Integrative analysis of cancer imaging readouts by networks

Synergistic cytotoxicity of erianin, a bisbenzyl in the dietetic Chinese herb Dendrobium against breast cancer cells

Erianin (ER), a dietary compound extracted from Dendrobium, a traditional Chinese medicinal edible herb, is well recognized for its potential anti-cancer activity. Nevertheless, its limitations, regarding its complex isolation procedure, low yield and low water solubility, limit large scale application. Combinatorial therapeutic regimen that combines several drugs to target different pathways in a characteristically synergistic manner at lower doses of drugs proved effective in several diseases treatment. Besides, new knowledge aimed at improving drug delivery into the intracellular environment is essential. In this study, ER was assessed for its cytotoxic effect in combination with doxorubicin hydrochloride (DOX·HCl) against breast cancer cells. Drug synergy was calculated by using combination index (CI) index and we discovered that they had positive effects. To ensure uniform delivery of both drugs to cells for a desired synergistic action, a dual drug loaded liposomes was developed using thin-film dispersion, and coated by a layer of folate-chitosan. Cytotoxicity and cell proliferation based assays revealed the increase of cell inhibition rate by more than 30% compared with free drugs. Fluorescence imaging revealed that liposomes can aid faster drugs accumulate in cancer cells. The study presented a novel strategy for the treatment of breast cancer.

Efficient synergistic combination effect of Quercetin with Curcumin on breast cancer cell apoptosis through their loading into Apo ferritin cavity

Combination of natural agents has received a great attention in cancer treatment because of synergistically increased apoptotic effect on cancer cell lines by triggering several apoptotic signaling pathways. However, the hydrophobic nature, poor bioavailability and low cellular uptake of most natural agents limit their therapeutic effectiveness. The purpose of this study was to design Apoferritin nanoparticles loaded with Quercetin and Curcumin (Que-Cur-HoS-Apo NPs) and to test their synergistic antitumor properties on a breast cancer cell line (MCF7). The physico-chemical characterization of the Que-Cur-HoS-Apo NPs by Size Exclusion Chromatography (FPLC) and Dynamic Light Scattering (DLS) confirmed the encapsulation of the compounds in the protein cage with narrow size distribution in the range 17.4 ± 1.2 nm. Cell viability study indicated that Que-Cur-HoS-Apo NPs were able to exert a more pronounced effect at lower dose on the MCF7 cell line when compared to the free combination of the drugs. The Que-Cur-HoS-Apo system allowed cellular uptake of natural agents thus triggering enhanced apoptosis. These effects were confirmed by Annexin-V/7-AAD Staining Assay and intracellular Reactive Oxygen Species (ROS) quantitative detection. These results suggest the potential of Que-Cur-HoS-Apo NPs as a promising anti-cancer agent in breast cancer therapy and pave the way to examine Que-Cur-HoS-Apo NPs effect in vivo.

From Medical Imaging to Radiomics: Role of Data Science for Advancing Precision Health

Treating disease according to precision health requires the individualization of therapeutic solutions as a cardinal step that is part of a process that typically depends on multiple factors. The starting point is the collection and assembly of data over time to assess the patient’s health status and monitor response to therapy. Radiomics is a very important component of this process. Its main goal is implementing a protocol to quantify the image informative contents by first mining and then extracting the most representative features. Further analysis aims to detect potential disease phenotypes through signs and marks of heterogeneity. As multimodal images hinge on various data sources, and these can be integrated with treatment plans and follow-up information, radiomics is naturally centered on dynamically monitoring disease progression and/or the health trajectory of patients. However, radiomics creates critical needs too. A concise list includes: (a) successful harmonization of intra/inter-modality radiomic measurements to facilitate the association with other data domains (genetic, clinical, lifestyle aspects, etc.); (b) ability of data science to revise model strategies and analytics tools to tackle multiple data types and structures (electronic medical records, personal histories, hospitalization data, genomic from various specimens, imaging, etc.) and to offer data-agnostic solutions for patient outcomes prediction; (c) and model validation with independent datasets to ensure generalization of results, clinical value of new risk stratifications, and support to clinical decisions for highly individualized patient management.

