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Interferons have been shown to mediate tumour rejection. Here, in a spontaneous mouse model of breast cancer, the authors show that this is disrupted owing to high levels of TGFβ produced in the tumour.

Abstract Concurrent amplifications of EGFR and PDGFRA have been reported in up to 5% of glioblastoma (GBM) and it remains unclear why such independent amplification events, and associated receptor overexpression, would be adaptive during glioma evolution. Here, we document that EGFR and PDGFRA protein co-expression occurs in 37% of GBM. There is wide cell-to-cell variation in the expressions of these receptor tyrosine kinases (RTKs) in stable tumor sphere lines, frequently defining tumor cell subpopulations with distinct sensitivities to growth factors and RTK inhibitors. We also find evidence for functional transactivation of PDGFRA by EGFR and EGF-induced receptor heterodimerization, both of which are abolished by EGFR inhibitors. These results indicate that GBM growth responses to targeted therapies previously tested in clinical trials are strongly influenced by the balance of EGFR and PDGFRA activation in individual cells, which is heterogeneous at baseline.

We have investigated the importance of interleukin-6 (IL-6) in promoting tumor growth and metastasis. In human primary breast cancers, increased levels of IL-6 were found at the tumor leading edge and positively correlated with advanced stage, suggesting a mechanistic link between tumor cell production of IL-6 and invasion. In support of this hypothesis, we showed that the IL-6/Janus kinase (JAK)/signal transducer and activator of transcription 3 (Stat3) pathway drives tumor progression through the stroma and metastatic niche. Overexpression of IL-6 in tumor cell lines promoted myeloid cell recruitment, angiogenesis, and induced metastases. We demonstrated the therapeutic potential of interrupting this pathway with IL-6 receptor blockade or by inhibiting its downstream effectors JAK1/2 or Stat3. These clinically relevant interventions did not inhibit tumor cell proliferation in vitro but had profound effects in vivo on tumor progression, interfering broadly with tumor-supportive stromal functions, including angiogenesis, fibroblast infiltration, and myeloid suppressor cell recruitment in both the tumor and pre-metastatic niche. This study provides the first evidence for IL-6 expression at the leading edge of invasive human breast tumors and demonstrates mechanistically that IL-6/JAK/Stat3 signaling plays a critical and pharmacologically targetable role in orchestrating the composition of the tumor microenvironment that promotes growth, invasion, and metastasis.

Systems biology has embraced computational modeling in response to the quantitative nature and increasing scale of contemporary data sets. The onslaught of data is accelerating as molecular profiling technology evolves. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) is a community effort to catalyze discussion about the design, application, and assessment of systems biology models through annual reverse-engineering challenges.We describe our assessments of the four challenges associated with the third DREAM conference which came to be known as the DREAM3 challenges: signaling cascade identification, signaling response prediction, gene expression prediction, and the DREAM3 in silico network challenge. The challenges, based on anonymized data sets, tested participants in network inference and prediction of measurements. Forty teams submitted 413 predicted networks and measurement test sets. Overall, a handful of best-performer teams were identified, while a majority of teams made predictions that were equivalent to random. Counterintuitively, combining the predictions of multiple teams (including the weaker teams) can in some cases improve predictive power beyond that of any single method.DREAM provides valuable feedback to practitioners of systems biology modeling. Lessons learned from the predictions of the community provide much-needed context for interpreting claims of efficacy of algorithms described in the scientific literature.

Chimeric antigen receptor T cells (CART) have demonstrated curative potential for hematological malignancies, but the optimal manufacturing has not yet been determined and may differ across products. The first step, T cell selection, removes contaminating cell types that can potentially suppress T cell expansion and transduction. While positive selection of CD4/CD8 T cells after leukapheresis is often used in clinical trials, it may modulate signaling cascades downstream of these co-receptors; indeed, the addition of a CD4/CD8-positive selection step altered CD22 CART potency and toxicity in patients. While negative selection may avoid this drawback, it is virtually absent from good manufacturing practices. Here, we performed both CD4/CD8-positive and -negative clinical scale selections of mononuclear cell apheresis products and generated CD22 CARTs per our ongoing clinical trial (NCT02315612NCT02315612). While the selection process did not yield differences in CART expansion or transduction, positively selected CART exhibited a significantly higher in vitro interferon-γ and IL-2 secretion but a lower in vitro tumor killing rate. Notably, though, CD22 CART generated from both selection protocols efficiently eradicated leukemia in NSG mice, with negatively selected cells exhibiting a significant enrichment in γδ CD22 CART. Thus, our study demonstrates the importance of the initial T cell selection process in clinical CART manufacturing.

