In silico model-based inference: a contemporary approach for hypothesis testing in network biology

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.

Multimodal network dynamics underpinning working memory

Complex human cognition arises from the integrated processing of multiple brain systems. However, little is known about how brain systems and their interactions might relate to, or perhaps even explain, human cognitive capacities. Here, we address this gap in knowledge by proposing a mechanistic framework linking frontoparietal system activity, default mode system activity, and the interactions between them, with individual differences in working memory capacity. We show that working memory performance depends on the strength of functional interactions between the frontoparietal and default mode systems. We find that this strength is modulated by the activation of two newly described brain regions, and demonstrate that the functional role of these systems is underpinned by structural white matter. Broadly, our study presents a holistic account of how regional activity, functional connections, and structural linkages together support integrative processing across brain systems in order for the brain to execute a complex cognitive process.

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 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.

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.

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.

BioNetGen 2.2: advances in rule-based modeling

: BioNetGen is an open-source software package for rule-based modeling of complex biochemical systems. Version 2.2 of the software introduces numerous new features for both model specification and simulation. Here, we report on these additions, discussing how they facilitate the construction, simulation and analysis of larger and more complex models than previously possible.

AVAILABILITY AND IMPLEMENTATION

Stable BioNetGen releases (Linux, Mac OS/X and Windows), with documentation, are available at http://bionetgen.org Source code is available at http://github.com/RuleWorld/bionetgen CONTACT: bionetgen.help@gmail.comSupplementary information: Supplementary data are available at Bioinformatics online.

Characterization of a Saccharomyces cerevisiae fermentation process for production of a therapeutic recombinant protein using a multivariate Bayesian approach

The principle of quality by design (QbD) has been widely applied to biopharmaceutical manufacturing processes. Process characterization is an essential step to implement the QbD concept to establish the design space and to define the proven acceptable ranges (PAR) for critical process parameters (CPPs). In this study, we present characterization of a Saccharomyces cerevisiae fermentation process using risk assessment analysis, statistical design of experiments (DoE), and the multivariate Bayesian predictive approach. The critical quality attributes (CQAs) and CPPs were identified with a risk assessment. The statistical model for each attribute was established using the results from the DoE study with consideration given to interactions between CPPs. Both the conventional overlapping contour plot and the multivariate Bayesian predictive approaches were used to establish the region of process operating conditions where all attributes met their specifications simultaneously. The quantitative Bayesian predictive approach was chosen to define the PARs for the CPPs, which apply to the manufacturing control strategy. Experience from the 10,000 L manufacturing scale process validation, including 64 continued process verification batches, indicates that the CPPs remain under a state of control and within the established PARs. The end product quality attributes were within their drug substance specifications. The probability generated with the Bayesian approach was also used as a tool to assess CPP deviations. This approach can be extended to develop other production process characterization and quantify a reliable operating region. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:799-812, 2016.

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 β-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 β-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 β-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 β-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.

CD8(+) T cell response to adenovirus vaccination and subsequent suppression of tumor growth: modeling, simulation and analysis

BACKGROUND

Using immune checkpoint modulators in the clinic to increase the number and activity of cytotoxic T lymphocytes that recognize tumor antigens can prolong survival for metastatic melanoma. Yet, only a fraction of the patient population receives clinical benefit. In short, these clinical trials demonstrate proof-of-principle but optimizing the specific therapeutic strategies remains a challenge. In many fields, CAD (computer-aided design) is a tool used to optimize integrated system behavior using a mechanistic model that is based upon knowledge of constitutive elements. The objective of this study was to develop a predictive simulation platform for optimizing anti-tumor immunity using different treatment strategies.

METHODS

To better understand the therapeutic role that cytotoxic CD8(+) T cells can play in controlling tumor growth, we developed a multi-scale mechanistic model of the biology using impulsive differential equations and calibrated it to a self-consistent data set.

RESULTS

The multi-scale model captures the activation and differentiation of naïve CD8(+) T cells into effector cytotoxic T cells in the lymph node following adenovirus-mediated vaccination against a tumor antigen, the trafficking of the resulting cytotoxic T cells into blood and tumor microenvironment, the production of cytokines within the tumor microenvironment, and the interactions between tumor cells, T cells and cytokines that control tumor growth. The calibrated model captures the modest suppression of tumor cell growth observed in the B16F10 model, a transplantable mouse model for metastatic melanoma, and was used to explore the impact of multiple vaccinations on controlling tumor growth.

CONCLUSIONS

Using the calibrated mechanistic model, we found that the cytotoxic CD8(+) T cell response was prolonged by multiple adenovirus vaccinations. However, the strength of the immune response cannot be improved enough by multiple adenovirus vaccinations to reduce tumor burden if the cytotoxic activity or local proliferation of cytotoxic T cells in response to tumor antigens is not greatly enhanced. Overall, this study illustrates how mechanistic models can be used for in silico screening of the optimal therapeutic dosage and timing in cancer treatment.