Head-to-Head Comparison of CCN4, DNMT3A, PTPN11, and SPARC as Suppressors of Anti-tumor Immunity.

Purpose: Emergent cancer cells likely secrete factors that inhibit anti-tumor immunity. To identify such factors, we applied a functional assay with proteomics to an immunotherapy resistant syngeneic mouse melanoma model. Four secreted factors were identified that potentially mediate immunosuppression and could become targets for novel immunotherapies. We tested for consistent clinical correlates in existing human data and verified in vivo whether knocking out tumor cell production of these factors improved immune-mediated control of tumor growth. Methods: Existing human data was analyzed for clinical correlates. A CRISPR/Cas9 approach to generate knockout cell lines and a kinetic analysis leveraging a Markov Chain Monte Carlo (MCMC) approach quantified the various knockouts' effect on cells' intrinsic growth rate. Flow cytometry was used to characterize differences in immune infiltration. Results: While all four gene products were produced by malignant melanocytes, only increased CCN4 expression was associated with reduced survival in primary melanoma patients. In immunocompetent C57BL/6 mice the CCN4 knockout increased survival while the other knockouts had no effect. This survival advantage was lost when the CCN4 knockout cells were injected into immunocompromised hosts, indicating that the effect of CCN4 may be immune mediated. Parameter estimation from the MCMC analysis shows that CCN4 was the only knockout tested that decreased the net tumor growth rate in immunocompetent mice. Flow cytometry showed an increase in NK cell infiltration in CCN4 knockout tumors. Conclusions: The results suggest that CCN4 is a mediator of immunosuppression in the melanoma tumor microenvironment and a potential collateral immunotherapy target. Supplementary Information: The online version contains supplementary material available at 10.1007/s12195-023-00787-7.

Data-driven learning how oncogenic gene expression locally alters heterocellular networks.

Developing drugs increasingly relies on mechanistic modeling and simulation. Models that capture causal relations among genetic drivers of oncogenesis, functional plasticity, and host immunity complement wet experiments. Unfortunately, formulating such mechanistic cell-level models currently relies on hand curation, which can bias how data is interpreted or the priority of drug targets. In modeling molecular-level networks, rules and algorithms are employed to limit a priori biases in formulating mechanistic models. Here we combine digital cytometry with Bayesian network inference to generate causal models of cell-level networks linking an increase in gene expression associated with oncogenesis with alterations in stromal and immune cell subsets from bulk transcriptomic datasets. We predict how increased Cell Communication Network factor 4, a secreted matricellular protein, alters the tumor microenvironment using data from patients diagnosed with breast cancer and melanoma. Predictions are then tested using two immunocompetent mouse models for melanoma, which provide consistent experimental results.

Cell Communication Network factor 4 promotes tumor-induced immunosuppression in melanoma.

Cell Communication Network factor 4 (CCN4/WISP1) is a matricellular protein secreted by cancer cells that promotes metastasis by inducing the epithelial-mesenchymal transition. While metastasis limits survival, limited anti-tumor immunity also associates with poor patient outcomes with recent work linking these two clinical correlates. Motivated by increased CCN4 correlating with dampened anti-tumor immunity in primary melanoma, we test for a direct causal link by knocking out CCN4 (CCN4 KO) in the B16F0 and YUMM1.7 mouse melanoma models. Tumor growth is reduced when CCN4 KO melanoma cells are implanted in immunocompetent but not in immunodeficient mice. Correspondingly, CD45+ tumor-infiltrating leukocytes are significantly increased in CCN4 KO tumors, with increased natural killer and CD8+ T cells and reduced myeloid-derived suppressor cells (MDSC). Among mechanisms linked to local immunosuppression, CCN4 suppresses IFN-gamma release by CD8+ T cells and enhances tumor secretion of MDSC-attracting chemokines like CCL2 and CXCL1. Finally, CCN4 KO potentiates the anti-tumor effect of immune checkpoint blockade (ICB) therapy. Overall, our results suggest that CCN4 promotes tumor-induced immunosuppression and is a potential target for therapeutic combinations with ICB.

