Perspective on the dynamics of cancer

Metal-based approaches to fight cervical cancer

Cervical cancer (CC) is one of the leading causes of death among women worldwide. The current treatments for this cancer consist of invasive methods such as chemotherapeutic drugs, radiation, immunotherapy and surgery, which could lead to severe side effects and hinder the patient's life quality. Although metal-based therapies, including cisplatin and ruthenium-based compounds, offer promising alternatives, they lack specificity and harm healthy cells. Combining metal nanoparticles with standard approaches has demonstrated remarkable efficacy and safety in the fight against CC. Overall, this review is intended to show the latest advancements and insights into metal-based strategies, creating a promising path for more effective and safer treatments in the battle against CC.

Lung Adenocarcinoma Systems Biomarker and Drug Candidates Identified by Machine Learning, Gene Expression Data, and Integrative Bioinformatics Pipeline

Lung adenocarcinoma (LUAD) is a significant planetary health challenge with its high morbidity and mortality rate, not to mention the marked interindividual variability in treatment outcomes and side effects. There is an urgent need for robust systems biomarkers that can help with early cancer diagnosis, prediction of treatment outcomes, and design of precision/personalized medicines for LUAD. The present study aimed at systems biomarkers of LUAD and deployed integrative bioinformatics and machine learning tools to harness gene expression data. Predictive models were developed to stratify patients based on prognostic outcomes. Importantly, we report here several potential key genes, for example, PMEL and BRIP1, and pathways implicated in the progression and prognosis of LUAD that could potentially be targeted for precision/personalized medicine in the future. Our drug repurposing analysis and molecular docking simulations suggested eight drug candidates for LUAD such as heat shock protein 90 inhibitors, cardiac glycosides, an antipsychotic agent (trifluoperazine), and a calcium ionophore (ionomycin). In summary, this study identifies several promising leads on systems biomarkers and drug candidates for LUAD. The findings also attest to the importance of integrative bioinformatics, structural biology and machine learning techniques in biomarker discovery, and precision oncology research and development.

Chronic arsenic exposure induces malignant transformation of human HaCaT cells through both deterministic and stochastic changes in transcriptome expression

Biological processes are inherently stochastic, i.e., are partially driven by hard to predict random probabilistic processes. Carcinogenesis is driven both by stochastic and deterministic (predictable non-random) changes. However, very few studies systematically examine the contribution of stochastic events leading to cancer development. In differential gene expression studies, the established data analysis paradigms incentivize expression changes that are uniformly different across the experimental versus control groups, introducing preferential inclusion of deterministic changes at the expense of stochastic processes that might also play a crucial role in the process of carcinogenesis. In this study, we applied simple computational techniques to quantify: (i) The impact of chronic arsenic (iAs) exposure as well as passaging time on stochastic gene expression and (ii) Which genes were expressed deterministically and which were expressed stochastically at each of the three stages of cancer development. Using biological coefficient of variation as an empirical measure of stochasticity we demonstrate that chronic iAs exposure consistently suppressed passaging related stochastic gene expression at multiple time points tested, selecting for a homogenous cell population that undergo transformation. Employing multiple balanced removal of outlier data, we show that chronic iAs exposure induced deterministic and stochastic changes in the expression of unique set of genes, that populate largely unique biological pathways. Together, our data unequivocally demonstrate that both deterministic and stochastic changes in transcriptome-wide expression are critical in driving biological processes, pathways and networks towards clonal selection, carcinogenesis, and tumor heterogeneity.

Globally invariant behavior of oncogenes and random genes at population but not at single cell level

Cancer is widely considered a genetic disease. Notably, recent works have highlighted that every human gene may possibly be associated with cancer. Thus, the distinction between genes that drive oncogenesis and those that are associated to the disease, but do not play a role, requires attention. Here we investigated single cells and bulk (cell-population) datasets of several cancer transcriptomes and proteomes in relation to their healthy counterparts. When analyzed by machine learning and statistical approaches in bulk datasets, both general and cancer-specific oncogenes, as defined by the Cancer Genes Census, show invariant behavior to randomly selected gene sets of the same size for all cancers. However, when protein-protein interaction analyses were performed, the oncogenes-derived networks show higher connectivity than those relative to random genes. Moreover, at single-cell scale, we observe variant behavior in a subset of oncogenes for each considered cancer type. Moving forward, we concur that the role of oncogenes needs to be further scrutinized by adopting protein causality and higher-resolution single-cell analyses.

