The field of machine learning has allowed researchers to generate and analyse vast amounts of data using a wide variety of methodologies. Artificial Neural Networks (ANN) are some of the most commonly used statistical models and have been successful in biomarker discovery studies in multiple disease types. This review seeks to explore and evaluate an integrated ANN pipeline for biomarker discovery and validation in Alzheimer's disease, the most common form of dementia worldwide with no proven cause and no available cure. The proposed pipeline consists of analysing public data with a categorical and continuous stepwise algorithm and further examination through network inference to predict gene interactions. This methodology can reliably generate novel markers and further examine known ones and can be used to guide future research in Alzheimer's disease.

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Background Metformin, a biguanide, is one of the most commonly prescribed treatments for type 2 diabetes and has recently been recommended as a potential drug candidate for advanced cancer therapy. Although Metformin has antiproliferative and proapoptotic effects on breast cancer, the heterogenous nature of this disease affects the response to metformin leading to the activation of pro-invasive signalling pathways that are mediated by the focal adhesion kinase PYK2 in pure HER2 phenotype breast cancer. Methods The effect of metformin on different breast cancer cell lines, representing the molecular heterogenicity of the disease was investigated using in vitro proliferation and apoptosis assays. The activation of PYK2 by metformin in pure HER2 phenotype (HER2+/ER−/PR-) cell lines was investigated by microarrays, quantitative real time PCR and immunoblotting. Cell migration and invasion PYK2-mediated and in response to metformin were determined by wound healing and invasion assays using HER2+/ER−/PR- PYK2 knockdown cell lines. Proteomic analyses were used to determine the role of PYK2 in HER2+/ER−/PR- proliferative, migratory and invasive cellular pathways and in response to metformin. The association between PYK2 expression and HER2+/ER−/PR- patients’ cancer-specific survival was investigated using bioinformatic analysis of PYK2 expression from patient gene expression profiles generated by the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) study. The effect of PYK2 and metformin on tumour initiation and invasion of HER2+/ER−/PR- breast cancer stem-like cells was performed using the in vitro stem cell proliferation and invasion assays. Results Our study showed for the first time that pure HER2 breast cancer cells are more resistant to metformin treatment when compared with the other breast cancer phenotypes. This drug resistance was associated with the activation of PTK2B/PYK2, a well-known mediator of signalling pathways involved in cell proliferation, migration and invasion. The role of PYK2 in promoting invasion of metformin resistant HER2 breast cancer cells was confirmed through investigating the effect of PYK2 knockdown and metformin on cell invasion and by proteomic analysis of associated cellular pathways. We also reveal a correlation between high level of expression of PYK2 and reduced survival in pure HER2 breast cancer patients. Moreover, we also report a role of PYK2 in tumour initiation and invasion-mediated by pure HER2 breast cancer stem-like cells. This was further confirmed by demonstrating a correlation between reduced survival in pure HER2 breast cancer patients and expression of PYK2 and the stem cell marker CD44 . Conclusions We provide evidence of a PYK2-driven pro-invasive potential of metformin in pure HE...

Hematological malignancies originate and progress in primary and secondary lymphoid organs, where they establish a uniquely immune-suppressive tumour microenvironment. Although high-throughput transcriptomic and proteomic approaches are being employed to interrogate immune surveillance and escape mechanisms in patients with solid tumours, and to identify actionable targets for immunotherapy, our knowledge of the immunological landscape of hematological malignancies, as well as our understanding of the molecular circuits that underpin the establishment of immune tolerance, is not comprehensive. Areas covered: This article will discuss how multiplexed immunohistochemistry, flow cytometry/mass cytometry, proteomic and genomic techniques can be used to dynamically capture the complexity of tumour-immune interactions. Moreover, the analysis of multi-dimensional, clinically annotated data sets obtained from public repositories such as Array Express, TCGA and GEO is crucial to identify immune biomarkers, to inform the rational design of immune therapies and to predict clinical benefit in individual patients. We will also highlight how artificial neural network models and alternative methodologies integrating other algorithms can support the identification of key molecular drivers of immune dysfunction. Expert commentary: High-dimensional technologies have the potential to enhance our understanding of immune-cancer interactions and will support clinical decision making and the prediction of therapeutic benefit from immune-based interventions.

Objective: Full-field digital mammography (FFDM) has limited sensitivity for cancer in younger women with denser breasts. Digital breast tomosynthesis (DBT) can reduce the risk of cancer being obscured by overlying tissue. The primary study aim was to compare the sensitivity of FFDM, DBT and FFDM-plus-DBT in women under 60 years old with clinical suspicion of breast cancer. Methods: This multicentre study recruited 446 patients from UK breast clinics. Participants underwent both standard FFDM and DBT. A blinded retrospective multireader study involving 12 readers and 300 mammograms (152 malignant and 148 benign cases) was conducted. Results: Sensitivity for cancer was 86.6% with FFDM [95% CI (85.2–88.0%)], 89.1% with DBT [95% CI (88.2–90%)], and 91.7% with FFDM+DBT [95% CI (90.7–92.6%)]. In the densest breasts, the maximum sensitivity increment with FFDM +DBT over FFDM alone was 10.3%, varying by density measurement method. Overall specificity was 81.4% with FFDM [95% CI (80.5–82.3%)], 84.6% with DBT [95% CI (83.9–85.3%)], and 79.6% with FFDM +DBT [95% CI (79.0–80.2%)]. No differences were detected in accuracy of tumour measurement in unifocal cases. Conclusions: Where available, DBT merits first-line use in the under 60 age group in symptomatic breast clinics, particularly in women known to have very dense breasts. Advances in knowledge: This study is one of very few to address the accuracy of DBT in symptomatic rather than screening patients. It quantifies the diagnostic gains of DBT in direct comparison with standard digital mammography, supporting informed decisions on appropriate use of DBT in this population.