An Unsupervised Strategy for Identifying Epithelial-Mesenchymal Transition State Metrics in Breast Cancer and Melanoma.

Digital cytometry aims to identify different cell types in the tumor microenvironment, with the current focus on immune cells. Yet, identifying how changes in tumor cell phenotype, such as the epithelial-mesenchymal transition, influence the immune contexture is emerging as an important question. To extend digital cytometry, we developed an unsupervised feature extraction and selection strategy to capture functional plasticity tailored to breast cancer and melanoma separately. Specifically, principal component analysis coupled with resampling helped develop gene expression-based state metrics that characterize differentiation within an epithelial to mesenchymal-like state space and independently correlate with metastatic potential. First developed using cell lines, the orthogonal state metrics were refined to exclude the contributions of normal fibroblasts and provide tissue-level state estimates using bulk tissue RNA-seq measures. The resulting metrics for differentiation state aim to inform a more holistic view of how the malignant cell phenotype influences the immune contexture within the tumor microenvironment.

Interleukin-12 elicits a non-canonical response in B16 melanoma cells to enhance survival.

BACKGROUND: Oncogenesis rewires signaling networks to confer a fitness advantage to malignant cells. For instance, the B16F0 melanoma cell model creates a cytokine sink for Interleukin-12 (IL-12) to deprive neighboring cells of this important anti-tumor immune signal. While a cytokine sink provides an indirect fitness advantage, does IL-12 provide an intrinsic advantage to B16F0 cells? METHODS: Acute in vitro viability assays were used to compare the cytotoxic effect of imatinib on a melanoma cell line of spontaneous origin (B16F0) with a normal melanocyte cell line (Melan-A) in the presence of IL-12. The results were analyzed using a mathematical model coupled with a Markov Chain Monte Carlo approach to obtain a posterior distribution in the parameters that quantified the biological effect of imatinib and IL-12. Intracellular signaling responses to IL-12 were compared using flow cytometry in 2D6 cells, a cell model for canonical signaling, and B16F0 cells, where potential non-canonical signaling occurs. Bayes Factors were used to select among competing signaling mechanisms that were formulated as mathematical models. Analysis of single cell RNAseq data from human melanoma patients was used to explore generalizability. RESULTS: Functionally, IL-12 enhanced the survival of B16F0 cells but not normal Melan-A melanocytes that were challenged with a cytotoxic agent. Interestingly, the ratio of IL-12 receptor components (IL12RB2:IL12RB1) was increased in B16F0 cells. A similar pattern was observed in human melanoma. To identify a mechanism, we assayed the phosphorylation of proteins involved in canonical IL-12 signaling, STAT4, and cell survival, Akt. In contrast to T cells that exhibited a canonical response to IL-12 by phosphorylating STAT4, IL-12 stimulation of B16F0 cells predominantly phosphorylated Akt. Mechanistically, the differential response in B16F0 cells is explained by both ligand-dependent and ligand-independent aspects to initiate PI3K-AKT signaling upon IL12RB2 homodimerization. Namely, IL-12 promotes IL12RB2 homodimerization with low affinity and IL12RB2 overexpression promotes homodimerization via molecular crowding on the plasma membrane. CONCLUSIONS: The data suggest that B16F0 cells shifted the intracellular response to IL-12 from engaging immune surveillance to favoring cell survival. Identifying how signaling networks are rewired in model systems of spontaneous origin can inspire therapeutic strategies in humans. Interleukin-12 is a key cytokine that promotes anti-tumor immunity, as it is secreted by antigen presenting cells to activate Natural Killer cells and T cells present within the tumor microenvironment. Thinking of cancer as an evolutionary process implies that an immunosuppressive tumor microenvironment could arise during oncogenesis by interfering with endogenous anti-tumor immune signals, like IL-12. Previously, we found that B16F0 cells, a cell line derived from a spontaneous melanoma, interrupts this secreted heterocellular signal by sequestering IL-12, which provides an indirect fitness advantage. Normally, IL-12 signals via a receptor comprised of two components, IL12RB1 and IL12RB2, that are expressed in a 1:1 ratio and activates STAT4 as a downstream effector. Here, we report that B16F0 cells gain an intrinsic advantage by rewiring the canonical response to IL-12 to instead initiate PI3K-AKT signaling, which promotes cell survival. The data suggest a model where overexpressing one component of the IL-12 receptor, IL12RB2, enables melanoma cells to shift the functional response via both IL-12-mediated and molecular crowding-based IL12RB2 homodimerization. To explore the generalizability of these results, we also found that the expression of IL12RB2:IL12RB1 is similarly skewed in human melanoma based on transcriptional profiles of melanoma cells and tumor-infiltrating lymphocytes. Additional file 6: Video abstract. (MP4 600 kb).

