The TICking clock of EGFR therapy resistance in glioblastoma: Target Independence or target Compensation.

Targeted therapy against driver mutations responsible for cancer progression has been shown to be effective in many tumor types. For glioblastoma (GBM), the epidermal growth factor receptor (EGFR) gene is the most frequently mutated oncogenic driver and has therefore been considered an attractive target for therapy. However, so far responses to EGFR-pathway inhibitors have been disappointing. We performed an exhaustive analysis of the mechanisms that might account for therapy resistance against EGFR inhibition. We define two major mechanisms of resistance and propose modalities to overcome them. The first resistance mechanism concerns target independence. In this case, cells have lost expression of the EGFR protein and experience no negative impact of EGFR targeting. Loss of extrachromosomally encoded EGFR as present in double minute DNA is a frequent mechanism for this type of drug resistance. The second mechanism concerns target compensation. In this case, cells will counteract EGFR inhibition by activation of compensatory pathways that render them independent of EGFR signaling. Compensatory pathway candidates are platelet-derived growth factor ß (PDGFß), Insulin-like growth factor 1 (IGFR1) and cMET and their downstream targets, all not commonly mutated at the time of diagnosis alongside EGFR mutation. Given that both mechanisms make cells independent of EGFR expression, other means have to be found to eradicate drug resistant cells. To this end we suggest rational strategies which include the use of multi-target therapies that hit truncation mutations (mechanism 1) or multi-target therapies to co-inhibit compensatory proteins (mechanism 2).

A federated learning architecture for secure and private neuroimaging analysis.

The amount of biomedical data continues to grow rapidly. However, collecting data from multiple sites for joint analysis remains challenging due to security, privacy, and regulatory concerns. To overcome this challenge, we use federated learning, which enables distributed training of neural network models over multiple data sources without sharing data. Each site trains the neural network over its private data for some time and then shares the neural network parameters (i.e., weights and/or gradients) with a federation controller, which in turn aggregates the local models and sends the resulting community model back to each site, and the process repeats. Our federated learning architecture, MetisFL, provides strong security and privacy. First, sample data never leave a site. Second, neural network parameters are encrypted before transmission and the global neural model is computed under fully homomorphic encryption. Finally, we use information-theoretic methods to limit information leakage from the neural model to prevent a "curious" site from performing model inversion or membership attacks. We present a thorough evaluation of the performance of secure, private federated learning in neuroimaging tasks, including for predicting Alzheimer's disease and for brain age gap estimation (BrainAGE) from magnetic resonance imaging (MRI) studies in challenging, heterogeneous federated environments where sites have different amounts of data and statistical distributions.

Designing a multi-epitope vaccine against Chlamydia pneumoniae by integrating the core proteomics, subtractive proteomics and reverse vaccinology-based immunoinformatics approaches.

Chlamydia pneumoniae, a pneumonia causing specie belonging to chlamydia bacterium. C. pneumonia is considered as a leading cause of pneumonia. Apart from that, C. pneumoniae infection can also cause a variety of inflammatory disorders. There is an urgent need to tackle the major concerns arises due to infections causing by C. pneumoniae as no licensed vaccine available against this bacterial infection. In the framework of this study, a core proteome was generated C. pneumoniae strains was generated which revealed a total of 4754 core proteins. Later, 4 target proteins were obtained from 4754 core proteins by applying subtractive proteomics pipeline. Finally, MEV construct was designed by applying reverse vaccinology-based immunoinformatics approach on four target proteins. Molecular docking analysis were conducted to better understand thermodynamic stability, binding affinities, and interaction of vaccine. The binding interactions of MEV construct against TLR4, MHCII and MHCII showed that these candidate vaccines perfectly fit into the binding domains of the receptors. In addition, MEV construct has a better binding energy of 103.7 ± 15.4, 72.1 ± 9.1, and 70.4 ± 16.0 kcal/mol against TLR4, MHCII and MHCI. MD simulation was run at 200ns on docked complexes which further strengthened the current findings. Respective codon of vaccine construct was optimized and then in silico cloned into an E. coli expression host to ensure maximum vaccine protein expression. Despite the fact that the in-silico analysis used in this research produced reliable results, more studies are needed to validate the effectiveness and performance of proposed vaccine candidate.