Tackling the cancer signal transduction “Labyrinth”: A combinatorial use of biochemical tools with mathematical models will enhance the identification of optimal targets for each molecular defect

Corrected score methods for estimating Bayesian networks with error-prone nodes

Motivated by inferring cellular signaling networks using noisy flow cytometry data, we develop procedures to draw inference for Bayesian networks based on error-prone data. Two methods for inferring causal relationships between nodes in a network are proposed based on penalized estimation methods that account for measurement error and encourage sparsity. We discuss consistency of the proposed network estimators and develop an approach for selecting the tuning parameter in the penalized estimation methods. Empirical studies are carried out to compare the proposed methods with a naive method that ignores measurement error. Finally, we apply these methods to infer signaling networks using single cell flow cytometry data.

Addressing the elephant in the room, therapeutic resistance in non-small cell lung cancer, with epigenetic therapies

Like Chinese boxes nesting inside each other, the classification of non-small cell lung cancer (NSCLC) is subdivided into smaller and smaller subtypes on the basis of histological and molecular attributes. The latter characterizes NSCLC by its molecular alterations and the identification of inhibitors that target these cancer-specific "driver" mutations. Despite the initial promise of precision-guided therapies to inhibit a finer and finer array of molecular subcategories, despite even the curative potential of immunotherapeutic checkpoint blockade, in particular, casualties still abound and true clinical success stories are few and far between; the ever-present, if sometimes unmentioned, "elephant in the room", is the acquisition of resistance, which, sooner or later, rears its ugly head to undermine treatment success and shorten survival. Emerging data suggests that epigenetic therapies are able to reprogram the aberrant tumor-associated epigenome and 'tame the beast of resistance', thereby prolonging survival. This article reviews the role of epigenetic dysregulation in NSCLC, explores PFS2 as a possible surrogate endpoint, briefly mentions possible biomarkers and highlights combinatorial treatment epigenetic strategies to "prime" tumors and reverse resistance.

Targeting Androgen/Estrogen Receptors Crosstalk in Cancer

The actions of estrogens are mediated by estrogen receptors, ERα and ERβ. Recent genomic landscaping of ERα- and ERβ-binding sites has revealed important distinctions regarding their transcriptional activity. ERβ and its isoforms have been correlated with endocrine treatment responsiveness in breast tumors, while post-translational modifications, receptor dimerization patterns, and subcellular localization are increasingly recognized as crucial modulators in prostate carcinogenesis. Androgen receptor (AR) is essential for the development and progression of prostate cancer as well as of certain breast cancer types. The balance between the activity of these two hormone receptors and their molecular interactions in different clinical settings is influenced by several coregulators. This comprises a dynamic regulatory network enhancing or limiting the activity of AR-directed treatments in breast and prostate tumorigenesis. In this review, we discuss the molecular background regarding the therapeutic targeting of androgen/estrogen receptor crosstalk in breast and prostate cancer.

Conditional robustness analysis for fragility discovery and target identification in biochemical networks and in cancer systems biology

Background

The study of cancer therapy is a key issue in the field of oncology research and the development of target therapies is one of the main problems currently under investigation. This is particularly relevant in different types of tumor where traditional chemotherapy approaches often fail, such as lung cancer.

Results

We started from the general definition of robustness introduced by Kitano and applied it to the analysis of dynamical biochemical networks, proposing a new algorithm based on moment independent analysis of input/output uncertainty. The framework utilizes novel computational methods which enable evaluating the model fragility with respect to quantitative performance measures and parameters such as reaction rate constants and initial conditions. The algorithm generates a small subset of parameters that can be used to act on complex networks and to obtain the desired behaviors. We have applied the proposed framework to the EGFR-IGF1R signal transduction network, a crucial pathway in lung cancer, as an example of Cancer Systems Biology application in drug discovery. Furthermore, we have tested our framework on a pulse generator network as an example of Synthetic Biology application, thus proving the suitability of our methodology to the characterization of the input/output synthetic circuits.

Conclusions

The achieved results are of immediate practical application in computational biology, and while we demonstrate their use in two specific examples, they can in fact be used to study a wider class of biological systems.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-015-0216-5) contains supplementary material, which is available to authorized users.

The potential for chemical mixtures from the environment to enable the cancer hallmark of sustained proliferative signalling

The aim of this work is to review current knowledge relating the established cancer hallmark, sustained cell proliferation to the existence of chemicals present as low dose mixtures in the environment. Normal cell proliferation is under tight control, i.e. cells respond to a signal to proliferate, and although most cells continue to proliferate into adult life, the multiplication ceases once the stimulatory signal disappears or if the cells are exposed to growth inhibitory signals. Under such circumstances, normal cells remain quiescent until they are stimulated to resume further proliferation. In contrast, tumour cells are unable to halt proliferation, either when subjected to growth inhibitory signals or in the absence of growth stimulatory signals. Environmental chemicals with carcinogenic potential may cause sustained cell proliferation by interfering with some cell proliferation control mechanisms committing cells to an indefinite proliferative span.