The Impact of Inadequate Exposure to Epidermal Growth Factor Receptor-Tyrosine Kinase Inhibitors on the Development of Resistance in Non-Small-Cell Lung Cancer Cells.

Epidermal growth factor receptor (EGFR)-mutant non-small-cell lung cancer (NSCLC) patients treated with EGFR-tyrosine kinase inhibitors (TKIs) inevitably develop resistance through several biological mechanisms. However, little is known on the molecular mechanisms underlying acquired resistance to suboptimal EGFR-TKI doses, due to pharmacodynamics leading to inadequate drug exposure. To evaluate the effects of suboptimal EGFR-TKI exposure on resistance in NSCLC, we obtained HCC827 and PC9 cell lines resistant to suboptimal fixed and intermittent doses of gefitinib and compared them to cells exposed to higher doses of the drug. We analyzed the differences in terms of EGFR signaling activation and the expression of epithelial-mesenchymal transition (EMT) markers, whole transcriptomes byRNA sequencing, and cell motility. We observed that the exposure to low doses of gefitinib more frequently induced a partial EMT associated with an induced migratory ability, and an enhanced transcription of cancer stem cell markers, particularly in the HCC827 gefitinib-resistant cells. Finally, the HCC827 gefitinib-resistant cells showed increased secretion of the EMT inducer transforming growth factor (TGF)-ß1, whose inhibition was able to partially restore gefitinib sensitivity. These data provide evidence that different levels of exposure to EGFR-TKIs in tumor masses might promote different mechanisms of acquired resistance.

A review on drug repurposing applicable to COVID-19.

Drug repurposing involves the identification of new applications for existing drugs at a lower cost and in a shorter time. There are different computational drug-repurposing strategies and some of these approaches have been applied to the coronavirus disease 2019 (COVID-19) pandemic. Computational drug-repositioning approaches applied to COVID-19 can be broadly categorized into (i) network-based models, (ii) structure-based approaches and (iii) artificial intelligence (AI) approaches. Network-based approaches are divided into two categories: network-based clustering approaches and network-based propagation approaches. Both of them allowed to annotate some important patterns, to identify proteins that are functionally associated with COVID-19 and to discover novel drug-disease or drug-target relationships useful for new therapies. Structure-based approaches allowed to identify small chemical compounds able to bind macromolecular targets to evaluate how a chemical compound can interact with the biological counterpart, trying to find new applications for existing drugs. AI-based networks appear, at the moment, less relevant since they need more data for their application.

A multiple network-based bioinformatics pipeline for the study of molecular mechanisms in oncological diseases for personalized medicine.

MOTIVATION: Assessment of genetic mutations is an essential element in the modern era of personalized cancer treatment. Our strategy is focused on 'multiple network analysis' in which we try to improve cancer diagnostics by using biological networks. Genetic alterations in some important hubs or in driver genes such as BRAF and TP53 play a critical role in regulating many important molecular processes. Most of the studies are focused on the analysis of the effects of single mutations, while tumors often carry mutations of multiple driver genes. The aim of this work is to define an innovative bioinformatics pipeline focused on the design and analysis of networks (such as biomedical and molecular networks), in order to: (1) improve the disease diagnosis; (2) identify the patients that could better respond to a given drug treatment; and (3) predict what are the primary and secondary effects of gene mutations involved in human diseases. RESULTS: By using our pipeline based on a multiple network approach, it has been possible to demonstrate and validate what are the joint effects and changes of the molecular profile that occur in patients with metastatic colorectal carcinoma (mCRC) carrying mutations in multiple genes. In this way, we can identify the most suitable drugs for the therapy for the individual patient. This information is useful to improve precision medicine in cancer patients. As an application of our pipeline, the clinically significant case studies of a cohort of mCRC patients with the BRAF V600E-TP53 I195N missense combined mutation were considered. AVAILABILITY: The procedures used in this paper are part of the Cytoscape Core, available at (www.cytoscape.org). Data used here on mCRC patients have been published in [55]. SUPPLEMENTARY INFORMATION: A supplementary file containing a more detailed discussion of this case study and other cases is available at the journal site as Supplementary Data.

Investigating the Effects of Amino Acid Variations in Human Menin.

