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.

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.

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.

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).

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 .

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.

Induction of Wnt-inducible signaling protein-1 correlates with invasive breast cancer oncogenesis and reduced type 1 cell-mediated cytotoxic immunity: a retrospective study.

Innate and type 1 cell-mediated cytotoxic immunity function as important extracellular control mechanisms that maintain cellular homeostasis. Interleukin-12 (IL12) is an important cytokine that links innate immunity with type 1 cell-mediated cytotoxic immunity. We recently observed in vitro that tumor-derived Wnt-inducible signaling protein-1 (WISP1) exerts paracrine action to suppress IL12 signaling. The objective of this retrospective study was three fold: 1) to determine whether a gene signature associated with type 1 cell-mediated cytotoxic immunity was correlated with overall survival, 2) to determine whether WISP1 expression is increased in invasive breast cancer, and 3) to determine whether a gene signature consistent with inhibition of IL12 signaling correlates with WISP1 expression. Clinical information and mRNA expression for genes associated with anti-tumor immunity were obtained from the invasive breast cancer arm of the Cancer Genome Atlas study. Patient cohorts were identified using hierarchical clustering. The immune signatures associated with the patient cohorts were interpreted using model-based inference of immune polarization. Reverse phase protein array, tissue microarray, and quantitative flow cytometry in breast cancer cell lines were used to validate observed differences in gene expression. We found that type 1 cell-mediated cytotoxic immunity was correlated with increased survival in patients with invasive breast cancer, especially in patients with invasive triple negative breast cancer. Oncogenic transformation in invasive breast cancer was associated with an increase in WISP1. The gene expression signature in invasive breast cancer was consistent with WISP1 as a paracrine inhibitor of type 1 cell-mediated immunity through inhibiting IL12 signaling and promoting type 2 immunity. Moreover, model-based inference helped identify appropriate immune signatures that can be used as design constraints in genetically engineering better pre-clinical models of breast cancer.

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.

Inferring alterations in cell-to-cell communication in HER2+ breast cancer using secretome profiling of three cell models.

Challenges in demonstrating durable clinical responses to molecular-targeted therapies have sparked a re-emergence in viewing cancer as an evolutionary process. In somatic evolution, cellular variants are introduced through a random process of somatic mutation and are selected for improved fitness through a competition for survival. In contrast to Darwinian evolution, cellular variants that are retained may directly alter the fitness competition. If cell-to-cell communication is important for selection, the biochemical cues secreted by malignant cells that emerge should be altered to bias this fitness competition. To test this hypothesis, we compared the proteins secreted in vitro by two human HER2+ breast cancer cell lines (BT474 and SKBR3) relative to a normal human mammary epithelial cell line (184A1) using a proteomics workflow that leveraged two-dimensional gel electrophoresis (2DE) and MALDI-TOF mass spectrometry. Supported by the 2DE secretome maps and identified proteins, the two breast cancer cell lines exhibited secretome profiles that were similar to each other and, yet, were distinct from the 184A1 secretome. Using protein-protein interaction and pathway inference tools for functional annotation, the results suggest that all three cell lines secrete exosomes, as confirmed by scanning electron microscopy. Interestingly, the HER2+ breast cancer cell line exosomes are enriched in proteins involved in antigen-processing and presentation and glycolytic metabolism. These pathways are associated with two of the emerging hallmarks of cancer: evasion of tumor immunosurveillance and deregulating cellular energetics.

Differential proteomic analysis of caveolin-1 KO cells reveals Sh2b3 and Clec12b as novel interaction partners of caveolin-1 and Capns1 as a potential mediator of caveolin-1-induced apoptosis.

