Data-Driven Modeling of Breast Cancer Tumors Using Boolean Networks.

Cancer is a genomic disease involving various intertwined pathways with complex cross-communication links. Conceptually, this complex interconnected system forms a network, which allows one to model the dynamic behavior of the elements that characterize it to describe the entire system's development in its various evolutionary stages of carcinogenesis. Knowing the activation or inhibition status of the genes that make up the network during its temporal evolution is necessary for the rational intervention on the critical factors for controlling the system's dynamic evolution. In this report, we proposed a methodology for building data-driven boolean networks that model breast cancer tumors. We defined the network components and topology based on gene expression data from RNA-seq of breast cancer cell lines. We used a Boolean logic formalism to describe the network dynamics. The combination of single-cell RNA-seq and interactome data enabled us to study the dynamics of malignant subnetworks of up-regulated genes. First, we used the same Boolean function construction scheme for each network node, based on canalyzing functions. Using single-cell breast cancer datasets from The Cancer Genome Atlas, we applied a binarization algorithm. The binarized version of scRNA-seq data allowed identifying attractors specific to patients and critical genes related to each breast cancer subtype. The model proposed in this report may serve as a basis for a methodology to detect critical genes involved in malignant attractor stability, whose inhibition could have potential applications in cancer theranostics.

Advances in the use of MOFs for Cancer Diagnosis and Treatment: An Overview.

Nanoparticles as drug delivery systems and diagnostic agents have gained much attention in recent years, especially for cancer treatment. Nanocarriers improve the therapeutic efficiency and bioavailability of antitumor drugs, besides providing preferential accumulation at the target site. Among different types of nanocarriers for drug delivery assays, metal-organic frameworks (MOFs) have attracted increasing interest in the academic community. MOFs are an emerging class of coordination polymers constructed of metal nodes or clusters and organic linkers that show the capacity to combine a porous structure with high drug loading through distinct kinds of interactions, overcoming the limitations of traditional drug carriers explored up to date. Despite the rational design and synthesis of MOFs, structural aspects and some applications of these materials like gas adsorption have already been comprehensively described in recent years; it is time to demonstrate their potential applications in biomedicine. In this context, MOFs can be used as drug delivery systems and theranostic platforms due to their ability to release drugs and accommodate imaging agents. This review describes the intrinsic characteristics of nanocarriers used in cancer therapy and highlights the latest advances in MOFs as anticancer drug delivery systems and diagnostic agents.