Human menin is a nuclear protein that participates in many cellular processes, as transcriptional regulation, DNA damage repair, cell signaling, cell division, proliferation, and migration, by interacting with many other proteins. Mutations of the gene encoding menin cause multiple endocrine neoplasia type 1 (MEN1), a rare autosomal dominant disorder associated with tumors of the endocrine glands. In order to characterize the structural and functional effects at protein level of the hundreds of missense variations, we investigated by computational methods the wild-type menin and more than 200 variants, predicting the amino acid variations that change secondary structure, solvent accessibility, salt-bridge and H-bond interactions, protein thermostability, and altering the capability to bind known protein interactors. The structural analyses are freely accessible online by means of a web interface that integrates also a 3D visualization of the structure of the wild-type and variant proteins. The results of the study offer insight into the effects of the amino acid variations in view of a more complete understanding of their pathological role.

STL1, a New AKT Inhibitor, Synergizes with Flavonoid Quercetin in Enhancing Cell Death in A Chronic Lymphocytic Leukemia Cell Line.

Using a pharmacophore model based on the experimental structure of AKT-1, we recently identified the compound STL1 (ZINC2429155) as an allosteric inhibitor of AKT-1. STL1, was able to reduce Ser473 phosphorylation, thus inhibiting the PI3K/AKT pathway. Moreover, we demonstrated that the flavonoid quercetin downregulated the phosphorylated and active form of AKT. However, in this case, quercetin inhibited the PI3K/AKT pathway by directly binding the kinases CK2 and PI3K. In the present work, we investigated the antiproliferative effects of the co-treatment quercetin plus STL1 in HG-3 cells, derived from a patient affected by chronic lymphocytic leukemia. Quercetin and STL1 in the mono-treatment maintained the capacity to inhibit AKT phosphorylation on Ser473, but did not significantly reduce cell viability. On the contrary, they activated a protective form of autophagy. When the HG-3 cells were co-treated with quercetin and STL1, their association synergistically (combination index < 1) inhibited cell growth and induced apoptosis. The combined treatment caused the switch from protective to non-protective autophagy. This work demonstrated that cytotoxicity could be enhanced in a drug-resistant cell line by combining the effects of different inhibitors acting in concert on PI3K and AKT kinases.

Virtual Screening of Natural Compounds as Potential PI3K-AKT1 Signaling Pathway Inhibitors and Experimental Validation.

A computational screening for natural compounds suitable to bind the AKT protein has been performed after the generation of a pharmacophore model based on the experimental structure of AKT1 complexed with IQO, a well-known inhibitor. The compounds resulted as being most suitable from the screening have been further investigated by molecular docking, ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) analysis and toxicity profiles. Two compounds selected at the end of the computational analysis, i.e., ZINC2429155 (also named STL1) and ZINC1447881 (also named AC1), have been tested in an experimental assay, together with IQO as a positive control and quercetin as a negative control. Only STL1 clearly inhibited AKT activation negatively modulating the PI3K/AKT pathway.

PAX8 expression in high-grade serous ovarian cancer positively regulates attachment to ECM via Integrin ß3.

BACKGROUND: Ovarian cancer is the third most common cause of death among gynecologic malignancies worldwide. Understanding the biology and molecular pathogenesis of ovarian epithelial tumors is key to developing improved prognostic indicators and effective therapies. We aimed to determine the effects of PAX8 expression on the migrative, adhesive and survival capabilities of high-grade serous carcinoma cells. METHODS: PAX8 depleted Fallopian tube secretory cells and ovarian cancer cells were generated using short interfering siRNA. Anoikis resistance, cell migration and adhesion properties of PAX8 silenced cells were analyzed by means of specific assays. Chromatin immunoprecipitation (ChIP) was carried out using a PAX8 polyclonal antibody to demonstrate that PAX8 is able to bind to the 5'-flanking region of the ITGB3 gene positively regulating its expression. RESULTS: Here, we report that RNAi silencing of PAX8 sensitizes non-adherent cancer cells to anoikis and affects their tumorigenic properties. We show that PAX8 plays a critical role in migration and adhesion of both Fallopian tube secretory epithelial cells and ovarian cancer cells. Inhibition of PAX8 gene expression reduces the ability of ovarian cancer cells to migrate and adhere to the ECM and specifically to fibronectin and/or collagen substrates. Moreover, loss of PAX8 strongly reduces ITGB3 expression and consequently the correct expression of the αvß3 heterodimer on the plasma membrane. CONCLUSIONS: Our results demonstrate that PAX8 modulates the interaction of tumor cells with the extracellular matrix (ECM). Notably, we also highlight a novel pathway downstream this transcription factor. Overall, PAX8 could be a potential therapeutic target for high-grade serous carcinoma.