Caveolin-1 (Cav1) is a small scaffolding protein involved in a variety of cellular functions, including cell signaling, lipid transport and membrane traffic. The objective of this study was to use comparative proteomics to identify differentially expressed proteins in Cav1 knockout (KO) mouse embryonic fibroblasts. These deregulated proteins were then analyzed using systems biology tools to gain insight into the local network properties and to identify the interaction partners of Cav1. We identified five proteins that were up-regulated and ten proteins that were down-regulated in Cav1 KO cells, suggesting that the local network behaves as a complex system. Protein interaction network analysis revealed two proteins, Sh2b3 and Clec12b, as novel interaction partners of Cav1. Functional annotation showed apoptosis signaling as the most significant pathway. To validate this functional annotation, Cav1 KO cells showed more than 1.5-fold increase in caspase-3 activity over wild type cells upon apoptotic stimulation. We also found that calpain small subunit 1 is up-regulated in Cav1 KO cells and directly influences the cell response to apoptotic stimuli. Moreover, Capns1 was reduced in Cav1 KO cells following re-expression of Cav1, and suppression of Capns1 expression in Cav1 KO cells significantly inhibited the cells to apoptotic stimuli, as measured by caspase 3 activity. In conclusion, our results suggest that Sh2b3 and Clec12b functionally interact with Cav1 and that calpain small subunit 1 may mediate Cav1-induced apoptosis.

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.

Protein-based identification of quantitative trait loci associated with malignant transformation in two HER2+ cellular models of breast cancer.

BACKGROUND: A contemporary view of the cancer genome reveals extensive rearrangement compared to normal cells. Yet how these genetic alterations translate into specific proteomic changes that underpin acquiring the hallmarks of cancer remains unresolved. The objectives of this study were to quantify alterations in protein expression in two HER2+ cellular models of breast cancer and to infer differentially regulated signaling pathways in these models associated with the hallmarks of cancer. RESULTS: A proteomic workflow was used to identify proteins in two HER2 positive tumorigenic cell lines (BT474 and SKBR3) that were differentially expressed relative to a normal human mammary epithelial cell line (184A1). A total of 64 (BT474-184A1) and 69 (SKBR3-184A1) proteins were uniquely identified that were differentially expressed by at least 1.5-fold. Pathway inference tools were used to interpret these proteins in terms of functionally enriched pathways in the tumor cell lines. We observed "protein ubiquitination" and "apoptosis signaling" pathways were both enriched in the two breast cancer models while "IGF signaling" and "cell motility" pathways were enriched in BT474 and "amino acid metabolism" were enriched in the SKBR3 cell line. CONCLUSION: While "protein ubiquitination" and "apoptosis signaling" pathways were common to both the cell lines, the observed patterns of protein expression suggest that the evasion of apoptosis in each tumorigenic cell line occurs via different mechanisms. Evidently, apoptosis is regulated in BT474 via down regulation of Bid and in SKBR3 via up regulation of Calpain-11 as compared to 184A1.

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.

Inferring relevant control mechanisms for interleukin-12 signaling in naïve CD4+ T cells.

Interleukin-12 (IL-12) is a key cytokine involved in shaping the cell-mediated immunity to intracellular pathogens. IL-12 initiates a cellular response through the IL-12 signaling pathway, a member of the Janus kinase/signal transducer and activator of transcription (JAK/STAT) family of signaling networks. The JAK/STAT pathway includes several regulatory elements; however, the dynamics of these mechanisms are not fully understood. Therefore, the objective of this study was to infer the relative importance of regulatory mechanisms that modulate the activation of STAT4 in naïve CD4(+) T cells. Dynamic changes in protein expression and activity were measured using flow cytometry and these data were used to calibrate a mathematical model of IL-12 signaling. An empirical Bayesian approach was used to infer the relative strengths of the different regulatory mechanisms in the system. The model predicted that IL-12 receptor expression is regulated by a dynamic, autonomous program that was independent of STAT4 activation. In summary, a mathematical model of the canonical IL-12 signaling pathway used in conjunction with a Bayesian framework provided high-confidence predictions of the system-specific control mechanisms from the available experimental observations.

A multiscale systems perspective on cancer, immunotherapy, and Interleukin-12.

