Systems Biology is an interdisciplinary field that aims to interrogate and predict complex behaviors of multivariate biological systems. It employs quantitative approaches to understand the integrated behaviors of multiple biological components. In contrast to reductionist approaches, which seek to identify how individual components affect particular phenotypes, systems biology attempts to query the simultaneous responses of many elements to uncover how they work in concert to elicit a given response. It is predicated upon the belief that many biological processes cannot be comprehensively understood by analyses of individual components alone (e.g. a single molecule, cell, etc.), but rather require a holistic appreciation of entire networks and systems (e.g. signaling networks, heterotypic cell-cell interactions, physiologic interplay between organs, etc.). By combining mathematical modeling and computation with experimental and clinical data, systems biologists can construct a framework for understanding the multiscale and temporal elements regulating biological responses and elucidate emergent behaviors.

While the discipline of systems biology became well established around 2000 (Ideker et al., 2001), its underlying concepts have been appreciated for over half a century (Waterman and Theory, 1968; Kitano, 2002). Indeed, some have suggested that the study of medicine, which requires an understanding of the complex interactions between multiple molecules, cell types, and organ systems in response to different treatments over time, represents an original implementation of Systems Biology (Germain, 2018). Nonetheless, recent advances in technologies and computational approaches have enabled researchers to query systems-level dynamics at scales not possible in previous decades (Hood et al., 2004).

Recently, researchers in the fields of both cancer biology and immunology have embraced systems approaches to advance their disciplines. In cancer biology, genomics and proteomics approaches have been implemented to identify the effects of defects in signaling networks on malignant transformation and progression (Sanchez-Vega et al., 2018; Mertins et al., 2016). Next-generation sequencing (NGS) has enabled studies of tumor heterogeneity and clonal evolution (Jacoby et al., 2015). In the United States, the National Cancer Institute formed the Cancer Systems Biology Consortium to promote applications of systems approaches to cancer.

Immunology represents a field that is readily amenable to systems level approaches. Deciphering the immune system requires an understanding of the interactions between numerous cell types, immune receptors, and cytokines as they traverse multiple anatomical locations and organ systems in order to orchestrate effective immune responses. While the multivariate components governing an immune response have been slowly elucidated through reductionist approaches, they have recently become subject to a much more comprehensive characterization through advances in modeling and high-throughput technologies (Davis et al., 2017).

Although the study of tumor immunology can be traced back at least to the advent of Coley’s toxins at the turn of the twentieth century (Starnes, 1992), the recent clinical successes of immunotherapies in the treatment of advanced stage cancers have catalyzed renewed interest in the field. Consequently, cancer systems immunology represents a new avenue of interrogation for understanding how the immune system interacts with tumors during tumorigenesis, progression, and treatment. Cancer systems biology and systems immunology have been reviewed elsewhere (Davis et al., 2017; Faratian, 2010; Suhail et al., 2019; Germain et al., 2011; Vera, 2015; Werner et al., 2014; Korsunsky et al., 2014; Kreeger and Lauffenburger, 2010; Chuang et al., 2010). In this review, we will discuss approaches to the nascent field of cancer systems immunology as well as their potential applications and current limitations.

Traditionally, animal models have served as critical tools to cancer biologists and immunologists as they try to decipher how tumors affect the host organism or how the immune response is orchestrated across multiple tissues, respectively. Nonetheless, animal models are frequently imperfect surrogates for human biology. While orthologous genes typically elicit similar functions across species, there are many instances where there exists a stark divergence in phenotypes for orthologs of different species (Gharib and Robinson-Rechavi, 2011; Koonin, 2005). Furthermore, there are even greater discrepancies between gene products that elicit the same functions, often reflecting a high degree of convergent evolution (Koonin, 2003). For example, inhibitory signaling in natural killer (NK) cells following recognition of major histocompatibility (MHC) class Ia molecules is achieved by Ly49 family members in mice but killer immunoglobulin-like receptors (KIRs) in humans (Lanier, 2005; Karlhofer et al., 1992; Moretta et al., 1990). In addition to differences in orthology, the cellular immune repertoires and the very existence of their associated effector molecules can vary significantly between species (Mestas and Hughes, 2004). All these factors frequently conspire to yield failed translation of therapeutic approaches when moving from preclinical models to human clinical trials (Denayer et al., 2014). As discussed throughout our report, systems biology offers potential solutions to this otherwise vexing problem through its ability to bridge data sets and models across species. Indeed, systems biology approaches have already provided predictive insights into human responses where preclinical models alone may be insufficient or inaccurate.

