Circulating tumor cells (CTCs) are important for metastatic dissemination of cancer. They can provide useful information, regarding biological features and tumor heterogeneity; however, their detection and characterization are difficult due to their limited number in the bloodstream and their mesenchymal characteristics. Therefore, new biomarkers are needed to address these questions. Bioinformatics functional enrichment analysis revealed a subgroup of 24 genes, potentially overexpressed in CTCs. Among these genes, the chemokine receptor CXCR4 plays a central role. After prioritization according to the CXCR4 corresponding pathways, five molecules (JUNB, YWHAB, TYROBP, NFYA, and PRDX1) were selected for further analysis in biological samples. The SKBR3, MDA-MB231, and MCF7 cell lines, as well as PBMCs from normal (n = 10) blood donors, were used as controls to define the expression pattern of all the examined molecules. Consequently, 100 previously untreated metastatic breast cancer (mBC) patients (n = 100) were analyzed using the following combinations of antibodies: CK (cytokeratin)/CXCR4/JUNB, CK/NFYA/ΥWHΑΒ (14-3-3), and CK/TYROBP/PRDX1. A threshold value for every molecule was considered the mean expression in normal PBMCs. Quantification of CXCR4 revealed overexpression of the receptor in SKBR3 and in CTCs, following the subsequent scale (SKBR3>CTCs>Hela>MCF7>MDA-MB231). JUNB was also overexpressed in CTCs (SKBR3>CTCs>MCF7>MDA-MB231>Hela). According to the defined threshold for each molecule, CXCR4-positive CTCs were identified in 90% of the patients with detectable tumor cells in their blood. In addition, 65%, 75%, 14.3%, and 12.5% of the patients harbored JUNB-, TYROBP-, NFYA-, and PRDX-positive CTCs, respectively. Conversely, none of the patients revealed YWHAB-positive CTCs. Interestingly, JUNB expression in CTCs was phenotypically and statistically enhanced compared to patients’ blood cells (p = 0.002) providing a possible new biomarker for CTCs. Furthermore, the detection of JUNB-positive CTCs in patients was associated with poorer PFS (p = 0.015) and OS (p = 0.002). Moreover, JUNB staining of 11 primary and 4 metastatic tumors from the same cohort of patients revealed a dramatic increase of JUNB expression in metastasis. CXCR4, JUNB, and TYROBP were overexpressed in CTCs, but only the expression of JUNB was associated with poor prognosis, providing a new biomarker and a potential therapeutic target for the elimination of CTCs.

A plethora of publicly available biomedical resources do currently exist and are constantly increasing at a fast rate. In parallel, specialized repositories are been developed, indexing numerous clinical and biomedical tools. The main drawback of such repositories is the difficulty in locating appropriate resources for a clinical or biomedical decision task, especially for non-Information Technology expert users. In parallel, although NLP research in the clinical domain has been active since the 1960s, progress in the development of NLP applications has been slow and lags behind progress in the general NLP domain. The aim of the present study is to investigate the use of semantics for biomedical resources annotation with domain specific ontologies and exploit Natural Language Processing methods in empowering the non-Information Technology expert users to efficiently search for biomedical resources using natural language. A Natural Language Processing engine which can “translate” free text into targeted queries, automatically transforming a clinical research question into a request description that contains only terms of ontologies, has been implemented. The implementation is based on information extraction techniques for text in natural language, guided by integrated ontologies. Furthermore, knowledge from robust text mining methods has been incorporated to map descriptions into suitable domain ontologies in order to ensure that the biomedical resources descriptions are domain oriented and enhance the accuracy of services discovery. The framework is freely available as a web application at ( http://calchas.ics.forth.gr/ ). For our experiments, a range of clinical questions were established based on descriptions of clinical trials from the ClinicalTrials.gov registry as well as recommendations from clinicians. Domain experts manually identified the available tools in a tools repository which are suitable for addressing the clinical questions at hand, either individually or as a set of tools forming a computational pipeline. The results were compared with those obtained from an automated discovery of candidate biomedical tools. For the evaluation of the results, precision and recall measurements were used. Our results indicate that the proposed framework has a high precision and low recall, implying that the system returns essentially more relevant results than irrelevant. There are adequate biomedical ontologies already available, sufficiency of existing NLP tools and quality of biomedical annotation systems for the implementation of a biomedical resources discovery framework, based on the semantic annotation of resources and the use on NLP techniques. The results of the present study demonstrate the clinical utility of the application of the proposed framework which aims to bridge the gap between clinical question in natural language and efficient dynamic biomedical resources discovery.

