacy. CD38 is one such enzyme. While the role of CD38 in hematological malignancies has been extensively studied, the impact of CD38 expression within solid tumors is largely unknown, though most current data indicate an immunosuppressive role for CD38. However, CD38 is far from a simple enzyme, and there are several remaining questions that require further study. To effectively treat solid tumors, we must learn as much about this multifaceted protein as possible—i.e., which infiltrating immune cell types express CD38 for functional activities, the most effective CD38 inhibitor(s) to employ, and the influence of other similarly functioning enzymes that may also contribute towards an immunosuppressive microenvironment. Gathering knowledge such as this will allow for intelligent targeting of CD38, the reinvigoration of immune functionality and, ultimately, tumor elimination

A live-cell platform to isolate phenotypically defined subpopulations for spatial multi-omic profiling.

Numerous techniques have been employed to deconstruct the heterogeneity observed in normal and diseased cellular populations, including single cell RNA sequencing, in situ hybridization, and flow cytometry. While these approaches have revolutionized our understanding of heterogeneity, in isolation they cannot correlate phenotypic information within a physiologically relevant live-cell state with molecular profiles. This inability to integrate a live-cell phenotype-such as invasiveness, cell:cell interactions, and changes in spatial positioning-with multi-omic data creates a gap in understanding cellular heterogeneity. We sought to address this gap by employing lab technologies to design a detailed protocol, termed Spatiotemporal Genomic and Cellular Analysis (SaGA), for the precise imaging-based selection, isolation, and expansion of phenotypically distinct live cells. This protocol requires cells expressing a photoconvertible fluorescent protein and employs live cell confocal microscopy to photoconvert a user-defined single cell or set of cells displaying a phenotype of interest. The total population is then extracted from its microenvironment, and the optically highlighted cells are isolated using fluorescence activated cell sorting. SaGA-isolated cells can then be subjected to multi-omics analysis or cellular propagation for in vitro or in vivo studies. This protocol can be applied to a variety of conditions, creating protocol flexibility for user-specific research interests. The SaGA technique can be accomplished in one workday by non-specialists and results in a phenotypically defined cellular subpopulations for integration with multi-omics techniques. We envision this approach providing multi-dimensional datasets exploring the relationship between live cell phenotypes and multi-omic heterogeneity within normal and diseased cellular populations

SaGA schematic to isolate distinct cell(s) based upon live, user-defined phenotypic criteria.

Schematic showing three broad steps of SaGA: 1) Preparation, 2) Selection and isolation, and 3) Analysis. SaGA can be applied to a variety of cell conditions, such as non-adherent, 3-dimensional (3D), and 2-dimensional (2D), for selection, isolation, and analysis of live subpopulations within a parental population. Cells stably expressing a photoconvertible tag can be precisely photoconverted (from green to red) based upon live, user-defined, phenotypic criteria. These red photoconverted cells are then isolated utilizing fluorescence activated cell sorting (FACS) for multi-omic analysis and/or cell cultivation for long-term in vitro and in vivo analyses. Created with Biorender.com.</p

3D spheroid invasive area and circularity quantification.

3D spheroid invasive area and circularity quantification.</p

Example photoconversion in different cell culture conditions.

a. Cells stably expressing a photoconvertible tag (ex: H2B-Dendra2, Pal-Dendra2) can be prepared under non-adherent, 3D, or 2D experimental conditions which illicit distinct and imageable cellular response for photoconversion. Non-adherent conditions were performed with RPMI8226 myeloma cells; H1299 lung cancer cells were used for all other conditions. Scale bar, 50 μm. b, c. Integrated density (relative fluorescence units) quantification of 6 or more cells pre- and post- photoconversion in the green (b) and red (c) channels, emission peaks, 507 nm, and 573 nm, respectively. d, e. Quantification of integrated density percent change of 6 or more cells pre- and post- photoconversion in the green (d) and red (e) channels.</p

Troubleshooting table.

