Niche inheritance: a cooperative pathway to enhance cancer cell fitness though ecosystem engineering

Cancer cells can be described as an invasive species that is able to establish itself in a new environment. The concept of niche construction can be utilized to describe the process by which cancer cells terraform their environment, thereby engineering an ecosystem that promotes the genetic fitness of the species. Ecological dispersion theory can then be utilized to describe and model the steps and barriers involved in a successful diaspora as the cancer cells leave the original host organ and migrate to new host organs to successfully establish a new metastatic community. These ecological concepts can be further utilized to define new diagnostic and therapeutic areas for lethal cancers.Comment: 8 pages, 1 Table, 4 Figure

Opportunities for organoids as new models of aging.

The biology of aging is challenging to study, particularly in humans. As a result, model organisms are used to approximate the physiological context of aging in humans. However, the best model organisms remain expensive and time-consuming to use. More importantly, they may not reflect directly on the process of aging in people. Human cell culture provides an alternative, but many functional signs of aging occur at the level of tissues rather than cells and are therefore not readily apparent in traditional cell culture models. Organoids have the potential to effectively balance between the strengths and weaknesses of traditional models of aging. They have sufficient complexity to capture relevant signs of aging at the molecular, cellular, and tissue levels, while presenting an experimentally tractable alternative to animal studies. Organoid systems have been developed to model many human tissues and diseases. Here we provide a perspective on the potential for organoids to serve as models for aging and describe how current organoid techniques could be applied to aging research

Translational Oncogenomics and Human Cancer Interactome Networks

An overview of translational, human oncogenomics, transcriptomics and cancer interactomic networks is presented together with basic concepts and potential, new applications to Oncology and Integrative Cancer Biology. Novel translational oncogenomics research is rapidly expanding through the application of advanced technology, research findings and computational tools/models to both pharmaceutical and clinical problems. A self-contained presentation is adopted that covers both fundamental concepts and the most recent biomedical, as well as clinical, applications. Sample analyses in recent clinical studies have shown that gene expression data can be employed to distinguish between tumor types as well as to predict outcomes. Potentially important applications of such results are individualized human cancer therapies or, in general, ‘personalized medicine’. Several cancer detection techniques are currently under development both in the direction of improved detection sensitivity and increased time resolution of cellular events, with the limits of single molecule detection and picosecond time resolution already reached. The urgency for the complete mapping of a human cancer interactome with the help of such novel, high-efficiency / low-cost and ultra-sensitive techniques is also pointed out

The Evolution of Failure: Explaining Cancer as an Evolutionary Process

One of the major developments in cancer research in recent years has been the construction of models that treat cancer as a cellular population subject to natural selection. We expand on this idea, drawing upon multilevel selection theory. Cancer is best understood in our view from a multilevel perspective, as both a by-product of selection at other levels of organization, and as subject to selection (and drift) at several levels of organization. Cancer is a by-product in two senses. First, cancer cells co-opt signaling pathways that are otherwise adaptive at the organismic level. Second, cancer is also a by-product of features distinctive to the metazoan lineage: cellular plasticity and modularity. Applying the multilevel perspective in this way permits one to explain transitions in complexity and individuality in cancer progression. Our argument is a reply to Germain’s (2012) scepticism towards the explanatory relevance of natural selection for cancer. The extent to which cancer fulfills the conditions for being a paradigmatic Darwinian population depends on the scale of analysis, and the details of the purported selective scenario. Taking a multilevel perspective clarifies some of the complexities surrounding how to best understand the relevance of evolutionary thinking in cancer progression

Computational Cancer Biology: An Evolutionary Perspective

ISSN:1553-734XISSN:1553-735

Cell population heterogeneity and evolution towards drug resistance in cancer: Biological and mathematical assessment, theoretical treatment optimisation