Computational biologists have developed a variety of tools to translate findings from preclinical models to humans when simple matching of orthologs is insufficient for predicting responses (Brubaker and Lauffenburger, 2020). To overcome differences in gene-to-function relationships and predict human responses from rodent data, researchers used Bayesian analysis of gene expression to define ‘functional orthologs’ across species (Chikina and Troyanskaya, 2011), and others have applied unsupervised and semi-supervised machine learning approaches to transcriptomic and proteomic data generated in rodent models to predict human responses (Brubaker et al., 2019). On collaborative initiatiative, termed SBV-IMPROVER (Systems Biology Verification for Industrial Methodology for PROcess VErification in Research), sought to develop computational methods capable of cross-species translation using multimodal datasets including transcriptomics, phosphoproteomics, and cytokine data (Poussin et al., 2014; Rhrissorrakrai et al., 2015). Solutions ranged from approaches using support vector machines, neural networks, random forest trees, and more, with no one algorithm outperforming the others across all datasets. These types of cross-species comparisons have been extended to single-cell analyses, wherein cell types can be defined separately in the different organisms and matched manually, through correlation analyses, or through the use of random forest machine learning (Tosches et al., 2018; Butler et al., 2018; Shafer, 2019; Elyada et al., 2019). Nonetheless, the choice of preclinical model is critical as a given model may not be as informative as another (Olson et al., 2018). For example, transplantable pancreatic cancer models did not predict the limited efficacy of gemcitabine as accurately as autochthonous models due to a lack of desmoplastic stroma in the transplantable setting (Olive et al., 2009).

Even with improved animal models, however, there are many instances where they fail to predict clinical responses, and only 8% of drugs entering clinical trials succeed in Phase I (Mak et al., 2014). Thus, the use of systems approaches, informed by existing data sets, to accurately predict human responses in the absence of accurate animal models represents an important opportunity to improve translation. Mathematical models have proven effective at accurately predicting many aspects of cancer biology ranging from growth kinetics and tumor evolution to responses to therapy (Altrock et al., 2015). For example, such models have tracked the development of resistance in CML (Michor et al., 2005) or have served as the basis for clinical trials altering dosing strategies (Norton and Simon, 1977; Citron et al., 2003). Critically, mathematical models have been used to inform clinical decisions. In an approach they term ‘adaptive therapy’, one group used an evolutionary game theory model to predict patient-specific treatment responses in patients with castration-resistant prostate cancer, modifying their treatment accordingly, and in doing so, extended the time to progression in these patients (Zhang et al., 2017a). This study highlights the profound impact systems-level mathematical modeling can impart on clinical decision-making. Systems immunology has been applied particularly in the field of vaccinology to reduce the reliance upon animal models (Davis et al., 2017), and these types of modeling approaches have recently been extended to cancer immunology to understand how treatment regimens can be tailored to improve immune responses (Park et al., 2019). By extending these approaches further, cancer systems immunology holds the potential to inform clinical approaches when animal models are insufficient.

The multivariate nature of systems biology has rendered it particularly applicable to comprehensive datasets derived from quantification of systems-level parameters (e.g. genomics, transcriptomics, metabolomics, proteomics, etc.). Indeed, the beginning of modern-era systems biology largely coincides with the Human Genome Project (Lander et al., 2001; Venter et al., 2001; International Human Genome Sequencing Consortium, 2004), which enabled researchers to interrogate the genome-wide contributions of mutations to diseases. It is worth noting that systems biology does not require the use of any of these advanced technologies, and many mathematical and computational models were derived from experimental evidence collected with conventional assays. Similarly, the use of these next-generation technologies for a particular study does not, in and of itself, constitute a systems biology approach. Frequently, such technologies are used to screen for targets that are subsequently subjected to conventional reductionist analyses (e.g. differential gene expression (DGE) analyses of RNA-seq data to identify a gene of interest). Such approaches alone do not provide a systems-level understanding of a particular phenomenon, as they do not describe emergent behavior that could not be uncovered with reductionist approaches. Nonetheless, a wide range of new technologies has enabled researchers to examine the breath and dynamics of entire systems in order to better understand the interplay of multiple elements and networks (Figure 1; Hood et al., 2004; Ideker, 2001). Here, we describe some of the major technologies adopted by cancer systems immunologists to uncover new biology. Strengths and weaknesses for the various genomic and epigenomic profiling technologies are highlighted in Tables 1–3.

Figure 1

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While often disparate fields, mathematical modeling, computational modeling, informatics tools, and statistical analyses are often inextricably linked, as mathematical modeling (e.g. differential equations) can be aided by computational approaches and bioinformatics tools often rely upon novel or existing mathematical analyses for their implementation. The breadth of modeling approaches and informatics tools in the field of Systems Biology (applied to cancer, the immune system, or both) is too large to review here and has been discussed elsewhere (Germain et al., 2011; Altrock et al., 2015; Meyer and Heiser, 2019; Machado et al., 2011; Norton et al., 2019; Woelke et al., 2010; Stuart and Satija, 2019; Materi and Wishart, 2007). Many modeling approaches, such as Petri Nets (Petri, 1962; Sackmann et al., 2006; Koch, 2015) and Boolean Networks (Thomas, 1973; Saez-Rodriguez et al., 2007; Mai and Liu, 2009; Stoll et al., 2012), have been used extensively to model regulatory and signaling networks within cells (Vieira and Vera-Licona, 2019). While such networks play important roles in governing tumor-immune interactions, the multiscale nature and complexity of immune responses to tumors often renders these approaches challenging to scale up for many applications in cancer systems immunology. Here, we focus on some general principles and tools that are particularly germane to the field of tumor immunology.