Cell Communication Network Factor 4 (CCN4/WISP1) Shifts Melanoma Cells from a Fragile Proliferative State to a Resilient Metastatic State.

INTRODUCTION: Cellular communication network factor 4 (CCN4/WISP1) is a secreted matricellular protein that stimulates metastasis in multiple malignancies but has an unclear impact on phenotypic changes in melanoma. Recent data using cells edited via a double-nickase CRISPR/Cas9 approach suggest that CCN4/WISP1 stimulates invasion and metastasis of melanoma cells. While these data also suggest that loss of CCN4/WISP1 increases cell proliferative, the CRISPR approach used may be an alternative explanation rather than the loss of gene function. METHODS: To test whether CCN4/WISP1 also influences the proliferative phenotype of melanoma cells, we used mouse melanoma models and knocked out Ccn4 using a homology-directed repair CRISPR/Cas9 system to generate pools of Ccn4-knockout cells. The resulting edited cell pools were compared to parental cell lines using an ensemble of in vitro and in vivo assays. RESULTS: In vitro assays using knockout pools supported previous findings that CCN4/WISP1 promoted an epithelial-mesenchymal-like transition in melanoma cells and stimulated invasion and metastasis. While Ccn4 knockout also enhanced cell growth in optimal 2D culture conditions, the knockout suppressed certain cell survival signaling pathways and rendered cells less resistant to stress conditions. Tumor cell growth assays at sub-optimal conditions in vitro, quantitative analysis of tumor growth assays in vivo, and transcriptomics analysis of human melanoma cell lines were also used to quantify changes in phenotype and generalize the findings. CONCLUSIONS: In addition to stimulating invasion and metastasis of melanoma cells, the results suggested that CCN4/WISP1 repressed cell growth and simultaneously enhanced cell survival.

An in silico exploration of combining Interleukin-12 with Oxaliplatin to treat liver-metastatic colorectal cancer.

BACKGROUND: Combining anti-cancer therapies with orthogonal modes of action, such as direct cytotoxicity and immunostimulatory, hold promise for expanding clinical benefit to patients with metastatic disease. For instance, a chemotherapy agent Oxaliplatin (OXP) in combination with Interleukin-12 (IL-12) can eliminate pre-existing liver metastatic colorectal cancer and protect from relapse in a murine model. However, the underlying dynamics associated with the targeted biology and the combinatorial space consisting of possible dosage and timing of each therapy present challenges for optimizing treatment regimens. To address some of these challenges, we developed a predictive simulation platform for optimizing dose and timing of the combination therapy involving Mifepristone-induced IL-12 and chemotherapy agent OXP. METHODS: A multi-scale mathematical model comprised of impulsive ordinary differential equations was developed to describe the interaction between the immune system and tumor cells in response to the combined IL-12 and OXP therapy. An ensemble of model parameters were calibrated to published experimental data using a genetic algorithm and used to represent three different phenotypes: responders, partial-responders, and non-responders. RESULTS: The multi-scale model captures tumor growth patterns of the three phenotypic responses observed in mice in response to the combination therapy against a tumor re-challenge and was used to explore the impacts of changing the dose and timing of the mixed immune-chemotherapy on tumor growth subjected to a tumor re-challenge in mice. An increased ratio of CD8 + T effectors to regulatory T cells during and after treatment was key to improve tumor control in the responder cohort. Sensitivity analysis indicates that combined OXP and IL-12 therapy worked more efficiently in responders by increased priming of T cells, enhanced CD8 + T cell-mediated killing, and functional inhibition of regulatory T cells. In a virtual cohort that mimics non-responders and partial-responders, simulations show that an increased dose of OXP alone would improve the response. In addition, enhanced IL-12 expression alone or an increased number of treatment cycles of the mixed immune-chemotherapy can barely improve tumor control for non-responders and partial responders. CONCLUSIONS: Overall, this study illustrates how mechanistic models can be used for in silico screening of the optimal therapeutic dose and timing in combined cancer treatment strategies.