Monoclonal antibodies represent some of the most promising molecular targeted immunotherapies. However, understanding mechanisms by which tumors evade elimination by the immune system of the host presents a significant challenge for developing effective cancer immunotherapies. The interaction of cancer cells with the host is a complex process that is distributed across a variety of time and length scales. The time scales range from the dynamics of protein refolding (i.e., microseconds) to the dynamics of disease progression (i.e., years). The length scales span the farthest reaches of the human body (i.e., meters) down to the range of molecular interactions (i.e., nanometers). Limited ranges of time and length scales are used experimentally to observe and quantify changes in physiology due to cancer. Translating knowledge obtained from the limited scales observed experimentally to predict patient response is an essential prerequisite for the rational design of cancer immunotherapies that improve clinical outcomes. In studying multiscale systems, engineers use systems analysis and design to identify important components in a complex system and to test conceptual understanding of the integrated system behavior using simulation. The objective of this review is to summarize interactions between the tumor and cell-mediated immunity from a multiscale perspective. Interleukin-12 and its role in coordinating antibody-dependent cell-mediated cytotoxicity is used illustrate the different time and length scale that underpin cancer immunoediting. An underlying theme in this review is the potential role that simulation can play in translating knowledge across scales.

Inferring predominant pathways in cellular models of breast cancer using limited sample proteomic profiling.

BACKGROUND: Molecularly targeted drugs inhibit aberrant signaling within oncogenic pathways. Identifying the predominant pathways at work within a tumor is a key step towards tailoring therapies to the patient. Clinical samples pose significant challenges for proteomic profiling, an attractive approach for identifying predominant pathways. The objective of this study was to determine if information obtained from a limited sample (i.e., a single gel replicate) can provide insight into the predominant pathways in two well-characterized breast cancer models. METHODS: A comparative proteomic analysis of total cell lysates was obtained from two cellular models of breast cancer, BT474 (HER2+/ER+) and SKBR3 (HER2+/ER-), using two-dimensional electrophoresis and MALDI-TOF mass spectrometry. Protein interaction networks and canonical pathways were extracted from the Ingenuity Pathway Knowledgebase (IPK) based on association with the observed pattern of differentially expressed proteins. RESULTS: Of the 304 spots that were picked, 167 protein spots were identified. A threshold of 1.5-fold was used to select 62 proteins used in the analysis. IPK analysis suggested that metabolic pathways were highly associated with protein expression in SKBR3 cells while cell motility pathways were highly associated with BT474 cells. Inferred protein networks were confirmed by observing an up-regulation of IGF-1R and profilin in BT474 and up-regulation of Ras and enolase in SKBR3 using western blot. CONCLUSION: When interpreted in the context of prior information, our results suggest that the overall patterns of differential protein expression obtained from limited samples can still aid in clinical decision making by providing an estimate of the predominant pathways that underpin cellular phenotype.

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.

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 empirical Bayesian approach for model-based inference of cellular signaling networks.

BACKGROUND: A common challenge in systems biology is to infer mechanistic descriptions of biological process given limited observations of a biological system. Mathematical models are frequently used to represent a belief about the causal relationships among proteins within a signaling network. Bayesian methods provide an attractive framework for inferring the validity of those beliefs in the context of the available data. However, efficient sampling of high-dimensional parameter space and appropriate convergence criteria provide barriers for implementing an empirical Bayesian approach. The objective of this study was to apply an Adaptive Markov chain Monte Carlo technique to a typical study of cellular signaling pathways. RESULTS: As an illustrative example, a kinetic model for the early signaling events associated with the epidermal growth factor (EGF) signaling network was calibrated against dynamic measurements observed in primary rat hepatocytes. A convergence criterion, based upon the Gelman-Rubin potential scale reduction factor, was applied to the model predictions. The posterior distributions of the parameters exhibited complicated structure, including significant covariance between specific parameters and a broad range of variance among the parameters. The model predictions, in contrast, were narrowly distributed and were used to identify areas of agreement among a collection of experimental studies. CONCLUSION: In summary, an empirical Bayesian approach was developed for inferring the confidence that one can place in a particular model that describes signal transduction mechanisms and for inferring inconsistencies in experimental measurements.