Precision cancer medicine promises to tailor clinical decisions to patients using genomic information. Indeed, successes of drugs targeting genetic alterations in tumors, such as imatinib that targets BCR-ABL in chronic myelogenous leukemia, have demonstrated the power of this approach. However, biological systems are complex, and patients may differ not only by the specific genetic alterations in their tumor, but also by more subtle interactions among such alterations. Systems biology and more specifically, network analysis, provides a framework for advancing precision medicine beyond clinical actionability of individual mutations. Here we discuss applications of network analysis to study tumor biology, early methods for N-of-1 tumor genome analysis, and the path for such tools to the clinic.

Background The major histocompatibility complex class I (MHC-I) molecule is a protein complex that displays intracellular peptides to T cells, allowing the immune system to recognize and destroy infected or cancerous cells. MHC-I is composed of a highly polymorphic HLA-encoded alpha chain that binds the peptide and a Beta-2-microglobulin (B2M) protein that acts as a stabilizing scaffold. HLA mutations have been implicated as a mechanism of immune evasion during tumorigenesis, and B2M is considered a tumor suppressor gene. However, the implications of somatic HLA and B2M mutations have not been fully explored in the context of antigen presentation via the MHC-I molecule during tumor development. To understand the effect that B2M and HLA MHC-I molecule mutations have on mutagenesis, we analyzed the accumulation of mutations in patients from The Cancer Genome Atlas according to their MHC-I molecule mutation status. Results Somatic B2M and HLA mutations in microsatellite stable tumors were associated with higher overall mutation burden and a larger fraction of HLA-binding neoantigens when compared to B2M and HLA wild type tumors. B2M and HLA mutations were highly enriched in patients with microsatellite instability. B2M mutations tended to occur relatively early during patients’ respective tumor development, whereas HLA mutations were either early or late events. In addition, B2M and HLA mutated patients had higher levels of immune infiltration by natural killer and CD8+ T cells and higher levels of cytotoxicity. Conclusions Our findings add to a growing body of evidence that somatic B2M and HLA mutations are a mechanism of immune evasion by demonstrating that such mutations are associated with a higher load of neoantigens that should be presented via MHC-I. Electronic supplementary material The online version of this article (10.1186/s12920-019-0544-1) contains supplementary material, which is available to authorized users.

More reliable and cheaper sequencing technologies have revealed the vast mutational landscapes characteristic of many phenotypes. The analysis of such genetic variants has led to successful identification of altered proteins underlying many Mendelian disorders. Nevertheless the simple one‐variant one‐phenotype model valid for many monogenic diseases does not capture the complexity of polygenic traits and disorders. Although experimental and computational approaches have improved detection of functionally deleterious variants and important interactions between gene products, the development of comprehensive models relating genotype and phenotypes remains a challenge in the field of genomic medicine. In this context, a new view of the pathologic state as significant perturbation of the network of interactions between biomolecules is crucial for the identification of biochemical pathways associated with complex phenotypes. Seminal studies in systems biology combined the analysis of genetic variation with protein–protein interaction networks to demonstrate that even as biological systems evolve to be robust to genetic variation, their topologies create disease vulnerabilities. More recent analyses model the impact of genetic variants as changes to the “wiring” of the interactome to better capture heterogeneity in genotype–phenotype relationships. These studies lay the foundation for using networks to predict variant effects at scale using machine‐learning or algorithmic approaches. A wealth of databases and resources for the annotation of genotype–phenotype relationships have been developed to support developments in this area. This overview describes how study of the molecular interactome has generated insights linking the organization of biological systems to disease mechanism, and how this information can enable precision medicine. This article is categorized under: Translational, Genomic, and Systems Medicine > Translational Medicine Biological Mechanisms > Cell Signaling Models of Systems Properties and Processes > Mechanistic Models Analytical and Computational Methods > Computational Methods

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The objective of this study is to make comprehensive cytotoxicity evaluation and in vitro characterization of Jeffamine-cored polyamidoamine (PAMAM) dendrimers on L929 cell lines for oral drug delivery purposes. Ester-, amine- and carboxylic acid-terminated PAMAMs were investigated for their cytotoxicity on L929 cells at different generations and concentrations. Cationic surface charge caused highest cytotoxicity on L929 cells, while ester-terminated PAMAMs showed generation- and concentration-dependent toxicity. Anionic dendrimers were determined as the lowest cytotoxic group, and highest generation number presented lowest cellular toxicity. Encapsulation studies were performed with anionic PAMAMs at 2.5, 3.5 and 4.5 generations and different concentrations. Increasing generation number provides greater loaded naproxen amounts and larger particle size. Moreover, formulations provide controlled release at simulated terminal ileum conditions. Consequently, Jeffamine-cored carboxylic acid-terminated PAMAMs can be a promising option for oral drug delivery of poorly water-soluble drugs.

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Nanoparticles loaded with 5-fluorouracil (5-FU) for colon cancer therapies were prepared using the solvent evaporation technique, which involved lyophilization by freeze-drying. Formulations produced a substantially high encapsulation efficiency of approximately 93%. A positive correlation was seen when increasing polycaprolactone (PCL) and/or PVA concentrations and the size of nanoparticles produced. Increasing PCL concentration had a considerable influence on PDI while increasing PVA concentration had a lesser effect. All nanoparticles possessed a negative zeta potential, particularly in formulations with low polymer and polymer emulsifier concentrations. The formulation with the lowest PCL and PVA concentration was characterized by the most optimal properties; which accounts for the desirable delayed release profile of the active drug in dissolution testing indicating an improved targeting capability and enhanced bioavailability at the action site. Cytotoxicity studies showed that 5-FU loaded PCL nanoparticles had higher antiproliferative effect than free 5-FU on Caco-2 cell line (p < 0.05). The encouraging results obtained offer reasons for optimism regarding the future of 5-FU nanoparticles as a promising drug delivery system which could be further improved by including either enteric coating or encapsulating the nanoparticles onto microparticles to overcome unanticipated degradation.

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