An elastic-net logistic regression approach to generate classifiers and gene signatures for types of immune cells and T helper cell subsets.

BACKGROUND: Host immune response is coordinated by a variety of different specialized cell types that vary in time and location. While host immune response can be studied using conventional low-dimensional approaches, advances in transcriptomics analysis may provide a less biased view. Yet, leveraging transcriptomics data to identify immune cell subtypes presents challenges for extracting informative gene signatures hidden within a high dimensional transcriptomics space characterized by low sample numbers with noisy and missing values. To address these challenges, we explore using machine learning methods to select gene subsets and estimate gene coefficients simultaneously. RESULTS: Elastic-net logistic regression, a type of machine learning, was used to construct separate classifiers for ten different types of immune cell and for five T helper cell subsets. The resulting classifiers were then used to develop gene signatures that best discriminate among immune cell types and T helper cell subsets using RNA-seq datasets. We validated the approach using single-cell RNA-seq (scRNA-seq) datasets, which gave consistent results. In addition, we classified cell types that were previously unannotated. Finally, we benchmarked the proposed gene signatures against other existing gene signatures. CONCLUSIONS: Developed classifiers can be used as priors in predicting the extent and functional orientation of the host immune response in diseases, such as cancer, where transcriptomic profiling of bulk tissue samples and single cells are routinely employed. Information that can provide insight into the mechanistic basis of disease and therapeutic response. The source code and documentation are available through GitHub: https://github.com/KlinkeLab/ImmClass2019 .

WNT1-inducible signaling pathway protein 1 (WISP1/CCN4) stimulates melanoma invasion and metastasis by promoting the epithelial-mesenchymal transition.

Besides intrinsic changes, malignant cells also release soluble signals that reshape their microenvironment. Among these signals is WNT1-inducible signaling pathway protein 1 (WISP1), a secreted matricellular protein whose expression is elevated in several cancers, including melanoma, and is associated with reduced survival of patients diagnosed with primary melanoma. Here, we found that WISP1 knockout increases cell proliferation and represses wound healing, migration, and invasion of mouse and human melanoma cells in multiple in vitro assays. Metastasis assays revealed that WISP1 knockout represses tumor metastasis of B16F10 and YUMM1.7 melanoma cells in both C57BL/6Ncrl and NOD-scid IL2Rγnull (NSG) mice. WT B16F10 cells having an invasion phenotype in a transwell assay possessed a gene expression signature similar to that observed in the epithelial-mesenchymal transition (EMT), including E-cadherin repression and fibronectin and N-cadherin induction. Upon WISP1 knockout, expression of these EMT signature genes went in the opposite direction in both mouse and human cell lines, and EMT-associated gene expression was restored upon exposure to media containing WISP1 or to recombinant WISP1 protein. In vivo, Wisp1 knockout-associated metastasis repression was reversed by the reintroduction of either WISP1 or snail family transcriptional repressor 1 (SNAI1). Experiments testing EMT gene activation and inhibition with recombinant WISP1 or kinase inhibitors in B16F10 and YUMM1.7 cells suggested that WISP1 activates AKT Ser/Thr kinase and that MEK/ERK signaling pathways shift melanoma cells from proliferation to invasion. Our results indicate that WISP1 present within the tumor microenvironment stimulates melanoma invasion and metastasis by promoting an EMT-like process.