Deltex E3 ubiquitin ligase 3 inhibits colorectal cancer cell growth and regulates cell cycle progression via upregulating E2F transcription factor 1

Background

The mortality rate of colorectal cancer (CRC) remains high in developing countries. Interventions that can inhibit the proliferation of tumor cells represent promising strategies in CRC treatment. Deltex E3 ubiquitin ligase 3 (DTX3) plays an essential role in tumor development and may predict the outcome of cancer patients. This study aimed to investigate the regulatory mechanisms of DTX3 in CRC progression.

Methods and results

The expression of DTX3 was significantly downregulated in CRC tissues relative to normal colorectal tissues. DTX3 overexpression inhibited, while DTX3 knockout promoted the colony-forming capacity and proliferation of CRC cells. E2F transcription factor 1 (E2F1) is a key mediator of cell cycle progression that participates in the progression, metastasis, and chemoresistance of CRC. Further analysis revealed that DTX3 regulated the transcriptional activity of E2F1 in CRC cells. The transcription by E2F1 was significantly reduced with the increase in the cellular level of DTX3, while DTX3 knockout exerted an opposite effect. DTX3 knockout also increased the expression of E2F1 target genes involved in cell cycle progression, CDC2 and Cyclin D3, while PD 0332991, an inhibitor of E2F1 transcription, inhibited the expression of both proteins.

Conclusions

In conclusion, DTX3 regulated CRC cell growth via regulating E2F1 and its downstream genes. These findings support further exploration of DTX3 as a potential therapeutic target for CRC.

Application of Advanced Mass Spectrometry-Based Proteomics to Study Hypoxia Driven Cancer Progression

Cancer is one of the largest contributors to the burden of chronic disease in the world and is the second leading cause of death globally. It is associated with episodes of low-oxygen stress (hypoxia or ischemia/reperfusion) that promotes cancer progression and therapeutic resistance. Efforts have been made in the past using traditional proteomic approaches to decipher oxygen deprivation stress-related mechanisms of the disease initiation and progression and to identify key proteins as a therapeutic target for the treatment and prevention. Despite the potential benefits of proteomic in translational research for the discovery of new drugs, the therapeutic outcome with this approach has not met expectations in clinical trials. This is mainly due to the disease complexity which possess a multifaceted molecular pathology. Therefore, novel strategies to identify and characterize clinically important sets of modulators and molecular events for multi-target drug discovery are needed. Here, we review important past and current studies on proteomics in cancer with an emphasis on recent pioneered labeling approaches in mass spectrometry (MS)-based systematic quantitative analysis to improve clinical success. We also discuss the results of the selected innovative publications that integrate advanced proteomic technologies (e.g. MALDI-MSI, pSILAC/SILAC/iTRAQ/TMT-LC-MS/MS, MRM-MS) for comprehensive analysis of proteome dynamics in different biosystems, including cell type, cell species, and subcellular proteome (i.e. secretome and chromatome). Finally, we discuss the future direction and challenges in the application of these technological advancements in mass spectrometry within the context of cancer and hypoxia.

Modeling Cancer Dynamics

Cancer is a complex disease that continues to pose formidable challenges to therapeutic interventions. An increased understanding of cancer complexity and in particular tumor growth dynamics is critical to the development of more effective therapies. In this respect, an agent-based model of tumor growth is explored with the consideration of cancer reprograming of metabolism and the immune response, to seek insight about the coupling between these two key determinants of tumor growth dynamics. Ultimately, this exploration is intended to inform the development of therapies that can induce a more effective immune response despite the metabolic constraints of the tumor microenvironment.