Numerous techniques have been employed to deconstruct the heterogeneity observed in normal and diseased cellular populations, including single cell RNA sequencing, in situ hybridization, and flow cytometry. While these approaches have revolutionized our understanding of heterogeneity, in isolation they cannot correlate phenotypic information within a physiologically relevant live-cell state with molecular profiles. This inability to integrate a live-cell phenotype—such as invasiveness, cell:cell interactions, and changes in spatial positioning—with multi-omic data creates a gap in understanding cellular heterogeneity. We sought to address this gap by employing lab technologies to design a detailed protocol, termed Spatiotemporal Genomic and Cellular Analysis (SaGA), for the precise imaging-based selection, isolation, and expansion of phenotypically distinct live cells. This protocol requires cells expressing a photoconvertible fluorescent protein and employs live cell confocal microscopy to photoconvert a user-defined single cell or set of cells displaying a phenotype of interest. The total population is then extracted from its microenvironment, and the optically highlighted cells are isolated using fluorescence activated cell sorting. SaGA-isolated cells can then be subjected to multi-omics analysis or cellular propagation for in vitro or in vivo studies. This protocol can be applied to a variety of conditions, creating protocol flexibility for user-specific research interests. The SaGA technique can be accomplished in one workday by non-specialists and results in a phenotypically defined cellular subpopulations for integration with multi-omics techniques. We envision this approach providing multi-dimensional datasets exploring the relationship between live cell phenotypes and multi-omic heterogeneity within normal and diseased cellular populations.</div

I cell selection and isolation optimization.

a. Flow plots illustrating stepwise isolation of live photoconverted cells. 8% 405 nm laser line intensity utilized in positive control. b. False positive photoconverted cells due to light reflection off the glass plate at varying photoconversion laser intensities at 405 nm. c. Representative merged image showing photoconversion of multiple cells (orange and yellow cells) in 3D, where intensity change is measured in a neighboring, non-photoconverted cell (representative nearby cell circled in blue). Quantification of 6 or more cells showing fold change of normalized red emission after rounds of photoconversion are complete. d. Representative merged image showing photoconversion in multiple cells (orange and yellow cells) in 2D, where intensity change is measured in a neighboring, non-photoconverted cell (representative nearby cell circled in blue). Quantification of 6 or more cells showing fold change of normalized red emission after rounds of photoconversion are complete. *p < 0.05 by one-way ANOVA with Tukey’s multiple comparisons test. Scale bar, 50 μm.</p

Photoconversion time course guidelines.

Numerous techniques have been employed to deconstruct the heterogeneity observed in normal and diseased cellular populations, including single cell RNA sequencing, in situ hybridization, and flow cytometry. While these approaches have revolutionized our understanding of heterogeneity, in isolation they cannot correlate phenotypic information within a physiologically relevant live-cell state with molecular profiles. This inability to integrate a live-cell phenotype—such as invasiveness, cell:cell interactions, and changes in spatial positioning—with multi-omic data creates a gap in understanding cellular heterogeneity. We sought to address this gap by employing lab technologies to design a detailed protocol, termed Spatiotemporal Genomic and Cellular Analysis (SaGA), for the precise imaging-based selection, isolation, and expansion of phenotypically distinct live cells. This protocol requires cells expressing a photoconvertible fluorescent protein and employs live cell confocal microscopy to photoconvert a user-defined single cell or set of cells displaying a phenotype of interest. The total population is then extracted from its microenvironment, and the optically highlighted cells are isolated using fluorescence activated cell sorting. SaGA-isolated cells can then be subjected to multi-omics analysis or cellular propagation for in vitro or in vivo studies. This protocol can be applied to a variety of conditions, creating protocol flexibility for user-specific research interests. The SaGA technique can be accomplished in one workday by non-specialists and results in a phenotypically defined cellular subpopulations for integration with multi-omics techniques. We envision this approach providing multi-dimensional datasets exploring the relationship between live cell phenotypes and multi-omic heterogeneity within normal and diseased cellular populations.</div

Step-by-step protocol, also available on protocols.io.

Step-by-step protocol, also available on protocols.io.</p