Human Breast Cancer Xenograft Model Implicates Peroxisome Proliferator-activated Receptor Signaling as Driver of Cancer-induced Muscle Fatigue.

PURPOSE: This study tested the hypothesis that a patient-derived orthotopic xenograft (PDOX) model would recapitulate the common clinical phenomenon of breast cancer-induced skeletal muscle (SkM) fatigue in the absence of muscle wasting. This study additionally sought to identify drivers of this condition to facilitate the development of therapeutic agents for patients with breast cancer experiencing muscle fatigue. EXPERIMENTAL DESIGN: Eight female BC-PDOX-bearing mice were produced via transplantation of tumor tissue from 8 female patients with breast cancer. Individual hind limb muscles from BC-PDOX mice were isolated at euthanasia for RNA-sequencing, gene and protein analyses, and an ex vivo muscle contraction protocol to quantify tumor-induced aberrations in SkM function. Differentially expressed genes (DEG) in the BC-PDOX mice relative to control mice were identified using DESeq2, and multiple bioinformatics platforms were employed to contextualize the DEGs. RESULTS: We found that SkM from BC-PDOX-bearing mice showed greater fatigability than control mice, despite no differences in absolute muscle mass. PPAR, mTOR, IL6, IL1, and several other signaling pathways were implicated in the transcriptional changes observed in the BC-PDOX SkM. Moreover, 3 independent in silico analyses identified PPAR signaling as highly dysregulated in the SkM of both BC-PDOX-bearing mice and human patients with early-stage nonmetastatic breast cancer. CONCLUSIONS: Collectively, these data demonstrate that the BC-PDOX model recapitulates the expected breast cancer-induced SkM fatigue and further identify aberrant PPAR signaling as an integral factor in the pathology of this condition.

Exosomes derived from B16F0 melanoma cells alter the transcriptome of cytotoxic T cells that impacts mitochondrial respiration.

While recent clinical studies demonstrate the promise of cancer immunotherapy, a barrier for broadening the clinical benefit is identifying how tumors locally suppress cytotoxic immunity. As an emerging mode of intercellular communication, exosomes secreted by malignant cells can deliver a complex payload of coding and noncoding RNA to cells within the tumor microenvironment. Here, we quantified the RNA payload within tumor-derived exosomes and the resulting dynamic transcriptomic response to cytotoxic T cells upon exosome delivery to better understand how tumor-derived exosomes can alter immune cell function. Exosomes derived from B16F0 melanoma cells were enriched for a subset of coding and noncoding RNAs that did not reflect the abundance in the parental cell. Upon exosome delivery, RNAseq revealed the dynamic changes in the transcriptome of CTLL2 cytotoxic T cells. In analyzing transiently coexpressed gene clusters, pathway enrichment suggested that the B16F0 exosomal payload altered mitochondrial respiration, which was confirmed independently, and upregulated genes associated with the Notch signaling pathway. Interestingly, exosomal miRNA appeared to have no systematic effect on downregulating target mRNA levels. DATABASES: Gene expression data are available in the GEO database under the accession SuperSeries number GSE102951.

Quantifying spontaneous metastasis in a syngeneic mouse melanoma model using real time PCR.