The strength and duration of interleukin-2 (IL-2) signals is known to regulate T cell memory. Here the authors show that T cell receptor signalling strength controls the requirement for IL-2 signals to form CD8+ T cell memory with high functional activity and a wide range of genes with open chromatin regions.

Data clustering plays a significant role in biomedical sciences, particularly in single-cell data analysis. Researchers use clustering algorithms to group individual cells into populations that can be evaluated across different levels of disease progression, drug response, and other clinical statuses. In many cases, multiple sets of clusters must be generated to assess varying levels of cluster specificity. For example, there are many subtypes of leukocytes (e.g. T cells), whose individual preponderance and phenotype must be assessed for statistical/functional significance. In this report, we introduce a novel hierarchical density clustering algorithm (HAL-x) that uses supervised linkage methods to build a cluster hierarchy on raw single-cell data. With this new approach, HAL-x can quickly predict multiple sets of labels for immense datasets, achieving a considerable improvement in computational efficiency on large datasets compared to existing methods. We also show that cell clusters generated by HAL-x yield near-perfect F1-scores when classifying different clinical statuses based on single-cell profiles. Our hierarchical density clustering algorithm achieves high accuracy in single cell classification in a scalable, tunable and rapid manner.

The Type I Interferon family of cytokines all act through the same cell surface receptor and induce phosphorylation of the same subset of response regulators of the STAT family. Despite their shared receptor, different Type I Interferons have different functions during immune response to infection. In particular, they differ in the potency of their induced anti-viral and anti-proliferative responses in target cells. It remains not fully understood how these functional differences can arise in a ligand-specific manner both at the level of STAT phosphorylation and the downstream function. We use a minimal computational model of Type I Interferon signaling, focusing on Interferon-α and Interferon-β. We validate the model with quantitative experimental data to identify the key determinants of specificity and functional plasticity in Type I Interferon signaling. We investigate different mechanisms of signal discrimination, and how multiple system components such as binding affinity, receptor expression levels and their variability, receptor internalization, short-term negative feedback by SOCS1 protein, and differential receptor expression play together to ensure ligand specificity on the level of STAT phosphorylation. Based on these results, we propose phenomenological functional mappings from STAT activation to downstream anti-viral and anti-proliferative activity to investigate differential signal processing steps downstream of STAT phosphorylation. We find that the negative feedback by the protein USP18, which enhances differences in signaling between Interferons via ligand-dependent refractoriness, can give rise to functional plasticity in Interferon-α and Interferon-β signaling, and explore other factors that control functional plasticity. Beyond Type I Interferon signaling, our results have a broad applicability to questions of signaling specificity and functional plasticity in signaling systems with multiple ligands acting through a bottleneck of a small number of shared receptors.