Herbal medicinal products (HMPs) are the subject of increasing interest regarding their benefits for health. However, a serious concern is the potential appearance of clinically significant drug–herb interactions in patients. This work provides an overview of drug–herb interactions and an evaluation of their clinical significance. We discuss how personalized health services and mobile health applications can utilize tools that provide essential information to patients to avoid drug–HMP interactions. There is a specific mention to PharmActa, a dedicated mobile app for personalized pharmaceutical care with information regarding drug–HMPs interactions. Several studies over the years have shown that for some HMPs, the potential to present clinically significant interactions is evident, especially for many of the top selling HMPs. Towards that, PharmActa presents how we can improve the way that information regarding potential drug–herb interactions can be disseminated to the public. The utilization of technologies focusing on medical information and context awareness introduce a new era in healthcare. The exploitation of eHealth tools and pervasive mobile monitoring technologies in the case of HMPs will allow the citizens to be informed and avoid potential drug–HMPs interactions enhancing the effectiveness and ensuring safety for HMPs.

PURPOSE: The explosion of big data and artificial intelligence has rapidly increased the need for integrated, homogenized, and harmonized health data. Many common data models (CDMs) and standard vocabularies have appeared in an attempt to offer harmonized access to the available information, with Observational Medical Outcomes Partnership (OMOP)-CDM being one of the most prominent ones, allowing the standardization and harmonization of health care information. However, despite its flexibility, still capturing imaging metadata along with the corresponding clinical data continues to pose a challenge. This challenge arises from the absence of a comprehensive standard representation for image-related information and subsequent image curation processes and their interlinkage with the respective clinical information. Successful resolution of this challenge holds the potential to enable imaging and clinical data to become harmonized, quality-checked, annotated, and ready to be used in conjunction, in the development of artificial intelligence models and other data-dependent use cases. METHODS: To address this challenge, we introduce medical imaging (MI)-CDM—an extension of the OMOP-CDM specifically designed for registering medical imaging data and curation-related processes. Our modeling choices were the result of iterative numerous discussions among clinical and AI experts to enable the integration of imaging and clinical data in the context of the ProCAncer-I project, for answering a set of clinical questions across the prostate cancer's continuum. RESULTS: Our MI-CDM extension has been successfully implemented for the use case of prostate cancer for integrating imaging and curation metadata along with clinical information by using the OMOP-CDM and its oncology extension. CONCLUSION: By using our proposed terminologies and standardized attributes, we demonstrate how diverse imaging modalities can be seamlessly integrated in the future.

Radiotranscriptomics is an emerging field that aims to investigate the relationships between the radiomic features extracted from medical images and gene expression profiles that contribute in the diagnosis, treatment planning, and prognosis of cancer. This study proposes a methodological framework for the investigation of these associations with application on non-small-cell lung cancer (NSCLC). Six publicly available NSCLC datasets with transcriptomics data were used to derive and validate a transcriptomic signature for its ability to differentiate between cancer and non-malignant lung tissue. A publicly available dataset of 24 NSCLC-diagnosed patients, with both transcriptomic and imaging data, was used for the joint radiotranscriptomic analysis. For each patient, 749 Computed Tomography (CT) radiomic features were extracted and the corresponding transcriptomics data were provided through DNA microarrays. The radiomic features were clustered using the iterative K-means algorithm resulting in 77 homogeneous clusters, represented by meta-radiomic features. The most significant differentially expressed genes (DEGs) were selected by performing Significance Analysis of Microarrays (SAM) and 2-fold change. The interactions among the CT imaging features and the selected DEGs were investigated using SAM and a Spearman rank correlation test with a False Discovery Rate (FDR) of 5%, leading to the extraction of 73 DEGs significantly correlated with radiomic features. These genes were used to produce predictive models of the meta-radiomics features, defined as p-metaomics features, by performing Lasso regression. Of the 77 meta-radiomic features, 51 can be modeled in terms of the transcriptomic signature. These significant radiotranscriptomics relationships form a reliable basis to biologically justify the radiomics features extracted from anatomic imaging modalities. Thus, the biological value of these radiomic features was justified via enrichment analysis on their transcriptomics-based regression models, revealing closely associated biological processes and pathways. Overall, the proposed methodological framework provides joint radiotranscriptomics markers and models to support the connection and complementarities between the transcriptome and the phenotype in cancer, as demonstrated in the case of NSCLC.