Role of Complex Networks for Integrating Medical Images and Radiomic Features of Intracranial Ependymoma Patients in Response to Proton Radiotherapy

Human cancers exhibit phenotypic diversity that medical imaging can precisely and non-invasively detect. Multiple factors underlying innovations and progresses in the medical imaging field exert diagnostic and therapeutic impacts. The emerging field of radiomics has shown unprecedented ability to use imaging information in guiding clinical decisions. To achieve clinical assessment that exploits radiomic knowledge sources, integration between diverse data types is required. A current gap is the accuracy with which radiomics aligns with clinical endpoints. We propose a novel methodological approach that synergizes data volumes (images), tissue-contextualized information breadth, and network-driven resolution depth. Following the Precision Medicine paradigm, disease monitoring and prognostic assessment are tackled at the individual level by examining medical images acquired from two patients affected by intracranial ependymoma (with and without relapse). The challenge of spatially characterizing intratumor heterogeneity is tackled by a network approach that presents two main advantages: (a) Increased detection in the image domain power from high spatial resolution, (b) Superior accuracy in generating hypotheses underlying clinical decisions.

Current Advances in Chitosan Nanoparticles Based Drug Delivery and Targeting

Nanoparticles (NPs) have been found to be potential targeted and controlled release drug delivery systems. Various drugs can be loaded in the NPs to achieve targeted delivery. Chitosan NPs being biodegradable, biocompatible, less toxic and easy to prepare, are an effective and potential tool for drug delivery. Chitosan is natural biopolymer which can be easily functionalized to obtain the desired targeted results and is also approved by GRAS (Generally Recognized as Safe by the United States Food and Drug Administration [US FDA]). Various methods for preparation of chitosan NPs include, ionic cross-linking, covalent cross-linking, reverse micellar method, precipitation and emulsion-droplet coalescence method. Chitosan NPs are found to have plethora of applications in drug delivery diagnosis and other biological applications. The key applications include ocular drug delivery, per-oral delivery, pulmonary drug delivery, nasal drug delivery, mucosal drug delivery, gene delivery, buccal drug delivery, vaccine delivery, vaginal drug delivery and cancer therapy. The present review describes the formation of chitosan, synthesis of chitosan NPs and their various applications in drug delivery.

Phosphoserine aminotransferase 1 is associated to poor outcome on tamoxifen therapy in recurrent breast cancer

In a previous study, we detected a significant association between phosphoserine aminotransferase 1 (PSAT1) hyper-methylation and mRNA levels to outcome to tamoxifen treatment in recurrent disease. We here aimed to study the association of PSAT1 protein levels to outcome upon tamoxifen treatment and to obtain more insight in its role in tamoxifen resistance. A cohort of ER positive, hormonal therapy naïve primary breast carcinomas was immunohistochemically (IHC) stained for PSAT1. Staining was analyzed for association with patient's time to progression (TTP) and overall response on first-line tamoxifen for recurrent disease. PSAT1 mRNA levels were also assessed by reverse transcriptase quantitative polymerase chain reaction (RT-qPCR; n = 161) and Affymetrix GeneChip (n = 155). Association of PSAT1 to biological pathways on tamoxifen outcome were assessed by global test. PSAT1 protein and mRNA levels were significantly associated to poor outcome to tamoxifen treatment. When comparing PSAT1 protein and mRNA levels, IHC and RT-qPCR data showed a significant association. Global test results showed that cytokine and JAK-STAT signaling were associated to PSAT1 expression. We hereby report that PSAT1 protein and mRNA levels measured in ER positive primary tumors are associated with poor clinical outcome to tamoxifen.