The role of nitric oxide (NO) in cancer progression has largely been studied in the context of tumor NOS2 expression. However, pro- versus anti-tumor signaling is also affected by tumor cell-macrophage interactions. While these cell-cell interactions are partly regulated by NO, the functional effects of NO flux on proinflammatory (M1) macrophages are unknown. Using a triple negative murine breast cancer model, we explored the potential role of macrophage Nos2 on 4T1 tumor progression. The effects of NO on macrophage phenotype were examined in bone marrow derived macrophages from wild type and Nos2−/− mice following in vitro stimulation with cytokine/LPS combinations to produce low, medium, and high NO flux. Remarkably, Nos2 induction was spatially distinct, where Nos2high cells expressed low cyclooxygenase-2 (Cox2) and vice versa. Importantly, in vitro M1 polarization with IFNγ+LPS induced high NO flux that was restricted to cells harboring depolarized mitochondria. This flux altered the magnitude and spatial extent of hypoxic gradients. Metabolic and single cell analyses demonstrated that single cell Nos2 induction limited the generation of hypoxic gradients in vitro, and Nos2-dependent and independent features may collaborate to regulate M1 functionality. It was found that Cox2 expression was important for Nos2high cells to maintain NO tolerance. Furthermore, Nos2 and Cox2 expression in 4T1 mouse tumors was spatially orthogonal forming distinct cellular neighborhoods. In summary, the location and type of Nos2high cells, NO flux, and the inflammatory status of other cells, such as Cox2high cells in the tumor niche contribute to Nos2 inflammatory mechanisms that promote disease progression of 4T1 tumors. Keywords: Nos2, Macrophage, Tumor, Metabolism

Many drugs are small molecule inhibitors of cell signalling. Through single cell analysis and mathematical modelling here the authors show that cell-to-cell variability diversifies inhibition response into digital and analogue, and that the two translate into distinct long-term functional responses.

T lymphocytes’ ability to discriminate between structurally related antigens has been attributed to the unique signaling properties of the T cell receptor. However, recent studies have suggested that the output of this discrimination process is conditioned by environmental cues. Here, we demonstrate how the IL-2 cytokine, collectively generated by strongly activated T cell clones, can induce weaker T cell clones to proliferate. We identify the PI3K pathway as being critical for integrating the antigen and cytokine responses and for controlling cell-cycle entry. We build a hybrid stochastic/deterministic computational model that accounts for such signal synergism and demonstrates quantitatively how T cells tune their cell-cycle entry according to environmental cytokine cues. Our findings indicate that antigen discrimination by T cells is not solely an intrinsic cellular property but rather a product of integration of multiple cues, including local cues such as antigen quality and quantity, to global ones like the extracellular concentration of inflammatory cytokines.

Abstract Understanding how the immune system decides between tolerance and activation by antigens requires addressing cytokine regulation as a highly dynamic process. We quantified the dynamics of interleukin‐2 (IL‐2) signaling in a population of T cells during an immune response by combining in silico modeling and single‐cell measurements in vitro. We demonstrate that IL‐2 receptor expression levels vary widely among T cells creating a large variability in the ability of the individual cells to consume, produce and participate in IL‐2 signaling within the population. Our model reveals that at the population level, these heterogeneous cells are engaged in a tug‐of‐war for IL‐2 between regulatory (Treg) and effector (Teff) T cells, whereby access to IL‐2 can either increase the survival of Teff cells or the suppressive capacity of Treg cells. This tug‐of‐war is the mechanism enforcing, at the systems level, a core function of Treg cells, namely the specific suppression of survival signals for weakly activated Teff cells but not for strongly activated cells. Our integrated model yields quantitative, experimentally validated predictions for the manipulation of Treg suppression.

Single-cell RNA and protein concentrations dynamically fluctuate because of stochastic ("noisy") regulation. Consequently, biological signaling and genetic networks not only translate stimuli with functional response but also random fluctuations. Intuitively, this feature manifests as the accumulation of fluctuations from the network source to the target. Taking advantage of the fact that noise propagates directionally, we developed a method for causation prediction that does not require time-lagged observations and therefore can be applied to data generated by destructive assays such as immunohistochemistry. Our method for causation prediction, "Inference of Network Directionality Using Covariance Elements (INDUCE)," exploits the theoretical relationship between a change in the strength of a causal interaction and the associated changes in the single cell measured entries of the covariance matrix of protein concentrations. We validated our method for causation prediction in two experimental systems where causation is well established: in an E. coli synthetic gene network, and in MEK to ERK signaling in mammalian cells. We report the first analysis of covariance elements documenting noise propagation from a kinase to a phosphorylated substrate in an endogenous mammalian signaling network.