Both malignant and immune cells respond to their microenvironments. Factors such as the mechanics of the extracellular matrix (ECM), gradients in cytokines and chemokines, availability of nutrients, and much more drive cells to alter their behavior. Frequently, tumor cells and immune cells influence their own behavior and that of each other by inducing changes in these factors (e.g. immune cells secreting cytokines to recruit and activate more immune cells or cancer cells invading along stiff ECM). To model these elements, mathematicians have frequently turned to systems of ordinary differential equations (ODEs, e.g. for mechanics or chemical reactions) (Ideker, 2001; Bangasser and Odde, 2013) and partial differential equations (PDEs, e.g. for reaction-diffusion analyses) (Turing, 1952; Matzavinos et al., 2004; Khain and Sander, 2006). These models are both intuitive and adaptable and can be solved under many conditions with the assistance of computational approaches. Indeed, such deterministic models have been employed to evaluate tumor-immune interactions (Bellomo et al., 1999; Matzavinos et al., 2004; Owen and Sherratt, 1998). With increasing complexity (e.g. inclusion of many cell types, all producing gradients of multiple soluble factors and responding to them outside the assumptions of steady state conditions), however, analysis of ODEs and PDEs can become cumbersome and computationally impractical, and in such instances, alternate approaches can be more computationally feasible.

A variety of approaches have been used to model competition between immune cells and different clones within tumors. Probabilistic models that utilize evolutionary game theory (EGT) can be amenable to such modeling (Basanta et al., 2012; Pacheco et al., 2014; Stanková et al., 2019; Nowak and Sigmund, 2004; Archetti, 2013; Bellomo and Delitala, 2008). Rule-based approaches have gained popularity for modeling complex systems. Cellular automaton (CA) models can be used to study effects of the microenvironment on tumor growth (Jiao and Torquato, 2011; Gerlee and Anderson, 2007) and immune system homeostasis (Seiden and Celada, 1992; Kaufman et al., 1985). These models place cells within lattices and rely on defined rules to simulate their interactions. Stochasticity (e.g. Brownian motion) can be incorporated into such models as well as the ability to evaluate temporal responses. Perhaps, one of the most powerful modeling approaches in systems biology is agent-based modeling (ABM) (Soheilypour and Mofrad, 2018; Holcombe et al., 2012; Pogson et al., 2008; Odell and Foe, 2008; Drasdo and Höhme, 2005; Ghaffarizadeh et al., 2018; Poleszczuk et al., 2016; An, 2008; Chiacchio et al., 2014). ABM is based upon the interactions of agents, which can be anything from molecules, to cells, to organisms. Their interactions and behaviors are governed by a set of rules, but unlike CA, they are not restricted to a lattice. The agents are autonomous; they can evolve, and their interactions can reveal collective emergent behavior. The ability to simulate interactions between thousands of agents, which could represent a myriad of cell types or molecules, renders this approach particularly amenable to the study of tumor immune interactions (Norton et al., 2019). A number of studies have used ABM to model these interactions during tumor progression (Enderling et al., 2012; Alfonso et al., 2016; Pourhasanzade et al., 2017; Dréau, 2009) or response to immunotherapy (Pappalardo et al., 2011; Gong et al., 2017). PhysiCell is an ABM tool that has been used to interrogate adaptive immune responses to tumors (Ghaffarizadeh et al., 2018; Ozik et al., 2018). In this model, oxygen consumption by cells can result in necrosis and is determined by a normally-distributed parameter representing ‘oncoprotein’ expression. This parameter also affects immunogenicity of a given cell (as an approximation for neoantigen burden) and affects the degree to which immune cells attack the tumor. PhysiCell analyses have revealed how gradients of immunogenicity can ‘trap’ immune cells at the tumor center and allow tumor cells at the outside to escape immune attack and reestablish a tumor (Ozik et al., 2018). Another recent study combined EGT with ABM to model tumor invasion and show how subpopulations within tumors can co-opt distinct macrophage populations to both degrade stroma and suppress immune responses (Gatenbee, 2019).

