Cancer Systems Biology Research Articles

Morphological Stability for in silico Models of Avascular Tumors

The landscape of computational modeling in cancer systems biology is diverse, offering a spectrum of models and frameworks, each with its own trade-offs and advantages. Ideally, models are meant to be useful in refining hypotheses, to sharpen experimental procedures and, in the longer run, even for applications in personalized medicine. One of the greatest challenges is to balance model realism and detail with experimental data to eventually produce useful data-driven models. We contribute to this quest by developing a transparent, highly parsimonious, first principle in silico model of a growing avascular tumor. We initially formulate the physiological considerations and the specific model within a stochastic cell-based framework. We next formulate a corresponding mean-field model using partial differential equations which is amenable to mathematical analysis. Despite a few notable differences between the two models, we are in this way able to successfully detail the impact of all parameters in the stability of the growth process and on the eventual tumor fate of the stochastic model. This facilitates the deduction of Bayesian priors for a given situation, but also provides important insights into the underlying mechanism of tumor growth and progression. Although the resulting model framework is relatively simple and transparent, it can still reproduce the full range of known emergent behavior. We identify a novel model instability arising from nutrient starvation and we also discuss additional insight concerning possible model additions and the effects of those. Thanks to the framework’s flexibility, such additions can be readily included whenever the relevant data become available.

Spatial Cancer Systems Biology Resolves Heterotypic Interactions and Identifies Disruption of Spatial Hierarchy as a Pathological Driver Event

Spatial Cancer Systems Biology Resolves Heterotypic Interactions and Identifies Disruption of Spatial Hierarchy as a Pathological Driver Event

Cancer Systems Biology and Epidemiology: Target Identification, Combination Therapy, and Personalized Treatment in Cancer Medicine.

Cancer Systems Biology and Epidemiology: Target Identification, Combination Therapy, and Personalized Treatment in Cancer Medicine.

Clinical Network Systems Biology: Traversing the Cancer Multiverse

In recent decades, cancer biology and medicine have ushered in a new age of precision medicine through high-throughput approaches that led to the development of novel targeted therapies and immunotherapies for different cancers. The availability of multifaceted high-throughput omics data has revealed that cancer, beyond its genomic heterogeneity, is a complex system of microenvironments, sub-clonal tumor populations, and a variety of other cell types that impinge on the genetic and non-genetic mechanisms underlying the disease. Thus, a systems approach to cancer biology has become instrumental in identifying the key components of tumor initiation, progression, and the eventual emergence of drug resistance. Through the union of clinical medicine and basic sciences, there has been a revolution in the development and approval of cancer therapeutic drug options including tyrosine kinase inhibitors, antibody–drug conjugates, and immunotherapy. This ‘Team Medicine’ approach within the cancer systems biology framework can be further improved upon through the development of high-throughput clinical trial models that utilize machine learning models, rapid sample processing to grow patient tumor cell cultures, test multiple therapeutic options and assign appropriate therapy to individual patients quickly and efficiently. The integration of systems biology into the clinical network would allow for rapid advances in personalized medicine that are often hindered by a lack of drug development and drug testing.

Bromodomain and extraterminal (BET) domain proteins regulate immune checkpoints through AMPK-dependent and -independent pathways in peripheral T cell subsets

Abstract Immune checkpoint blockade unleashes anti-tumor immunity of tumor-infiltrating CD8+ T cells. Yet, many cancer patients are refractory: unknown mechanisms resolve responders from non-responders. Analysis of transcriptional networks regulating immune checkpoint genes may provide insight. We used multi-parameter FACS to explore expression of several checkpoint inhibition molecules, focusing on functional roles of 5′-Adenosine Monophosphate-activated Protein Kinase (AMPK) and BET transcriptional co-regulators (BRD2, BRD3 and BRD4), both known to be key signaling pathways disturbed in obesity and cancer, resulting in exhaustion of effector CD8+ T cells and tumor progression. Here, human primary PBMCs from normal, healthy, adult donors were activated ex vivowith anti-CD3 and anti-CD28. We then measured expression of inhibitory receptors PD1, CTLA4, TIM3 and TIGIT on T cell subsets. Small molecule inhibitors of BET protein expression, including pan-BET inhibitor JQ1 and BRD4-selective PROTAC degrader MZ1, identified BET protein-regulated targets. We found that BRD4 regulates PD1, CTLA4 and TIM3 (but not TIGIT) in both CD4+ and CD8+ T cells. Treatment with Compound C, a modulator of AMPK, defined a unique PD1 and CTLA4 network. We confirmed that T cells with elevated expression of inhibitory receptors were impaired in cytokine secretion and proliferation. We conclude that separate signaling pathways likely converge on BRD2 and/or BRD3 and/or BRD4, acting independently as effectors at each promoter. Patterns of AMPK-regulated metabolism and BET gene expression are personalized and must be considered when evaluating immune checkpoint blockade in cancer therapy. Supported by grants from the Cancer Systems Biology Consortium of the National Cancer Institute (U01CA182898,U01CA243004; PI Denis), NCI Cancer Moonshot (R01CA222170; PI Denis) and Boston University's Immunology Training Program (T32AI007309-32; PI Kepler).