Variability within isogenic T cell populations yields heterogeneous ‘local’ signaling responses to shared antigenic stimuli, but responding clones may communicate ‘global’ antigen load through paracrine messengers, such as cytokines. Such coordination of individual cell responses within multicellular populations is critical for accurate collective reactions to shared environmental cues. However, cytokine production may saturate as a function of antigen input, or be dominated by the precursor frequency of antigen-specific T cells. Surprisingly, we found that T cells scale their collective output of IL-2 to total antigen input over a large dynamic range, independently of population size. Through experimental quantitation and computational modeling, we demonstrate that this scaling is enforced by an inhibitory cross-talk between antigen and IL-2 signaling, and a nonlinear acceleration of IL-2 secretion per cell. Our study reveals how time-integration of these regulatory loops within individual cell signaling generates scaled collective responses and can be leveraged for immune monitoring.

Chronic Lymphocytic Leukemia (CLL) is defined by a perturbed B-cell receptor-mediated signaling machinery. We aimed to model differential signaling behavior between B cells from CLL and healthy individuals to pinpoint modes of dysregulation.We developed an experimental methodology combining immunophenotyping, multiplexed phosphospecific flow cytometry, and multifactorial statistical modeling. Utilizing patterns of signaling network covariance, we modeled BCR signaling in 67 CLL patients using Partial Least Squares Regression (PLSR). Results from multidimensional modeling were validated using an independent test cohort of 38 patients.We identified a dynamic and variable imbalance between proximal (pSYK, pBTK) and distal (pPLCγ2, pBLNK, ppERK) phosphoresponses. PLSR identified the relationship between upstream tyrosine kinase SYK and its target, PLCγ2, as maximally predictive and sufficient to distinguish CLL from healthy samples, pointing to this juncture in the signaling pathway as a hallmark of CLL B cells. Specific BCR pathway signaling signatures that correlate with the disease and its degree of aggressiveness were identified. Heterogeneity in the PLSR response variable within the B cell population is both a characteristic mark of healthy samples and predictive of disease aggressiveness.Single-cell multidimensional analysis of BCR signaling permitted focused analysis of the variability and heterogeneity of signaling behavior from patient-to-patient, and from cell-to-cell. Disruption of the pSYK/pPLCγ2 relationship is uncovered as a robust hallmark of CLL B cell signaling behavior. Together, these observations implicate novel elements of the BCR signal transduction as potential therapeutic targets.

T-lymphocyte activation displays a remarkable combination of speed, sensitivity, and discrimination in response to peptide-major histocompatibility complex (pMHC) ligand engagement of clonally distributed antigen receptors (T cell receptors or TCRs). Even a few foreign pMHCs on the surface of an antigen-presenting cell trigger effective signaling within seconds, whereas 1 x 10(5)-1 x 10(6) self-pMHC ligands that may differ from the foreign stimulus by only a single amino acid fail to elicit this response. No existing model accounts for this nearly absolute distinction between closely related TCR ligands while also preserving the other canonical features of T-cell responses. Here we document the unexpected highly amplified and digital nature of extracellular signal-regulated kinase (ERK) activation in T cells. Based on this observation and evidence that competing positive- and negative-feedback loops contribute to TCR ligand discrimination, we constructed a new mathematical model of proximal TCR-dependent signaling. The model made clear that competition between a digital positive feedback based on ERK activity and an analog negative feedback involving SH2 domain-containing tyrosine phosphatase (SHP-1) was critical for defining a sharp ligand-discrimination threshold while preserving a rapid and sensitive response. Several nontrivial predictions of this model, including the notion that this threshold is highly sensitive to small changes in SHP-1 expression levels during cellular differentiation, were confirmed by experiment. These results combining computation and experiment reveal that ligand discrimination by T cells is controlled by the dynamics of competing feedback loops that regulate a high-gain digital amplifier, which is itself modulated during differentiation by alterations in the intracellular concentrations of key enzymes. The organization of the signaling network that we model here may be a prototypic solution to the problem of achieving ligand selectivity, low noise, and high sensitivity in biological responses.