Understanding cancer complexome using networks, spectral graph theory and multilayer framework

Cancer complexome comprises a heterogeneous and multifactorial milieu that varies in cytology, physiology, signaling mechanisms and response to therapy. The combined framework of network theory and spectral graph theory along with the multilayer analysis provides a comprehensive approach to analyze the proteomic data of seven different cancers, namely, breast, oral, ovarian, cervical, lung, colon and prostate. Our analysis demonstrates that the protein-protein interaction networks of the normal and the cancerous tissues associated with the seven cancers have overall similar structural and spectral properties. However, few of these properties implicate unsystematic changes from the normal to the disease networks depicting difference in the interactions and highlighting changes in the complexity of different cancers. Importantly, analysis of common proteins of all the cancer networks reveals few proteins namely the sensors, which not only occupy significant position in all the layers but also have direct involvement in causing cancer. The prediction and analysis of miRNAs targeting these sensor proteins hint towards the possible role of these proteins in tumorigenesis. This novel approach helps in understanding cancer at the fundamental level and provides a clue to develop promising and nascent concept of single drug therapy for multiple diseases as well as personalized medicine.

Time-course gene profiling and networks in demethylated retinoblastoma cell line

Retinoblastoma, a very aggressive cancer of the developing retina, initiatiates by the biallelic loss of RB1 gene, and progresses very quickly following RB1 inactivation. While its genome is stable, multiple pathways are deregulated, also epigenetically. After reviewing the main findings in relation with recently validated markers, we propose an integrative bioinformatics approach to include in the previous group new markers obtained from the analysis of a single cell line subject to epigenetic treatment. In particular, differentially expressed genes are identified from time course microarray experiments on the WERI-RB1 cell line treated with 5-Aza-2′-deoxycytidine (decitabine; DAC). By inducing demethylation of CpG island in promoter genes that are involved in biological processes, for instance apoptosis, we performed the following main integrative analysis steps: i) Gene expression profiling at 48h, 72h and 96h after DAC treatment; ii) Time differential gene co-expression networks and iii) Context-driven marker association (transcriptional factor regulated protein networks, master regulatory paths). The observed DAC-driven temporal profiles and regulatory connectivity patterns are obtained by the application of computational tools, with support from curated literature. It is worth emphasizing the capacity of networks to reconcile multi-type evidences, thus generating testable hypotheses made available by systems scale predictive inference power. Despite our small experimental setting, we propose through such integrations valuable impacts of epigenetic treatment in terms of gene expression measurements, and then validate evidenced apoptotic effects.

Immunomediated Pan-cancer Regulation Networks are Dominant Fingerprints After Treatment of Cell Lines with Demethylation

Pan-cancer studies are particularly relevant not only for addressing the complexity of the inherently observed heterogeneity but also for identifying clinically relevant features that may be common to the cancer types. Immune system regulations usually reveal synergistic modulation with other cancer mechanisms and in combination provide insights on possible advances in cancer immunotherapies. Network inference is a powerful approach to decipher pan-cancer systems dynamics. The methodology proposed in this study elucidates the impacts of epigenetic treatment on the drivers of complex pan-cancer regulation circuits involving cell lines of five cancer types. These patterns were observed from differential gene expression measurements following demethylation with 5-azacytidine. Networks were built to establish associations of phenotypes at molecular level with cancer hallmarks through both transcriptional and post-transcriptional regulation mechanisms. The most prominent feature that emerges from our integrative network maps, linking pathway landscapes to disease and drug-target associations, refers primarily to a mosaic of immune-system crosslinked influences. Therefore, characteristics initially evidenced in single cancer maps become motifs well summarized by network cores and fingerprints.

Integrative analysis of cancer imaging readouts by networks

Cancer is a multifactorial and heterogeneous disease. The corresponding complexity appears at multiple levels: from the molecular and the cellular constitution to the macroscopic phenotype, and at the diagnostic and therapeutic management stages. The overall complexity can be approximated to a certain extent, e.g. characterized by a set of quantitative phenotypic observables recorded in time‐space resolved dimensions by using multimodal imaging approaches. The transition from measures to data can be made effective through various computational inference methods, including networks, which are inherently capable of mapping variables and data to node‐ and/or edge‐valued topological properties, dynamic modularity configurations, and functional motifs. We illustrate how networks can integrate imaging data to explain cancer complexity, and assess potential pre‐clinical and clinical impact.

Computational Multiplexing Imaging merges imaging and networks.

Networks show signatures of tumor heterogeneity and phenotypic profiles observed in‐vivo.

A profile ensemble establishes a tumor fingerprint, and this constitutes a novel type of marker.

Personalized treatment is embedded in a systems medicine approach.