Modeling metastasis in vivo with animals is a priority for both revealing mechanisms of tumor dissemination and developing therapeutic methods. While conventional intravenous injection of tumor cells provides an efficient and consistent system for studying tumor cell extravasation and colonization, studying spontaneous metastasis derived from orthotopic tumor sites has the advantage of modeling more aspects of the metastatic cascade, but is challenging as it is difficult to detect small numbers of metastatic cells. In this work, we developed an approach for quantifying spontaneous metastasis in the syngeneic mouse B16 system using real time PCR. We first transduced B16 cells with lentivirus expressing firefly luciferase Luc2 gene for bioluminescence imaging. Next, we developed a real time quantitative PCR (qPCR) method for the detection of luciferase-expressing, metastatic tumor cells in mouse lungs and other organs. To illustrate the approach, we quantified lung metastasis in both spontaneous and experimental scenarios using B16F0 and B16F10 cells in C57BL/6Ncrl and NOD-Scid Gamma (NSG) mice. We tracked B16 melanoma metastasis with both bioluminescence imaging and qPCR, which were found to be self-consistent. Using this assay, we can quantitatively detect one Luc2 positive tumor cell out of 104 tissue cells, which corresponds to a metastatic burden of 1.8 × 104 metastatic cells per whole mouse lung. More importantly, the qPCR method was at least a factor of 10 more sensitive in detecting metastatic cell dissemination and should be combined with bioluminescence imaging as a high-resolution, end-point method for final metastatic cell quantitation. Given the rapid growth of primary tumors in many mouse models, assays with improved sensitivity can provide better insight into biological mechanisms that underpin tumor metastasis.

Melanoma exosomes deliver a complex biological payload that upregulates PTPN11 to suppress T lymphocyte function.

As exosomes are emerging as a new mode of intercellular communication, we hypothesized that the payload contained within exosomes is shaped by somatic evolution. To test this, we assayed the impact on primary CD8+ T-cell function, a key mechanism for antitumor immunity, of exosomes derived from three melanoma-related cell lines. While morphologically similar, exosomes from each cell line were functionally different, as B16F0 exosomes dose-dependently suppressed T-cell proliferation. In contrast, Cloudman S91 exosomes promoted T-cell proliferation and Melan-A exosomes had a negligible effect on primary CD8+ T cells. Mechanistically, transcript profiling suggested that exosomal mRNA is enriched for full-length mRNAs that target immune-related pathways. Interestingly, B16F0 exosomes were unique in that they contained both protein and mRNA for PTPN11, which inhibited T-cell proliferation. Collectively, the results suggest that upregulation of PTPN11 by B16F0 exosomes to tumor infiltrating lymphocytes would bypass the extracellular control of the immune checkpoints.

Eavesdropping on altered cell-to-cell signaling in cancer by secretome profiling.

In the past decade, cumulative clinical experiences with molecular targeted therapies and immunotherapies for cancer have promoted a shift in our conceptual understanding of cancer. This view shifted from viewing solid tumors as a homogeneous mass of malignant cells to viewing tumors as heterogeneous structures that are dynamically shaped by intercellular interactions among the variety of stromal, immune, and malignant cells present within the tumor microenvironment. As in any dynamic system, identifying how cells communicate to maintain homeostasis and how this communication is altered during oncogenesis are key hurdles for developing therapies to restore normal tissue homeostasis. Here, I discuss tissues as dynamic systems, using the mammary gland as an example, and the evolutionary concepts applied to oncogenesis. Drawing from these concepts, I present 2 competing hypotheses for how intercellular communication might be altered during oncogenesis. As an initial test of these competing hypotheses, a recent secretome comparison between normal human mammary and HER2+ breast cancer cell lines suggested that the particular proteins secreted by the malignant cells reflect a convergent evolutionary path associated with oncogenesis in a specific anatomical niche, despite arising in different individuals. Overall, this study illustrates the emerging power of secretome proteomics to probe, in an unbiased way, how intercellular communication changes during oncogenesis.

Identifying causal networks linking cancer processes and anti-tumor immunity using Bayesian network inference and metagene constructs.