Mathematical Oncology to Cancer Systems Medicine: Translation from Academic Pursuit to Individualized Therapy with MORA

Aim & Objective: This article is aimed to understand the gradual development of cancer systems medicine and how this provides a better therapeutic strategy (in terms of drug selection, dose and duration) and patients care. Hence, this study is focused to understand the need and the evolving nature of the analytical models for the assessment of the outcome of different cancer therapeutics. Background: Presently, cancer is viewed from a quantitative standpoint; hence, several analytical models on different cancers have developed. From the information of cancer development to therapeutic advantage, mathematical oncology has contributed significantly. With a fewer number of variables, models in this area have successfully synchronized the model output with real-life dynamical data. However, with the availability of large scale data for different cancers, systems biology has gained importance. It provides biomedical insights among a large number of variables. And to get information for clinically relevant variables especially, the controlling variable(s), cancer systems medicine is suggested. Methods: In this article, we have reviewed the gradual development of the field from mathematical oncology to cancer systems biology to cancer systems medicine. An intensive search with PubMed, IEEE Xplorer and Google for cancer model, analytical model and cancer systems biology was made and the latest developments have been noted. Results: Gradual development of cancer systems biology entails the importance of the development of models towards a unified model of cancer treatment. For this, the model should be flexible so that different types of cancer and/or its therapy can be included within the same model. With the existing knowledge, relevant variables are included in the same model, followed by simulation studies that will enrich the knowledge base further. Such a deductive approach in the modelling and simulations efforts can help to tackle the adversity of individual cancer cases in future. This approach is indeed important to encompass the fourth industrial revolution in health sector. Conclusion: Towards the development of a unified modelling effort, a multi-scale modelling approach could be suitable; so that different researchers across the globe can add their contribution to enrich the same model. Moreover, with this, the identification of controlling variables may be possible. Towards this goal, middle-out rationalist approach (MORA) is working on analytical models for cancer treatment.

Senescent cell-derived extracellular vesicles are critical elements in senescence surveillance by recruiting antigen-presenting cells

Abstract Cellular senescence is a tumor suppressor mechanism that arrests cell cycle and initiates immune-mediated removal of senescent cells, a process known as senescence surveillance (SS). Senescent cells interact with their microenvironment through senescence-associated secretory phenotype (SASP). Current knowledge about the SASP has focused on soluble elements like cytokines. Extracellular vesicles (EVs) are membrane-bound, nanometer-sized particles that are emerging as a novel class of SASP factors. Whether EVs play a role in SS is unknown. Elucidating the role of EVs in SS will open new avenues for therapeutically targeting cellular senescence. In order to study if and how EVs contribute to SS, we inhibited EV release from senescent cells using dominant-negative mutant Rab35. This approach preserved secretion of soluble SASP elements while decreasing >95% the release of EVs from senescent cells. C57BL6 mice were challenged with EV-inhibited senescent cells and their numbers at the site of injection as well as immune infiltrates were quantified. Interfering with EV release led to the reduction of antigen-presenting cells including dendritic cells, macrophages, and B cells, which may contribute to the observed persistence of senescent cells. Interestingly, EV-inhibition reverted cellular senescence, promoted proliferation and tumor formation. Proteomic analysis revealed 35 proteins that were exclusively present on senescent cell-derived EVs, compared to those from their non-senescence counterparts. These results suggest that EVs have critical role in SS possibly via signaling to immune cells. Future work will explore whether T cells respond to EV-bound senescent cell antigens. Integrated Training in Quantitative and Experimental Cancer Systems Biology, 5T32CA254888-02 and CRUK/OHSU A29681

A community-based approach to image analysis of cells, tissues and tumors.