Bioinformatics: From NGS Data to Biological Complexity in Variant Detection and Oncological Clinical Practice.

The use of next-generation sequencing (NGS) techniques for variant detection has become increasingly important in clinical research and in clinical practice in oncology. Many cancer patients are currently being treated in clinical practice or in clinical trials with drugs directed against specific genomic alterations. In this scenario, the development of reliable and reproducible bioinformatics tools is essential to derive information on the molecular characteristics of each patient's tumor from the NGS data. The development of bioinformatics pipelines based on the use of machine learning and statistical methods is even more relevant for the determination of complex biomarkers. In this review, we describe some important technologies, computational algorithms and models that can be applied to NGS data from Whole Genome to Targeted Sequencing, to address the problem of finding complex cancer-associated biomarkers. In addition, we explore the future perspectives and challenges faced by bioinformatics for precision medicine both at a molecular and clinical level, with a focus on an emerging complex biomarker such as homologous recombination deficiency (HRD).

Low Impact of Clonal Hematopoiesis on the Determination of RAS Mutations by Cell-Free DNA Testing in Routine Clinical Diagnostics.

Targeted sequencing of circulating cell-free DNA (cfDNA) is used in routine clinical diagnostics for the identification of predictive biomarkers in cancer patients in an advanced stage. The presence of KRAS mutations associated with clonal hematopoiesis of indeterminate potential (CHIP) might represent a confounding factor. We used an amplicon-based targeted sequencing panel, covering selected regions of 52 genes, for circulating cell-free total nucleic acid (cfTNA) analysis of 495 plasma samples from cancer patients. The cfDNA test failed in 4 cases, while circulating cell-free RNA (cfRNA) sequencing was invalid in 48 cases. In the 491 samples successfully tested on cfDNA, at least one genomic alteration was found in 222 cases (45.21%). We identified 316 single nucleotide variants (SNVs) in 21 genes. The most frequently mutated gene was TP53 (74 variants), followed by KRAS (71), EGFR (56), PIK3CA (33) and BRAF (19). Copy number variations (CNVs) were detected in 36 cases, while sequencing of cfRNA revealed 6 alterations. Analysis with droplet digital PCR (ddPCR) of peripheral blood leukocyte (PBL)-derived genomic DNA did not identify any KRAS mutations in 39 cases that showed KRAS mutations at cfDNA analysis. These findings suggest that the incidence of CHIP-associated KRAS mutations is relatively rare in routine clinical diagnostics.

Searching for Chymase Inhibitors among Chamomile Compounds Using a Computational-Based Approach.

Inhibitors of chymase have good potential to provide a novel therapeutic approach for the treatment of cardiovascular diseases. We used a computational approach based on pharmacophore modeling, docking, and molecular dynamics simulations to evaluate the potential ability of 13 natural compounds from chamomile extracts to bind chymase enzyme. The results indicated that some chamomile compounds can bind to the active site of human chymase. In particular, chlorogenic acid had a predicted binding energy comparable or even better than that of some known chymase inhibitors, interacted stably with key amino acids in the chymase active site, and appeared to be more selective for chymase than other serine proteases. Therefore, chlorogenic acid is a promising starting point for developing new chymase inhibitors.

A theoretical study on predicted protein targets of apple polyphenols and possible mechanisms of chemoprevention in colorectal cancer.

We investigated the potential role of apple phenolic compounds in human pathologies by integrating chemical characterization of phenolic compounds in three apple varieties, computational approaches to identify potential protein targets of the compounds, bioinformatics analyses on data from public archive of gene expression data, and functional analyses to hypothesize the effects of the selected compounds in molecular pathways. Starting by the analytic characterization of phenolic compounds in three apple varieties, i.e. Annurca, Red Delicious, and Golden Delicious, we used computational approaches to verify by reverse docking the potential protein targets of the identified compounds. Direct docking validation of the potential protein-ligand interactions has generated a short list of human proteins potentially bound by the apple phenolic compounds. By considering the known chemo-preventive role of apple antioxidants' extracts against some human pathologies, we performed a functional analysis by comparison with experimental gene expression data and interaction networks, obtained from public repositories. The results suggest the hypothesis that chemo-preventive effects of apple extracts in human pathologies, in particular for colorectal cancer, may be the interference with the activity of nucleotide metabolism and methylation enzymes, similarly to some classes of anticancer drugs.