Cancer arises from a deregulation of both intracellular and intercellular networks that maintain system homeostasis. Identifying the architecture of these networks and how they are changed in cancer is a pre-requisite for designing drugs to restore homeostasis. Since intercellular networks only appear in intact systems, it is difficult to identify how these networks become altered in human cancer using many of the common experimental models. To overcome this, we used the diversity in normal and malignant human tissue samples from the Cancer Genome Atlas (TCGA) database of human breast cancer to identify the topology associated with intercellular networks in vivo. To improve the underlying biological signals, we constructed Bayesian networks using metagene constructs, which represented groups of genes that are concomitantly associated with different immune and cancer states. We also used bootstrap resampling to establish the significance associated with the inferred networks. In short, we found opposing relationships between cell proliferation and epithelial-to-mesenchymal transformation (EMT) with regards to macrophage polarization. These results were consistent across multiple carcinomas in that proliferation was associated with a type 1 cell-mediated anti-tumor immune response and EMT was associated with a pro-tumor anti-inflammatory response. To address the identifiability of these networks from other datasets, we could identify the relationship between EMT and macrophage polarization with fewer samples when the Bayesian network was generated from malignant samples alone. However, the relationship between proliferation and macrophage polarization was identified with fewer samples when the samples were taken from a combination of the normal and malignant samples. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:470-479, 2016.

Inferring the Impact of Regulatory Mechanisms that Underpin CD8+ T Cell Control of B16 Tumor Growth In vivo Using Mechanistic Models and Simulation.

A major barrier for broadening the efficacy of immunotherapies for cancer is identifying key mechanisms that limit the efficacy of tumor infiltrating lymphocytes. Yet, identifying these mechanisms using human samples and mouse models for cancer remains a challenge. While interactions between cancer and the immune system are dynamic and non-linear, identifying the relative roles that biological components play in regulating anti-tumor immunity commonly relies on human intuition alone, which can be limited by cognitive biases. To assist natural intuition, modeling and simulation play an emerging role in identifying therapeutic mechanisms. To illustrate the approach, we developed a multi-scale mechanistic model to describe the control of tumor growth by a primary response of CD8+ T cells against defined tumor antigens using the B16 C57Bl/6 mouse model for malignant melanoma. The mechanistic model was calibrated to data obtained following adenovirus-based immunization and validated to data obtained following adoptive transfer of transgenic CD8+ T cells. More importantly, we use simulation to test whether the postulated network topology, that is the modeled biological components and their associated interactions, is sufficient to capture the observed anti-tumor immune response. Given the available data, the simulation results also provided a statistical basis for quantifying the relative importance of different mechanisms that underpin CD8+ T cell control of B16F10 growth. By identifying conditions where the postulated network topology is incomplete, we illustrate how this approach can be used as part of an iterative design-build-test cycle to expand the predictive power of the model.

Exosomes: improved methods to characterize their morphology, RNA content, and surface protein biomarkers.

As a type of secreted membrane vesicle, exosomes are an emerging mode of cell-to-cell communication. Yet as exosome samples are commonly contaminated with other extracellular vesicles, the biological roles of exosomes in regulating immunity and promoting oncogenesis remain controversial. Wondering whether existing methods could distort our view of exosome biology, we compared two direct methods for imaging extracellular vesicles and quantified the impact of different production and storage conditions on the quality of exosome samples. Scanning electron microscopy (SEM) was compared to transmission electron microscopy (TEM) as alternatives to examine the morphology of exosomes. Using SEM, we were able to distinguish exosomes from other contaminating extracellular vesicles based on the size distribution. More importantly, freezing of samples prior to SEM imaging made it more difficult to distinguish exosomes from extracellular vesicles secreted during cell death. In addition to morphology, the quality of RNA contained within the exosomes was characterized under different storage conditions, where freezing of samples also degraded RNA. Finally, we developed a new flow cytometry approach to assay transmembrane proteins on exosomes. While high-copy-number proteins could be readily detected, detecting low-copy-number proteins was improved using a lipophilic tracer that clustered exosomes. To illustrate this, we observed that exosomes derived from SKBR3 cells, a cell model for human HER2+ breast cancer, contained both HER1 and HER2 but at different levels of abundance. Collectively, these new methods will help to ensure a consistent framework to identify specific roles that exosomes play in regulating cell-to-cell communication.