One of the most promising aspects of systems biology analyses is their potential to integrate observations across multiple physical and temporal scales to discover emergent behavior that is not discernable from analysis of the individual components. This potential is especially attractive in the tumor setting where orchestration of immune responses occurs across all these levels (Figure 2A). To address these types of problems, systems biologists often use multi-level or hybrid analyses (Norton et al., 2019; Gerlee and Anderson, 2007; Letort et al., 2019; Jeon et al., 2010; Deisboeck et al., 2011; Chamseddine and Rejniak, 2019). Such modeling approaches combine continuous or deterministic models (e.g. ODEs and PDEs) with discrete models such as agent-based models (ABMs). By combining discrete and continuum models, researchers can bridge scales and combine bottom-up approaches that efficiently model cell-cell interactions with rules governed by differential equations spanning larger physical scales to achieve a systems-level understanding of the biology (Figure 2B). For example, combining CA models with PDEs has facilitated investigation of tumor-immune interactions during tumor growth (Mallet and De Pillis, 2006). One hybrid model revealed how spatial and phenotypic heterogeneity can lead to immunosuppression (Wells et al., 2015). These hybrid approaches will enable researchers to bridge existing multivariate datasets and observations to generate more holistic models of tumor immune interactions capable of predicting emergent phenomena that may be difficult to discern with existing experimental approaches.

Figure 2

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In addition to the use of computational approaches to develop models of tumor-immune interactions, bioinformaticians have developed a large array of tools for analyzing the data that are produced by the technologies described earlier. While the full extent of these tools is beyond the scope of this review, we highlight a few topics that are particularly relevant to cancer systems immunology.

The reduced cost and increased accessibility of DNA microarrays and NGS have resulted in vast amounts of publicly available human datasets. The Immunological Genome Project (ImmGen) provides transcriptional profiles from all the major immune subsets in mouse and human (Heng et al., 2008; Shay and Kang, 2013). Resources such as InnateDB, ImmuneXpresso, and the Human Immunology Project Consortium also provide large databases of immune expression profiles and interaction networks (Breuer et al., 2013; Kveler et al., 2018; Brusic et al., 2014). Projects such as the Cancer Genome Atlas (TCGA) have collected –omics data from tens of thousands of patients (The Cancer Genome Atlas, 2006). A recent herculean effort by multiple investigators used TCGA data across 33 cancer types and 10,000 tumors to identify six immune subtypes conserved across cancers that can inform outcome predictions and identify regulatory networks independent of tumor type (Thorsson et al., 2018). By performing a pan-cancer meta-analysis of transcriptional profiles in 18,000 patients using a tool called PRECOG, researchers identified specific signatures of distinct leukocyte infiltration that correlates with outcome (Gentles et al., 2015), while another study correlated the antigenicity of tumors with immune responses to characterize the ‘antigenome’ of tumors (Angelova et al., 2015). One of the challenges that arises when analyzing these data is the heterogeneous nature of the tissue collected for these bulk analyses. The sequenced tumors typically contain not only malignant cells but also a variety of stromal and immune populations making it difficult to determine whether observed differences in for example, gene expression or allele frequency, reflect changes within a tumor, changes in other cells, or simply differences in cellular makeup. To address this challenge, bioinformaticians have developed deconvolution algorithms that can approximate cellular content from bulk transcriptomic data (Racle et al., 2017; Newman et al., 2015; Newman et al., 2019; Ahn et al., 2013; Yoshihara et al., 2013; Gong and Szustakowski, 2013; Li et al., 2016b; Becht et al., 2016; Shen-Orr et al., 2010; Zhong et al., 2013; Aran et al., 2017; Quon et al., 2013; Shen-Orr and Gaujoux, 2013; Vallania et al., 2018; Du et al., 2019). CIBERSORTx is capable of not only deconvolving cellular mixtures, but also quantifying cell-type-specific gene expression profiles (Newman et al., 2019). It performs this analysis by developing signature matrices derived from scRNA-seq and FACS sorted bulk RNA-seq datasets. These types of approaches will allow systems biologists to leverage the prodigious amounts of publicly available bulk RNA-seq data to discover how specific immune subsets interact with tumors and influence clinical outcome.