Contribution of Angiogenesis to Inflammation and Cancer

During carcinogenesis, advanced tumors are surrounded by both stromal and immune cells, which support tumor development. In addition, inflammation and angiogenesis are processes that play important roles in the development of cancer, from the initiation of carcinogenesis, tumor in situ and advanced stages of cancer. During acute inflammation, vascular hyperpermeability allows inflammatory mediators and immune response cells, including leukocytes and monocytes/macrophages, to infiltrate the site of damage. As a factor that regulates vascular permeability, vascular endothelial growth factor (VEGF) also plays a vital role as a multifunctional molecule and growth factor. Furthermore, stromal and immune cells secrete soluble factors that activate endothelial cells and favor their transmigration to eliminate the aggressive agent. In this review, we present a comprehensive view of both the relationship between chronic inflammation and angiogenesis during carcinogenesis and the participation of endothelial cells in the inflammatory process. In addition, the regulatory mechanisms that contribute to the endothelium returning to its basal permeability state after acute inflammation are discussed. Moreover, the manner in which immune cells participate in pathological angiogenesis release pro-angiogenic factors that contribute to early tumor vascularization, even before the angiogenic switch occurs, is also examined. Also, we discuss the role of hypoxia as a mechanism that drives the acquisition of tumor hallmarks that make certain cancers more aggressive. Finally, some combinations of therapies that inhibit the angiogenesis process and that may be a successful strategy for cancer patients are indicated.

Breast Cancer Tumor Stroma: Cellular Components, Phenotypic Heterogeneity, Intercellular Communication, Prognostic Implications and Therapeutic Opportunities

Although the mechanisms underlying the genesis and progression of breast cancer are better understood than ever, it is still the most frequent malignant tumor in women and one of the leading causes of cancer death. Therefore, we need to establish new approaches that lead us to better understand the prognosis of this heterogeneous systemic disease and to propose new therapeutic strategies. Cancer is not only a malignant transformation of the epithelial cells merely based on their autonomous or acquired proliferative capacity. Today, data support the concept of cancer as an ecosystem based on a cellular sociology, with diverse components and complex interactions between them. Among the different cell types that make up the stroma, which have a relevant role in the dynamics of tumor/stromal cell interactions, the main ones are cancer associated fibroblasts, endothelial cells, immune cells and mesenchymal stromal cells. Several factors expressed by the stroma of breast carcinomas are associated with the development of metastasis, such as matrix metalloproteases, their tissular inhibitors or some of their regulators like integrins, cytokines or toll-like receptors. Based on the expression of these factors, two types of breast cancer stroma can be proposed with significantly different influence on the prognosis of patients. In addition, there is evidence about the existence of bi-directional signals between cancer cells and tumor stroma cells with prognostic implications, suggesting new therapeutic strategies in breast cancer.

The Adaptive Complexity of Cancer

Cancer treatment options are expanding to the benefit of significant segments of patients. However, their therapeutic power is not equally realized for all cancer patients due to drug toxicity and disease resistance. Overcoming these therapeutic challenges would require a better understanding of the adaptive survival mechanisms of cancer. In this respect, an integrated view of the disease as a complex adaptive system is proposed as a framework to explain the dynamic coupling between the various drivers underlying tumor growth and cancer resistance to therapy. In light of this system view of cancer, the immune system is in principal the most appropriate and naturally available therapeutic instrument that can thwart the adaptive survival mechanisms of cancer. In this respect, new cancer therapies should aim at restoring immunosurveillance by priming the induction of an effective immune response through a judicious targeting of immunosuppression, inflammation, and the tumor nutritional lifeline extended by the tumor microenvironment.

An Introduction to the Mathematical Modeling in the Study of Cancer Systems Biology

BACKGROUND

Frequently occurring in cancer are the aberrant alterations of regulatory onco-metabolites, various oncogenes/epigenetic stochasticity, and suppressor genes, as well as the deficient mismatch repair mechanism, chronic inflammation, or those deviations belonging to the other cancer characteristics. How these aberrations that evolve overtime determine the global phenotype of malignant tumors remains to be completely understood. Dynamic analysis may have potential to reveal the mechanism of carcinogenesis and can offer new therapeutic intervention.

AIMS

We introduce simplified mathematical tools to model serial quantitative data of cancer biomarkers. We also highlight an introductory overview of mathematical tools and models as they apply from the viewpoint of known cancer features.