Emerging multiplexed imaging platforms provide an unprecedented view of an increasing number of molecular markers at subcellular resolution and the dynamic evolution of tumor cellular composition. As such, they are capable of elucidating cell-to-cell interactions within the tumor microenvironment that impact clinical outcome and therapeutic response. However, the rapid development of these platforms has far outpaced the computational methods for processing and analyzing the data they generate. While being technologically disparate, all imaging assays share many computational requirements for post-collection data processing. As such, our Image Analysis Working Group (IAWG), composed of researchers in the Cancer Systems Biology Consortium (CSBC) and the Physical Sciences - Oncology Network (PS-ON), convened a workshop on "Computational Challenges Shared by Diverse Imaging Platforms" to characterize these common issues and a follow-up hackathon to implement solutions for a selected subset of them. Here, we delineate these areas that reflect major axes of research within the field, including image registration, segmentation of cells and subcellular structures, and identification of cell types from their morphology. We further describe the logistical organization of these events, believing our lessons learned can aid others in uniting the imaging community around self-identified topics of mutual interest, in designing and implementing operational procedures to address those topics and in mitigating issues inherent in image analysis (e.g., sharing exemplar images of large datasets and disseminating baseline solutions to hackathon challenges through open-source code repositories).

Towards the First Principles in Biology and Cancer: New Vistas in Computational Systems Biology of Cancer.

These days many leading scientists argue for a new paradigm for cancer research and propose a complex systems-view of cancer supported by empirical evidence. As an example, Thea Newman (2021) has applied “the lessons learned from physical systems to a critique of reductionism in medical research, with an emphasis on cancer”. It is the understanding of this author that the mesoscale constructs that combine the bottom-up as well as top-down approaches, are very close to the concept of emergence. The mesoscale constructs can be said to be those effective components through which the system allows itself to be understood. A short list of basic concepts related to life/biology fundamentals are first introduced to demonstrate a lack of emphasis on these matters in literature. It is imperative that physical and chemical approaches are introduced and incorporated in biology to make it more conceptually sound, quantitative, and based on the first principles. Non-equilibrium thermodynamics is the only tool currently available for making progress in this direction. A brief outline of systems biology, the discovery of emergent properties, and metabolic modeling are introduced in the second part. Then, different cancer initiation concepts are reviewed, followed by application of non-equilibrium thermodynamics in the metabolic and genomic analysis of initiation and development of cancer, stressing the endogenous network hypothesis (ENH). Finally, extension of the ENH is suggested to include a cancer niche (exogenous network hypothesis). It is expected that this will lead to a unifying systems–biology approach for a future combination of the analytical and synthetic arms of two major hypotheses of cancer models (SMT and TOFT).

Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics.

Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of scale-specific biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built using this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- and multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multi-scale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for the development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.

Oncogenic Viruses as Entropic Drivers of Cancer Evolution.

Viral infection is an indisputable causal factor for nearly 17% of all human cancers. However, the diversity and complexity of oncogenic mechanisms raises new questions as to the mechanistic role of viruses in cancer. Classical viral oncogenes have been identified for all tumor-associated viruses. These oncogenes can have multiple oncogenic activities that may or may not be utilized in a particular tumor cell. In addition, stochastic events, like viral mutation and integration, as well as heritable host susceptibilities and immune deficiencies are also implicated in tumorigenesis. A more contemporary view of tumor biology highlights the importance of evolutionary forces that select for phenotypes better adapted to a complex and changing environment. Given the challenges of prioritizing singular mechanistic causes, it may be necessary to integrate concepts from evolutionary theory and systems biology to better understand viral cancer-driving forces. Here, we propose that viral infection provides a biological “entropy” that increases genetic variation and phenotypic plasticity, accelerating the main driving forces of cancer cell evolution. Viruses can also influence the evolutionary selection criteria by altering the tumor microenvironment and immune signaling. Utilizing concepts from cancer cell evolution, population genetics, thermodynamics, and systems biology may provide new perspectives on viral oncogenesis and identify novel therapeutic strategies for treating viruses and cancer.

How systems biology of cancer signaling pathways can inform optimized combination chemotherapy design

This talk presents an overview of the systems biology of cancer applied to specific protein-protein interaction signaling pathways. We argue that network analysis augmented with thermodynamics measures such as Gibbs free energy can lead to optimized design of drug combinations for cancer chemotherapy.

Building patient-specific models for receptor tyrosine kinase signaling networks.

