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Macrophages are ubiquitous innate immune cells that play a central role in health and disease by adopting distinct phenotypes, which are broadly divided into classical inflammatory responses and alternative responses that promote immune suppression and wound healing. Although macrophages are attractive therapeutic targets, incomplete understanding of this functional choice limits clinical manipulation. While individual stimuli, pathways, and genes involved in macrophage functional responses have been identified, how macrophages evaluate complex in vivo milieus comprising multiple divergent stimuli remains poorly understood. Here, we used combinations of “incoherent” stimuli—those that individually promote distinct macrophage phenotypes—to elucidate how the immunosuppressive, IL-10-driven macrophage phenotype is induced, maintained, and modulated under such combinatorial stimuli. The IL-10-induced immunosuppressive phenotype was largely insensitive to co-administered IL-12, which has been reported to modulate macrophage phenotype, but maintaining the immunosuppressive phenotype required sustained exposure to IL-10. Our data implicate the intracellular protein, BCL3, as a key mediator of the IL-10-driven phenotype. Notably, co-administration of IFN-γ disrupted an IL-10-mediated positive feedback loop that may reinforce the immunosuppressive phenotype. This novel combinatorial perturbation approach thus generated new insights into macrophage decision making and local immune network function.

Macrophage-initiated inflammation is tightly regulated to eliminate threats such as infections while suppressing harmful immune activation. However, individual cells’ signaling responses to pro-inflammatory cues are heterogeneous, with subpopulations emerging with high or low activation states. Here, we use single-cell tracking and dynamical modeling to develop and validate a revised model for lipopolysaccharide (LPS)-induced macrophage activation that invokes a mechanism we term quorum licensing. The results show that bimodal phenotypic partitioning of macrophages is primed during the resting state, dependent on cumulative history of cell density, predicted by extrinsic noise in transcription factor expression, and independent of canonical LPS-induced intercellular feedback in the tumor necrosis factor (TNF) response. Our analysis shows how this density-dependent coupling produces a nonlinear effect on collective TNF production. We speculate that by linking macrophage density to activation, this mechanism could amplify local responses to threats and prevent false alarms., Macrophage activation is tightly regulated to maintain immune homeostasis, yet activation is also heterogeneous. Here, the authors show that macrophages coordinate activation by partitioning into two phenotypes that can nonlinearly amplify collective inflammatory cytokine production as a function of cell density.

Tumor growth involves a dynamic interplay between cancer cells and host cells, which collectively form a tumor microenvironmental network that either suppresses or promotes tumor growth under different conditions. The transition from tumor suppression to tumor promotion is mediated by a tumor-induced shift in the local immune state, and despite the clinical challenge this shift poses, little is known about how such dysfunctional immune states are initiated. Clinical and experimental observations have indicated that differences in both the composition and spatial distribution of different cell types and/or signaling molecules within the tumor microenvironment can strongly impact tumor pathogenesis and ultimately patient prognosis. How such “functional” and “spatial” heterogeneities confer such effects, however, is not known. To investigate these phenomena at a level currently inaccessible by direct observation, we developed a computational model of a nascent metastatic tumor capturing salient features of known tumor-immune interactions that faithfully recapitulates key features of existing experimental observations. Surprisingly, over a wide range of model formulations, we observed that heterogeneity in both spatial organization and cell phenotype drove the emergence of immunosuppressive network states. We determined that this observation is general and robust to parameter choice by developing a systems-level sensitivity analysis technique, and we extended this analysis to generate other parameter-independent, experimentally testable hypotheses. Lastly, we leveraged this model as an in silico test bed to evaluate potential strategies for engineering cell-based therapies to overcome tumor associated immune dysfunction and thereby identified modes of immune modulation predicted to be most effective. Collectively, this work establishes a new integrated framework for investigating and modulating tumor-immune networks and provides insights into how such interactions may shape early stages of tumor formation., Author Summary Over the course of tumor growth, cancer cells interact with normal cells via processes that are difficult to understand by experiment alone. This challenge is particularly pronounced at early stages of tumor formation, when experimental observation is most limited. Elucidating such interactions could inform both understanding of cancer and clinical practice. To address this need we developed a computational model capturing the current understanding of how individual metastatic tumor cells and immune cells sense and contribute to the tumor environment, which in turn enabled us to investigate the complex, collective behavior of these systems. Surprisingly, we discovered that tumor escape from immune control was enhanced by the existence of small differences (or heterogeneities) in the responses of individual immune cells to their environment, as well as by heterogeneities in the way that cells and the molecules they secrete are arranged in space. These conclusions held true over a range of model formulations, suggesting that this is a general feature of these tumor-immune networks. Finally, we used this model as a test bed to evaluate potential strategies for enhancing immunological control of early tumors, ultimately predicting that specifically modulating tumor-associated immune dysfunction may be more effective than simply enhanced tumor killing.

