Non-small cell lung cancer (NSCLC) is one of the most prevalent and lethal types of cancers worldwide and its high incidence and mortality rates pose a significant public health challenge. Despite sig Show more
Non-small cell lung cancer (NSCLC) is one of the most prevalent and lethal types of cancers worldwide and its high incidence and mortality rates pose a significant public health challenge. Despite significant advances in targeted therapy and immunotherapy, the overall prognosis of patients with NSCLC remains poor. Hypoxia is a critical driving factor in tumor progression, influencing the biological behavior of tumor cells through complex molecular mechanisms. The present review systematically examined the role of the hypoxic microenvironment in NSCLC, demonstrating its crucial role in promoting tumor cell growth, invasion and metastasis. Additionally, it has been previously reported that the hypoxic microenvironment enhances tumor cell resistance by activating hypoxia-inducible factor and regulating exosome secretion. The hypoxic microenvironment also enables tumor cells to adapt to low oxygen and nutrient-deficient conditions by enhancing metabolic reprogramming, such as through upregulating glycolysis. Further studies have shown that the hypoxic microenvironment facilitates immune escape by modulating tumor-associated immune cells and suppressing the antitumor response of the immune system. Moreover, the hypoxic microenvironment increases tumor resistance to radiotherapy, chemotherapy and other types of targeted therapy through various pathways, significantly reducing the therapeutic efficacy of these treatments. Therefore, it could be suggested that early detection of cellular hypoxia and targeted therapy based on hypoxia may offer new therapeutic approaches for patients with NSCLC. The present review not only deepened the current understanding of the mechanisms of action and role of the hypoxic microenvironment in NSCLC but also provided a solid theoretical basis for the future development of precision treatments for patients with NSCLC. Show less
Mitochondria are central actors in diverse physiological phenomena ranging from energy metabolism to stress signaling and immune modulation. Accumulating scientific evidence points to the critical inv Show more
Mitochondria are central actors in diverse physiological phenomena ranging from energy metabolism to stress signaling and immune modulation. Accumulating scientific evidence points to the critical involvement of specific mitochondrial-associated events, including mitochondrial quality control, intercellular mitochondrial transfer, and mitochondrial genetics, in potentiating the metastatic cascade of neoplastic cells. Furthermore, numerous recent studies have consistently emphasized the highly significant role mitochondria play in coordinating the regulation of tumor-infiltrating immune cells and immunotherapeutic interventions. This review provides a comprehensive and rigorous scholarly investigation of this subject matter, exploring the intricate mechanisms by which mitochondria contribute to tumor metastasis and examining the progress of mitochondria-targeted cancer therapies. Show less
The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the drug discovery process, but it is important in order to rationalise phenotypic findings and to anticipate potenti Show more
The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the drug discovery process, but it is important in order to rationalise phenotypic findings and to anticipate potential side-effects. Bioinformatic approaches, advances in machine learning techniques and the increasing deposition of high-throughput data in public databases have significantly contributed to recent advances in the field, but it is not straightforward to decide which data and methods are most suitable to use in a given case. In this review, we focus on these methods and data and their applications in generating MoA hypotheses for subsequent experimental validation. We discuss compound-specific data such as -omics, cell morphology and bioactivity data, as well as commonly used supplementary prior knowledge such as network and pathway data, and provide information on databases where this data can be accessed. In terms of methodologies, we discuss both well-established methods (connectivity mapping, pathway enrichment) as well as more developing methods (neural networks and multi-omics integration). Finally, we review case studies where the MoA of a compound was successfully suggested from computational analysis by incorporating multiple data modalities and/or methodologies. Our aim for this review is to provide researchers with insights into the benefits and drawbacks of both the data and methods in terms of level of understanding, biases and interpretation – and to highlight future avenues of investigation which we foresee will improve the field of MoA elucidation, including greater public access to -omics data and methodologies which are capable of data integration. Show less
The task of drug-target interaction prediction holds significant importance in pharmacology and therapeutic drug design. In this paper, we present FRnet-DTI, an auto-encoder based feature manipulation Show more
The task of drug-target interaction prediction holds significant importance in pharmacology and therapeutic drug design. In this paper, we present FRnet-DTI, an auto-encoder based feature manipulation and a convolutional neural network based classifier for drug target interaction prediction. Two convolutional neural networks are proposed: FRnet-Encode and FRnet-Predict. Here, one model is used for feature manipulation and the other one for classification. Using the first method FRnet-Encode, we generate 4096 features for each of the instances in each of the datasets and use the second method, FRnet-Predict, to identify interaction probability employing those features. We have tested our method on four gold standard datasets extensively used by other researchers. Experimental results shows that our method significantly improves over the state-of-the-art method on three out of four drug-target interaction gold standard datasets on both area under curve for Receiver Operating Characteristic (auROC) and area under Precision Recall curve (auPR) metric. We also introduce twenty new potential drug-target pairs for interaction based on high prediction scores. The source codes and implementation details of our methods are available from https://github.com/farshidrayhanuiu/FRnet-DTI/ and also readily available to use as an web application from http://farshidrayhan.pythonanywhere.com/FRnet-DTI/ . Show less
Plasticity of the cell state has been proposed to drive resistance to multiple classes of cancer therapies, thereby limiting their effectiveness. A high-mesenchymal cell state observed in human tumour Show more
Plasticity of the cell state has been proposed to drive resistance to multiple classes of cancer therapies, thereby limiting their effectiveness. A high-mesenchymal cell state observed in human tumours and cancer cell lines has been associated with resistance to multiple treatment modalities across diverse cancer lineages, but the mechanistic underpinning for this state has remained incompletely understood. Here we molecularly characterize this therapy-resistant high-mesenchymal cell state in human cancer cell lines and organoids and show that it depends on a druggable lipid-peroxidase pathway that protects against ferroptosis, a non-apoptotic form of cell death induced by the build-up of toxic lipid peroxides. We show that this cell state is characterized by activity of enzymes that promote the synthesis of polyunsaturated lipids. These lipids are the substrates for lipid peroxidation by lipoxygenase enzymes. This lipid metabolism creates a dependency on pathways converging on the phospholipid glutathione peroxidase (GPX4), a selenocysteine-containing enzyme that dissipates lipid peroxides and thereby prevents the iron-mediated reactions of peroxides that induce ferroptotic cell death. Dependency on GPX4 was found to exist across diverse therapy-resistant states characterized by high expression of ZEB1, including epithelial-mesenchymal transition in epithelial-derived carcinomas, TGFβ-mediated therapy-resistance in melanoma, treatment-induced neuroendocrine transdifferentiation in prostate cancer, and sarcomas, which are fixed in a mesenchymal state owing to their cells of origin. We identify vulnerability to ferroptic cell death induced by inhibition of a lipid peroxidase pathway as a feature of therapy-resistant cancer cells across diverse mesenchymal cell-state contexts. Show less