Cancer progresses due to changes in the dynamic interactions of multidimensional factors associated with gene mutations. Cancer research has actively adopted computational methods, including data-driven and mathematical model-driven approaches, to identify causative factors and regulatory rules that can explain the complexity and diversity of cancers. A data-driven, statistics-based approach revealed correlations between gene alterations and clinical outcomes in many types of cancers. A model-driven mathematical approach has elucidated the dynamic features of cancer networks and identified the mechanisms of drug efficacy and resistance. More recently, machine learning methods have emerged that can be used for mining omics data and classifying patient. However, as the strengths and weaknesses of each method becoming apparent, new analytical tools are emerging to combine and improve the methodologies and maximize their predictive power for classifying cancer subtypes and prognosis. Here, we introduce recent advances in cancer systems biology aimed at personalized medicine, with focus on the receptor tyrosine kinase signaling network.

The NCI Cancer Tissue Engineering Collaborative Research Program is a highly interdisciplinary and focused community

The NCI Cancer Tissue Engineering Collaborative Research Program is a highly interdisciplinary and focused community

Systems Biology and Experimental Model Systems of Cancer.

Over the past decade, we have witnessed an increasing number of large-scale studies that have provided multi-omics data by high-throughput sequencing approaches. This has particularly helped with identifying key (epi)genetic alterations in cancers. Importantly, aberrations that lead to the activation of signaling networks through the disruption of normal cellular homeostasis is seen both in cancer cells and also in the neighboring tumor microenvironment. Cancer systems biology approaches have enabled the efficient integration of experimental data with computational algorithms and the implementation of actionable targeted therapies, as the exceptions, for the treatment of cancer. Comprehensive multi-omics data obtained through the sequencing of tumor samples and experimental model systems will be important in implementing novel cancer systems biology approaches and increasing their efficacy for tailoring novel personalized treatment modalities in cancer. In this review, we discuss emerging cancer systems biology approaches based on multi-omics data derived from bulk and single-cell genomics studies in addition to existing experimental model systems that play a critical role in understanding (epi)genetic heterogeneity and therapy resistance in cancer.

In Memoriam: Mortimer L. Mendelsohn: (1 December, 1925-8 January, 2020).

In Memoriam: Mortimer L. Mendelsohn: (1 December, 1925-8 January, 2020).

Computational and systems biology of cancer

Computational and systems biology of cancer

Systems Biology of Gastric Cancer: Perspectives on the Omics-Based Diagnosis and Treatment.

Gastric cancer is the fifth most diagnosed cancer in the world, affecting more than a million people and causing nearly 783,000 deaths each year. The prognosis of advanced gastric cancer remains extremely poor despite the use of surgery and adjuvant therapy. Therefore, understanding the mechanism of gastric cancer development, and the discovery of novel diagnostic biomarkers and therapeutics are major goals in gastric cancer research. Here, we review recent progress in application of omics technologies in gastric cancer research, with special focus on the utilization of systems biology approaches to integrate multi-omics data. In addition, the association between gastrointestinal microbiota and gastric cancer are discussed, which may offer insights in exploring the novel microbiota-targeted therapeutics. Finally, the application of data-driven systems biology and machine learning approaches could provide a predictive understanding of gastric cancer, and pave the way to the development of novel biomarkers and rational design of cancer therapeutics.

Navigating 2D Monoelemental Materials (Xenes) for Cancer Nanomedicine

Contributing to this issue of Matter, Wei Tao, Tian Xie, and colleagues now demonstrate the killing of two birds (inhibition of tumor recurrence and wound infection) with one stone (germanene-based surgical adjuvant theranostic materials): another breakthrough of two-dimensional monoelemental materials (2D Xenes) as cancer nanomedicine.

A census of pathway maps in cancer systems biology.

A key goal of cancer systems biology is to use big data to elucidate the molecular networks by which cancer develops. However, to date there has been no systematic evaluation of how far these efforts have progressed. In this Analysis, we survey six major systems biology approaches for mapping and modelling cancer pathways with attention to how well their resulting network maps cover and enhance current knowledge. Our sample of 2,070 systems biology maps captures all literature-curated cancer pathways with significant enrichment, although the strong tendency is for these maps to recover isolated mechanisms rather than entire integrated processes. Systems biology maps also identify previously underappreciated functions, such as a potential role for human papillomavirus-induced chromosomal alterations in ovarian tumorigenesis, and they add new genes to known cancer pathways, such as those related to metabolism, Hippo signalling and immunity. Notably, we find that many cancer networks have been provided only in journal figures and not for programmatic access, underscoring the need to deposit network maps in community databases to ensure they can be readily accessed. Finally, few of these findings have yet been clinically translated, leaving ample opportunity for future translational studies. Periodic surveys of cancer pathway maps, such as the one reported here, are critical to assess progress in the field and identify underserved areas of methodology and cancer biology.