Even with advances in deconvolution, however, scRNA-seq remains one of the most effective methods for evaluating immune profiles within tumors (Shalek et al., 2013; Zilionis et al., 2019; Puram et al., 2017; Azizi et al., 2018; Tirosh et al., 2016; Schelker et al., 2017; Qiu et al., 2019; Zheng et al., 2017). This approach enables interrogation of the immune composition of tumors in an unbiased fashion. The amount of data and diversity of cell types can render interpretation of the datasets challenging, however. Computational biologists have developed a number of tools to analyze these datasets and present the findings in an interpretable fashion (Stegle et al., 2015; Chen et al., 2019a). Typically, expression profiles are clustered in an unsupervised manner and visualized using dimensional reduction techniques (Macosko et al., 2015; Jaitin et al., 2014; Maaten and Hinton, 2008; Žurauskienė and Yau, 2016; Xu and Su, 2015; Guo et al., 2015; Grün et al., 2015; Kiselev et al., 2017; Becht et al., 2019; Levine et al., 2015). Dimensional reduction and visualization approaches such as principal component analysis (PCA) (Pearson, 1901), diffusion maps (Coifman et al., 2005), t-distributed stochastic neighbor embedding (t-SNE) (Maaten and Hinton, 2008), and uniform approximation and projection (UMAP) (McInnes et al., 2018) enable researchers to quickly visualize changes in cell populations. Many ‘all-in-one’ pipelines, such as Seurat, incorporate the normalization, clustering, and visualization algorithms in a single R package to assist in efficient interpretation (Butler et al., 2018; Stuart et al., 2019). A major challenge in scRNA-seq analysis is the occurrence of batch effects. For example, the effects may result in the T cells of one patient clustering more closely with macrophages from the same patient than with T cells from another patient. Such effects are especially prominent when attempting to compare data acquired on different platforms (e.g. 10X Chromium vs. inDrop vs. Fluidigm C1). Furthermore, scRNA-seq captures only a small percentage of the transcripts in any given cell due to undersampling and technical limitationss, resulting in dropout. A variety of techniques have been developed to deal with batch effects and dropout by imputing expression of zero-read genes and defining anchors onto which clusters can be mapped (Stuart et al., 2019; van Dijk et al., 2018). Using a method termed ‘Biscuit’, one approach normalizes and clusters cells simultaneously using co-expression of genes to identify cell types, which are then normalized independently and dropout gene expression is imputed (Azizi et al., 2018; Prabhakaran, 2016). This approach enabled robust comparisons of scRNA-seq datasets across platforms, and was used to demonstrate the diversity of T cell states in human breast tumors.

Single-cell cytometry and imaging technologies also produce large high-parameter datasets whose interpretation has been aided by the development of computational tools. For example, spanning-tree progression analysis of density-normalized events (SPADE) overcomes the limitations of biaxial plots frequently employed in the analysis of conventional flow cytometry to facilitate analysis of mass cytometry data (Bendall et al., 2011; Qiu et al., 2011). This approach uses agglomerative clustering on down-sampled data along with a minimum spanning tree approach to generate a visual representation upon which the original data is displayed. Like many of the mass cytometry analysis tools, SPADE does not rely upon preexisting knowledge and enables an agnostic interpretation of the data. Other methods include viSNE (an adapted version of t-SNE) (Amir et al., 2013), FlowSOM (Van Gassen, 2015), Citrus (Bruggner et al., 2014), PhenoGraph (Levine et al., 2015), X-shift (Samusik et al., 2016). Another tool, Wanderlust, is capable of inferring a trajectory continuum of cell states from mass cytometry data and has been applied to understanding the transitions during B cell development (Bendall et al., 2014). In collaboration with the lab of Garry Nolan, our group developed a tool known as Scaffold Maps that uses manual gating of mass cytometry data to define landmark nodes in combination with force-directed layouts to generate a graphical reference map of the immune system that can be used to compare tissues, species, or other parameters (Spitzer et al., 2015). By augmenting this approach with statistical inference adopted from the significance analysis of microarrays (SAM) (Bair and Tibshirani, 2004), we applied this approach to interrogating cancer immunotherapy in genetically-engineered mouse models of breast cancer and melanoma (Spitzer et al., 2017). These analyses revealed the importance of secondary lymphoid tissues in orchestrating anti-tumor immune responses as well as identified an emergent CD4⁺ T cell population that is a key element of effective immunotherapy. As with mass cytometry, high-parameter imaging modalities require the advent of new analysis tools. A recent study exploring immune infiltration in triple-negative breast cancer patients by MIBI used a deep-learning approach to aid in image segmentation and revealed populational co-occurrence patterns that correlate with prognosis (Keren et al., 2018). Deconvolution approaches have also been applied in combination with multiplexed fluorescence confocal imaging to interrogate immune interactions in entire organs, such as lymph nodes (Gerner et al., 2012). This approach was recently used to demonstrate how autoreactive T cells are regulated by clustering with Tregs and migratory dendritic cells in lymph nodes (Liu et al., 2015). To visualize interactions between immune cells such as T cell interactions with antigen-presenting DCs, researchers have traditionally used two-photon excitation microscopy (Stoll et al., 2002; Mempel et al., 2004; Miller et al., 2002). In a recent study, researchers developed a deep convolution neural network to identify and characterize DC-T cell interactions in fixed tissue samples, thus enabling quantification of these interactions tissues from mice and humans without requiring the use of transgenic animals expressing fluorescent reporters (Liarski et al., 2019). These analyses could have important implications for understanding how tumors interact with the immune system during malignant progression.