In silico model-based inference: an emerging approach for inverse problems in engineering better medicines.

Identifying the network of biochemical interactions that underpin disease pathophysiology is a key hurdle in drug discovery. While many components involved in these biological processes are identified, how components organize differently in health and disease remains unclear. In chemical engineering, mechanistic modeling provides a quantitative framework to capture our understanding of a reactive system and test this knowledge against data. Here, we describe an emerging approach to test this knowledge against data that leverages concepts from probability, Bayesian statistics, and chemical kinetics by focusing on two related inverse problems. The first problem is to identify the causal structure of the reaction network, given uncertainty as to how the reactive components interact. The second problem is to identify the values of the model parameters, when a network is known a priori.

Interlocked positive and negative feedback network motifs regulate ß-catenin activity in the adherens junction pathway.

The integrity of epithelial tissue architecture is maintained through adherens junctions that are created through extracellular homotypic protein-protein interactions between cadherin molecules. Cadherins also provide an intracellular scaffold for the formation of a multiprotein complex that contains signaling proteins, including ß-catenin. Environmental factors and controlled tissue reorganization disrupt adherens junctions by cleaving the extracellular binding domain and initiating a series of transcriptional events that aim to restore tissue homeostasis. However, it remains unclear how alterations in cell adhesion coordinate transcriptional events, including those mediated by ß-catenin in this pathway. Here were used quantitative single-cell and population-level in vitro assays to quantify the endogenous pathway dynamics after the proteolytic disruption of the adherens junctions. Using prior knowledge of isolated elements of the overall network, we interpreted these data using in silico model-based inference to identify the topology of the regulatory network. Collectively the data suggest that the regulatory network contains interlocked network motifs consisting of a positive feedback loop, which is used to restore the integrity of adherens junctions, and a negative feedback loop, which is used to limit ß-catenin-induced gene expression.

Enhancing the discovery and development of immunotherapies for cancer using quantitative and systems pharmacology: Interleukin-12 as a case study.

Recent clinical successes of immune checkpoint modulators have unleashed a wave of enthusiasm associated with cancer immunotherapy. However, this enthusiasm is dampened by persistent translational hurdles associated with cancer immunotherapy that mirror the broader pharmaceutical industry. Specifically, the challenges associated with drug discovery and development stem from an incomplete understanding of the biological mechanisms in humans that are targeted by a potential drug and the financial implications of clinical failures. Sustaining progress in expanding the clinical benefit provided by cancer immunotherapy requires reliably identifying new mechanisms of action. Along these lines, quantitative and systems pharmacology (QSP) has been proposed as a means to invigorate the drug discovery and development process. In this review, I discuss two central themes of QSP as applied in the context of cancer immunotherapy. The first theme focuses on a network-centric view of biology as a contrast to a "one-gene, one-receptor, one-mechanism" paradigm prevalent in contemporary drug discovery and development. This theme has been enabled by the advances in wet-lab capabilities to assay biological systems at increasing breadth and resolution. The second theme focuses on integrating mechanistic modeling and simulation with quantitative wet-lab studies. Drawing from recent QSP examples, large-scale mechanistic models that integrate phenotypic signaling-, cellular-, and tissue-level behaviors have the potential to lower many of the translational hurdles associated with cancer immunotherapy. These include prioritizing immunotherapies, developing mechanistic biomarkers that stratify patient populations and that reflect the underlying strength and dynamics of a protective host immune response, and facilitate explicit sharing of our understanding of the underlying biology using mechanistic models as vehicles for dialogue. However, creating such models require a modular approach that assumes that the biological networks remain similar in health and disease. As oncogenesis is associated with re-wiring of these biological networks, I also describe an approach that combines mechanistic modeling with quantitative wet-lab experiments to identify ways in which malignant cells alter these networks, using Interleukin-12 as an example. Collectively, QSP represents a new holistic approach that may have profound implications for how translational science is performed.