e20655 Background: ICOGEN was a randomized Phase III clinical trial comparing two EGFR tyrosine kinase inhibitors (TKI), icotinib (I) and gefitinib (G), in EGFR gene mutation status unknown non-small-cell lung cancer (NSCLC) patients previously treated with platinum doublet chemotherapy. Plasma samples from study patients were retrospectively analyzed with a commercially-available blood-based proteomic test which has been shown to have prognostic and predictive properties in NSCLC. This study represents an updated analysis of ICOGEN to evaluate the ability of the test to predict outcome based on therapeutic regimen. Methods: Available pre-treatment plasma samples from ICOGEN were retrospectively analyzed with the proteomic test which classifies subjects as Good or Poor. Progression free survival (PFS ) and over-all survival (OS) were analyzed within treatment (TX) arms and test classification. Results: 352 subjects were evaluated, with 277(78.7%) classified as Good and 75(21.3%) classified as Poor. Among all patients evaluated with the proteomic test, the median PFS was 4.9 mo. and 2.3 mo. (HR [95% CI] 0.61 [0.46 – 0.81]; p = 0.0004) and median OS was 16.6 mo. and 5.5 mo. (HR [95% CI] 0.39 [0.29 – 0.50]; p < 0.0001) for the Good and Poor sub-groups, respectively. Association between test result and PFS was significant in patients treated with I (6.0 mo vs. 1.9 mo, HR = 0.43, p < 0.0001), but not significant in patients treated with G (3.7 mo vs. 2.5 mo, HR = 0.85, p = 0.429). Further evaluation demonstrated that the proteomic test predicted differential therapeutic benefit between icotinib and gefitinib for PFS (pint = 0.036). In OS, a significant association between test result and outcome was shown in both the I (16.3 mo vs. 4.0 mo, HR [95% CI] 0.27[0.19-0.39]; p < 0.0001) and G (16.6 mo vs. 7.0 mo, HR [95% CI] 0.51[0.35-0.76; p = 0.0008) arms, which trended towards prediction of differential therapeutic benefit between icotinib and gefitinib for OS (pint = 0.086). Conclusions: For ICOGEN’s primary study endpoint, the proteomic test was predictive of differential therapeutic benefit between icotinib and gefitinib in EGFR mutations status unknown NSCLC patients.

Engineered cell-based therapies are uniquely capable of performing sophisticated therapeutic functions in vivo, and this strategy is yielding promising clinical benefits for treating cancer. In this review, we discuss key opportunities and challenges for engineering customized cellular functions using cell-based therapy for cancer as a representative case study. We examine the historical development of chimeric antigen receptor (CAR) therapies as an illustration of the engineering design cycle. We also consider the potential roles that the complementary disciplines of systems biology and synthetic biology may play in realizing safe and effective treatments for a broad range of patients and diseases. In particular, we discuss how systems biology may facilitate both fundamental research and clinical translation, and we describe how the emerging field of synthetic biology is providing novel modalities for building customized cellular functions to overcome existing clinical barriers. Together, these approaches provide a powerful set of conceptual and experimental tools for transforming information into understanding, and for translating understanding into novel therapeutics to establish a new framework for design-driven medicine.

Engineered cell-based therapies are uniquely capable of performing sophisticated therapeutic functions in vivo, and this strategy is yielding promising clinical benefits for treating cancer. In this review, we discuss key opportunities and challenges for engineering customized cellular functions using cell-based therapy for cancer as a representative case study. We examine the historical development of chimeric antigen receptor (CAR) therapies as an illustration of the engineering design cycle. We also consider the potential roles that the complementary disciplines of systems biology and synthetic biology may play in realizing safe and effective treatments for a broad range of patients and diseases. In particular, we discuss how systems biology may facilitate both fundamental research and clinical translation, and we describe how the emerging field of synthetic biology is providing novel modalities for building customized cellular functions to overcome existing clinical barriers. Together, these approaches provide a powerful set of conceptual and experimental tools for transforming information into understanding, and for translating understanding into novel therapeutics to establish a newframework for design-driven medicine.

Macrophages functionally polarize towards either immunostimulatory (M1) or immunosuppressive (M2) phenotypes, and tumor-associated macrophages (TAMs) exhibit an M2-like phenotype that impedes immunotherapy. Although the importance of M2 cells in tumor progression is well established, the process by which macrophages “decide” to adopt an M2 phenotype in complex environments is not well understood and is the subject of this study. Most prior investigations have applied “coherent” stimuli (exclusively pro-M1 or pro-M2 signals), yet these stimuli rarely exist independently in vivo. To understand how macrophages integrate incoherent stimuli (both pro-M1 and pro-M2 simultaneously), we first exposed macrophages to dosed combinations of IL-10 and IL-12. Contrary to reports that IL-12 directly alters macrophage polarization state, we observed that when competing directly with IL-10, IL-12 had little direct effect on macrophage polarization. Instead, our data suggest that maintenance of the IL-10-induced M2 state is mediated via the NFκB inhibitor BCL-3, and the M2 state was disrupted by either removal of IL-10 or co-treatment with the pro-M1 stimulus IFNγ. Together, these data suggest that IL-12 may indirectly promote M1 phenotypes by inducing innate and adaptive immune cells to produce IFNγ in the tumor microenvironment. To investigate how interactions between macrophages may influence polarization outcomes, we next examined polarization in individual cells using flow cytometry. Surprisingly, we observed that M1 and M2 cells co-existed in vitro, and that the probability of polarization towards M2 was dose-dependent on IL-10 and independent of IL-12 co-treatment. These data represent the first evidence to date the macrophage polarization is “stochastic” at a coarse-grained level. Heterogeneity was pronounced immediately following activation via LPS and evolved via dynamics that differed based upon cytokine pretreatment. Finally, we investigated macrophage polarization dynamics in vivo. Although conventional wisdom states that macrophages transition from M1 to M2 during tumor progression, this putative transition is poorly understood and sparsely characterized at early stages of tumor development. Therefore, we characterized the dynamics of macrophage polarization during melanoma progression in a syngeneic murine model (B16F0 cells and C57/Bl6 mice). Heterogeneous macrophage polarization was observed at early time points post-tumor implantation, macrophages transitioned from M1 to M2 (systemically and at the tumor) as CD8+ CTL transitioned into regulatory T cell phenotypes. Such insights reshape our understanding of immune responses and should help to identify novel therapeutic targets and strategies for treating cancer.