Studies investigating the immune status of tumors have revealed that the prognosis of patients often strongly correlates with the degree of T cell infiltration into the local tumor microenvironment (TME) (Galon et al., 2006; Binnewies et al., 2018; Gajewski et al., 2013). Immune infiltrates have also served as effective prognostic biomarkers of response to immune checkpoint blockade (ICB) (Herbst et al., 2014; Tumeh et al., 2014). Tumor immunologists frequently refer to tumors as ‘hot’ or ‘cold’ (or ‘deserts’, in extreme cases) to describe the degree of infiltration of immune cells beyond the tumor margin, but these crude epithets fail to capture the breadth of nuance, and presumably prognostic value, that can be extracted using systems biology approaches to interrogate the TME. Furthermore, the TME does not exist in isolation, but rather is the product of constant communication with the entire organism (Egeblad et al., 2010) rendering its analysis particularly amenable to systems approaches. While most systems analyses have focused on immune responses within the primary tumor or peripheral blood, some recent studies have extended their analyses to the TME of metastatic sites such as LNs (Puram et al., 2017; Tirosh et al., 2016; Kim et al., 2020). Further studies are needed to comprehensively interrogate systemic immune responses to metastases, as lymph node metastases can render the systemic immune response permissive to tumor progression (Reticker-Flynn et al., 2020), and the invocation of systemic immunity is required for the efficacy of immunotherapy (Spitzer et al., 2017).

Macrophages

In the mid-nineteenth century, Rudolf Virchow first recognized that tumors frequently contain leukocytes. He posited that chronic inflammation lies at the origins of tumors (Virchow, 1863; Virchow, 1858), leading Dvorak, 1986, to describe tumors as ‘wounds that do not heal’, a century later. Many of the initial investigations into the TME focused on the role of myeloid cells in promoting tumorigenesis and metastasis (Joyce and Pollard, 2009; Qian and Pollard, 2010; Coussens and Werb, 2002; Engblom et al., 2016). In particular, researchers have focused on macrophages as key instigators of both tumor-promoting inflammation as well as immunosuppression. While macrophages represent essential components of innate immunity due to their capacity to scavenge for microbial pathogens, drive new blood vessel formation, and process and present antigens to lymphocytes, they have long been associated with a range of pathologies including atherosclerosis, cirrhosis, neurodegeneration, and malignancy (Murray and Wynn, 2011). Nearly all solid tumors exhibit evidence of macrophage involvement regardless of the presence of other infiltrating immune types. While tumor-associated macrophages (TAMs) have been reviewed extensively elsewhere (Lewis and Pollard, 2006), we focus here on recent systems biology approaches employed to elucidate their roles in the TME.

Macrophages within tissues are derived from one of two sources: either, they differentiate from circulating bone-marrow-derived monocytes that have extravasated from blood vessels or, in the case of some specialized tissues, they were seeded during development by macrophages derived from the yolk sac or monocytes from the fetal liver (Ginhoux and Guilliams, 2016; Epelman et al., 2014). Langerhans cells of the epidermis (Merad et al., 2002), microglia of the brain (Ginhoux et al., 2010; Ajami et al., 2007; Yona et al., 2013), Kupffer cells of the liver (Yona et al., 2013), and alveolar macrophages of the lungs (Yona et al., 2013; Hashimoto et al., 2013; Guilliams et al., 2013) are all examples of the latter ontogeny and are known as tissue-resident macrophages. Circulating bone-marrow-derived monocytes serve as the other major macrophage source and the primary source after birth. The majority of these monocytes are known as inflammatory or classical monocytes. They express high levels of Ly6C (in mice) and are recruited to sites of inflammation, often through CCR2, where they differentiate into macrophages (although such differentiation typically does not occur during steady state conditions) (Jakubzick et al., 2013; Tsou et al., 2007; Serbina and Pamer, 2006). Ly6C^lo nonclassical or patrolling monocytes typically remain within vessels and crawl along the endothelial walls to clean up debris from dying endothelium (Auffray et al., 2007; Carlin et al., 2013).

The majority of TAM research has focused on macrophages derived from inflammatory monocytes and recruited to the TME (Kitamura et al., 2015; Richards et al., 2013). Classically, macrophages have been divided into M1 and M2 macrophages in an attempt to mimic the nomenclature of T helper (Th) cells (Mills et al., 2000). M1 macrophages (or classically activated macrophages) were thought to represent a state of polarization induced by Th1-derived cytokines such as IFN-γ or microbial stimuli (e.g. LPS). They exhibit elevated MHC-II expression and produce nitrous oxide (NO), reactive oxygen species (ROS), TNF-α, and a milieu of inflammatory cytokines (e.g. IL-12, IL-23, IL-6, IL-1, etc.) (Dalton et al., 1993; Mantovani et al., 2004; Atri et al., 1801). In contrast, M2 macrophages (alternatively-activated macrophages) were initially reported to be polarized by Th2 cytokines such as IL-4 and IL-13 that drive alternative macrophage activation (Stein et al., 1992; Doyle et al., 1994; Gordon, 2003). These macrophages express arginase 1, IL-10, CCL17, CCL22, and CCL24 and are generally considered anti-inflammatory and immunosuppressive (Biswas and Mantovani, 2010). Consequently, M1 macrophages have traditionally been associated with anti-tumor effects while M2 macrophages were considered pro-tumor (though M1 ROS production has been suggested to play a role in tumorigenesis) (Kratochvill et al., 2015; Zhang et al., 2013). Over time, it became apparent that the initial nomenclature, derived primarily from in vitro activation experiments, was likely not sufficient to classify the M2 macrophage states, in particular. Distinctions between Th2 cytokine production, IL-10-mediated immunosuppression, and immune complex and TLR stimulation have led to an effort to subset M2 macrophages into three groups (Mantovani et al., 2004).