Background Drug-induced drug resistance in cancer has been attributed to diverse biological mechanisms at the individual cell or cell population scale, relying on stochastically or epigenetically varying expression of phenotypes at the single cell level, and on the adaptability of tumours at the cell population level. Scope of review We focus on intra-tumour heterogeneity, namely between-cell variability within cancer cell populations, to account for drug resistance. To shed light on such heterogeneity, we review evolutionary mechanisms that encompass the great evolution that has designed multicellular organisms, as well as smaller windows of evolution on the time scale of human disease. We also present mathematical models used to predict drug resistance in cancer and optimal control methods that can circumvent it in combined therapeutic strategies. Major conclusions Plasticity in cancer cells, i.e., partial reversal to a stem-like status in individual cells and resulting adaptability of cancer cell populations, may be viewed as backward evolution making cancer cell populations resistant to drug insult. This reversible plasticity is captured by mathematical models that incorporate between-cell heterogeneity through continuous phenotypic variables. Such models have the benefit of being compatible with optimal control methods for the design of optimised therapeutic protocols involving combinations of cytotoxic and cytostatic treatments with epigenetic drugs and immunotherapies. General significance Gathering knowledge from cancer and evolutionary biology with physiologically based mathematical models of cell population dynamics should provide oncologists with a rationale to design optimised therapeutic strategies to circumvent drug resistance, that still remains a major pitfall of cancer therapeutics. This article is part of a Special Issue entitled “System Genetics” Guest Editor: Dr. Yudong Cai and Dr. Tao Huang

Cellular interactions in the tumor microenvironment: the role of secretome

Over the past years, it has become evident that cancer initiation and progression depends on several components of the tumor microenvironment, including inflammatory and immune cells, fibroblasts, endothelial cells, adipocytes, and extracellular matrix. These components of the tumor microenvironment and the neoplastic cells interact with each other providing pro and antitumor signals. The tumor-stroma communication occurs directly between cells or via a variety of molecules secreted, such as growth factors, cytokines, chemokines and microRNAs. This secretome, which derives not only from tumor cells but also from cancer-associated stromal cells, is an important source of key regulators of the tumorigenic process. Their screening and characterization could provide useful biomarkers to improve cancer diagnosis, prognosis, and monitoring of treatment responses.Agência financiadora Fundação de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP) FAPESP 10/51168-0 12/06048-2 13/03839-1 National Council for Scientific and Technological Development (CNPq) CNPq 306216/2010-8 Fundacao para a Ciencia e a Tecnologia (FCT) UID/BIM/04773/2013 CBMR 1334info:eu-repo/semantics/publishedVersio

Study Regarding Properties of Solid Tumors in Mammals

Based on the mass balance (conservation) equation and the ubiquitous ¼ power law in biological allometry, we derive a general governing equation for organism growth. It is the same as a previously published model by West et al., which was developed using a conservation of total energy formalism with respect to the maintenance of already existing tissue and newly created one. In the present model, the energy/nutrition (including oxygen) supply or consumption in metabolism is reflected in the production and death rates. We start by dividing an organism into different small systems which have identical cells and then unite them by introducing similarity of growth. Normal cells follow the rules for similarity of growth but tumor cells may not. This model is applied to tumor therapy. We model the response of tumor cells to the major standard tumor treatments: surgery, radiation and chemotherapy. This model explains the survival curves quite well (better than all previous models). Also, it consistently explains cell response to high and low LET (linear energy transfer) radiations. This work shows that the LQ model is an approximate result of the present model under specific conditions. Tumor interstitial fluid pressure (TIFP) has the potential to predict tumor response to non-surgical cancer treatments such as radiation and chemotherapies. We present the mathematical framework for a quantitative, non-invasive measure of TIFP. It describes the distribution of interstitial fluid pressure in three distinct tumor regions: vascularized tumor rim, central tumor region and normal tissue. We demonstrate that the acquisition of serial images of a tumor after the injection of a contrast agent can provide a non-invasive and potentially quantitative measure of TIFP

Engineering simulations for cancer systems biology

Computer simulation can be used to inform in vivo and in vitro experimentation, enabling rapid, low-cost hypothesis generation and directing experimental design in order to test those hypotheses. In this way, in silico models become a scientific instrument for investigation, and so should be developed to high standards, be carefully calibrated and their findings presented in such that they may be reproduced. Here, we outline a framework that supports developing simulations as scientific instruments, and we select cancer systems biology as an exemplar domain, with a particular focus on cellular signalling models. We consider the challenges of lack of data, incomplete knowledge and modelling in the context of a rapidly changing knowledge base. Our framework comprises a process to clearly separate scientific and engineering concerns in model and simulation development, and an argumentation approach to documenting models for rigorous way of recording assumptions and knowledge gaps. We propose interactive, dynamic visualisation tools to enable the biological community to interact with cellular signalling models directly for experimental design. There is a mismatch in scale between these cellular models and tissue structures that are affected by tumours, and bridging this gap requires substantial computational resource. We present concurrent programming as a technology to link scales without losing important details through model simplification. We discuss the value of combining this technology, interactive visualisation, argumentation and model separation to support development of multi-scale models that represent biologically plausible cells arranged in biologically plausible structures that model cell behaviour, interactions and response to therapeutic interventions