METHODS

Mathematical modeling of potentially actionable genomic products and how they proceed overtime during tumorigenesis are explored. This report is intended to be instinctive without being overly technical.

RESULTS

To date, many mathematical models of the common features of cancer have been developed. However, the dynamic of integrated heterogeneous processes and their cross talks related to carcinogenesis remains to be resolved.

CONCLUSIONS

In cancer research, outlining mathematical modeling of experimentally obtained data snapshots of molecular species may provide insights into a better understanding of the multiple biochemical circuits. Recent discoveries have provided support for the existence of complex cancer progression in dynamics that span from a simple 1-dimensional deterministic system to a stochastic (ie, probabilistic) or to an oscillatory and multistable networks. Further research in mathematical modeling of cancer progression, based on the evolving molecular kinetics (time series), could inform a specific and a predictive behavior about the global systems biology of vulnerable tumor cells in their earlier stages of oncogenesis. On this footing, new preventive measures and anticancer therapy could then be constructed.

Immuno-Oncology: Emerging Targets and Combination Therapies

Host immunity recognizes and eliminates most early tumor cells, yet immunological checkpoints, exemplified by CTLA-4, PD-1, and PD-L1, pose a significant obstacle to effective antitumor immune responses. T-lymphocyte co-inhibitory pathways influence intensity, inflammation and duration of antitumor immunity. However, tumors and their immunosuppressive microenvironments exploit them to evade immune destruction. Recent PD-1 checkpoint inhibitors yielded unprecedented efficacies and durable responses across advanced-stage melanoma, showcasing potential to replace conventional radiotherapy regimens. Neverthless, many clinical problems remain in terms of efficacy, patient-to-patient variability, and undesirable outcomes and side effects. In this review, we evaluate recent advances in the immuno-oncology field and discuss ways forward. First, we give an overview of current immunotherapy modalities, involving mainy single agents, including inhibitor monoclonal antibodies (mAbs) targeting T-cell checkpoints of PD-1 and CTLA-4. However, neoantigen recognition alone cannot eliminate tumors effectively in vivo given their inherent complex micro-environment, heterogeneous nature and stemness. Then, based mainly upon CTLA-4 and PD-1 checkpoint inhibitors as a "backbone," we cover a range of emerging ("second-generation") therapies incorporating other immunotherapies or non-immune based strategies in synergistic combination. These include targeted therapies such as tyrosine kinase inhibitors, co-stimulatory mAbs, bifunctional agents, epigenetic modulators (such as inhibitors of histone deacetylases or DNA methyltransferase), vaccines, adoptive-T-cell therapy, nanoparticles, oncolytic viruses, and even synthetic "gene circuits." A number of novel immunotherapy co-targets in pre-clinical development are also introduced. The latter include metabolic components, exosomes and ion channels. We discuss in some detail of the personalization of immunotherapy essential for ultimate maximization of clinical outcomes. Finally, we outline possible future technical and conceptual developments including realistic in vitro and in vivo models and inputs from physics, engineering, and artificial intelligence. We conclude that the breadth and quality of immunotherapeutic approaches and the types of cancers that can be treated will increase significantly in the foreseeable future.

How do cancer cells replenish their fuel supply?

BACKGROUND

Multiple genetic changes, availability of cellular nutrients and metabolic alterations play a pivotal role in oncogenesis AIMS: We focus on cancer cell's metabolic properties, and we outline the cross talks between cellular oncogenic growth pathways in cancer metabolism. The review also provides a synopsis of the relevant cancer drugs targeting metabolic activities that are at various stages of clinical development.

METHODS

We review literature published within the last decade to include select articles that have highlighted energy metabolism crucial to the development of cancer phenotypes.

RESULTS

Cancer cells maintain their potent metabolism and keep a balanced redox status by enhancing glycolysis and autophagy and rerouting Krebs cycle intermediates and products of β-oxydation.

CONCLUSIONS

The processes underlying cancer pathogenesis are extremely complex and remain elusive. The new field of systems biology provides a mathematical framework in which these homeostatic dysregulation principles may be examined for better understanding of cancer phenotypes. Knowledge of key players in cancer-related metabolic reprograming may pave the way for new therapeutic metabolism-targeted drugs and ultimately improve patient care.