Recently, a number of studies that have employed systems approaches have revealed that this binary interpretation of macrophage polarization is vastly oversimplified and fails to capture the diversity of phenotypes exhibited by macrophages (Mosser and Edwards, 2008; Xue et al., 2014; Murray et al., 2014; Martinez and Gordon, 2014; Lavin et al., 2014; Ginhoux et al., 2016; Reinartz et al., 2014; Zhang et al., 2019). One group of investigators interrogated the regulatory landscape of tissue-resident macrophages across a variety of organs by combining RNA-seq, ChIP-seq, and ATAC-seq (Lavin et al., 2014). In this study, the corresponding tissue-resident macrophages from the brain, lungs, liver, spleen, peritoneum, ileum, and colon were purified by FACS, and subjected to these analyses to reveal distinct gene expression, enhancer methylation, and chromatin states depending on their tissue of residence. In accordance with reports describing the considerable plasticity of macrophages, this study also showed that transplanted macrophages could be reprogrammed by their new tissue of residence in a manner that mimics the regulatory landscape of macrophages in those tissues. Seeking to characterize the diversity of macrophage activation states, other investigators performed transcriptional profiling of human macrophages following in vitro activation with a variety of stimuli and found that the cells exhibit a range of distinct states that extend well beyond the conventional M1/M2 polarization axis (Xue et al., 2014). Similarly, TAMs frequently fail to conform to the traditional M1/M2 polarization model (Aras and Zaidi, 2017). In one study that used mass cytometry to profile macrophages and T cells within the tumors of 73 clear cell renal cell carcinoma patients, the authors characterized 17 distinct TAM phenotypes (Chevrier et al., 2017). In lung adenocarcinoma, researchers used both MARS-seq and mass cytometry to reveal that tumor-specific macrophages exhibit distinct profiles from other mononuclear phagocytes, including lung-specific macrophages, conventional macrophages, DCs, and monocytes (Lavin et al., 2017). Characterization of myeloid cell diversity in lung adenocarcinoma using scRNA-seq identified multiple populations of neutrophils, DCs, monocytes, and macrophages that were conserved across patients and species (Zilionis et al., 2019). Of note, while some of the macrophage populations exhibited enrichment for M2 gene signatures, they did not cluster to a singular state and the myeloid cells, in general, exhibit a spectrum of states within tumors. Similarly, another study performed scRNA-seq on 45,000 leukocytes from human breast tumors along with corresponding blood, lymph nodes, and normal breast tissue (Azizi et al., 2018). Using diffusion maps (Coifman et al., 2005), they characterized the heterogeneity in both the T cell and myeloid compartments. In particular, they noted considerable diversity in the myeloid compartments, and surprisingly, found a strong positive correlation between M1 and M2 gene signatures (Figure 3). The three TAM populations that they identified often expressed both gene signatures in the same cells, again suggesting that the original M1/M2 polarization concept does not recapitulate what occurs in vivo, particularly in human tumors. Taken together, these studies suggest that macrophages within tumors do not comport to the theorized discrete M1/M2 polarization states, but rather exist within a spectrum of activation states that even permits concomitant expression of the two profiles. While mixtures of the transcriptional and epigenetic repression by the opposing cytokines IL-4 and IFN-γ may be partially responsible for the mixed phenotypes exhibited in vivo where complex mixtures of cytokines are present (Piccolo et al., 2017), it still remains unclear how such inhibitory programs could result in coexpression of both gene sets within the same cells. Adding to the complexity of TAM characterization, a number of recent studies investigating macrophage ontogeny in the context of tumors have revealed that TAMs are not only derived from bone marrow-derived monocyte precursors, but also tissue-resident macrophages seeded during embryogenesis. A recent study that used parabiosis and congenic mice revealed that TAMs in pancreatic ductal adenocarcinoma are derived from both blood monocytes and tissue-resident macrophages and that these populations serve distinct functions during tumor progression (Zhu et al., 2017). Similarly, lineage-tracing approaches, scRNA-seq, and ATAC-seq have all been used to reveal that both microglia and bone marrow-derived macrophages are involved in gliomas and exhibit distinct profiles and contributions to tumor progression (Bowman et al., 2016; Müller et al., 2017). Through an appreciation of the diversity, ontogeny, and complexity of TAM phenotypes cancer systems biologists are beginning to reveal how the interplay of these populations can facilitate tumor progression.

Figure 3

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Systems approaches to designing, monitoring, and evaluating clinical responses to tumor immunotherapy

Cancer immunotherapy, and ICB in particular, represents one of the most significant advances in oncology in decades and has the ability to elicit durable responses and cures in some patients with advanced stage malignancies (Rosenberg and Restifo, 2015; Brahmer et al., 2012; Hamid et al., 2013; Hodi et al., 2010; Nghiem et al., 2016; Pardoll, 2012; Robert et al., 2015; Sharma and Allison, 2015; Rizvi et al., 2015b; Grupp et al., 2013; Brentjens et al., 2013; Porter et al., 2011; June et al., 2018). Nonetheless, while subsets of patients with melanoma, RCC, Hodgkin’s lymphoma, NSCLC, urothelial cancer, and MSI high GI cancers have experienced durable responses and even complete tumor regression (Zappasodi et al., 2018), the majority of patients who receive the therapy do not exhibit such responses. Thus, there is an urgent need to distinguish in advance of treatment which patients will respond to which therapies as well as learn why certain patients exhibit durable responses while others either fail to respond at all or relapse after varying periods of response. The tools and approaches used by systems biologists to investigate tumor immune biology have enabled a variety of new approaches for designing and evaluating clinical responses. In many ways, most clinical practice could be construed as systems biology. Clinicians integrate cellular and molecular blood biomarkers (e.g. blood chemistry, complete blood counts, hematocrit, serum immunoglobulin levels, etc.), imaging, and biophysical measurements (e.g. temperature, blood pressure, etc.) in a longitudinal manner to gain an understanding of disease progression. Thus, the practice of medicine epitomizes the multiscale integrated analyses that lie at the core of systems biology. Indeed, pharmaceutical companies have used systems biology approaches in drug discovery and clinical trial design (Butcher et al., 2004). This holistic integration of multi-level datasets and the quantitation tools that accompany them render medicine amenable to the recent advances in cancer systems immunology and their application to evaluating tumor immunotherapy (Figure 4).

Figure 4

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Many of the systems approaches discussed in this review are now being applied to advancing our understanding of the effects of existing immunotherapies and designing new approaches. As discussed in the previous section, researchers and clinicians have combined WES of patient tumors with HLA-binding and TCR prediction algorithms to design personalized neoantigen peptide vaccines (Figure 4; Ott et al., 2017; Hu et al., 2017b; Keskin et al., 2019). Staining for targeted costimulatory molecules (e.g. PD-L1) on tumors has exhibited variable predictive power in ICB, showing positive correlations in many studies but failing to predict responders whose tumors appear negative for the markers (Topalian et al., 2012; Nishino et al., 2017). In addition to the utility of WES in vaccine design, evaluation of neoantigen burden by WES has revealed that the response to anti-CTLA-4 (Snyder et al., 2014) and anti-PD1 (Rizvi et al., 2015a) therapies correlates with mutational burden. Recent studies have sought to exploit mass cytometry to develop high-dimensional cellular biomarkers of treatment response to ICB (Krieg et al., 2018; Wistuba-Hamprecht et al., 2017). One study identified not only differences in lymphocyte populations but also evidence of classical monocyte activation in PBMCs of patients who responded to anti-PD1 (Krieg et al., 2018). Additionally, standardized mass cytometry-based immunophenotyping approaches have been developed for monitoring clinical responses to immunotherapies (Hartmann et al., 2019), and this approach is currently being applied to monitoring responses to DC vaccination trials (Nowicki et al., 2019). Similarly, scRNA-seq approaches are being considered for monitoring immune responses to therapies and evaluating tumor heterogeneity, and will become increasingly feasible with the continual reduction in cost of these technologies (Shalek and Benson, 2017; Kim et al., 2016).

In addition to monitoring therapy in patients, systems approaches can reveal underlying biology about responses to treatment. A recent study revealing the clonal replacement of exhausted T cells following ICB is one such example demonstrating the power of integrative systems analyses to improve our understanding of the mechanisms of ICB (Figure 4; Yost et al., 2019). We believe that it is important to gain a holistic understanding of how immunity is orchestrated across an organism in order to understand the basis of immune responses in cancer. By combining our visualization method known as Statistical Scaffold with mass cytometry, we were able to generate an organism-wide map of differences in immune responses that distinguish effective from ineffective immunotherapy (Spitzer et al., 2017). This approach allowed us to compare immune repertoires in distinct tissues to each other and across treatment conditions, facilitating a systems-level understanding of immune responses across an organism. In addition to identifying differences in key immune populations such as a subset activated memory CD4⁺ T cells, we found that the key differentiator of effective immune responses is its orchestration from extratumoral sites such as LNs rather than a simple reinvigoration of existing TILs. Information gleaned from these types of systems-level analyses should facilitate the design of approaches that augment our current arsenal of immunotherapies (Figure 4).

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